diff --git a/single-page/Quantconnect-Cloud-Platform.html b/single-page/Quantconnect-Cloud-Platform.html index 55dbcf4f7c..bae9c5d16a 100644 --- a/single-page/Quantconnect-Cloud-Platform.html +++ b/single-page/Quantconnect-Cloud-Platform.html @@ -142,94 +142,111 @@

Table of Content

  • 11.4 Strategies
  • 11.5 Deployment
  • 11.6 Results
    • -
  • 12 Object Store
    • -
  • 13 Community
    • -
  • 13.1 Code of Conduct
    • -
  • 13.2 Forum
    • -
  • 13.3 Discord
    • -
  • 13.4 Profile
    • -
  • 13.5 Academic Grants
    • -
  • 13.6 Integration Partners
    • -
  • 13.7 Affiliates
    • -
  • 13.8 Research
    • -
  • 13.9 Strategies
    • -
  • 14 API Reference
    • -
  • 14.1 Authentication
    • -
  • 14.2 Project Management
    • -
  • 14.2.1 Create Project
    • -
  • 14.2.2 Read Project
    • -
  • 14.2.3 Update Project
    • -
  • 14.2.4 Delete Project
    • -
  • 14.2.5 Collaboration
    • -
  • 14.2.5.1 Create Project Collaborator
    • -
  • 14.2.5.2 Read Project Collaborators
    • -
  • 14.2.5.3 Update Project Collaborator
    • -
  • 14.2.5.4 Delete Project Collaborator
    • -
  • 14.2.5.5 Lock Project
    • -
  • 14.2.6 Nodes
    • -
  • 14.2.6.1 Read Project Nodes
    • -
  • 14.2.6.2 Update Project Nodes
    • -
  • 14.3 File Management
    • -
  • 14.3.1 Create File
    • -
  • 14.3.2 Read File
    • -
  • 14.3.3 Update File
    • -
  • 14.3.4 Delete File
    • -
  • 14.4 Compiling Code
    • -
  • 14.4.1 Create Compilation Job
    • -
  • 14.4.2 Read Compilation Result
    • -
  • 14.5 Backtest Management
    • -
  • 14.5.1 Create Backtest
    • -
  • 14.5.2 Read Backtest
    • -
  • 14.5.2.1 Backtest Statistics
    • -
  • 14.5.2.2 Charts
    • -
  • 14.5.2.3 Orders
    • -
  • 14.5.2.4 Insights
    • -
  • 14.5.3 Update Backtest
    • -
  • 14.5.4 Delete Backtest
    • -
  • 14.5.5 List Backtests
    • -
  • 14.6 Live Management
    • -
  • 14.6.1 Create Live Algorithm
    • -
  • 14.6.2 Read Live Algorithm
    • -
  • 14.6.2.1 Live Algorithm Statistics
    • -
  • 14.6.2.2 Charts
    • -
  • 14.6.2.3 Portfolio State
    • -
  • 14.6.2.4 Orders
    • -
  • 14.6.2.5 Insights
    • -
  • 14.6.2.6 Logs
    • -
  • 14.6.3 Update Live Algorithm
    • -
  • 14.6.3.1 Liquidate Live Portfolio
    • -
  • 14.6.3.2 Stop Live Algorithm
    • -
  • 14.6.4 List Live Algorithms
    • -
  • 14.6.5 Live Commands
    • -
  • 14.6.5.1 Create Live Command
    • -
  • 14.6.5.2 Broadcast Live Command
    • -
  • 14.7 Optimization Management
    • -
  • 14.7.1 Create Optimization
    • -
  • 14.7.2 Read Optimization
    • -
  • 14.7.3 Update Optimization
    • -
  • 14.7.4 Delete Optimization
    • -
  • 14.7.5 Abort Optimization
    • -
  • 14.7.6 List Optimization
    • -
  • 14.7.7 Estimate Optimization Cost
    • -
  • 14.8 Object Store Management
    • -
  • 14.8.1 Upload Object Store Files
    • -
  • 14.8.2 Get Object Store Metadata
    • -
  • 14.8.3 Get Object Store File
    • -
  • 14.8.4 Delete Object Store File
    • -
  • 14.8.5 List Object Store Files
    • -
  • 14.9 AI Assistance
    • -
  • 14.9.1 Tools
    • -
  • 14.9.1.1 Backtest Initialization
    • -
  • 14.9.1.2 Code Completion
    • -
  • 14.9.1.3 Error Enhancement
    • -
  • 14.9.1.4 PEP8 Conversion
    • -
  • 14.9.1.5 Syntax Check
    • -
  • 14.9.1.6 Search
    • -
  • 14.10 Reports
    • -
  • 14.10.1 Backtest Report
    • -
  • 14.11 Account
    • -
  • 14.12 Lean Version
    • -
  • 14.13 Examples
    • +
  • 12 Research Pipeline
    • +
  • 13 Object Store
    • +
  • 14 Community
    • +
  • 14.1 Code of Conduct
    • +
  • 14.2 Forum
    • +
  • 14.3 Discord
    • +
  • 14.4 Profile
    • +
  • 14.5 Academic Grants
    • +
  • 14.6 Integration Partners
    • +
  • 14.7 Affiliates
    • +
  • 14.8 Research
    • +
  • 14.9 Strategies
    • +
  • 15 API Reference
    • +
  • 15.1 Authentication
    • +
  • 15.2 Project Management
    • +
  • 15.2.1 Create Project
    • +
  • 15.2.2 Read Project
    • +
  • 15.2.3 Update Project
    • +
  • 15.2.4 Delete Project
    • +
  • 15.2.5 Collaboration
    • +
  • 15.2.5.1 Create Project Collaborator
    • +
  • 15.2.5.2 Read Project Collaborators
    • +
  • 15.2.5.3 Update Project Collaborator
    • +
  • 15.2.5.4 Delete Project Collaborator
    • +
  • 15.2.5.5 Lock Project
    • +
  • 15.2.6 Nodes
    • +
  • 15.2.6.1 Read Project Nodes
    • +
  • 15.2.6.2 Update Project Nodes
    • +
  • 15.3 File Management
    • +
  • 15.3.1 Create File
    • +
  • 15.3.2 Read File
    • +
  • 15.3.3 Update File
    • +
  • 15.3.4 Delete File
    • +
  • 15.4 Compiling Code
    • +
  • 15.4.1 Create Compilation Job
    • +
  • 15.4.2 Read Compilation Result
    • +
  • 15.5 Backtest Management
    • +
  • 15.5.1 Create Backtest
    • +
  • 15.5.2 Read Backtest
    • +
  • 15.5.2.1 Backtest Statistics
    • +
  • 15.5.2.2 Charts
    • +
  • 15.5.2.3 Orders
    • +
  • 15.5.2.4 Insights
    • +
  • 15.5.3 Update Backtest
    • +
  • 15.5.4 Delete Backtest
    • +
  • 15.5.5 List Backtests
    • +
  • 15.6 Live Management
    • +
  • 15.6.1 Create Live Algorithm
    • +
  • 15.6.2 Read Live Algorithm
    • +
  • 15.6.2.1 Live Algorithm Statistics
    • +
  • 15.6.2.2 Charts
    • +
  • 15.6.2.3 Portfolio State
    • +
  • 15.6.2.4 Orders
    • +
  • 15.6.2.5 Insights
    • +
  • 15.6.2.6 Logs
    • +
  • 15.6.3 Update Live Algorithm
    • +
  • 15.6.3.1 Liquidate Live Portfolio
    • +
  • 15.6.3.2 Stop Live Algorithm
    • +
  • 15.6.4 List Live Algorithms
    • +
  • 15.6.5 Live Commands
    • +
  • 15.6.5.1 Create Live Command
    • +
  • 15.6.5.2 Broadcast Live Command
    • +
  • 15.7 Optimization Management
    • +
  • 15.7.1 Create Optimization
    • +
  • 15.7.2 Read Optimization
    • +
  • 15.7.3 Update Optimization
    • +
  • 15.7.4 Delete Optimization
    • +
  • 15.7.5 Abort Optimization
    • +
  • 15.7.6 List Optimization
    • +
  • 15.7.7 Estimate Optimization Cost
    • +
  • 15.8 Object Store Management
    • +
  • 15.8.1 Upload Object Store Files
    • +
  • 15.8.2 Get Object Store Metadata
    • +
  • 15.8.3 Get Object Store File
    • +
  • 15.8.4 Delete Object Store File
    • +
  • 15.8.5 List Object Store Files
    • +
  • 15.9 AI Assistance
    • +
  • 15.9.1 Tools
    • +
  • 15.9.1.1 Backtest Initialization
    • +
  • 15.9.1.2 Code Completion
    • +
  • 15.9.1.3 Error Enhancement
    • +
  • 15.9.1.4 PEP8 Conversion
    • +
  • 15.9.1.5 Syntax Check
    • +
  • 15.9.1.6 Search
    • +
  • 15.10 Reports
    • +
  • 15.10.1 Backtest Report
    • +
  • 15.11 Account
    • +
  • 15.12 Lean Version
    • +
  • 15.13 Agent Management
    • +
  • 15.13.1 Agents
    • +
  • 15.13.2 Tasks
    • +
  • 15.13.2.1 Create Task
    • +
  • 15.13.2.2 Read Task
    • +
  • 15.13.2.3 Update Task
    • +
  • 15.13.2.4 Delete Task
    • +
  • 15.13.2.5 List Tasks
    • +
  • 15.13.3 Deployments
    • +
  • 15.13.3.1 Create Deployment
    • +
  • 15.13.3.2 Read Deployment
    • +
  • 15.13.3.3 Update Deployment
    • +
  • 15.13.3.4 List Deployments
    • +
  • 15.13.3.5 Stop Deployment
    • +
  • 15.13.3.6 Delete Deployment
    • +
  • 15.13.3.7 Read Conversation
    • +
  • 15.14 Examples

    •  

    @@ -565,7 +582,11 @@

    Physical access to our servers is limited to a few dedicated team members whom QuantConnect has vetted. Only those credentialed team members can access the physical servers, and we schedule all work in advance. - Work on the servers is always done in pairs to prevent single rogue actors from accessing the servers. We host our servers in a world-class security facility (Equinix) with security staff 24/7. + Work on the servers is always done in pairs to prevent single rogue actors from accessing the servers. We host our servers in a world-class security facility, on co-located servers racked in + + Equinix + + , with security staff 24/7.

    Information and Digital Security @@ -614,7 +635,7 @@

    Code Encryption

    -

    On-Premises Installations

    +

    On-Premise Installations

    @@ -1481,7 +1502,11 @@

    Live Trading Nodes

    - Live trading nodes enable you to deploy live algorithms to our professionally-managed, co-located servers. + Live trading nodes enable you to deploy live algorithms to our professionally-managed, co-located servers racked in + + Equinix + + . You need a live trading node for each algorithm that you deploy to our co-located servers. Several models of live trading nodes are available. More powerful live trading nodes allow you to run algorithms with larger universes and give you @@ -1639,6 +1664,133 @@

    Live Trading Nodes

    +

    Assistant Nodes

    + + +

    + Assistant nodes enable you to deploy assistant tasks. + The more assistant nodes your organization has, the more concurrent assistant tasks that you can run. + More powerful assistant nodes have more cores and RAM to handle larger, more demanding tasks. + The following table shows the specifications of the assistant node models: +

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + Name + + Number of Cores + + RAM (GB) + + Agents +
    + A-MICRO + + 1 + + 1 + + 1 +
    + A1-1 + + 1 + + 1 + + 1 +
    + A4-8 + + 4 + + 8 + + 4 +
    + A16-32 + + 16 + + 32 + + 16 +
    + +

    + Refer to the + + Pricing + + page to see the price of each assistant node model. + Your first organization includes one free A-MICRO assistant node, which is capped at 100,000 tokens per month. + The token cap is lifted and the node is replaced when you + + upgrade your organization to a paid tier + + and + + add a new assistant node + + . +

    +

    + To view the status of all of your organization's nodes, see the + + Resources panel + + of the IDE. + When you launch an assistant task, it uses the best-performing resource by default, but you can + + select a specific resource to use + + . +

    + + +

    Sharing Resources

    @@ -3036,7 +3188,7 @@

    Introduction

    There are three tiers of support seats and each tier provides different services. You can file support tickets to get private assistance with issues, but support tickets should not replace your efforts of performing your own research and reading through the documentation. If you need further assistance than what our Support Team offers, consider hiring an - + Integration Partner . @@ -3059,11 +3211,11 @@

    email , - + Discord , or the - + forum . We will review your submission. If we confirm you've found a bug, we will create a GitHub Issue to have it resolved. Subscribe to the GitHub Issue to track our progress in fixing the bug. @@ -3156,8 +3308,8 @@

    For more information on this feature, see - - Strategy Development + + AI Assistance .

    @@ -3470,7 +3622,7 @@

    LEAN CLI , and the - + QuantConnect REST API , which includes installation and subjects that don't apply to the Cloud Platform. @@ -4052,7 +4204,7 @@

    Ticket Quotas

    Chat with - + Mia for immediate assistance. We trained it on hundreds of algorithms and thousands of documentation pages to provide contextual assistance for most issues you may encounter when developing a strategy. @@ -5801,7 +5953,7 @@

    Giving to Others

    To show your appreciation for contributions in the forum, - + give some QuantConnect Credit (QCC) rewards . The following table shows the available QCC rewards: @@ -7020,7 +7172,7 @@

    Backtesting
    - + Sharing Backtests
    @@ -7407,9 +7559,12 @@

    Delete a parameter
  • Remove the - + GetParameter + + get_parameter + calls that were associated with the parameter from your code files.
    • @@ -7420,7 +7575,19 @@

    Delete Projects

    - You can delete a project when it is open or closed. + You can delete a project when it is open or closed. Deleted projects are moved to the + + Recycle Bin + + directory on the + + My Projects + + page, where you can recover them if you delete one by mistake. Projects in the + + Recycle Bin + + are permanently deleted after 30 days.

    Delete Open Projects @@ -7466,6 +7633,57 @@

    "Project deleted" displays.

    +

    + Recover Deleted Projects +

    +

    + When you delete a project, it moves to the + + Recycle Bin + + directory instead of being permanently removed. Projects in the + + Recycle Bin + + are permanently deleted after 30 days, so recover them within that window. Follow these steps to recover a project you deleted by mistake: +

    +
      +
    1. + Open the + + My Projects + + page. +
      2. +
    2. + Click the + + Recycle Bin + + directory. +
      3. +
    3. + Click the project to open it. +
      4. +
    4. + + Rename the project + + to remove the + + Recycle Bin/ + + prefix from its path (for example, rename + + Recycle Bin/My Legacy Project + + to + + My Legacy Project + + ). +
      5. +
    @@ -9333,6 +9551,87 @@

    +

    Order Tagging

    + + +

    + You can add + + tags + + to your orders to track why each trade was placed. This is useful for debugging your algorithm's trading logic because you can include the indicator values or conditions that triggered the order. +

    +
    + 
    public class OrderTaggingAlgorithm : QCAlgorithm
+{
+    private ExponentialMovingAverage _emaShort;
+    private ExponentialMovingAverage _emaLong;
+    private Symbol _symbol;
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+
+        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
+        _emaShort = EMA(_symbol, 10);
+        _emaLong = EMA(_symbol, 30);
+    }
+
+    public override void OnData(Slice data)
+    {
+        if (!_emaShort.IsReady || !_emaLong.IsReady) return;
+
+        if (_emaShort > _emaLong && !Portfolio[_symbol].IsLong)
+        {
+            MarketOrder(_symbol, 100, tag: $"BUY: ema-short: {_emaShort:F4} > ema-long: {_emaLong:F4}");
+        }
+        else if (_emaShort < _emaLong && !Portfolio[_symbol].IsShort)
+        {
+            MarketOrder(_symbol, -100, tag: $"SELL: ema-short: {_emaShort:F4} < ema-long: {_emaLong:F4}");
+        }
+    }
+}
    + 
    class OrderTaggingAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+
+        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
+        self._ema_short = self.ema(self._symbol, 10)
+        self._ema_long = self.ema(self._symbol, 30)
+
+    def on_data(self, data: Slice) -> None:
+        if not self._ema_short.is_ready or not self._ema_long.is_ready:
+            return
+
+        ema_short = self._ema_short.current.value
+        ema_long = self._ema_long.current.value
+        if ema_short > ema_long and not self.portfolio[self._symbol].is_long:
+            self.market_order(self._symbol, 100, tag=f'BUY: ema-short: {ema_short:.4f} > ema-long: {ema_long:.4f}')
+        elif ema_short < ema_long and not self.portfolio[self._symbol].is_short:
+            self.market_order(self._symbol, -100, tag=f'SELL: ema-short: {ema_short:.4f} < ema-long: {ema_long:.4f}')
    +
    +

    + The tag is saved to the + + result files + + that you can download for local analysis. Unlike + + logging statements + + , order tags are attached directly to each order, so you don't need to search through logs to match a message to a specific trade, and they don't consume your + + log quota + + . +

    + + +

    Charting

    @@ -9370,7 +9669,7 @@

    Object Store

    The Object Store is a key-value data store for low-latency information storage and retrieval. - During a backtest, you can build large objects you’d like to analyze and write them for later analysis. + During a backtest, you can build large objects you'd like to analyze and write them for later analysis. This workflow can be helpful when the objects are large and plotting is impossible or when you want to perform analysis across many backtests.

    @@ -10592,8 +10891,10 @@

    Example

    bbdf[['price', 'lowerband', 'middleband', 'upperband']].plot();
    - 
    #load "../QuantConnect.csx"
-using QuantConnect;
+   
    #load "../QuantConnect.csx"
    
+
    +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -11667,7 +11968,7 @@ 
    Out of Sample Period
    
  
 
 
-
    On-Premises Backtests
    
+
    On-Premise Backtests
    
 
  
   
    
@@ -11735,11 +12036,11 @@ 
    Share Backtests

    To attach the embedded backtest result to a forum discussion, see - + Create Discussions or - + Post Comments . @@ -14174,7 +14475,7 @@

    method or the - + /backtests/orders/read endpoint. @@ -14391,11 +14692,11 @@

    Share Results

    To attach the embedded backtest result to a forum discussion, see - + Create Discussions or - + Post Comments . @@ -18483,7 +18784,11 @@

    Delivery

    - Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters. + Most live trading algorithms run on co-located servers racked in + + Equinix + + . Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

    Live data takes time to travel from the source to your algorithm. @@ -18665,7 +18970,11 @@

    Delivery

    - Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters. + Most live trading algorithms run on co-located servers racked in + + Equinix + + . Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

    Live data takes time to travel from the source to your algorithm. @@ -18813,7 +19122,11 @@

    Delivery

    - Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters. + Most live trading algorithms run on co-located servers racked in + + Equinix + + . Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

    Live data takes time to travel from the source to your algorithm. @@ -18948,7 +19261,11 @@

    Delivery

    - Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters. + Most live trading algorithms run on co-located servers racked in + + Equinix + + . Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

    Live data takes time to travel from the source to your algorithm. @@ -19083,7 +19400,11 @@

    Delivery

    - Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters. + Most live trading algorithms run on co-located servers racked in + + Equinix + + . Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

    Live data takes time to travel from the source to your algorithm. @@ -19249,7 +19570,11 @@

    Delivery

    - Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters. + Most live trading algorithms run on co-located servers racked in + + Equinix + + . Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

    Live data takes time to travel from the source to your algorithm. @@ -19389,7 +19714,11 @@

    Delivery

    , include a live stream. In these cases, we deliver the data as a live stream to your algorithm.

    - Most live trading algorithms run on co-located servers racked in Equinix. Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters. + Most live trading algorithms run on co-located servers racked in + + Equinix + + . Co-location reduces several factors that can interfere with your algorithm, including downtime from internet outages, equipment repairs, and natural disasters.

    Live data takes time to travel from the source to your algorithm. The latency of the alternative data depends on the specific dataset you're using. @@ -20415,7 +20744,7 @@

    Hybrid Data Provider

    When you - + deploy a live algorithm with the Charles Schwab brokerage , you can use a third-party data provider, the Charles Schwab data provider, or both. @@ -21666,7 +21995,11 @@

    Introduction

    - A live algorithm is an algorithm that trades in real-time with real market data. QuantConnect enables you to run your algorithms in live mode with real-time market data. Deploy your algorithms using QuantConnect because our infrastructure is battle-tested. The algorithms that our members create are run on co-located servers and the trading infrastructure is maintained at all times by our team of engineers. It's common for members to achieve 6-months of uptime with no interruptions. + A live algorithm is an algorithm that trades in real-time with real market data. QuantConnect enables you to run your algorithms in live mode with real-time market data. Deploy your algorithms using QuantConnect because our infrastructure is battle-tested. The algorithms that our members create are run on co-located servers racked in + + Equinix + + and the trading infrastructure is maintained at all times by our team of engineers. It's common for members to achieve 6-months of uptime with no interruptions.

    @@ -22037,7 +22370,7 @@

    Clear Live Algorithms History

    -

    On-Premises Live Algorithms

    +

    On-Premise Live Algorithms

    @@ -23531,6 +23864,110 @@

    +

    FIX Integration

    + + +

    + FIX (Financial Information eXchange) integration lets existing Interactive Brokers (IB) clients route orders from QuantConnect through their IB account. If you already hold an IB account, you can subscribe to QuantConnect directly from the IB Client Portal to enable FIX order routing. +

    +

    + Before you start, make sure you have an active IB account and access to the IB Client Portal. +

    +

    + Follow these steps to add FIX support to your IB account: +

    +
      +
    1. + Log in to the + + IB Client Portal + + , open + + Settings + + , click + + Research Subscriptions + + , open + + News & Research Subscriptions + + , and then click + + Manage Subscriptions + + . +
      2. +
    2. + Select + + QuantConnect + + from the list of subscriptions to create the FIX session, and then click + + Continue + + . +
      3. +
    3. + On the + + Review Your Research Subscriptions + + screen, review the market-data agreements and disclosures, and then confirm to proceed. +
      4. +
    4. + On the + + Service Account Configuration + + screen, click the + + Configure + + gear next to QuantConnect. +
      5. +
    5. + In the + + Service Account Configuration for QuantConnect + + dialog, select the IB account(s) you want to link, enter your QuantConnect user name in the + + Quantconnect Username + + field, and then click + + Save + + . +
      6. +

      + The user name you enter is the default subscriber for the QuantConnect service. You can select multiple accounts here. When you later connect from QuantConnect to IB, you enter this same user name to authenticate. +

      +
    6. + Back on the + + Service Account Configuration + + screen, click + + Continue + + . +
      7. +
    7. + Acknowledge and confirm the OMS Riders and Addendum. +
      8. +

      + After you confirm, your subscription is complete and the selected information is added to the FIX connection for order validation. +

      +
    + + +

    Asset Classes

    @@ -24801,6 +25238,141 @@

    Deploy Live Algorithms

    +

    Deploy Live Algorithms FIX

    + + +

    + You must have an available + + live trading node + + for each live trading algorithm you deploy. +

    +

    + Follow these steps to deploy a live algorithm: +

    +
      +
    1. + + Open the project + + you want to deploy. +
      2. +
    2. + Click the + Lightning icon + + Deploy Live + + icon. +
      3. +
    3. + On the Deploy Live page, click the + + Brokerage + + field and then click + + + Interactive Brokers + + + from the drop-down menu. +
      4. +
    4. + Enter your + + IB + + user name (see + + FIX Integration + + ). +
      5. +
    5. + Click the + + Node + + field and then click the live trading node that you want to use from the drop-down menu. +
      6. +
    6. + + (Optional) + + In the + + Data Provider + + section, click + + Show + + and change the data provider or add additional providers. +
      7. +

      + In most cases, we suggest using + + both the QC and IB data providers + + . +

      +

      + If you use + + IB + + data provider and trade with a paper trading account, you need to share the market data subscription with your paper trading account. For instructions on sharing market data subscription, see + + Account Types + + . +

      +
    7. + + (Optional) + + + Set up notifications + + . +
      8. +
    8. + Configure the + + Automatically restart algorithm + + setting. +
      9. +

      + By enabling + + automatic restarts + + , the algorithm will use best efforts to restart the algorithm if it fails due to a runtime error. This can help improve the algorithm's resilience to temporary outages such as a brokerage API disconnection. +

      +
    9. + Click + + Deploy + + . +
      10. +
    10. + If your IB account has 2FA enabled, tap the notification on your IB Key device and then enter your pin. +
      11. +
    +

    + The deployment process can take up to 5 minutes. When the algorithm deploys, the + + live results page + + displays. If you know your brokerage positions before you deployed, you can verify they have been loaded properly by checking your equity value in the runtime statistics, your cashbook holdings, and your position holdings. +

    + + +

    Troubleshooting

    @@ -27962,6 +28534,15 @@

    +

    Rate Limit

    + + +

    + Charles Schwab limits order requests (place, update, and cancel) to 120 requests per minute and 4,000 order requests per day. To avoid hitting these limits, design your algorithm to issue orders sparingly. +

    + + +

    Fees

    @@ -32262,13 +32843,19 @@

    Account Types

    Use - + AccountType.Cash + + AccountType.CASH + to connect to your Exchange wallet or - + AccountType.Margin + + AccountType.MARGIN + to connect to your Margin wallet. You can not connect to your Funding or Capital Raise wallet. If you provide the wrong AccountType @@ -32326,9 +32913,12 @@

  • Use - + AccountType.Cash + + AccountType.CASH + in your algorithms.
    • @@ -36301,7 +36891,7 @@

    Demo Algorithm

    if self.portfolio.invested: return for chain in slice.future_chains: - contracts = sorted(chain.Value, key=lambda c: c.expiry) + contracts = sorted(chain.value, key=lambda c: c.expiry) if not contracts: continue contract = contracts[0] @@ -38460,7 +39050,11 @@

    Introduction

    - Deploy your trading algorithms live to receive real-time market data and submit orders on our co-located servers. As your algorithms run, you can view their performance in the Algorithm Lab. Since the algorithms run in QuantConnect Cloud, you can close the IDE without interrupting the execution of your algorithms. Deploying your algorithms to live trading through QuantConnect is cheaper than purchasing server space, setting up data feeds, and maintaining the software on your own. To deploy your algorithms on QuantConnect, you just need to follow the + Deploy your trading algorithms live to receive real-time market data and submit orders on our co-located servers racked in + + Equinix + + . As your algorithms run, you can view their performance in the Algorithm Lab. Since the algorithms run in QuantConnect Cloud, you can close the IDE without interrupting the execution of your algorithms. Deploying your algorithms to live trading through QuantConnect is cheaper than purchasing server space, setting up data feeds, and maintaining the software on your own. To deploy your algorithms on QuantConnect, you just need to follow the Deploy Live Algorithms @@ -38477,7 +39071,11 @@

    Resources

    - Live trading nodes enable you to deploy live algorithms to our professionally-managed, co-located servers. + Live trading nodes enable you to deploy live algorithms to our professionally-managed, co-located servers racked in + + Equinix + + . You need a live trading node for each algorithm that you deploy to our co-located servers. Several models of live trading nodes are available. More powerful live trading nodes allow you to run algorithms with larger universes and give you @@ -39293,7 +39891,7 @@


    Collection of insights emitted since the last notification. See - + Insight for the schema of each object. @@ -39311,7 +39909,7 @@


    Collection of order events since the last notification. For the conceptual model, see - + OrderEvent in the API Reference . The webhook serialization differs from the API response. See the Order Event Schema table below for the webhook-specific schema. @@ -40577,16 +41175,16 @@

    To programmatically analyze orders, call the - + - ReadBacktestOrders + ReadLiveOrders read_live_orders method or the - + /live/orders/read endpoint. @@ -41195,6 +41793,13 @@

    Introduction

    If your algorithm is perfectly reconciled, it has an exact overlap between its live and OOS backtest equity curves. Deviations mean that the performance of your algorithm has differed between the two execution modes. Several factors can contribute to the deviations.

    +

    + To generate the OOS reconciliation curve for a live deployment and overlay live vs. backtest equity and order fills from the Research Environment, see + + Live Reconciliation + + . +

    @@ -42124,7 +42729,7 @@

    -

    On-Premises Optimizations

    +

    On-Premise Optimizations

    @@ -42323,9 +42928,12 @@

    Delete a parameter
  • Remove the - + GetParameter + + get_parameter + calls that were associated with the parameter from your code files.
    • @@ -43674,9 +44282,414 @@

     

    -

    Object Store

    +

    Research Pipeline

    +

    Research Pipeline

    + +
    +
    +

    Introduction

    + + +

    + The Research Pipeline is a kanban board that enables you to easily monitor the state of your organization. + Think of it as a structured approach to bringing your ideas through the stages of research validation, backtesting, paper trading, and ending with live trading. + Each card represents one project in QuantConnect Cloud. + Simply add projects to the board, then drag-and-drop them, to move them throughout the various stages of the pipeline as your team completes tasks. + To view the board, + + log in to the Algorithm Lab + + and then click the + + Research Pipeline + + tab. +

    + Research Pipeline kanban board with cards across Ideas, Research, Backtest, Paper Trading, and Live Trading lists. +

    + To supercharge your productivity, add some + + AI assistants + + to your Research Pipeline. + We offer a set of highly-skilled AI assistants that you can add to your organization. + They are each pre-configured to specialize in a particular phase of the Research Pipeline, but you can always define custom assistants to fit your organization's needs. +

    + + + +

    Stage 0: Ideas

    + + +

    + The + + Ideas + + list on the Research Pipeline should contain cards that describe new trading ideas for your team's researchers to explore. + To add ideas to the list, click the + + + Add card + + icon in the top-right corner of the list, enter a description of the trading idea, and then click + + Save + + . +

    +

    + If you need some help generating some trading ideas, an + + Ideas Assistant + + can add a steady stream of novel trading ideas to your organization's + + Ideas + + list. + This assistant analyzes recent financial news articles and trading blog posts. + It then adds a card to the list with a concise description of the strategy, including the underlying logic, the types of assets it would trade, and any specific indicators or datasets it would utilize. +

    + + + +

    Stage 1: Research

    + + +

    + The + + Research + + list on the Research Pipeline should contain a card for each project that your research team is currently investigating. + During the research stage, the goal is to perform preliminary analysis to assess the viability of the trading idea before committing resources to a formal backtest. + This stage helps filter out weak ideas early, refine hypotheses, and identify potential pitfalls. +

    +

    + To add cards to the + + Research + + list, drag-and-drop a card from the + + Ideas + + list. + Alternatively, click the + + + Add card + + icon in the top-right corner of the list, enter a description of the trading idea, and then click + + Save + + . +

    +

    + If you need some help researching ideas, add + + Research + + and + + Research Validation Assistants + + to your organization. + The Research Assistant has access to tools that let it write, read, and execute research notebooks, and it produces a research report in the project that documents its process and findings — typically covering empirical evidence, statistical significance, and a deep analysis of the fundamental reason for the edge of the trading idea. +

    +

    + The Research Validation Assistant then pressure-tests that work, using the same notebook tools to check the model's residuals for stationarity, serial correlation, heteroscedasticity, and stability between training and testing windows. + Together, they decide whether an idea has earned its place in the backtesting queue or whether it should be sent back before any engineering time is spent on it. +

    + + + +

    Stage 2: Backtest

    + + +

    + The + + Backtest + + list on the Research Pipeline should contain a card for each project that your backtest team is currently investigating. + During the backtest stage, the goal is to implement the trading idea in an error-free, event-driven algorithm. + This stage helps filter out strategies that passed the research phase but have poor performance after running a backtest with realistic + + reality modeling + + . +

    +

    + To add cards to the + + Backtest + + list, drag-and-drop a card from the + + Ideas + + or + + Research + + lists. + Alternatively, click the + + + Add card + + icon in the top-right corner of the list, enter a description of the trading idea, and then click + + Save + + . +

    +

    + If you need some help backtesting ideas, add a + + Backtest Assistant + + to your organization. + This assistant has access to tools that enable it to write algorithms, fix coding errors, run backtests, interpret backtest results, and optimize parameters. + The output of this assistant is an algorithm that's proven to underperform the market or an algorithm that's ready for testing in the paper trading environment. +

    + + + +

    Stage 3: Paper Trading

    + + +

    + The + + Paper Trading + + list on the Research Pipeline should contain a card for each project that your team is currently running live with the + + paper trading brokerage + + . + During the paper trading stage, the goal is to confirm the strategy works as expected in a live environment and diagnose any deviations. +

    +

    + To add cards to the + + Paper Trading + + list, drag-and-drop a card from the + + Backtest + + list. +

    +

    + If you need some help validating ideas with paper trading, add a + + Paper Testing Assistant + + to your organization. + This assistant has access to tools to deploy algorithms, analyze their live performance, and adjust the algorithm logic if necessary. + As it runs, it verifies the following questions: +

    +
      +
    • + Does the strategy execute trades exactly as the code dictates? +
      • +
    • + Does the paper trading performance match a backtest run over the identical date range? +
      • +
    • + Does the out-of-sample performance fall within the statistical envelope of the original backtest? +
      • +
    +

    + When any of these questions fail validation, the assistant reviews the code, debugs the algorithm, edits the logic, and redeploys the project. + If the assistant determines the strategy is not ready for live trading with real money, it stops the algorithm and sends you a report. +

    + + + +

    Stage 4: Live Trading

    + + +

    + The + + Live Trading + + list on the Research Pipeline should contain a card for each project that your organization is running live with real money. + During this final stage, the goal is to ensure the algorithm continues running and to intervene when necessary. + To add cards to the + + Live Trading + + list, drag-and-drop a card from the + + Paper Trading + + list. +

    +

    + If you need some help monitoring your live algorithms, add a + + Live Monitoring Assistant + + to your organization. + This assistant has access to tools to deploy algorithms, read their current positions, and scan for material news events that affect the positions. + As it runs, it assesses the directional risk of your holdings given breaking news events. + When it detects an event that can significantly reduce the value of your holdings, it notifies you so you can act. +

    + + + +

    Project Archive

    + + +

    + The + + Archived Items + + section at the bottom of the Research Pipeline should contain a card for each project to which you're no longer allocating resources. + These may be ideas that failed the research validation stage, strategies that underperformed in the backtesting stage, or algorithms that lost their edge in live trading. +

    +

    + To add projects to the archive, drag-and-drop a card from one of the stages of the Research Pipeline. + You can always recover projects from the archive later on. +

    + + + +

    Update Cards

    + + +

    + Follow these steps to update the title or body of a card on the Research Pipeline: +

    +
      +
    1. + Click on a card. +

      + The + + Card + + tab shows the current title and description. +

      + Card tab showing the current title and description. +
      2. +
    2. + Click + + Edit Idea + + . +
      3. +
    3. + Enter the new title and description. +
      4. +
    4. + Click + + Save + + . +
      5. +
    + + + +

    View Deployments

    + + +

    + When you assign a card to an Assistant and the Assistant is currently working on the task, the card displays “Running”. +

    + Card on the Research Pipeline displaying the Running status. +

    + To view the deployment details, click + + Running + + . + The deployment view page displays the output of the Assistant(s) assigned to the task. +

    + Deployment view showing the output of the Assistants assigned to the task. +

    + To open the project associated with the task, click the project link at the top of the deployment view. +

    +

    + To interrupt the assistant during its run, in the top-right corner of the deployment view, click the + + + stop + + icon. +

    +

    + To view all the current and historical tasks, follow these steps: +

    +
      +
    1. + Log in to the + + Algorithm Lab + + . +
      2. +
    2. + In the left navigation bar, click + + Organization > Assistants + + . +
      3. +
    3. + Click the + + Tasks + + tab. +
      4. +
    + Tasks tab on the Assistants page showing current and historical tasks. +

    + From this view, you can click the + + Status + + column to open the deployment view of the task or click + + Delete + + to remove the task from the view. +

    + + + +

    Quotas

    + + +

    + Assistants have a fair-use quota, but are essentially uncapped. + The number of agents you can concurrently run in your organization and the amount of tokens you can use depends on your team's + + Assistant Nodes + + . +

    + + + +

     

    +

    Object Store

    +
    +

    Object Store

    @@ -44039,7 +45052,7 @@

    Usage by Project

     

    Community

    -
    +

    Community

    @@ -44055,7 +45068,7 @@

    Community

    -
    +

    Code of Conduct @@ -44067,7 +45080,7 @@

    -
    +

    Forum @@ -44079,7 +45092,7 @@

    -
    +

    Discord @@ -44091,7 +45104,7 @@

    -
    +

    Profile @@ -44103,7 +45116,7 @@

    -
    +

    Academic Grants @@ -44115,7 +45128,7 @@

    -
    +

    Integration Partners @@ -44127,7 +45140,7 @@

    -
    +

    Affiliates @@ -44139,7 +45152,7 @@

    -
    +

    Research @@ -44151,7 +45164,7 @@

    -
    +

    Strategies @@ -44165,11 +45178,11 @@

    See Also

    -     + Using the Forum
    - + Managing Your Profile
    @@ -44181,7 +45194,7 @@

     

    Community > Code of Conduct

    -
    +

    Community

    Code of Conduct

    @@ -44419,7 +45432,7 @@

    About This Code of Conduct

     

    Community > Forum

    -
    +

    Community

    Forum

    @@ -44429,7 +45442,7 @@

    Introduction

    The QuantConnect forum is a place to discuss with other community members, Mia (our AI assistant), the core QuantConnect team, and our - + Integration Partners . Use the forum to spark interesting discussions, ask for assistance from other members, and voice your opinion on our products. You must complete 30% of the Bootcamp lessons in the @@ -44523,7 +45536,7 @@

    Credit

    To show your appreciation for contributions in the forum, - + give some QuantConnect Credit (QCC) rewards . The following table shows the available QCC rewards: @@ -44874,7 +45887,7 @@

    To toggle your subscription to individual discussions, add or remove a - + bookmark to the discussions. @@ -45377,7 +46390,7 @@

    Manage Bookmarks

     

    Community > Discord

    -
    +

    Community

    Discord

    @@ -45399,7 +46412,7 @@

    Code of Conduct

    The Discord server is governed by the normal rules in the - + Code of Conduct . @@ -45410,7 +46423,7 @@

    Code of Conduct

     

    Community > Profile

    -
    +

    Community

    Profile

    @@ -45773,7 +46786,7 @@

    Badges

    Connect your QuantConnect account with - + our Discord server . @@ -46486,7 +47499,7 @@

    Remove Account

     

    Community > Academic Grants

    -
    +

    Community

    Academic Grants

    @@ -46566,7 +47579,7 @@

    Republishing Permission

     

    Community > Integration Partners

    -
    +

    Community

    Integration Partners

    @@ -46613,7 +47626,7 @@

    Join the Integration Partner Team

     

    Community > Affiliates

    -
    +

    Community

    Affiliates

    @@ -46668,7 +47681,7 @@

    Become a Partner

     

    Community > Research

    -
    +

    Community

    Research

    @@ -46963,7 +47976,7 @@

    Examples

     

    Community > Strategies

    -
    +

    Community

    Strategies

    @@ -47000,7 +48013,7 @@

    Leaderboard

    The score is the Sharpe ratio over one year with a penalty for strategies with less than one year of out-of-sample data. The penalty is proportional to the fraction of the year they have been in-of-sample. For example, a strategy that has been in-of-sample for six months will have its Sharpe ratio halved to calculate its score. A strategy submitted for over a year will have no penalty applied, and its score matches its Sharpe ratio.

    - + Submit your strategies to join the competition. @@ -47201,7 +48214,7 @@

    Submission Guidelines

     

    API Reference

    -
    +

    API Reference

    @@ -47217,7 +48230,7 @@

    API Reference

    -
    +

    Authentication @@ -47229,7 +48242,7 @@

    -
    +

    Project Management @@ -47241,7 +48254,7 @@

    -
    +

    File Management @@ -47253,7 +48266,7 @@

    -
    +

    Compiling Code @@ -47265,7 +48278,7 @@

    -
    +

    Backtest Management @@ -47277,7 +48290,7 @@

    -
    +

    Live Management @@ -47289,7 +48302,7 @@

    -
    +

    Optimization Management @@ -47301,7 +48314,7 @@

    -
    +

    Object Store Management @@ -47313,7 +48326,7 @@

    -
    +

    AI Assistance @@ -47325,7 +48338,7 @@

    -
    +

    Reports @@ -47337,7 +48350,7 @@

    -
    +

    Account @@ -47349,7 +48362,7 @@

    -
    +

    Lean Version @@ -47359,9 +48372,20 @@

    +
    + +
    +
    +

    + Agent Management +

    +
    +
    +     +
    -
    +

    Examples @@ -47379,7 +48403,7 @@

     

    API Reference > Authentication

    -
    +

    API Reference

    Authentication

    @@ -47460,44 +48484,48 @@ 

    // Generate a timestamped SHA-256 hashed API token for secure authentication
-#r "nuget:RestSharp"
-using System;
 using System.Security.Cryptography;
-using RestSharp;
-
-// Request your API token on https://www.quantconnect.com/settings/ and replace the below values.
-var yourUserId = 0;
-var yourApiToken = "_____";
+using System.Text;
 
-// Get timestamp
-var stamp = ((DateTimeOffset)DateTime.UtcNow).ToUnixTimeSeconds();
-var timeStampedToken = $"{<yourApiToken>}:{stamp}";
+// Set the QC_USER_ID and QC_API_TOKEN environment variables with values from https://www.quantconnect.com/settings/.
+var yourUserId = Environment.GetEnvironmentVariable("QC_USER_ID") ?? "0";
+var yourApiToken = Environment.GetEnvironmentVariable("QC_API_TOKEN") ?? "_____";
 
-// Get hashed API token
-var crypt = new SHA256Managed();
-var hashToken = crypt.ComputeHash(Encoding.UTF8.GetBytes(timeStampedToken), 0, Encoding.UTF8.GetByteCount(timeStampedToken));
-var hash = new StringBuilder();
-foreach (var theByte in hashToken)
+Dictionary<string, string> GetHeaders()
 {
-    hash.Append(theByte.ToString("x2"));
+    // Get timestamp
+    var timestamp = ((DateTimeOffset)DateTime.UtcNow).ToUnixTimeSeconds().ToString();
+    var timeStampedToken = $"{yourApiToken}:{timestamp}";
+
+    // Get hashed API token
+    var hashBytes = SHA256.HashData(Encoding.UTF8.GetBytes(timeStampedToken));
+    var hash = BitConverter.ToString(hashBytes).Replace("-", "").ToLowerInvariant();
+    var authentication = Convert.ToBase64String(Encoding.UTF8.GetBytes($"{yourUserId}:{hash}"));
+
+    // Create headers dictionary.
+    return new Dictionary<string, string>
+    {
+        { "Authorization", $"Basic {authentication}" },
+        { "Timestamp", timestamp }
+    };
 }
-var apiToken = hash.ToString();
 
    # Generate a timestamped SHA-256 hashed API token for secure authentication
+from os import environ
 from base64 import b64encode
 from hashlib import sha256
 from time import time
 
-# Request your API token on https://www.quantconnect.com/settings/ and replace the below values.
-USER_ID = 0
-API_TOKEN = '_____'
+# Set the QC_USER_ID and QC_API_TOKEN environment variables with values from https://www.quantconnect.com/settings/.
+USER_ID = environ.get('QC_USER_ID', 0)
+API_TOKEN = environ.get('QC_API_TOKEN', '_____')
 
 def get_headers():
     # Get timestamp
     timestamp = f'{int(time())}'
     time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
 
-    # Get hased API token
+    # Get hashed API token
     hashed_token = sha256(time_stamped_token).hexdigest()
     authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
     authentication = b64encode(authentication).decode('ascii')
@@ -47512,27 +48540,22 @@ 
    
   
    
    Make API Request
   
    
-  
    
-   Follow the below example to install the hashing into the authenticator and make an API request.
-  
    
-  
    
-   Follow the below example to install the hashing into the headings and make an API request.
+  
    
+   Follow the below example to set the authentication headers and make an API request.
   
    
   
    
-   
    // Create REST client and install authenticator.
-var client = new RestClient("<requestUrl>");
-client.Authenticator = new HttpBasicAuthenticator(
-    "<yourUserId>",
-    hash.ToString()
-);
-
-// Create Request and add timestamp header (optional: Json Content).
-var request = new RestRequest();
-request.AddHeader("Timestamp", stamp.ToString());
+   
    // Create HTTP client with authentication headers.
+var client = new HttpClient();
+client.BaseAddress = new Uri("https://www.quantconnect.com/api/v2/");
+foreach (var header in GetHeaders())
+{
+    client.DefaultRequestHeaders.Add(header.Key, header.Value);
+}
 
 // Make POST request.
-var response = await client.PostAsync(request);
-var content = response.Content
    
+var request = new StringContent("{}", Encoding.UTF8, "application/json");
+var response = await client.PostAsync("<requestUrl>", request);
+var content = await response.Content.ReadAsStringAsync();
    
    
    # Create POST Request with headers
 from requests import post
 BASE_URL = 'https://www.quantconnect.com/api/v2/'
@@ -47542,6 +48565,61 @@ 
    
                 json = {})    # Use json keyword to send the payload
 content = response.text
    
   
    
+  
    
+   Using cURL
+  
    
+  
    
+   Follow the below example to make an API request with authentication using cURL.
+  
    
+  
    
+   
    # Set the QC_USER_ID and QC_API_TOKEN environment variables with values from https://www.quantconnect.com/settings/.
+YOUR_USER_ID=${QC_USER_ID:-0}
+YOUR_API_TOKEN=${QC_API_TOKEN:-_____}
+
+# Get timestamp
+TIMESTAMP=$(date +%s)
+TIME_STAMPED_TOKEN="${YOUR_API_TOKEN}:${TIMESTAMP}"
+
+# Get hashed API token
+HASHED_TOKEN=$(echo -n "$TIME_STAMPED_TOKEN" | sha256sum | cut -d ' ' -f 1)
+AUTHENTICATION=$(echo -n "${YOUR_USER_ID}:${HASHED_TOKEN}" | base64 -w 0)
+
+# Make POST request with authentication headers
+curl -X POST "https://www.quantconnect.com/api/v2/<request_url>" \
+    -H "Authorization: Basic ${AUTHENTICATION}" \
+    -H "Timestamp: ${TIMESTAMP}" \
+    -H "Content-Type: application/json" \
+    -d '{}'
    
+  
    
+  
    
+   Using PowerShell
+  
    
+  
    
+   Follow the below example to make an API request with authentication using PowerShell.
+  
    
+  
    
+   
    # Set the QC_USER_ID and QC_API_TOKEN environment variables with values from https://www.quantconnect.com/settings/.
+$yourUserId = if ($env:QC_USER_ID) { $env:QC_USER_ID } else { "0" }
+$yourApiToken = if ($env:QC_API_TOKEN) { $env:QC_API_TOKEN } else { "_____" }
+
+# Get timestamp
+$timestamp = [Math]::Floor((([DateTimeOffset]::UtcNow).ToUnixTimeSeconds()))
+$timeStampedToken = "${yourApiToken}:${timestamp}"
+
+# Get hashed API token
+$hasher = [System.Security.Cryptography.SHA256]::Create()
+$hashBytes = $hasher.ComputeHash([System.Text.Encoding]::UTF8.GetBytes($timeStampedToken))
+$hash = -join ($hashBytes | ForEach-Object { $_.ToString("x2") })
+$authentication = [Convert]::ToBase64String([System.Text.Encoding]::UTF8.GetBytes("${yourUserId}:${hash}"))
+
+# Make POST request with authentication headers
+$headers = @{
+    "Authorization" = "Basic $authentication"
+    "Timestamp" = "$timestamp"
+}
+$response = Invoke-RestMethod -Uri "https://www.quantconnect.com/api/v2/<requestUrl>" -Method Post -Headers $headers -ContentType "application/json" -Body "{}"
+Write-Host "Response: $($response | ConvertTo-Json)"
    
+  
    
  
 
 
@@ -47641,7 +48719,7 @@ 
    
 
     
    
 
    API Reference > Project Management
    
 
    
-    
    
+    
    
         
    API Reference
    
         
    Project Management
    
     
    
@@ -47657,7 +48735,7 @@ 
    Project Management
    
    
    
     
    
      
-      
    
+      
    
        
    
         
    
          Create Project
@@ -47668,7 +48746,7 @@ 
    
     
    
     
    
      
-      
    
+      
    
        
    
         
    
          Read Project
@@ -47679,7 +48757,7 @@ 
    
     
    
     
    
      
-      
    
+      
    
        
    
         
    
          Update Project
@@ -47690,7 +48768,7 @@ 
    
     
    
     
    
      
-      
    
+      
    
        
    
         
    
          Delete Project
@@ -47712,7 +48790,7 @@ 
    
     
    
     
    
      
-      
    
+      
    
        
    
         
    
          Nodes
@@ -47729,7 +48807,7 @@ 
    
 
     
    
 
    API Reference > Project Management > Create Project
    
 
    
-    
    
+    
    
         
    Project Management
    
         
    Create Project
    
     
    
@@ -48115,7 +49193,7 @@ 
    
         "name": "string"
       },
       "isPinned": true,
-      "maxFileSize": "string",
+      "maxFileSize": 0,
       "sharingTokenBacktest": "q"
     }
   ],
@@ -48439,7 +49517,7 @@ 
    
     
     
      
-      object
+      Grid object
       
    
       
        
@@ -48448,7 +49526,7 @@ 
    
       
      
      
    
-     Configuration of the backtest view grid.
+     The grid arrangement of charts.
     
    
    
@@ -48457,7 +49535,7 @@ 
    
     
     
      
-      object
+      Grid object
       
    
       
        
@@ -48466,7 +49544,7 @@ 
    
       
      
      
    
-     Configuration of the live view grid.
+     The grid arrangement of charts.
     
    
    
@@ -48511,7 +49589,7 @@ 
    
     
     
      
-      object
+      LiveForm object
       
    
       
        
@@ -48520,7 +49598,7 @@ 
    
       
      
      
    
-     The last live wizard content used.
+     The live wizard content used.
     
    
    
@@ -48583,7 +49661,7 @@ 
    
     
     
      
-      object
+      EncryptionKey object
       
    
       
        
@@ -48592,7 +49670,7 @@ 
    
       
      
      
    
-     Text file with at least 32 characters to be used to encrypt the project.
+     Encryption key details.
     
    
    
@@ -48619,7 +49697,7 @@ 
    
     
     
      
-      int
+      integer
       
    
       
        
@@ -48850,7 +49928,7 @@ 
    
     "name": "string"
   },
   "isPinned": true,
-  "maxFileSize": "string",
+  "maxFileSize": 0,
   "sharingTokenBacktest": "q"
 }
    @@ -49495,7 +50573,7 @@

    - object + BrokerageData object
    @@ -49504,7 +50582,7 @@


    - /. + Brokerage data saved on live deployment. @@ -51229,7 +52307,7 @@

     

    API Reference > Project Management > Read Project

    -
    +

    Project Management

    Read Project

    @@ -51615,7 +52693,7 @@

    "name": "string" }, "isPinned": true, - "maxFileSize": "string", + "maxFileSize": 0, "sharingTokenBacktest": "q" } ], @@ -51939,7 +53017,7 @@

    - object + Grid object
    @@ -51948,7 +53026,7 @@


    - Configuration of the backtest view grid. + The grid arrangement of charts. @@ -51957,7 +53035,7 @@

    - object + Grid object
    @@ -51966,7 +53044,7 @@


    - Configuration of the live view grid. + The grid arrangement of charts. @@ -52011,7 +53089,7 @@

    - object + LiveForm object
    @@ -52020,7 +53098,7 @@


    - The last live wizard content used. + The live wizard content used. @@ -52083,7 +53161,7 @@

    - object + EncryptionKey object
    @@ -52092,7 +53170,7 @@


    - Text file with at least 32 characters to be used to encrypt the project. + Encryption key details. @@ -52119,7 +53197,7 @@

    - int + integer
    @@ -52350,7 +53428,7 @@

    "name": "string" }, "isPinned": true, - "maxFileSize": "string", + "maxFileSize": 0, "sharingTokenBacktest": "q" }

    @@ -52995,7 +54073,7 @@

    - object + BrokerageData object
    @@ -53004,7 +54082,7 @@


    - /. + Brokerage data saved on live deployment. @@ -53656,7 +54734,7 @@

     

    API Reference > Project Management > Update Project

    -
    +

    Project Management

    Update Project

    @@ -54063,7 +55141,7 @@

     

    API Reference > Project Management > Delete Project

    -
    +

    Project Management

    Delete Project

    @@ -54432,14 +55510,14 @@

     

    API Reference > Project Management > Collaboration

    -
    +

    Project Management

    Collaboration

    API Reference > Project Management > Collaboration > Create Project Collaborator

    -
    +

    Collaboration

    Create Project Collaborator

    @@ -55000,16 +56078,86 @@

    Examples

    # Parse the JSON response into python managable dict result = response.json() # Check if the request was successful and print the result +if result['success']: + print(f"Project Collaborator Deleted Successfully: {result}") + if result['success']: print(f"Project Collaborator Deleted Successfully: {result}")
    +

    + The following example demonstrates how to add a collaborator to all projects that start with a given folder name. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# Define the folder prefix to match (e.g., "SubFolder/")
+folder_prefix = "SubFolder/"
+# Define collaborator ID (replace with actual user ID)
+collaborator_id = 'johnny_walker'
+# Send a POST request to the /projects/read endpoint to get all projects
+response = post(f'{BASE_URL}/projects/read', headers=get_headers())
+result = response.json()
+if result['success']:
+    # Filter projects that start with the given folder prefix
+    matching_projects = [
+        project for project in result['projects']
+        if project['name'].startswith(folder_prefix)
+    ]
+    # Add the collaborator to each matching project
+    for project in matching_projects:
+        response = post(f'{BASE_URL}/projects/collaboration/create', headers=get_headers(), json={
+            "projectId": project['projectId'],
+            "collaboratorUserId": collaborator_id,
+            "collaborationLiveControl": True,
+            "collaborationWrite": True
+        })
+        add_result = response.json()
+        if add_result['success']:
+            print(f"Added collaborator to project '{project['name']}' (ID: {project['projectId']})")
+        else:
+            print(f"Failed to add collaborator to project '{project['name']}': {add_result}")
    +

     

    API Reference > Project Management > Collaboration > Read Project Collaborators

    -
    +

    Collaboration

    Read Project Collaborators

    @@ -55521,16 +56669,86 @@

    Examples

    # Parse the JSON response into python managable dict result = response.json() # Check if the request was successful and print the result +if result['success']: + print(f"Project Collaborator Deleted Successfully: {result}") + if result['success']: print(f"Project Collaborator Deleted Successfully: {result}")
    +

    + The following example demonstrates how to add a collaborator to all projects that start with a given folder name. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# Define the folder prefix to match (e.g., "SubFolder/")
+folder_prefix = "SubFolder/"
+# Define collaborator ID (replace with actual user ID)
+collaborator_id = 'johnny_walker'
+# Send a POST request to the /projects/read endpoint to get all projects
+response = post(f'{BASE_URL}/projects/read', headers=get_headers())
+result = response.json()
+if result['success']:
+    # Filter projects that start with the given folder prefix
+    matching_projects = [
+        project for project in result['projects']
+        if project['name'].startswith(folder_prefix)
+    ]
+    # Add the collaborator to each matching project
+    for project in matching_projects:
+        response = post(f'{BASE_URL}/projects/collaboration/create', headers=get_headers(), json={
+            "projectId": project['projectId'],
+            "collaboratorUserId": collaborator_id,
+            "collaborationLiveControl": True,
+            "collaborationWrite": True
+        })
+        add_result = response.json()
+        if add_result['success']:
+            print(f"Added collaborator to project '{project['name']}' (ID: {project['projectId']})")
+        else:
+            print(f"Failed to add collaborator to project '{project['name']}': {add_result}")
    +

     

    API Reference > Project Management > Collaboration > Update Project Collaborator

    -
    +

    Collaboration

    Update Project Collaborator

    @@ -56091,16 +57309,86 @@

    Examples

    # Parse the JSON response into python managable dict result = response.json() # Check if the request was successful and print the result +if result['success']: + print(f"Project Collaborator Deleted Successfully: {result}") + if result['success']: print(f"Project Collaborator Deleted Successfully: {result}")
    +

    + The following example demonstrates how to add a collaborator to all projects that start with a given folder name. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# Define the folder prefix to match (e.g., "SubFolder/")
+folder_prefix = "SubFolder/"
+# Define collaborator ID (replace with actual user ID)
+collaborator_id = 'johnny_walker'
+# Send a POST request to the /projects/read endpoint to get all projects
+response = post(f'{BASE_URL}/projects/read', headers=get_headers())
+result = response.json()
+if result['success']:
+    # Filter projects that start with the given folder prefix
+    matching_projects = [
+        project for project in result['projects']
+        if project['name'].startswith(folder_prefix)
+    ]
+    # Add the collaborator to each matching project
+    for project in matching_projects:
+        response = post(f'{BASE_URL}/projects/collaboration/create', headers=get_headers(), json={
+            "projectId": project['projectId'],
+            "collaboratorUserId": collaborator_id,
+            "collaborationLiveControl": True,
+            "collaborationWrite": True
+        })
+        add_result = response.json()
+        if add_result['success']:
+            print(f"Added collaborator to project '{project['name']}' (ID: {project['projectId']})")
+        else:
+            print(f"Failed to add collaborator to project '{project['name']}': {add_result}")
    +

     

    API Reference > Project Management > Collaboration > Delete Project Collaborator

    -
    +

    Collaboration

    Delete Project Collaborator

    @@ -56611,16 +57899,86 @@

    Examples

    # Parse the JSON response into python managable dict result = response.json() # Check if the request was successful and print the result +if result['success']: + print(f"Project Collaborator Deleted Successfully: {result}") + if result['success']: print(f"Project Collaborator Deleted Successfully: {result}")
    +

    + The following example demonstrates how to add a collaborator to all projects that start with a given folder name. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# Define the folder prefix to match (e.g., "SubFolder/")
+folder_prefix = "SubFolder/"
+# Define collaborator ID (replace with actual user ID)
+collaborator_id = 'johnny_walker'
+# Send a POST request to the /projects/read endpoint to get all projects
+response = post(f'{BASE_URL}/projects/read', headers=get_headers())
+result = response.json()
+if result['success']:
+    # Filter projects that start with the given folder prefix
+    matching_projects = [
+        project for project in result['projects']
+        if project['name'].startswith(folder_prefix)
+    ]
+    # Add the collaborator to each matching project
+    for project in matching_projects:
+        response = post(f'{BASE_URL}/projects/collaboration/create', headers=get_headers(), json={
+            "projectId": project['projectId'],
+            "collaboratorUserId": collaborator_id,
+            "collaborationLiveControl": True,
+            "collaborationWrite": True
+        })
+        add_result = response.json()
+        if add_result['success']:
+            print(f"Added collaborator to project '{project['name']}' (ID: {project['projectId']})")
+        else:
+            print(f"Failed to add collaborator to project '{project['name']}': {add_result}")
    +

     

    API Reference > Project Management > Collaboration > Lock Project

    -
    +

    Collaboration

    Lock Project

    @@ -56914,23 +58272,93 @@

    Examples

    # Parse the JSON response into python managable dict result = response.json() # Check if the request was successful and print the result +if result['success']: + print(f"Project Collaborator Deleted Successfully: {result}") + if result['success']: print(f"Project Collaborator Deleted Successfully: {result}")
    +

    + The following example demonstrates how to add a collaborator to all projects that start with a given folder name. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# Define the folder prefix to match (e.g., "SubFolder/")
+folder_prefix = "SubFolder/"
+# Define collaborator ID (replace with actual user ID)
+collaborator_id = 'johnny_walker'
+# Send a POST request to the /projects/read endpoint to get all projects
+response = post(f'{BASE_URL}/projects/read', headers=get_headers())
+result = response.json()
+if result['success']:
+    # Filter projects that start with the given folder prefix
+    matching_projects = [
+        project for project in result['projects']
+        if project['name'].startswith(folder_prefix)
+    ]
+    # Add the collaborator to each matching project
+    for project in matching_projects:
+        response = post(f'{BASE_URL}/projects/collaboration/create', headers=get_headers(), json={
+            "projectId": project['projectId'],
+            "collaboratorUserId": collaborator_id,
+            "collaborationLiveControl": True,
+            "collaborationWrite": True
+        })
+        add_result = response.json()
+        if add_result['success']:
+            print(f"Added collaborator to project '{project['name']}' (ID: {project['projectId']})")
+        else:
+            print(f"Failed to add collaborator to project '{project['name']}': {add_result}")
    +

     

    API Reference > Project Management > Nodes

    -
    +

    Project Management

    Nodes

    API Reference > Project Management > Nodes > Read Project Nodes

    -
    +

    Nodes

    Read Project Nodes

    @@ -57834,7 +59262,7 @@

     

    API Reference > Project Management > Nodes > Update Project Nodes

    -
    +

    Nodes

    Update Project Nodes

    @@ -58753,7 +60181,7 @@

     

    API Reference > File Management

    -
    +

    API Reference

    File Management

    @@ -58769,7 +60197,7 @@

    File Management

    -
    +

    Create File @@ -58780,7 +60208,7 @@

    -
    +

    Read File @@ -58791,7 +60219,7 @@

    -
    +

    Update File @@ -58802,7 +60230,7 @@

    -
    +

    Delete File @@ -58813,7 +60241,7 @@

    -
    +

    Patch File @@ -58830,7 +60258,7 @@

     

    API Reference > File Management > Create File

    -
    +

    File Management

    Create File

    @@ -59210,7 +60638,7 @@

    Examples

     

    API Reference > File Management > Read File

    -
    +

    File Management

    Read File

    @@ -59704,7 +61132,7 @@

    Examples

     

    API Reference > File Management > Update File

    -
    +

    File Management

    Update File

    @@ -60165,7 +61593,7 @@

    Examples

     

    API Reference > File Management > Delete File

    -
    +

    File Management

    Delete File

    @@ -60563,7 +61991,7 @@

    Examples

     

    API Reference > Compiling Code

    -
    +

    API Reference

    Compiling Code

    @@ -60579,7 +62007,7 @@

    Compiling Code

    -
    +

    Create Compilation Job @@ -60590,7 +62018,7 @@

    -
    +

    Read Compilation Result @@ -60607,7 +62035,7 @@

     

    API Reference > Compiling Code > Create Compilation Job

    -
    +

    Compiling Code

    Create Compilation Job

    @@ -61089,7 +62517,7 @@

    Examples

     

    API Reference > Compiling Code > Read Compilation Result

    -
    +

    Compiling Code

    Read Compilation Result

    @@ -61418,7 +62846,7 @@

    Examples

     

    API Reference > Backtest Management

    -
    +

    API Reference

    Backtest Management

    @@ -61434,7 +62862,7 @@

    Backtest Management

    -
    +

    Create Backtest @@ -61445,7 +62873,7 @@

    -
    +

    Read Backtest @@ -61456,7 +62884,7 @@

    -
    +

    Update Backtest @@ -61467,7 +62895,7 @@

    -
    +

    Delete Backtest @@ -61478,7 +62906,7 @@

    -
    +

    List Backtests @@ -61834,7 +63262,7 @@

    Examples

     

    API Reference > Backtest Management > Create Backtest

    -
    +

    Backtest Management

    Create Backtest

    @@ -62086,98 +63514,7 @@

    }, "parameterSet": , "rollingWindow": { - "tradeStatistics": { - "startDateTime": "2021-11-26T15:18:27.693Z", - "endDateTime": "2021-11-26T15:18:27.693Z", - "totalNumberOfTrades": 0, - "numberOfWinningTrades": 0, - "numberOfLosingTrades": 0, - "totalProfitLoss": "string", - "totalProfit": "string", - "totalLoss": "string", - "largestProfit": "string", - "largestLoss": "string", - "averageProfitLoss": "string", - "averageProfit": "string", - "averageLoss": "string", - "averageTradeDuration": "string", - "averageWinningTradeDuration": "string", - "averageLosingTradeDuration": "string", - "medianTradeDuration": "string", - "medianWinningTradeDuration": "string", - "medianLosingTradeDuration": "string", - "maxConsecutiveWinningTrades": 0, - "maxConsecutiveLosingTrades": 0, - "profitLossRatio": "string", - "winLossRatio": "string", - "winRate": "string", - "lossRate": "string", - "averageMAE": "string", - "averageMFE": "string", - "largestMAE": "string", - "largestMFE": "string", - "maximumClosedTradeDrawdown": "string", - "maximumIntraTradeDrawdown": "string", - "profitLossStandardDeviation": "string", - "profitLossDownsideDeviation": "string", - "profitFactor": "string", - "sharpeRatio": "string", - "sortinoRatio": "string", - "profitToMaxDrawdownRatio": "string", - "maximumEndTradeDrawdown": "string", - "averageEndTradeDrawdown": "string", - "maximumDrawdownDuration": "string", - "totalFees": "string" - }, - "portfolioStatistics": { - "averageWinRate": "string", - "averageLossRate": "string", - "profitLossRatio": "string", - "winRate": "string", - "lossRate": "string", - "expectancy": "string", - "startEquity": "string", - "endEquity": "string", - "compoundingAnnualReturn": "string", - "drawdown": "string", - "totalNetProfit": "string", - "sharpeRatio": "string", - "probabilisticSharpeRatio": "string", - "sortinoRatio": "string", - "alpha": "string", - "beta": "string", - "annualStandardDeviation": "string", - "annualVariance": "string", - "informationRatio": "string", - "trackingError": "string", - "treynorRatio": "string", - "portfolioTurnover": "string", - "valueAtRisk99": "string", - "valueAtRisk95": "string", - "drawdownRecovery": "string" - }, - "closedTrades": [ - { - "symbol": { - "value": "string", - "id": "string", - "permtick": "string" - }, - "entryTime": "2021-11-26T15:18:27.693Z", - "entryPrice": 0, - "direction": 0, - "quantity": 0, - "exitTime": "2021-11-26T15:18:27.693Z", - "exitPrice": 0, - "profitLoss": 0, - "totalFees": 0, - "mae": 0, - "mfe": 0, - "duration": "string", - "endTradeDrawdown": 0 - } - ] - }, + }, "runtimeStatistics": { "Equity": "$100.00", "Fees": "-$100.00", @@ -62312,7 +63649,21 @@

    }, "nodeName": "string", "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z", - "outOfSampleDays": 0 + "outOfSampleDays": 0, + "analysis": [ + { + "name": "PerformanceRelativeToBenchmarkAnalysis", + "issue": "The strategy has a lower Sharpe ratio than the benchmark.", + "sample": { + "backtestSharpe": -0.2281345806218534, + "benchmarkSharpe": -0.22182175736916387 + }, + "count": 5, + "solutions": [ + "Try adjusting the trading rules and/or the universe to get a strategy that outperforms the benchmark." + ] + } + ] }, "debugging": true, "success": true, @@ -62701,7 +64052,7 @@

    - AlgorithmPerformance object + object
    Rolling window detailed statistics. @@ -62749,7 +64100,7 @@

    - object + AlgorithmPerformance object
    @@ -62758,7 +64109,7 @@


    - The algorithm performance statistics. + The AlgorithmPerformance class is a wrapper for TradeStatistics and PortfolioStatistics. @@ -62803,6 +64154,18 @@

    Number of days of out of sample days. + + + analysis + + + + AnalysisResult Array + +
    + /. + + Example @@ -62838,98 +64201,7 @@

    }, "parameterSet": , "rollingWindow": { - "tradeStatistics": { - "startDateTime": "2021-11-26T15:18:27.693Z", - "endDateTime": "2021-11-26T15:18:27.693Z", - "totalNumberOfTrades": 0, - "numberOfWinningTrades": 0, - "numberOfLosingTrades": 0, - "totalProfitLoss": "string", - "totalProfit": "string", - "totalLoss": "string", - "largestProfit": "string", - "largestLoss": "string", - "averageProfitLoss": "string", - "averageProfit": "string", - "averageLoss": "string", - "averageTradeDuration": "string", - "averageWinningTradeDuration": "string", - "averageLosingTradeDuration": "string", - "medianTradeDuration": "string", - "medianWinningTradeDuration": "string", - "medianLosingTradeDuration": "string", - "maxConsecutiveWinningTrades": 0, - "maxConsecutiveLosingTrades": 0, - "profitLossRatio": "string", - "winLossRatio": "string", - "winRate": "string", - "lossRate": "string", - "averageMAE": "string", - "averageMFE": "string", - "largestMAE": "string", - "largestMFE": "string", - "maximumClosedTradeDrawdown": "string", - "maximumIntraTradeDrawdown": "string", - "profitLossStandardDeviation": "string", - "profitLossDownsideDeviation": "string", - "profitFactor": "string", - "sharpeRatio": "string", - "sortinoRatio": "string", - "profitToMaxDrawdownRatio": "string", - "maximumEndTradeDrawdown": "string", - "averageEndTradeDrawdown": "string", - "maximumDrawdownDuration": "string", - "totalFees": "string" - }, - "portfolioStatistics": { - "averageWinRate": "string", - "averageLossRate": "string", - "profitLossRatio": "string", - "winRate": "string", - "lossRate": "string", - "expectancy": "string", - "startEquity": "string", - "endEquity": "string", - "compoundingAnnualReturn": "string", - "drawdown": "string", - "totalNetProfit": "string", - "sharpeRatio": "string", - "probabilisticSharpeRatio": "string", - "sortinoRatio": "string", - "alpha": "string", - "beta": "string", - "annualStandardDeviation": "string", - "annualVariance": "string", - "informationRatio": "string", - "trackingError": "string", - "treynorRatio": "string", - "portfolioTurnover": "string", - "valueAtRisk99": "string", - "valueAtRisk95": "string", - "drawdownRecovery": "string" - }, - "closedTrades": [ - { - "symbol": { - "value": "string", - "id": "string", - "permtick": "string" - }, - "entryTime": "2021-11-26T15:18:27.693Z", - "entryPrice": 0, - "direction": 0, - "quantity": 0, - "exitTime": "2021-11-26T15:18:27.693Z", - "exitPrice": 0, - "profitLoss": 0, - "totalFees": 0, - "mae": 0, - "mfe": 0, - "duration": "string", - "endTradeDrawdown": 0 - } - ] - }, +}, "runtimeStatistics": { "Equity": "$100.00", "Fees": "-$100.00", @@ -63064,7 +64336,21 @@

    }, "nodeName": "string", "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z", - "outOfSampleDays": 0 + "outOfSampleDays": 0, + "analysis": [ + { + "name": "PerformanceRelativeToBenchmarkAnalysis", + "issue": "The strategy has a lower Sharpe ratio than the benchmark.", + "sample": { + "backtestSharpe": -0.2281345806218534, + "benchmarkSharpe": -0.22182175736916387 + }, + "count": 5, + "solutions": [ + "Try adjusting the trading rules and/or the universe to get a strategy that outperforms the benchmark." + ] + } + ] }

    @@ -63175,442 +64461,347 @@

    - AlgorithmPerformance - - Model - The AlgorithmPerformance class is a wrapper for TradeStatistics and PortfolioStatistics. - - - - - - tradeStatistics - - - - TradeStatistics object - -
    - A set of statistics calculated from a list of closed trades. - - - - - portfolioStatistics - - - - PortfolioStatistics object - -
    - Represents a set of statistics calculated from equity and benchmark samples. - - - - - closedTrades - - - - Trade Array - -
    - The algorithm statistics on portfolio. - - - - - Example - - -
    - 
    -{
-  "tradeStatistics": {
-    "startDateTime": "2021-11-26T15:18:27.693Z",
-    "endDateTime": "2021-11-26T15:18:27.693Z",
-    "totalNumberOfTrades": 0,
-    "numberOfWinningTrades": 0,
-    "numberOfLosingTrades": 0,
-    "totalProfitLoss": "string",
-    "totalProfit": "string",
-    "totalLoss": "string",
-    "largestProfit": "string",
-    "largestLoss": "string",
-    "averageProfitLoss": "string",
-    "averageProfit": "string",
-    "averageLoss": "string",
-    "averageTradeDuration": "string",
-    "averageWinningTradeDuration": "string",
-    "averageLosingTradeDuration": "string",
-    "medianTradeDuration": "string",
-    "medianWinningTradeDuration": "string",
-    "medianLosingTradeDuration": "string",
-    "maxConsecutiveWinningTrades": 0,
-    "maxConsecutiveLosingTrades": 0,
-    "profitLossRatio": "string",
-    "winLossRatio": "string",
-    "winRate": "string",
-    "lossRate": "string",
-    "averageMAE": "string",
-    "averageMFE": "string",
-    "largestMAE": "string",
-    "largestMFE": "string",
-    "maximumClosedTradeDrawdown": "string",
-    "maximumIntraTradeDrawdown": "string",
-    "profitLossStandardDeviation": "string",
-    "profitLossDownsideDeviation": "string",
-    "profitFactor": "string",
-    "sharpeRatio": "string",
-    "sortinoRatio": "string",
-    "profitToMaxDrawdownRatio": "string",
-    "maximumEndTradeDrawdown": "string",
-    "averageEndTradeDrawdown": "string",
-    "maximumDrawdownDuration": "string",
-    "totalFees": "string"
-  },
-  "portfolioStatistics": {
-    "averageWinRate": "string",
-    "averageLossRate": "string",
-    "profitLossRatio": "string",
-    "winRate": "string",
-    "lossRate": "string",
-    "expectancy": "string",
-    "startEquity": "string",
-    "endEquity": "string",
-    "compoundingAnnualReturn": "string",
-    "drawdown": "string",
-    "totalNetProfit": "string",
-    "sharpeRatio": "string",
-    "probabilisticSharpeRatio": "string",
-    "sortinoRatio": "string",
-    "alpha": "string",
-    "beta": "string",
-    "annualStandardDeviation": "string",
-    "annualVariance": "string",
-    "informationRatio": "string",
-    "trackingError": "string",
-    "treynorRatio": "string",
-    "portfolioTurnover": "string",
-    "valueAtRisk99": "string",
-    "valueAtRisk95": "string",
-    "drawdownRecovery": "string"
-  },
-  "closedTrades": [
-    {
-      "symbol": {
-        "value": "string",
-        "id": "string",
-        "permtick": "string"
-      },
-      "entryTime": "2021-11-26T15:18:27.693Z",
-      "entryPrice": 0,
-      "direction": 0,
-      "quantity": 0,
-      "exitTime": "2021-11-26T15:18:27.693Z",
-      "exitPrice": 0,
-      "profitLoss": 0,
-      "totalFees": 0,
-      "mae": 0,
-      "mfe": 0,
-      "duration": "string",
-      "endTradeDrawdown": 0
-    }
-  ]
-}
    -
    - - - - - - - - - - - +
    - - TradeStatistics + RuntimeStatistics - Model - A set of statistics calculated from a list of closed trades. + Model
    - startDateTime - - - string($date-time) - -
    - The entry date/time of the first trade. -
    - endDateTime + Equity - string($date-time) + string +
    + + + example: $100.00 + +
    - The exit date/time of the first trade. + Total portfolio value.
    - totalNumberOfTrades + Fees - integer + string +
    + + + example: -$100.00 + +
    - The total number of trades. + Transaction fee.
    - numberOfWinningTrades + Holdings - integer + string +
    + + + example: $100.00 + +
    - The total number of winning trades. + Equity value of security holdings.
    - numberOfLosingTrades + Net Profit - integer + string +
    + + + example: $100.00 + +
    - The total number of losing trades. + Net profit.
    - totalProfitLoss + Probabilistic Sharpe Ratio string +
    + + + example: 50.00% + +
    - The total profit/loss for all trades (as symbol currency). + Probabilistic Sharpe Ratio.
    - totalProfit + Return string +
    + + + example: 50.00% + +
    - The total profit for all winning trades (as symbol currency). + Return.
    - totalLoss + Unrealized string +
    + + + example: $100.00 + +
    - The total loss for all losing trades (as symbol currency). + Unrealized profit/loss.
    - largestProfit + Volume string +
    + + + example: $100.00 + +
    - The largest profit in a single trade (as symbol currency). + Total transaction volume.
    - largestLoss + Example - - string - -
    - The largest loss in a single trade (as symbol currency). +
    + 
    +{
+  "Equity": "$100.00",
+  "Fees": "-$100.00",
+  "Holdings": "$100.00",
+  "Net Profit": "$100.00",
+  "Probabilistic Sharpe Ratio": "50.00%",
+  "Return": "50.00%",
+  "Unrealized": "$100.00",
+  "Volume": "$100.00"
+}
    +
    + + + + + + +
    + + StatisticsResult + + Model - Statistics information sent during the algorithm operations. +
    - averageProfitLoss + Total Orders string
    - The average profit/loss (a.k.a. Expectancy or Average Trade) for all trades (as symbol currency). + Total nuber of orders.
    - averageProfit + Average Win string
    - The average profit for all winning trades (as symbol currency). + The average rate of return for winning trades.
    - averageLoss + Average Loss string
    - The average loss for all winning trades (as symbol currency). + The average rate of return for losing trades.
    - averageTradeDuration + Compounding Annual Return string
    - The average duration for all trades. + Annual compounded returns statistic based on the final-starting capital and years.
    - averageWinningTradeDuration + Drawdown string
    - The average duration for all winning trades. + Drawdown maximum percentage.
    - averageLosingTradeDuration + Expectancy string
    - The average duration for all losing trades. + The expected value of the rate of return.
    - medianTradeDuration + Start Equity string
    - The median duration for all trades. + Initial Equity Total Value.
    - medianWinningTradeDuration + End Equity string
    - The median duration for all winning trades. + Final Equity Total Value.
    - medianLosingTradeDuration + Net Profit string
    - The median duration for all losing trades. + The total net profit percentage.
    - maxConsecutiveWinningTrades + Sharpe Ratio - integer + string
    - The maximum number of consecutive winning trades. + Sharpe ratio with respect to risk free rate; measures excess of return per unit of risk.
    - maxConsecutiveLosingTrades + Sortino Ratio - integer + string
    - The maximum number of consecutive losing trades. + Sortino ratio with respect to risk free rate; measures excess of return per unit of downside risk.
    - profitLossRatio + Probabilistic Sharpe Ratio string
    - The ratio of the average profit per trade to the average loss per trade. + Is a probability measure associated with the Sharpe ratio. It informs us of the probability that the estimated Sharpe ratio is greater than a chosen benchmark.
    - winLossRatio + Loss Rate string
    - The ratio of the number of winning trades to the number of losing trades. + The ratio of the number of losing trades to the total number of trades.
    - winRate + Win Rate @@ -63622,206 +64813,245 @@

    - lossRate + Profit-Loss Ratio string
    - The ratio of the number of losing trades to the total number of trades. + The ratio of the average win rate to the average loss rate.
    - averageMAE + Alpha string
    - The average Maximum Adverse Excursion for all trades. + Algorithm "Alpha" statistic - abnormal returns over the risk free rate and the relationshio (beta) with the benchmark returns.
    - averageMFE + Beta string
    - The average Maximum Adverse Excursion for all trades. + Algorithm "beta" statistic - the covariance between the algorithm and benchmark performance, divided by benchmark's variance.
    - largestMAE + Annual Standard Deviation string
    - The average Maximum Favorable Excursion for all trades. + Annualized standard deviation.
    - largestMFE + Annual Variance string
    - The largest Maximum Adverse Excursion in a single trade (as symbol currency). + Annualized variance statistic calculation using the daily performance variance and trading days per year.
    - maximumClosedTradeDrawdown + Information Ratio string
    - The maximum closed-trade drawdown for all trades (as symbol currency). + Information ratio - risk adjusted return.
    - maximumIntraTradeDrawdown + Tracking Error string
    - The maximum intra-trade drawdown for all trades (as symbol currency). + Tracking error volatility (TEV) statistic - a measure of how closely a portfolio follows the index to which it is benchmarked.
    - profitLossStandardDeviation + Treynor Ratio string
    - The standard deviation of the profits/losses for all trades (as symbol currency). + Treynor ratio statistic is a measurement of the returns earned in excess of that which could have been earned on an investment that has no diversifiable risk.
    - profitLossDownsideDeviation + Total Fees string
    - The downside deviation of the profits/losses for all trades (as symbol currency). + Total amount of fees.
    - profitFactor + Estimated Strategy Capacity string
    - The ratio of the total profit to the total loss. + The estimated total capacity of the strategy at a point in time.
    - sharpeRatio + Lowest Capacity Asset string
    - The ratio of the average profit/loss to the standard deviation. + Provide a reference to the lowest capacity symbol used in scaling down the capacity for debugging.
    - sortinoRatio + Portfolio Turnover string
    - The ratio of the average profit/loss to the downside deviation. + The average Portfolio Turnover.
    - profitToMaxDrawdownRatio + Drawdown Recovery string
    - The ratio of the total profit/loss to the maximum closed trade drawdown. + /.
    - maximumEndTradeDrawdown + Example - - string - -
    - The maximum amount of profit given back by a single trade before exit (as symbol currency). +
    + 
    +{
+  "Total Orders": "string",
+  "Average Win": "string",
+  "Average Loss": "string",
+  "Compounding Annual Return": "string",
+  "Drawdown": "string",
+  "Expectancy": "string",
+  "Start Equity": "string",
+  "End Equity": "string",
+  "Net Profit": "string",
+  "Sharpe Ratio": "string",
+  "Sortino Ratio": "string",
+  "Probabilistic Sharpe Ratio": "string",
+  "Loss Rate": "string",
+  "Win Rate": "string",
+  "Profit-Loss Ratio": "string",
+  "Alpha": "string",
+  "Beta": "string",
+  "Annual Standard Deviation": "string",
+  "Annual Variance": "string",
+  "Information Ratio": "string",
+  "Tracking Error": "string",
+  "Treynor Ratio": "string",
+  "Total Fees": "string",
+  "Estimated Strategy Capacity": "string",
+  "Lowest Capacity Asset": "string",
+  "Portfolio Turnover": "string",
+  "Drawdown Recovery": "string"
+}
    +
    + + + + + + @@ -63832,47 +65062,97 @@ 

     {
-  "startDateTime": "2021-11-26T15:18:27.693Z",
-  "endDateTime": "2021-11-26T15:18:27.693Z",
-  "totalNumberOfTrades": 0,
-  "numberOfWinningTrades": 0,
-  "numberOfLosingTrades": 0,
-  "totalProfitLoss": "string",
-  "totalProfit": "string",
-  "totalLoss": "string",
-  "largestProfit": "string",
-  "largestLoss": "string",
-  "averageProfitLoss": "string",
-  "averageProfit": "string",
-  "averageLoss": "string",
-  "averageTradeDuration": "string",
-  "averageWinningTradeDuration": "string",
-  "averageLosingTradeDuration": "string",
-  "medianTradeDuration": "string",
-  "medianWinningTradeDuration": "string",
-  "medianLosingTradeDuration": "string",
-  "maxConsecutiveWinningTrades": 0,
-  "maxConsecutiveLosingTrades": 0,
-  "profitLossRatio": "string",
-  "winLossRatio": "string",
-  "winRate": "string",
-  "lossRate": "string",
-  "averageMAE": "string",
-  "averageMFE": "string",
-  "largestMAE": "string",
-  "largestMFE": "string",
-  "maximumClosedTradeDrawdown": "string",
-  "maximumIntraTradeDrawdown": "string",
-  "profitLossStandardDeviation": "string",
-  "profitLossDownsideDeviation": "string",
-  "profitFactor": "string",
-  "sharpeRatio": "string",
-  "sortinoRatio": "string",
-  "profitToMaxDrawdownRatio": "string",
-  "maximumEndTradeDrawdown": "string",
-  "averageEndTradeDrawdown": "string",
-  "maximumDrawdownDuration": "string",
-  "totalFees": "string"
+  "tradeStatistics": {
+    "startDateTime": "2021-11-26T15:18:27.693Z",
+    "endDateTime": "2021-11-26T15:18:27.693Z",
+    "totalNumberOfTrades": 0,
+    "numberOfWinningTrades": 0,
+    "numberOfLosingTrades": 0,
+    "totalProfitLoss": "string",
+    "totalProfit": "string",
+    "totalLoss": "string",
+    "largestProfit": "string",
+    "largestLoss": "string",
+    "averageProfitLoss": "string",
+    "averageProfit": "string",
+    "averageLoss": "string",
+    "averageTradeDuration": "string",
+    "averageWinningTradeDuration": "string",
+    "averageLosingTradeDuration": "string",
+    "medianTradeDuration": "string",
+    "medianWinningTradeDuration": "string",
+    "medianLosingTradeDuration": "string",
+    "maxConsecutiveWinningTrades": 0,
+    "maxConsecutiveLosingTrades": 0,
+    "profitLossRatio": "string",
+    "winLossRatio": "string",
+    "winRate": "string",
+    "lossRate": "string",
+    "averageMAE": "string",
+    "averageMFE": "string",
+    "largestMAE": "string",
+    "largestMFE": "string",
+    "maximumClosedTradeDrawdown": "string",
+    "maximumIntraTradeDrawdown": "string",
+    "profitLossStandardDeviation": "string",
+    "profitLossDownsideDeviation": "string",
+    "profitFactor": "string",
+    "sharpeRatio": "string",
+    "sortinoRatio": "string",
+    "profitToMaxDrawdownRatio": "string",
+    "maximumEndTradeDrawdown": "string",
+    "averageEndTradeDrawdown": "string",
+    "maximumDrawdownDuration": "string",
+    "totalFees": "string"
+  },
+  "portfolioStatistics": {
+    "averageWinRate": "string",
+    "averageLossRate": "string",
+    "profitLossRatio": "string",
+    "winRate": "string",
+    "lossRate": "string",
+    "expectancy": "string",
+    "startEquity": "string",
+    "endEquity": "string",
+    "compoundingAnnualReturn": "string",
+    "drawdown": "string",
+    "totalNetProfit": "string",
+    "sharpeRatio": "string",
+    "probabilisticSharpeRatio": "string",
+    "sortinoRatio": "string",
+    "alpha": "string",
+    "beta": "string",
+    "annualStandardDeviation": "string",
+    "annualVariance": "string",
+    "informationRatio": "string",
+    "trackingError": "string",
+    "treynorRatio": "string",
+    "portfolioTurnover": "string",
+    "valueAtRisk99": "string",
+    "valueAtRisk95": "string",
+    "drawdownRecovery": "string"
+  },
+  "closedTrades": [
+    {
+      "symbol": {
+        "value": "string",
+        "id": "string",
+        "permtick": "string"
+      },
+      "entryTime": "2021-11-26T15:18:27.693Z",
+      "entryPrice": 0,
+      "direction": 0,
+      "quantity": 0,
+      "exitTime": "2021-11-26T15:18:27.693Z",
+      "exitPrice": 0,
+      "profitLoss": 0,
+      "totalFees": 0,
+      "mae": 0,
+      "mfe": 0,
+      "duration": "string",
+      "endTradeDrawdown": 0
+    }
+  ]
 }
    @@ -63883,592 +65163,502 @@

    - - - - -
    + + AlgorithmPerformance + + Model - The AlgorithmPerformance class is a wrapper for TradeStatistics and PortfolioStatistics. +
    - averageEndTradeDrawdown + tradeStatistics - string + TradeStatistics object
    - The average amount of profit given back by all trades before exit (as symbol currency). + A set of statistics calculated from a list of closed trades.
    - maximumDrawdownDuration + portfolioStatistics - string + PortfolioStatistics object
    - The maximum amount of time to recover from a drawdown (longest time between new equity highs or peaks). + Represents a set of statistics calculated from equity and benchmark samples.
    - totalFees + closedTrades - string + Trade Array
    - The sum of fees for all trades. + The algorithm statistics on portfolio.
    - PortfolioStatistics + TradeStatistics - Model - Represents a set of statistics calculated from equity and benchmark samples. + Model - A set of statistics calculated from a list of closed trades.
    - averageWinRate + startDateTime - string + string($date-time)
    - The average rate of return for winning trades. + The entry date/time of the first trade.
    - averageLossRate + endDateTime - string + string($date-time)
    - The average rate of return for losing trades. + The exit date/time of the first trade.
    - profitLossRatio + totalNumberOfTrades - string + integer
    - The ratio of the average win rate to the average loss rate. + The total number of trades.
    - winRate + numberOfWinningTrades - string + integer
    - The ratio of the number of winning trades to the total number of trades. + The total number of winning trades.
    - lossRate + numberOfLosingTrades - string + integer
    - The ratio of the number of losing trades to the total number of trades. + The total number of losing trades.
    - expectancy + totalProfitLoss string
    - The expected value of the rate of return. + The total profit/loss for all trades (as symbol currency).
    - startEquity + totalProfit string
    - Initial Equity Total Value. + The total profit for all winning trades (as symbol currency).
    - endEquity + totalLoss string
    - Final Equity Total Value. + The total loss for all losing trades (as symbol currency).
    - compoundingAnnualReturn + largestProfit string
    - Annual compounded returns statistic based on the final-starting capital and years. + The largest profit in a single trade (as symbol currency).
    - drawdown + largestLoss string
    - Drawdown maximum percentage. + The largest loss in a single trade (as symbol currency).
    - totalNetProfit + averageProfitLoss string
    - The total net profit percentage. + The average profit/loss (a.k.a. Expectancy or Average Trade) for all trades (as symbol currency).
    - sharpeRatio + averageProfit string
    - Sharpe ratio with respect to risk free rate: measures excess of return per unit of risk. + The average profit for all winning trades (as symbol currency).
    - probabilisticSharpeRatio + averageLoss string
    - Probabilistic Sharpe Ratio is a probability measure associated with the Sharpe ratio. It informs us of the probability that the estimated Sharpe ratio is greater than a chosen benchmark. + The average loss for all winning trades (as symbol currency).
    - sortinoRatio + averageTradeDuration string
    - Sortino ratio with respect to risk free rate; measures excess of return per unit of downside risk. + The average duration for all trades.
    - alpha + averageWinningTradeDuration string
    - Algorithm "Alpha" statistic - abnormal returns over the risk free rate and the relationshio (beta) with the benchmark returns. + The average duration for all winning trades.
    - beta + averageLosingTradeDuration string
    - Algorithm beta statistic - the covariance between the algorithm and benchmark performance, divided by benchmark variance. + The average duration for all losing trades.
    - annualStandardDeviation + medianTradeDuration string
    - Annualized standard deviation. + The median duration for all trades.
    - annualVariance + medianWinningTradeDuration string
    - Annualized variance statistic calculation using the daily performance variance and trading days per year. + The median duration for all winning trades.
    - informationRatio + medianLosingTradeDuration string
    - Information ratio - risk adjusted return. + The median duration for all losing trades.
    - trackingError + maxConsecutiveWinningTrades - string + integer
    - Tracking error volatility (TEV) statistic - a measure of how closely a portfolio follows the index to which it is benchmarked. + The maximum number of consecutive winning trades.
    - treynorRatio + maxConsecutiveLosingTrades - string + integer
    - Treynor ratio statistic is a measurement of the returns earned in excess of that which could have been earned on an investment that has no diversifiable risk. + The maximum number of consecutive losing trades.
    - portfolioTurnover + profitLossRatio string
    - The average Portfolio Turnover. + The ratio of the average profit per trade to the average loss per trade.
    - valueAtRisk99 + winLossRatio string
    - The 1-day VaR for the portfolio, using the Variance-covariance approach. Assumes a 99% confidence level, 1 year lookback period, and that the returns are normally distributed. + The ratio of the number of winning trades to the number of losing trades.
    - valueAtRisk95 + winRate string
    - The 1-day VaR for the portfolio, using the Variance-covariance approach. Assumes a 95% confidence level, 1 year lookback period, and that the returns are normally distributed. + The ratio of the number of winning trades to the total number of trades.
    - drawdownRecovery + lossRate string
    - /. -
    - Example - -
    - 
    -{
-  "averageWinRate": "string",
-  "averageLossRate": "string",
-  "profitLossRatio": "string",
-  "winRate": "string",
-  "lossRate": "string",
-  "expectancy": "string",
-  "startEquity": "string",
-  "endEquity": "string",
-  "compoundingAnnualReturn": "string",
-  "drawdown": "string",
-  "totalNetProfit": "string",
-  "sharpeRatio": "string",
-  "probabilisticSharpeRatio": "string",
-  "sortinoRatio": "string",
-  "alpha": "string",
-  "beta": "string",
-  "annualStandardDeviation": "string",
-  "annualVariance": "string",
-  "informationRatio": "string",
-  "trackingError": "string",
-  "treynorRatio": "string",
-  "portfolioTurnover": "string",
-  "valueAtRisk99": "string",
-  "valueAtRisk95": "string",
-  "drawdownRecovery": "string"
-}
    -
    + The ratio of the number of losing trades to the total number of trades.
    - - - - - - - - - - -
    - - Trade - - Model - Represents a closed trade. -
    - symbol + averageMAE - Symbol object + string
    - Represents a unique security identifier. This is made of two components, the unique SID and the Value. The value is the current ticker symbol while the SID is constant over the life of a security. + The average Maximum Adverse Excursion for all trades.
    - entryTime + averageMFE - string($date-time) + string
    - The date and time the trade was opened. + The average Maximum Adverse Excursion for all trades.
    - entryPrice + largestMAE - number + string
    - The price at which the trade was opened (or the average price if multiple entries). + The average Maximum Favorable Excursion for all trades.
    - direction + largestMFE - integer Enum + string
    - Direction of a trade. 0=Long, 1=Short. Options : [0, 1] + The largest Maximum Adverse Excursion in a single trade (as symbol currency).
    - quantity + maximumClosedTradeDrawdown - number + string
    - The total unsigned quantity of the trade. + The maximum closed-trade drawdown for all trades (as symbol currency).
    - exitTime + maximumIntraTradeDrawdown - string($date-time) + string
    - The date and time the trade was closed. + The maximum intra-trade drawdown for all trades (as symbol currency).
    - exitPrice + profitLossStandardDeviation - number + string
    - The price at which the trade was closed (or the average price if multiple exits). + The standard deviation of the profits/losses for all trades (as symbol currency).
    - profitLoss + profitLossDownsideDeviation - number + string
    - The gross profit/loss of the trade (as account currency). + The downside deviation of the profits/losses for all trades (as symbol currency).
    - totalFees + profitFactor - number + string
    - The total fees associated with the trade (always positive value) (as account currency). + The ratio of the total profit to the total loss.
    - mae + sharpeRatio - number + string
    - The Maximum Adverse Excursion (as account currency). + The ratio of the average profit/loss to the standard deviation.
    - mfe + sortinoRatio - number + string
    - The Maximum Favorable Excursion (as account currency). + The ratio of the average profit/loss to the downside deviation.
    - duration + profitToMaxDrawdownRatio string
    - The duration of the trade. + The ratio of the total profit/loss to the maximum closed trade drawdown.
    - endTradeDrawdown + maximumEndTradeDrawdown - number + string
    - The amount of profit given back before the trade was closed. -
    - Example - -
    - 
    -{
-  "symbol": {
-    "value": "string",
-    "id": "string",
-    "permtick": "string"
-  },
-  "entryTime": "2021-11-26T15:18:27.693Z",
-  "entryPrice": 0,
-  "direction": 0,
-  "quantity": 0,
-  "exitTime": "2021-11-26T15:18:27.693Z",
-  "exitPrice": 0,
-  "profitLoss": 0,
-  "totalFees": 0,
-  "mae": 0,
-  "mfe": 0,
-  "duration": "string",
-  "endTradeDrawdown": 0
-}
    -
    + The maximum amount of profit given back by a single trade before exit (as symbol currency).
    - - - - - - @@ -64479,9 +65669,47 @@ 

     {
-  "value": "string",
-  "id": "string",
-  "permtick": "string"
+  "startDateTime": "2021-11-26T15:18:27.693Z",
+  "endDateTime": "2021-11-26T15:18:27.693Z",
+  "totalNumberOfTrades": 0,
+  "numberOfWinningTrades": 0,
+  "numberOfLosingTrades": 0,
+  "totalProfitLoss": "string",
+  "totalProfit": "string",
+  "totalLoss": "string",
+  "largestProfit": "string",
+  "largestLoss": "string",
+  "averageProfitLoss": "string",
+  "averageProfit": "string",
+  "averageLoss": "string",
+  "averageTradeDuration": "string",
+  "averageWinningTradeDuration": "string",
+  "averageLosingTradeDuration": "string",
+  "medianTradeDuration": "string",
+  "medianWinningTradeDuration": "string",
+  "medianLosingTradeDuration": "string",
+  "maxConsecutiveWinningTrades": 0,
+  "maxConsecutiveLosingTrades": 0,
+  "profitLossRatio": "string",
+  "winLossRatio": "string",
+  "winRate": "string",
+  "lossRate": "string",
+  "averageMAE": "string",
+  "averageMFE": "string",
+  "largestMAE": "string",
+  "largestMFE": "string",
+  "maximumClosedTradeDrawdown": "string",
+  "maximumIntraTradeDrawdown": "string",
+  "profitLossStandardDeviation": "string",
+  "profitLossDownsideDeviation": "string",
+  "profitFactor": "string",
+  "sharpeRatio": "string",
+  "sortinoRatio": "string",
+  "profitToMaxDrawdownRatio": "string",
+  "maximumEndTradeDrawdown": "string",
+  "averageEndTradeDrawdown": "string",
+  "maximumDrawdownDuration": "string",
+  "totalFees": "string"
 }
    @@ -64492,510 +65720,727 @@

    -
    - - Symbol - - Model - Represents a unique security identifier. This is made of two components, the unique SID and the Value. The value is the current ticker symbol while the SID is constant over the life of a security. -
    - value + averageEndTradeDrawdown string
    - The current symbol for this ticker. + The average amount of profit given back by all trades before exit (as symbol currency).
    - id + maximumDrawdownDuration string
    - The security identifier for this symbol. + The maximum amount of time to recover from a drawdown (longest time between new equity highs or peaks).
    - permtick + totalFees string
    - The ticker at IPO for this security. + The sum of fees for all trades.
    - RuntimeStatistics + PortfolioStatistics - Model + Model - Represents a set of statistics calculated from equity and benchmark samples.
    - Equity + averageWinRate string -
    - - - example: $100.00 - -
    - Total portfolio value. + The average rate of return for winning trades.
    - Fees + averageLossRate string -
    - - - example: -$100.00 - -
    - Transaction fee. + The average rate of return for losing trades.
    - Holdings + profitLossRatio string -
    - - - example: $100.00 - -
    - Equity value of security holdings. + The ratio of the average win rate to the average loss rate.
    - Net Profit + winRate string -
    - - - example: $100.00 - -
    - Net profit. + The ratio of the number of winning trades to the total number of trades.
    - Probabilistic Sharpe Ratio + lossRate string -
    - - - example: 50.00% - -
    - Probabilistic Sharpe Ratio. + The ratio of the number of losing trades to the total number of trades.
    - Return + expectancy string -
    - - - example: 50.00% - -
    - Return. + The expected value of the rate of return.
    - Unrealized + startEquity string -
    - - - example: $100.00 - -
    - Unrealized profit/loss. + Initial Equity Total Value.
    - Volume + endEquity string -
    - - - example: $100.00 - -
    - Total transaction volume. + Final Equity Total Value.
    - Example + compoundingAnnualReturn -
    - 
    -{
-  "Equity": "$100.00",
-  "Fees": "-$100.00",
-  "Holdings": "$100.00",
-  "Net Profit": "$100.00",
-  "Probabilistic Sharpe Ratio": "50.00%",
-  "Return": "50.00%",
-  "Unrealized": "$100.00",
-  "Volume": "$100.00"
-}
    -
    + + string + +
    + Annual compounded returns statistic based on the final-starting capital and years.
    - - - - - - + + +
    - - StatisticsResult - - Model - Statistics information sent during the algorithm operations. -
    - Total Orders + drawdown string
    - Total nuber of orders. + Drawdown maximum percentage.
    - Average Win + totalNetProfit string
    - The average rate of return for winning trades. + The total net profit percentage.
    - Average Loss + sharpeRatio string
    - The average rate of return for losing trades. + Sharpe ratio with respect to risk free rate: measures excess of return per unit of risk.
    - Compounding Annual Return + probabilisticSharpeRatio string
    - Annual compounded returns statistic based on the final-starting capital and years. + Probabilistic Sharpe Ratio is a probability measure associated with the Sharpe ratio. It informs us of the probability that the estimated Sharpe ratio is greater than a chosen benchmark.
    - Drawdown + sortinoRatio string
    - Drawdown maximum percentage. + Sortino ratio with respect to risk free rate; measures excess of return per unit of downside risk.
    - Expectancy + alpha string
    - The expected value of the rate of return. + Algorithm "Alpha" statistic - abnormal returns over the risk free rate and the relationshio (beta) with the benchmark returns.
    - Start Equity + beta string
    - Initial Equity Total Value. + Algorithm beta statistic - the covariance between the algorithm and benchmark performance, divided by benchmark variance.
    - End Equity + annualStandardDeviation string
    - Final Equity Total Value. + Annualized standard deviation.
    - Net Profit + annualVariance string
    - The total net profit percentage. + Annualized variance statistic calculation using the daily performance variance and trading days per year.
    - Sharpe Ratio + informationRatio string
    - Sharpe ratio with respect to risk free rate; measures excess of return per unit of risk. + Information ratio - risk adjusted return.
    - Sortino Ratio + trackingError string
    - Sortino ratio with respect to risk free rate; measures excess of return per unit of downside risk. + Tracking error volatility (TEV) statistic - a measure of how closely a portfolio follows the index to which it is benchmarked.
    - Probabilistic Sharpe Ratio + treynorRatio string
    - Is a probability measure associated with the Sharpe ratio. It informs us of the probability that the estimated Sharpe ratio is greater than a chosen benchmark. + Treynor ratio statistic is a measurement of the returns earned in excess of that which could have been earned on an investment that has no diversifiable risk.
    - Loss Rate + portfolioTurnover string
    - The ratio of the number of losing trades to the total number of trades. + The average Portfolio Turnover.
    - Win Rate + valueAtRisk99 string
    - The ratio of the number of winning trades to the total number of trades. + The 1-day VaR for the portfolio, using the Variance-covariance approach. Assumes a 99% confidence level, 1 year lookback period, and that the returns are normally distributed.
    - Profit-Loss Ratio + valueAtRisk95 string
    - The ratio of the average win rate to the average loss rate. + The 1-day VaR for the portfolio, using the Variance-covariance approach. Assumes a 95% confidence level, 1 year lookback period, and that the returns are normally distributed.
    - Alpha + drawdownRecovery string
    - Algorithm "Alpha" statistic - abnormal returns over the risk free rate and the relationshio (beta) with the benchmark returns. + /.
    - Beta + Example + +
    + 
    +{
+  "averageWinRate": "string",
+  "averageLossRate": "string",
+  "profitLossRatio": "string",
+  "winRate": "string",
+  "lossRate": "string",
+  "expectancy": "string",
+  "startEquity": "string",
+  "endEquity": "string",
+  "compoundingAnnualReturn": "string",
+  "drawdown": "string",
+  "totalNetProfit": "string",
+  "sharpeRatio": "string",
+  "probabilisticSharpeRatio": "string",
+  "sortinoRatio": "string",
+  "alpha": "string",
+  "beta": "string",
+  "annualStandardDeviation": "string",
+  "annualVariance": "string",
+  "informationRatio": "string",
+  "trackingError": "string",
+  "treynorRatio": "string",
+  "portfolioTurnover": "string",
+  "valueAtRisk99": "string",
+  "valueAtRisk95": "string",
+  "drawdownRecovery": "string"
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + Trade + + Model - Represents a closed trade. +
    + symbol - string + Symbol object
    - Algorithm "beta" statistic - the covariance between the algorithm and benchmark performance, divided by benchmark's variance. + Represents a unique security identifier. This is made of two components, the unique SID and the Value. The value is the current ticker symbol while the SID is constant over the life of a security.
    - Annual Standard Deviation + entryTime - string + string($date-time)
    - Annualized standard deviation. + The date and time the trade was opened.
    - Annual Variance + entryPrice - string + number
    - Annualized variance statistic calculation using the daily performance variance and trading days per year. + The price at which the trade was opened (or the average price if multiple entries).
    - Information Ratio + direction - string + integer Enum
    - Information ratio - risk adjusted return. + Direction of a trade. 0=Long, 1=Short. Options : [0, 1]
    - Tracking Error + quantity - string + number
    - Tracking error volatility (TEV) statistic - a measure of how closely a portfolio follows the index to which it is benchmarked. + The total unsigned quantity of the trade.
    - Treynor Ratio + exitTime + + + string($date-time) + +
    + The date and time the trade was closed. +
    + exitPrice + + + number + +
    + The price at which the trade was closed (or the average price if multiple exits). +
    + profitLoss + + + number + +
    + The gross profit/loss of the trade (as account currency). +
    + totalFees + + + number + +
    + The total fees associated with the trade (always positive value) (as account currency). +
    + mae + + + number + +
    + The Maximum Adverse Excursion (as account currency). +
    + mfe + + + number + +
    + The Maximum Favorable Excursion (as account currency). +
    + duration string
    - Treynor ratio statistic is a measurement of the returns earned in excess of that which could have been earned on an investment that has no diversifiable risk. + The duration of the trade.
    - Total Fees + endTradeDrawdown + + + number + +
    + The amount of profit given back before the trade was closed. +
    + Example + +
    + 
    +{
+  "symbol": {
+    "value": "string",
+    "id": "string",
+    "permtick": "string"
+  },
+  "entryTime": "2021-11-26T15:18:27.693Z",
+  "entryPrice": 0,
+  "direction": 0,
+  "quantity": 0,
+  "exitTime": "2021-11-26T15:18:27.693Z",
+  "exitPrice": 0,
+  "profitLoss": 0,
+  "totalFees": 0,
+  "mae": 0,
+  "mfe": 0,
+  "duration": "string",
+  "endTradeDrawdown": 0
+}
    +
    +
    + + + + + + + + + + +
    + + Symbol + + Model - Represents a unique security identifier. This is made of two components, the unique SID and the Value. The value is the current ticker symbol while the SID is constant over the life of a security. +
    + value string
    - Total amount of fees. + The current symbol for this ticker.
    - Estimated Strategy Capacity + id string
    - The estimated total capacity of the strategy at a point in time. + The security identifier for this symbol.
    - Lowest Capacity Asset + permtick string
    - Provide a reference to the lowest capacity symbol used in scaling down the capacity for debugging. + The ticker at IPO for this security.
    - Portfolio Turnover + Example + +
    + 
    +{
+  "value": "string",
+  "id": "string",
+  "permtick": "string"
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + @@ -65006,33 +66451,16 @@ 

     {
-  "Total Orders": "string",
-  "Average Win": "string",
-  "Average Loss": "string",
-  "Compounding Annual Return": "string",
-  "Drawdown": "string",
-  "Expectancy": "string",
-  "Start Equity": "string",
-  "End Equity": "string",
-  "Net Profit": "string",
-  "Sharpe Ratio": "string",
-  "Sortino Ratio": "string",
-  "Probabilistic Sharpe Ratio": "string",
-  "Loss Rate": "string",
-  "Win Rate": "string",
-  "Profit-Loss Ratio": "string",
-  "Alpha": "string",
-  "Beta": "string",
-  "Annual Standard Deviation": "string",
-  "Annual Variance": "string",
-  "Information Ratio": "string",
-  "Tracking Error": "string",
-  "Treynor Ratio": "string",
-  "Total Fees": "string",
-  "Estimated Strategy Capacity": "string",
-  "Lowest Capacity Asset": "string",
-  "Portfolio Turnover": "string",
-  "Drawdown Recovery": "string"
+  "name": "PerformanceRelativeToBenchmarkAnalysis",
+  "issue": "The strategy has a lower Sharpe ratio than the benchmark.",
+  "sample": {
+    "backtestSharpe": -0.2281345806218534,
+    "benchmarkSharpe": -0.22182175736916387
+  },
+  "count": 5,
+  "solutions": [
+    "Try adjusting the trading rules and/or the universe to get a strategy that outperforms the benchmark."
+  ]
 }
    @@ -65195,7 +66623,7 @@

    Examples

     

    API Reference > Backtest Management > Read Backtest

    -
    +

    Backtest Management

    Read Backtest

    @@ -65211,7 +66639,7 @@

    Read Backtest

    -
    +

    Backtest Statistics @@ -65222,7 +66650,7 @@

    -
    +

    Charts @@ -65233,7 +66661,7 @@

    -
    +

    Orders @@ -65244,7 +66672,7 @@

    -
    +

    Insights @@ -65261,7 +66689,7 @@

     

    API Reference > Backtest Management > Read Backtest > Backtest Statistics

    -
    +

    Read Backtest

    Backtest Statistics

    @@ -65473,138 +66901,47 @@

    }, "parameterSet": , "rollingWindow": { - "tradeStatistics": { - "startDateTime": "2021-11-26T15:18:27.693Z", - "endDateTime": "2021-11-26T15:18:27.693Z", - "totalNumberOfTrades": 0, - "numberOfWinningTrades": 0, - "numberOfLosingTrades": 0, - "totalProfitLoss": "string", - "totalProfit": "string", - "totalLoss": "string", - "largestProfit": "string", - "largestLoss": "string", - "averageProfitLoss": "string", - "averageProfit": "string", - "averageLoss": "string", - "averageTradeDuration": "string", - "averageWinningTradeDuration": "string", - "averageLosingTradeDuration": "string", - "medianTradeDuration": "string", - "medianWinningTradeDuration": "string", - "medianLosingTradeDuration": "string", - "maxConsecutiveWinningTrades": 0, - "maxConsecutiveLosingTrades": 0, - "profitLossRatio": "string", - "winLossRatio": "string", - "winRate": "string", - "lossRate": "string", - "averageMAE": "string", - "averageMFE": "string", - "largestMAE": "string", - "largestMFE": "string", - "maximumClosedTradeDrawdown": "string", - "maximumIntraTradeDrawdown": "string", - "profitLossStandardDeviation": "string", - "profitLossDownsideDeviation": "string", - "profitFactor": "string", - "sharpeRatio": "string", - "sortinoRatio": "string", - "profitToMaxDrawdownRatio": "string", - "maximumEndTradeDrawdown": "string", - "averageEndTradeDrawdown": "string", - "maximumDrawdownDuration": "string", - "totalFees": "string" - }, - "portfolioStatistics": { - "averageWinRate": "string", - "averageLossRate": "string", - "profitLossRatio": "string", - "winRate": "string", - "lossRate": "string", - "expectancy": "string", - "startEquity": "string", - "endEquity": "string", - "compoundingAnnualReturn": "string", - "drawdown": "string", - "totalNetProfit": "string", - "sharpeRatio": "string", - "probabilisticSharpeRatio": "string", - "sortinoRatio": "string", - "alpha": "string", - "beta": "string", - "annualStandardDeviation": "string", - "annualVariance": "string", - "informationRatio": "string", - "trackingError": "string", - "treynorRatio": "string", - "portfolioTurnover": "string", - "valueAtRisk99": "string", - "valueAtRisk95": "string", - "drawdownRecovery": "string" - }, - "closedTrades": [ - { - "symbol": { - "value": "string", - "id": "string", - "permtick": "string" - }, - "entryTime": "2021-11-26T15:18:27.693Z", - "entryPrice": 0, - "direction": 0, - "quantity": 0, - "exitTime": "2021-11-26T15:18:27.693Z", - "exitPrice": 0, - "profitLoss": 0, - "totalFees": 0, - "mae": 0, - "mfe": 0, - "duration": "string", - "endTradeDrawdown": 0 - } - ] - }, - "runtimeStatistics": { - "Equity": "$100.00", - "Fees": "-$100.00", - "Holdings": "$100.00", - "Net Profit": "$100.00", - "Probabilistic Sharpe Ratio": "50.00%", - "Return": "50.00%", - "Unrealized": "$100.00", - "Volume": "$100.00" - }, - "statistics": { - "Total Orders": "string", - "Average Win": "string", - "Average Loss": "string", - "Compounding Annual Return": "string", - "Drawdown": "string", - "Expectancy": "string", - "Start Equity": "string", - "End Equity": "string", - "Net Profit": "string", - "Sharpe Ratio": "string", - "Sortino Ratio": "string", - "Probabilistic Sharpe Ratio": "string", - "Loss Rate": "string", - "Win Rate": "string", - "Profit-Loss Ratio": "string", - "Alpha": "string", - "Beta": "string", - "Annual Standard Deviation": "string", - "Annual Variance": "string", - "Information Ratio": "string", - "Tracking Error": "string", - "Treynor Ratio": "string", - "Total Fees": "string", - "Estimated Strategy Capacity": "string", - "Lowest Capacity Asset": "string", - "Portfolio Turnover": "string", - "Drawdown Recovery": "string" - }, - "totalPerformance": { + }, + "runtimeStatistics": { + "Equity": "$100.00", + "Fees": "-$100.00", + "Holdings": "$100.00", + "Net Profit": "$100.00", + "Probabilistic Sharpe Ratio": "50.00%", + "Return": "50.00%", + "Unrealized": "$100.00", + "Volume": "$100.00" + }, + "statistics": { + "Total Orders": "string", + "Average Win": "string", + "Average Loss": "string", + "Compounding Annual Return": "string", + "Drawdown": "string", + "Expectancy": "string", + "Start Equity": "string", + "End Equity": "string", + "Net Profit": "string", + "Sharpe Ratio": "string", + "Sortino Ratio": "string", + "Probabilistic Sharpe Ratio": "string", + "Loss Rate": "string", + "Win Rate": "string", + "Profit-Loss Ratio": "string", + "Alpha": "string", + "Beta": "string", + "Annual Standard Deviation": "string", + "Annual Variance": "string", + "Information Ratio": "string", + "Tracking Error": "string", + "Treynor Ratio": "string", + "Total Fees": "string", + "Estimated Strategy Capacity": "string", + "Lowest Capacity Asset": "string", + "Portfolio Turnover": "string", + "Drawdown Recovery": "string" + }, + "totalPerformance": { "tradeStatistics": { "startDateTime": "2021-11-26T15:18:27.693Z", "endDateTime": "2021-11-26T15:18:27.693Z", @@ -65699,7 +67036,21 @@

    }, "nodeName": "string", "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z", - "outOfSampleDays": 0 + "outOfSampleDays": 0, + "analysis": [ + { + "name": "PerformanceRelativeToBenchmarkAnalysis", + "issue": "The strategy has a lower Sharpe ratio than the benchmark.", + "sample": { + "backtestSharpe": -0.2281345806218534, + "benchmarkSharpe": -0.22182175736916387 + }, + "count": 5, + "solutions": [ + "Try adjusting the trading rules and/or the universe to get a strategy that outperforms the benchmark." + ] + } + ] }, "debugging": true, "success": true, @@ -66088,7 +67439,7 @@

    @@ -66190,6 +67541,18 @@

    Number of days of out of sample days.

    + + + + @@ -66562,1223 +67848,1204 @@

    -
    + + AnalysisResult + + Model - The result of an analysis performed on a result. +
    + name string +
    + + + required + + +
    + + + example: PerformanceRelativeToBenchmarkAnalysis + +
    - The average Portfolio Turnover. + The name of the analysis that produced this result.
    - Drawdown Recovery + issue string +
    + + + required + + +
    + + + example: The strategy has a lower Sharpe ratio than the benchmark. + +
    - /. + A short description of why the analysis was triggered. +
    + sample + + + object +
    + + + required + + +
    + + + example: {'backtestSharpe': -0.2281345806218534, 'benchmarkSharpe': -0.22182175736916387} + + +     +
    + A representative sample value of the issue detected by the analysis. +
    + count + + + integer +
    + + + example: 5 + + +     +
    + The total number of matching occurrences found by the analysis. If not present, it should be assumed that there is only one occurrence. +
    + solutions + + + string Array +
    + + + required + + +     +
    + Human-readable suggestions for resolving the detected issue.
    - AlgorithmPerformance object + object
    Rolling window detailed statistics. @@ -66136,7 +67487,7 @@

    		- object + AlgorithmPerformance object
    @@ -66145,7 +67496,7 @@


    - The algorithm performance statistics. + The AlgorithmPerformance class is a wrapper for TradeStatistics and PortfolioStatistics.

    + analysis + + + AnalysisResult Array + +
    + /. +
    Example @@ -66225,98 +67588,7 @@

    }, "parameterSet": , "rollingWindow": { - "tradeStatistics": { - "startDateTime": "2021-11-26T15:18:27.693Z", - "endDateTime": "2021-11-26T15:18:27.693Z", - "totalNumberOfTrades": 0, - "numberOfWinningTrades": 0, - "numberOfLosingTrades": 0, - "totalProfitLoss": "string", - "totalProfit": "string", - "totalLoss": "string", - "largestProfit": "string", - "largestLoss": "string", - "averageProfitLoss": "string", - "averageProfit": "string", - "averageLoss": "string", - "averageTradeDuration": "string", - "averageWinningTradeDuration": "string", - "averageLosingTradeDuration": "string", - "medianTradeDuration": "string", - "medianWinningTradeDuration": "string", - "medianLosingTradeDuration": "string", - "maxConsecutiveWinningTrades": 0, - "maxConsecutiveLosingTrades": 0, - "profitLossRatio": "string", - "winLossRatio": "string", - "winRate": "string", - "lossRate": "string", - "averageMAE": "string", - "averageMFE": "string", - "largestMAE": "string", - "largestMFE": "string", - "maximumClosedTradeDrawdown": "string", - "maximumIntraTradeDrawdown": "string", - "profitLossStandardDeviation": "string", - "profitLossDownsideDeviation": "string", - "profitFactor": "string", - "sharpeRatio": "string", - "sortinoRatio": "string", - "profitToMaxDrawdownRatio": "string", - "maximumEndTradeDrawdown": "string", - "averageEndTradeDrawdown": "string", - "maximumDrawdownDuration": "string", - "totalFees": "string" - }, - "portfolioStatistics": { - "averageWinRate": "string", - "averageLossRate": "string", - "profitLossRatio": "string", - "winRate": "string", - "lossRate": "string", - "expectancy": "string", - "startEquity": "string", - "endEquity": "string", - "compoundingAnnualReturn": "string", - "drawdown": "string", - "totalNetProfit": "string", - "sharpeRatio": "string", - "probabilisticSharpeRatio": "string", - "sortinoRatio": "string", - "alpha": "string", - "beta": "string", - "annualStandardDeviation": "string", - "annualVariance": "string", - "informationRatio": "string", - "trackingError": "string", - "treynorRatio": "string", - "portfolioTurnover": "string", - "valueAtRisk99": "string", - "valueAtRisk95": "string", - "drawdownRecovery": "string" - }, - "closedTrades": [ - { - "symbol": { - "value": "string", - "id": "string", - "permtick": "string" - }, - "entryTime": "2021-11-26T15:18:27.693Z", - "entryPrice": 0, - "direction": 0, - "quantity": 0, - "exitTime": "2021-11-26T15:18:27.693Z", - "exitPrice": 0, - "profitLoss": 0, - "totalFees": 0, - "mae": 0, - "mfe": 0, - "duration": "string", - "endTradeDrawdown": 0 - } - ] - }, +}, "runtimeStatistics": { "Equity": "$100.00", "Fees": "-$100.00", @@ -66451,7 +67723,21 @@

    }, "nodeName": "string", "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z", - "outOfSampleDays": 0 + "outOfSampleDays": 0, + "analysis": [ + { + "name": "PerformanceRelativeToBenchmarkAnalysis", + "issue": "The strategy has a lower Sharpe ratio than the benchmark.", + "sample": { + "backtestSharpe": -0.2281345806218534, + "benchmarkSharpe": -0.22182175736916387 + }, + "count": 5, + "solutions": [ + "Try adjusting the trading rules and/or the universe to get a strategy that outperforms the benchmark." + ] + } + ] }

    - AlgorithmPerformance + RuntimeStatistics - Model - The AlgorithmPerformance class is a wrapper for TradeStatistics and PortfolioStatistics. + Model
    - tradeStatistics + Equity - TradeStatistics object + string +
    + + + example: $100.00 + +
    - A set of statistics calculated from a list of closed trades. + Total portfolio value.
    - portfolioStatistics + Fees - PortfolioStatistics object + string +
    + + + example: -$100.00 + +
    - Represents a set of statistics calculated from equity and benchmark samples. + Transaction fee.
    - closedTrades + Holdings - Trade Array + string +
    + + + example: $100.00 + +
    - The algorithm statistics on portfolio. + Equity value of security holdings.
    - Example + Net Profit -
    - 
    -{
-  "tradeStatistics": {
-    "startDateTime": "2021-11-26T15:18:27.693Z",
-    "endDateTime": "2021-11-26T15:18:27.693Z",
-    "totalNumberOfTrades": 0,
-    "numberOfWinningTrades": 0,
-    "numberOfLosingTrades": 0,
-    "totalProfitLoss": "string",
-    "totalProfit": "string",
-    "totalLoss": "string",
-    "largestProfit": "string",
-    "largestLoss": "string",
-    "averageProfitLoss": "string",
-    "averageProfit": "string",
-    "averageLoss": "string",
-    "averageTradeDuration": "string",
-    "averageWinningTradeDuration": "string",
-    "averageLosingTradeDuration": "string",
-    "medianTradeDuration": "string",
-    "medianWinningTradeDuration": "string",
-    "medianLosingTradeDuration": "string",
-    "maxConsecutiveWinningTrades": 0,
-    "maxConsecutiveLosingTrades": 0,
-    "profitLossRatio": "string",
-    "winLossRatio": "string",
-    "winRate": "string",
-    "lossRate": "string",
-    "averageMAE": "string",
-    "averageMFE": "string",
-    "largestMAE": "string",
-    "largestMFE": "string",
-    "maximumClosedTradeDrawdown": "string",
-    "maximumIntraTradeDrawdown": "string",
-    "profitLossStandardDeviation": "string",
-    "profitLossDownsideDeviation": "string",
-    "profitFactor": "string",
-    "sharpeRatio": "string",
-    "sortinoRatio": "string",
-    "profitToMaxDrawdownRatio": "string",
-    "maximumEndTradeDrawdown": "string",
-    "averageEndTradeDrawdown": "string",
-    "maximumDrawdownDuration": "string",
-    "totalFees": "string"
-  },
-  "portfolioStatistics": {
-    "averageWinRate": "string",
-    "averageLossRate": "string",
-    "profitLossRatio": "string",
-    "winRate": "string",
-    "lossRate": "string",
-    "expectancy": "string",
-    "startEquity": "string",
-    "endEquity": "string",
-    "compoundingAnnualReturn": "string",
-    "drawdown": "string",
-    "totalNetProfit": "string",
-    "sharpeRatio": "string",
-    "probabilisticSharpeRatio": "string",
-    "sortinoRatio": "string",
-    "alpha": "string",
-    "beta": "string",
-    "annualStandardDeviation": "string",
-    "annualVariance": "string",
-    "informationRatio": "string",
-    "trackingError": "string",
-    "treynorRatio": "string",
-    "portfolioTurnover": "string",
-    "valueAtRisk99": "string",
-    "valueAtRisk95": "string",
-    "drawdownRecovery": "string"
-  },
-  "closedTrades": [
-    {
-      "symbol": {
-        "value": "string",
-        "id": "string",
-        "permtick": "string"
-      },
-      "entryTime": "2021-11-26T15:18:27.693Z",
-      "entryPrice": 0,
-      "direction": 0,
-      "quantity": 0,
-      "exitTime": "2021-11-26T15:18:27.693Z",
-      "exitPrice": 0,
-      "profitLoss": 0,
-      "totalFees": 0,
-      "mae": 0,
-      "mfe": 0,
-      "duration": "string",
-      "endTradeDrawdown": 0
-    }
-  ]
-}
    -
    + + string +
    + + + example: $100.00 + + +     +
    + Net profit.
    - - - - - - +
    - - TradeStatistics - - Model - A set of statistics calculated from a list of closed trades. -
    - startDateTime + Probabilistic Sharpe Ratio - string($date-time) + string +
    + + + example: 50.00% + +
    - The entry date/time of the first trade. + Probabilistic Sharpe Ratio.
    - endDateTime + Return - string($date-time) + string +
    + + + example: 50.00% + +
    - The exit date/time of the first trade. + Return.
    - totalNumberOfTrades + Unrealized - integer + string +
    + + + example: $100.00 + +
    - The total number of trades. + Unrealized profit/loss.
    - numberOfWinningTrades + Volume - integer + string +
    + + + example: $100.00 + +
    - The total number of winning trades. + Total transaction volume.
    - numberOfLosingTrades + Example - - integer - -
    - The total number of losing trades. +
    + 
    +{
+  "Equity": "$100.00",
+  "Fees": "-$100.00",
+  "Holdings": "$100.00",
+  "Net Profit": "$100.00",
+  "Probabilistic Sharpe Ratio": "50.00%",
+  "Return": "50.00%",
+  "Unrealized": "$100.00",
+  "Volume": "$100.00"
+}
    +
    + + + + + + +
    + + StatisticsResult + + Model - Statistics information sent during the algorithm operations. +
    - totalProfitLoss + Total Orders string
    - The total profit/loss for all trades (as symbol currency). + Total nuber of orders.
    - totalProfit + Average Win string
    - The total profit for all winning trades (as symbol currency). + The average rate of return for winning trades.
    - totalLoss + Average Loss string
    - The total loss for all losing trades (as symbol currency). + The average rate of return for losing trades.
    - largestProfit + Compounding Annual Return string
    - The largest profit in a single trade (as symbol currency). + Annual compounded returns statistic based on the final-starting capital and years.
    - largestLoss + Drawdown string
    - The largest loss in a single trade (as symbol currency). + Drawdown maximum percentage.
    - averageProfitLoss + Expectancy string
    - The average profit/loss (a.k.a. Expectancy or Average Trade) for all trades (as symbol currency). + The expected value of the rate of return.
    - averageProfit + Start Equity string
    - The average profit for all winning trades (as symbol currency). + Initial Equity Total Value.
    - averageLoss + End Equity string
    - The average loss for all winning trades (as symbol currency). + Final Equity Total Value.
    - averageTradeDuration + Net Profit string
    - The average duration for all trades. + The total net profit percentage.
    - averageWinningTradeDuration + Sharpe Ratio string
    - The average duration for all winning trades. + Sharpe ratio with respect to risk free rate; measures excess of return per unit of risk.
    - averageLosingTradeDuration + Sortino Ratio string
    - The average duration for all losing trades. + Sortino ratio with respect to risk free rate; measures excess of return per unit of downside risk.
    - medianTradeDuration + Probabilistic Sharpe Ratio string
    - The median duration for all trades. + Is a probability measure associated with the Sharpe ratio. It informs us of the probability that the estimated Sharpe ratio is greater than a chosen benchmark.
    - medianWinningTradeDuration + Loss Rate string
    - The median duration for all winning trades. + The ratio of the number of losing trades to the total number of trades.
    - medianLosingTradeDuration + Win Rate string
    - The median duration for all losing trades. + The ratio of the number of winning trades to the total number of trades.
    - maxConsecutiveWinningTrades + Profit-Loss Ratio - integer + string
    - The maximum number of consecutive winning trades. + The ratio of the average win rate to the average loss rate.
    - maxConsecutiveLosingTrades + Alpha - integer + string
    - The maximum number of consecutive losing trades. + Algorithm "Alpha" statistic - abnormal returns over the risk free rate and the relationshio (beta) with the benchmark returns.
    - profitLossRatio + Beta string
    - The ratio of the average profit per trade to the average loss per trade. + Algorithm "beta" statistic - the covariance between the algorithm and benchmark performance, divided by benchmark's variance.
    - winLossRatio + Annual Standard Deviation string
    - The ratio of the number of winning trades to the number of losing trades. + Annualized standard deviation.
    - winRate + Annual Variance string
    - The ratio of the number of winning trades to the total number of trades. + Annualized variance statistic calculation using the daily performance variance and trading days per year.
    - lossRate + Information Ratio string
    - The ratio of the number of losing trades to the total number of trades. + Information ratio - risk adjusted return.
    - averageMAE + Tracking Error string
    - The average Maximum Adverse Excursion for all trades. + Tracking error volatility (TEV) statistic - a measure of how closely a portfolio follows the index to which it is benchmarked.
    - averageMFE + Treynor Ratio string
    - The average Maximum Adverse Excursion for all trades. + Treynor ratio statistic is a measurement of the returns earned in excess of that which could have been earned on an investment that has no diversifiable risk.
    - largestMAE + Total Fees string
    - The average Maximum Favorable Excursion for all trades. + Total amount of fees.
    - largestMFE + Estimated Strategy Capacity string
    - The largest Maximum Adverse Excursion in a single trade (as symbol currency). + The estimated total capacity of the strategy at a point in time.
    - maximumClosedTradeDrawdown + Lowest Capacity Asset string
    - The maximum closed-trade drawdown for all trades (as symbol currency). + Provide a reference to the lowest capacity symbol used in scaling down the capacity for debugging.
    - maximumIntraTradeDrawdown + Portfolio Turnover string
    - The maximum intra-trade drawdown for all trades (as symbol currency). + The average Portfolio Turnover.
    - profitLossStandardDeviation + Drawdown Recovery string
    - The standard deviation of the profits/losses for all trades (as symbol currency). + /.
    - profitLossDownsideDeviation + Example - - string - -
    - The downside deviation of the profits/losses for all trades (as symbol currency). +
    + 
    +{
+  "Total Orders": "string",
+  "Average Win": "string",
+  "Average Loss": "string",
+  "Compounding Annual Return": "string",
+  "Drawdown": "string",
+  "Expectancy": "string",
+  "Start Equity": "string",
+  "End Equity": "string",
+  "Net Profit": "string",
+  "Sharpe Ratio": "string",
+  "Sortino Ratio": "string",
+  "Probabilistic Sharpe Ratio": "string",
+  "Loss Rate": "string",
+  "Win Rate": "string",
+  "Profit-Loss Ratio": "string",
+  "Alpha": "string",
+  "Beta": "string",
+  "Annual Standard Deviation": "string",
+  "Annual Variance": "string",
+  "Information Ratio": "string",
+  "Tracking Error": "string",
+  "Treynor Ratio": "string",
+  "Total Fees": "string",
+  "Estimated Strategy Capacity": "string",
+  "Lowest Capacity Asset": "string",
+  "Portfolio Turnover": "string",
+  "Drawdown Recovery": "string"
+}
    +
    + + + + + + +
    + + AlgorithmPerformance + + Model - The AlgorithmPerformance class is a wrapper for TradeStatistics and PortfolioStatistics. +
    - profitFactor + tradeStatistics - string + TradeStatistics object
    - The ratio of the total profit to the total loss. + A set of statistics calculated from a list of closed trades.
    - sharpeRatio + portfolioStatistics - string + PortfolioStatistics object
    - The ratio of the average profit/loss to the standard deviation. + Represents a set of statistics calculated from equity and benchmark samples.
    - sortinoRatio + closedTrades - string + Trade Array
    - The ratio of the average profit/loss to the downside deviation. + The algorithm statistics on portfolio.
    - profitToMaxDrawdownRatio + Example - - string - -
    - The ratio of the total profit/loss to the maximum closed trade drawdown. +
    + 
    +{
+  "tradeStatistics": {
+    "startDateTime": "2021-11-26T15:18:27.693Z",
+    "endDateTime": "2021-11-26T15:18:27.693Z",
+    "totalNumberOfTrades": 0,
+    "numberOfWinningTrades": 0,
+    "numberOfLosingTrades": 0,
+    "totalProfitLoss": "string",
+    "totalProfit": "string",
+    "totalLoss": "string",
+    "largestProfit": "string",
+    "largestLoss": "string",
+    "averageProfitLoss": "string",
+    "averageProfit": "string",
+    "averageLoss": "string",
+    "averageTradeDuration": "string",
+    "averageWinningTradeDuration": "string",
+    "averageLosingTradeDuration": "string",
+    "medianTradeDuration": "string",
+    "medianWinningTradeDuration": "string",
+    "medianLosingTradeDuration": "string",
+    "maxConsecutiveWinningTrades": 0,
+    "maxConsecutiveLosingTrades": 0,
+    "profitLossRatio": "string",
+    "winLossRatio": "string",
+    "winRate": "string",
+    "lossRate": "string",
+    "averageMAE": "string",
+    "averageMFE": "string",
+    "largestMAE": "string",
+    "largestMFE": "string",
+    "maximumClosedTradeDrawdown": "string",
+    "maximumIntraTradeDrawdown": "string",
+    "profitLossStandardDeviation": "string",
+    "profitLossDownsideDeviation": "string",
+    "profitFactor": "string",
+    "sharpeRatio": "string",
+    "sortinoRatio": "string",
+    "profitToMaxDrawdownRatio": "string",
+    "maximumEndTradeDrawdown": "string",
+    "averageEndTradeDrawdown": "string",
+    "maximumDrawdownDuration": "string",
+    "totalFees": "string"
+  },
+  "portfolioStatistics": {
+    "averageWinRate": "string",
+    "averageLossRate": "string",
+    "profitLossRatio": "string",
+    "winRate": "string",
+    "lossRate": "string",
+    "expectancy": "string",
+    "startEquity": "string",
+    "endEquity": "string",
+    "compoundingAnnualReturn": "string",
+    "drawdown": "string",
+    "totalNetProfit": "string",
+    "sharpeRatio": "string",
+    "probabilisticSharpeRatio": "string",
+    "sortinoRatio": "string",
+    "alpha": "string",
+    "beta": "string",
+    "annualStandardDeviation": "string",
+    "annualVariance": "string",
+    "informationRatio": "string",
+    "trackingError": "string",
+    "treynorRatio": "string",
+    "portfolioTurnover": "string",
+    "valueAtRisk99": "string",
+    "valueAtRisk95": "string",
+    "drawdownRecovery": "string"
+  },
+  "closedTrades": [
+    {
+      "symbol": {
+        "value": "string",
+        "id": "string",
+        "permtick": "string"
+      },
+      "entryTime": "2021-11-26T15:18:27.693Z",
+      "entryPrice": 0,
+      "direction": 0,
+      "quantity": 0,
+      "exitTime": "2021-11-26T15:18:27.693Z",
+      "exitPrice": 0,
+      "profitLoss": 0,
+      "totalFees": 0,
+      "mae": 0,
+      "mfe": 0,
+      "duration": "string",
+      "endTradeDrawdown": 0
+    }
+  ]
+}
    +
    + + + + + + -
    + + TradeStatistics + + Model - A set of statistics calculated from a list of closed trades. +
    - maximumEndTradeDrawdown + startDateTime - string + string($date-time)
    - The maximum amount of profit given back by a single trade before exit (as symbol currency). + The entry date/time of the first trade.
    - averageEndTradeDrawdown + endDateTime - string + string($date-time)
    - The average amount of profit given back by all trades before exit (as symbol currency). + The exit date/time of the first trade.
    - maximumDrawdownDuration + totalNumberOfTrades - string + integer
    - The maximum amount of time to recover from a drawdown (longest time between new equity highs or peaks). + The total number of trades.
    - totalFees + numberOfWinningTrades - string + integer
    - The sum of fees for all trades. + The total number of winning trades.
    - Example + numberOfLosingTrades -
    - 
    -{
-  "startDateTime": "2021-11-26T15:18:27.693Z",
-  "endDateTime": "2021-11-26T15:18:27.693Z",
-  "totalNumberOfTrades": 0,
-  "numberOfWinningTrades": 0,
-  "numberOfLosingTrades": 0,
-  "totalProfitLoss": "string",
-  "totalProfit": "string",
-  "totalLoss": "string",
-  "largestProfit": "string",
-  "largestLoss": "string",
-  "averageProfitLoss": "string",
-  "averageProfit": "string",
-  "averageLoss": "string",
-  "averageTradeDuration": "string",
-  "averageWinningTradeDuration": "string",
-  "averageLosingTradeDuration": "string",
-  "medianTradeDuration": "string",
-  "medianWinningTradeDuration": "string",
-  "medianLosingTradeDuration": "string",
-  "maxConsecutiveWinningTrades": 0,
-  "maxConsecutiveLosingTrades": 0,
-  "profitLossRatio": "string",
-  "winLossRatio": "string",
-  "winRate": "string",
-  "lossRate": "string",
-  "averageMAE": "string",
-  "averageMFE": "string",
-  "largestMAE": "string",
-  "largestMFE": "string",
-  "maximumClosedTradeDrawdown": "string",
-  "maximumIntraTradeDrawdown": "string",
-  "profitLossStandardDeviation": "string",
-  "profitLossDownsideDeviation": "string",
-  "profitFactor": "string",
-  "sharpeRatio": "string",
-  "sortinoRatio": "string",
-  "profitToMaxDrawdownRatio": "string",
-  "maximumEndTradeDrawdown": "string",
-  "averageEndTradeDrawdown": "string",
-  "maximumDrawdownDuration": "string",
-  "totalFees": "string"
-}
    -
    + + integer + +
    + The total number of losing trades.
    - - - - - - -
    - - PortfolioStatistics - - Model - Represents a set of statistics calculated from equity and benchmark samples. -
    - averageWinRate + totalProfitLoss string
    - The average rate of return for winning trades. + The total profit/loss for all trades (as symbol currency).
    - averageLossRate + totalProfit string
    - The average rate of return for losing trades. + The total profit for all winning trades (as symbol currency).
    - profitLossRatio + totalLoss string
    - The ratio of the average win rate to the average loss rate. + The total loss for all losing trades (as symbol currency).
    - winRate + largestProfit string
    - The ratio of the number of winning trades to the total number of trades. + The largest profit in a single trade (as symbol currency).
    - lossRate + largestLoss string
    - The ratio of the number of losing trades to the total number of trades. + The largest loss in a single trade (as symbol currency).
    - expectancy + averageProfitLoss string
    - The expected value of the rate of return. + The average profit/loss (a.k.a. Expectancy or Average Trade) for all trades (as symbol currency).
    - startEquity + averageProfit string
    - Initial Equity Total Value. + The average profit for all winning trades (as symbol currency).
    - endEquity + averageLoss string
    - Final Equity Total Value. + The average loss for all winning trades (as symbol currency).
    - compoundingAnnualReturn + averageTradeDuration string
    - Annual compounded returns statistic based on the final-starting capital and years. + The average duration for all trades.
    - drawdown + averageWinningTradeDuration string
    - Drawdown maximum percentage. + The average duration for all winning trades.
    - totalNetProfit + averageLosingTradeDuration string
    - The total net profit percentage. + The average duration for all losing trades.
    - sharpeRatio + medianTradeDuration string
    - Sharpe ratio with respect to risk free rate: measures excess of return per unit of risk. + The median duration for all trades.
    - probabilisticSharpeRatio + medianWinningTradeDuration string
    - Probabilistic Sharpe Ratio is a probability measure associated with the Sharpe ratio. It informs us of the probability that the estimated Sharpe ratio is greater than a chosen benchmark. + The median duration for all winning trades.
    - sortinoRatio + medianLosingTradeDuration string
    - Sortino ratio with respect to risk free rate; measures excess of return per unit of downside risk. + The median duration for all losing trades.
    - alpha + maxConsecutiveWinningTrades - string + integer
    - Algorithm "Alpha" statistic - abnormal returns over the risk free rate and the relationshio (beta) with the benchmark returns. + The maximum number of consecutive winning trades.
    - beta + maxConsecutiveLosingTrades - string + integer
    - Algorithm beta statistic - the covariance between the algorithm and benchmark performance, divided by benchmark variance. + The maximum number of consecutive losing trades.
    - annualStandardDeviation + profitLossRatio string
    - Annualized standard deviation. + The ratio of the average profit per trade to the average loss per trade.
    - annualVariance + winLossRatio string
    - Annualized variance statistic calculation using the daily performance variance and trading days per year. + The ratio of the number of winning trades to the number of losing trades.
    - informationRatio + winRate string
    - Information ratio - risk adjusted return. + The ratio of the number of winning trades to the total number of trades.
    - trackingError + lossRate string
    - Tracking error volatility (TEV) statistic - a measure of how closely a portfolio follows the index to which it is benchmarked. + The ratio of the number of losing trades to the total number of trades.
    - treynorRatio + averageMAE string
    - Treynor ratio statistic is a measurement of the returns earned in excess of that which could have been earned on an investment that has no diversifiable risk. + The average Maximum Adverse Excursion for all trades.
    - portfolioTurnover + averageMFE string
    - The average Portfolio Turnover. + The average Maximum Adverse Excursion for all trades.
    - valueAtRisk99 + largestMAE string
    - The 1-day VaR for the portfolio, using the Variance-covariance approach. Assumes a 99% confidence level, 1 year lookback period, and that the returns are normally distributed. + The average Maximum Favorable Excursion for all trades.
    - valueAtRisk95 + largestMFE string
    - The 1-day VaR for the portfolio, using the Variance-covariance approach. Assumes a 95% confidence level, 1 year lookback period, and that the returns are normally distributed. + The largest Maximum Adverse Excursion in a single trade (as symbol currency).
    - drawdownRecovery + maximumClosedTradeDrawdown string
    - /. + The maximum closed-trade drawdown for all trades (as symbol currency).
    - Example + maximumIntraTradeDrawdown -
    - 
    -{
-  "averageWinRate": "string",
-  "averageLossRate": "string",
-  "profitLossRatio": "string",
-  "winRate": "string",
-  "lossRate": "string",
-  "expectancy": "string",
-  "startEquity": "string",
-  "endEquity": "string",
-  "compoundingAnnualReturn": "string",
-  "drawdown": "string",
-  "totalNetProfit": "string",
-  "sharpeRatio": "string",
-  "probabilisticSharpeRatio": "string",
-  "sortinoRatio": "string",
-  "alpha": "string",
-  "beta": "string",
-  "annualStandardDeviation": "string",
-  "annualVariance": "string",
-  "informationRatio": "string",
-  "trackingError": "string",
-  "treynorRatio": "string",
-  "portfolioTurnover": "string",
-  "valueAtRisk99": "string",
-  "valueAtRisk95": "string",
-  "drawdownRecovery": "string"
-}
    -
    + + string + +
    + The maximum intra-trade drawdown for all trades (as symbol currency).
    - - - - - - - - - - - - - - - - - - @@ -67789,23 +69056,47 @@ 

     {
-  "symbol": {
-    "value": "string",
-    "id": "string",
-    "permtick": "string"
-  },
-  "entryTime": "2021-11-26T15:18:27.693Z",
-  "entryPrice": 0,
-  "direction": 0,
-  "quantity": 0,
-  "exitTime": "2021-11-26T15:18:27.693Z",
-  "exitPrice": 0,
-  "profitLoss": 0,
-  "totalFees": 0,
-  "mae": 0,
-  "mfe": 0,
-  "duration": "string",
-  "endTradeDrawdown": 0
+  "startDateTime": "2021-11-26T15:18:27.693Z",
+  "endDateTime": "2021-11-26T15:18:27.693Z",
+  "totalNumberOfTrades": 0,
+  "numberOfWinningTrades": 0,
+  "numberOfLosingTrades": 0,
+  "totalProfitLoss": "string",
+  "totalProfit": "string",
+  "totalLoss": "string",
+  "largestProfit": "string",
+  "largestLoss": "string",
+  "averageProfitLoss": "string",
+  "averageProfit": "string",
+  "averageLoss": "string",
+  "averageTradeDuration": "string",
+  "averageWinningTradeDuration": "string",
+  "averageLosingTradeDuration": "string",
+  "medianTradeDuration": "string",
+  "medianWinningTradeDuration": "string",
+  "medianLosingTradeDuration": "string",
+  "maxConsecutiveWinningTrades": 0,
+  "maxConsecutiveLosingTrades": 0,
+  "profitLossRatio": "string",
+  "winLossRatio": "string",
+  "winRate": "string",
+  "lossRate": "string",
+  "averageMAE": "string",
+  "averageMFE": "string",
+  "largestMAE": "string",
+  "largestMFE": "string",
+  "maximumClosedTradeDrawdown": "string",
+  "maximumIntraTradeDrawdown": "string",
+  "profitLossStandardDeviation": "string",
+  "profitLossDownsideDeviation": "string",
+  "profitFactor": "string",
+  "sharpeRatio": "string",
+  "sortinoRatio": "string",
+  "profitToMaxDrawdownRatio": "string",
+  "maximumEndTradeDrawdown": "string",
+  "averageEndTradeDrawdown": "string",
+  "maximumDrawdownDuration": "string",
+  "totalFees": "string"
 }
    @@ -67816,573 +69107,515 @@

    - - - - -
    - - Trade - - Model - Represents a closed trade. -
    - symbol + profitLossStandardDeviation - Symbol object + string
    - Represents a unique security identifier. This is made of two components, the unique SID and the Value. The value is the current ticker symbol while the SID is constant over the life of a security. + The standard deviation of the profits/losses for all trades (as symbol currency).
    - entryTime + profitLossDownsideDeviation - string($date-time) + string
    - The date and time the trade was opened. -
    - entryPrice - - - number - -
    - The price at which the trade was opened (or the average price if multiple entries). -
    - direction - - - integer Enum - -
    - Direction of a trade. 0=Long, 1=Short. Options : [0, 1] -
    - quantity - - - number - -
    - The total unsigned quantity of the trade. + The downside deviation of the profits/losses for all trades (as symbol currency).
    - exitTime + profitFactor - string($date-time) + string
    - The date and time the trade was closed. + The ratio of the total profit to the total loss.
    - exitPrice + sharpeRatio - number + string
    - The price at which the trade was closed (or the average price if multiple exits). + The ratio of the average profit/loss to the standard deviation.
    - profitLoss + sortinoRatio - number + string
    - The gross profit/loss of the trade (as account currency). + The ratio of the average profit/loss to the downside deviation.
    - totalFees + profitToMaxDrawdownRatio - number + string
    - The total fees associated with the trade (always positive value) (as account currency). + The ratio of the total profit/loss to the maximum closed trade drawdown.
    - mae + maximumEndTradeDrawdown - number + string
    - The Maximum Adverse Excursion (as account currency). + The maximum amount of profit given back by a single trade before exit (as symbol currency).
    - mfe + averageEndTradeDrawdown - number + string
    - The Maximum Favorable Excursion (as account currency). + The average amount of profit given back by all trades before exit (as symbol currency).
    - duration + maximumDrawdownDuration string
    - The duration of the trade. + The maximum amount of time to recover from a drawdown (longest time between new equity highs or peaks).
    - endTradeDrawdown + totalFees - number + string
    - The amount of profit given back before the trade was closed. + The sum of fees for all trades.
    - Symbol + PortfolioStatistics - Model - Represents a unique security identifier. This is made of two components, the unique SID and the Value. The value is the current ticker symbol while the SID is constant over the life of a security. + Model - Represents a set of statistics calculated from equity and benchmark samples.
    - value + averageWinRate string
    - The current symbol for this ticker. + The average rate of return for winning trades.
    - id + averageLossRate string
    - The security identifier for this symbol. + The average rate of return for losing trades.
    - permtick + profitLossRatio string
    - The ticker at IPO for this security. -
    - Example - -
    - 
    -{
-  "value": "string",
-  "id": "string",
-  "permtick": "string"
-}
    -
    + The ratio of the average win rate to the average loss rate.
    - - - - - - -
    - - RuntimeStatistics - - Model -
    - Equity + winRate string -
    - - - example: $100.00 - -
    - Total portfolio value. + The ratio of the number of winning trades to the total number of trades.
    - Fees + lossRate string -
    - - - example: -$100.00 - -
    - Transaction fee. + The ratio of the number of losing trades to the total number of trades.
    - Holdings + expectancy string -
    - - - example: $100.00 - -
    - Equity value of security holdings. + The expected value of the rate of return.
    - Net Profit + startEquity string -
    - - - example: $100.00 - -
    - Net profit. + Initial Equity Total Value.
    - Probabilistic Sharpe Ratio + endEquity string -
    - - - example: 50.00% - -
    - Probabilistic Sharpe Ratio. + Final Equity Total Value.
    - Return + compoundingAnnualReturn string -
    - - - example: 50.00% - -
    - Return. + Annual compounded returns statistic based on the final-starting capital and years.
    - Unrealized + drawdown string -
    - - - example: $100.00 - -
    - Unrealized profit/loss. + Drawdown maximum percentage.
    - Volume + totalNetProfit string -
    - - - example: $100.00 - -
    - Total transaction volume. + The total net profit percentage.
    - Example + sharpeRatio -
    - 
    -{
-  "Equity": "$100.00",
-  "Fees": "-$100.00",
-  "Holdings": "$100.00",
-  "Net Profit": "$100.00",
-  "Probabilistic Sharpe Ratio": "50.00%",
-  "Return": "50.00%",
-  "Unrealized": "$100.00",
-  "Volume": "$100.00"
-}
    -
    + + string + +
    + Sharpe ratio with respect to risk free rate: measures excess of return per unit of risk.
    - - - - - - +
    - - StatisticsResult - - Model - Statistics information sent during the algorithm operations. -
    - Total Orders + probabilisticSharpeRatio string
    - Total nuber of orders. + Probabilistic Sharpe Ratio is a probability measure associated with the Sharpe ratio. It informs us of the probability that the estimated Sharpe ratio is greater than a chosen benchmark.
    - Average Win + sortinoRatio string
    - The average rate of return for winning trades. + Sortino ratio with respect to risk free rate; measures excess of return per unit of downside risk.
    - Average Loss + alpha string
    - The average rate of return for losing trades. + Algorithm "Alpha" statistic - abnormal returns over the risk free rate and the relationshio (beta) with the benchmark returns.
    - Compounding Annual Return + beta string
    - Annual compounded returns statistic based on the final-starting capital and years. + Algorithm beta statistic - the covariance between the algorithm and benchmark performance, divided by benchmark variance.
    - Drawdown + annualStandardDeviation string
    - Drawdown maximum percentage. + Annualized standard deviation.
    - Expectancy + annualVariance string
    - The expected value of the rate of return. + Annualized variance statistic calculation using the daily performance variance and trading days per year.
    - Start Equity + informationRatio string
    - Initial Equity Total Value. + Information ratio - risk adjusted return.
    - End Equity + trackingError string
    - Final Equity Total Value. + Tracking error volatility (TEV) statistic - a measure of how closely a portfolio follows the index to which it is benchmarked.
    - Net Profit + treynorRatio string
    - The total net profit percentage. + Treynor ratio statistic is a measurement of the returns earned in excess of that which could have been earned on an investment that has no diversifiable risk.
    - Sharpe Ratio + portfolioTurnover string
    - Sharpe ratio with respect to risk free rate; measures excess of return per unit of risk. + The average Portfolio Turnover.
    - Sortino Ratio + valueAtRisk99 string
    - Sortino ratio with respect to risk free rate; measures excess of return per unit of downside risk. + The 1-day VaR for the portfolio, using the Variance-covariance approach. Assumes a 99% confidence level, 1 year lookback period, and that the returns are normally distributed.
    - Probabilistic Sharpe Ratio + valueAtRisk95 string
    - Is a probability measure associated with the Sharpe ratio. It informs us of the probability that the estimated Sharpe ratio is greater than a chosen benchmark. + The 1-day VaR for the portfolio, using the Variance-covariance approach. Assumes a 95% confidence level, 1 year lookback period, and that the returns are normally distributed.
    - Loss Rate + drawdownRecovery string
    - The ratio of the number of losing trades to the total number of trades. + /.
    - Win Rate + Example - - string - -
    - The ratio of the number of winning trades to the total number of trades. +
    + 
    +{
+  "averageWinRate": "string",
+  "averageLossRate": "string",
+  "profitLossRatio": "string",
+  "winRate": "string",
+  "lossRate": "string",
+  "expectancy": "string",
+  "startEquity": "string",
+  "endEquity": "string",
+  "compoundingAnnualReturn": "string",
+  "drawdown": "string",
+  "totalNetProfit": "string",
+  "sharpeRatio": "string",
+  "probabilisticSharpeRatio": "string",
+  "sortinoRatio": "string",
+  "alpha": "string",
+  "beta": "string",
+  "annualStandardDeviation": "string",
+  "annualVariance": "string",
+  "informationRatio": "string",
+  "trackingError": "string",
+  "treynorRatio": "string",
+  "portfolioTurnover": "string",
+  "valueAtRisk99": "string",
+  "valueAtRisk95": "string",
+  "drawdownRecovery": "string"
+}
    +
    + + + + + + @@ -68393,45 +69626,240 @@ 

     {
-  "Total Orders": "string",
-  "Average Win": "string",
-  "Average Loss": "string",
-  "Compounding Annual Return": "string",
-  "Drawdown": "string",
-  "Expectancy": "string",
-  "Start Equity": "string",
-  "End Equity": "string",
-  "Net Profit": "string",
-  "Sharpe Ratio": "string",
-  "Sortino Ratio": "string",
-  "Probabilistic Sharpe Ratio": "string",
-  "Loss Rate": "string",
-  "Win Rate": "string",
-  "Profit-Loss Ratio": "string",
-  "Alpha": "string",
-  "Beta": "string",
-  "Annual Standard Deviation": "string",
-  "Annual Variance": "string",
-  "Information Ratio": "string",
-  "Tracking Error": "string",
-  "Treynor Ratio": "string",
-  "Total Fees": "string",
-  "Estimated Strategy Capacity": "string",
-  "Lowest Capacity Asset": "string",
-  "Portfolio Turnover": "string",
-  "Drawdown Recovery": "string"
-}
    -
    - -

    -
    + + Trade + + Model - Represents a closed trade. +
    - Profit-Loss Ratio + symbol - string + Symbol object
    - The ratio of the average win rate to the average loss rate. + Represents a unique security identifier. This is made of two components, the unique SID and the Value. The value is the current ticker symbol while the SID is constant over the life of a security.
    - Alpha + entryTime - string + string($date-time)
    - Algorithm "Alpha" statistic - abnormal returns over the risk free rate and the relationshio (beta) with the benchmark returns. + The date and time the trade was opened.
    - Beta + entryPrice - string + number
    - Algorithm "beta" statistic - the covariance between the algorithm and benchmark performance, divided by benchmark's variance. + The price at which the trade was opened (or the average price if multiple entries).
    - Annual Standard Deviation + direction - string + integer Enum
    - Annualized standard deviation. + Direction of a trade. 0=Long, 1=Short. Options : [0, 1]
    - Annual Variance + quantity - string + number
    - Annualized variance statistic calculation using the daily performance variance and trading days per year. + The total unsigned quantity of the trade.
    - Information Ratio + exitTime - string + string($date-time)
    - Information ratio - risk adjusted return. + The date and time the trade was closed.
    - Tracking Error + exitPrice - string + number
    - Tracking error volatility (TEV) statistic - a measure of how closely a portfolio follows the index to which it is benchmarked. + The price at which the trade was closed (or the average price if multiple exits).
    - Treynor Ratio + profitLoss - string + number
    - Treynor ratio statistic is a measurement of the returns earned in excess of that which could have been earned on an investment that has no diversifiable risk. + The gross profit/loss of the trade (as account currency).
    - Total Fees + totalFees - string + number
    - Total amount of fees. + The total fees associated with the trade (always positive value) (as account currency).
    - Estimated Strategy Capacity + mae - string + number
    - The estimated total capacity of the strategy at a point in time. + The Maximum Adverse Excursion (as account currency).
    - Lowest Capacity Asset + mfe - string + number
    - Provide a reference to the lowest capacity symbol used in scaling down the capacity for debugging. + The Maximum Favorable Excursion (as account currency).
    - Portfolio Turnover + duration string
    - The average Portfolio Turnover. + The duration of the trade.
    - Drawdown Recovery + endTradeDrawdown - string + number
    - /. + The amount of profit given back before the trade was closed.
    -

    - 401 Authentication Error -

    - - - - +
    + "symbol": { + "value": "string", + "id": "string", + "permtick": "string" + }, + "entryTime": "2021-11-26T15:18:27.693Z", + "entryPrice": 0, + "direction": 0, + "quantity": 0, + "exitTime": "2021-11-26T15:18:27.693Z", + "exitPrice": 0, + "profitLoss": 0, + "totalFees": 0, + "mae": 0, + "mfe": 0, + "duration": "string", + "endTradeDrawdown": 0 +} + + +
    + + + + + + + + + + + + + + + + + + + + + + +
    + + Symbol + + Model - Represents a unique security identifier. This is made of two components, the unique SID and the Value. The value is the current ticker symbol while the SID is constant over the life of a security. +
    + value + + + string + +
    + The current symbol for this ticker. +
    + id + + + string + +
    + The security identifier for this symbol. +
    + permtick + + + string + +
    + The ticker at IPO for this security. +
    + Example + +
    + 
    +{
+  "value": "string",
+  "id": "string",
+  "permtick": "string"
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + AnalysisResult + + Model - The result of an analysis performed on a result. +
    + name + + + string +
    + + + required + + +
    + + + example: PerformanceRelativeToBenchmarkAnalysis + + +     +
    + The name of the analysis that produced this result. +
    + issue + + + string +
    + + + required + + +
    + + + example: The strategy has a lower Sharpe ratio than the benchmark. + + +     +
    + A short description of why the analysis was triggered. +
    + sample + + + object +
    + + + required + + +
    + + + example: {'backtestSharpe': -0.2281345806218534, 'benchmarkSharpe': -0.22182175736916387} + + +     +
    + A representative sample value of the issue detected by the analysis. +
    + count + + + integer +
    + + + example: 5 + + +     +
    + The total number of matching occurrences found by the analysis. If not present, it should be assumed that there is only one occurrence. +
    + solutions + + + string Array +
    + + + required + + +     +
    + Human-readable suggestions for resolving the detected issue. +
    + Example + +
    + 
    +{
+  "name": "PerformanceRelativeToBenchmarkAnalysis",
+  "issue": "The strategy has a lower Sharpe ratio than the benchmark.",
+  "sample": {
+    "backtestSharpe": -0.2281345806218534,
+    "benchmarkSharpe": -0.22182175736916387
+  },
+  "count": 5,
+  "solutions": [
+    "Try adjusting the trading rules and/or the universe to get a strategy that outperforms the benchmark."
+  ]
+}
    +
    +
    +

    + 401 Authentication Error +

    + + + + + + + + + + + + + + + + + + +

    + 401 Authentication Error +

    +
    UnauthorizedError @@ -68582,7 +70010,7 @@

    Examples

     

    API Reference > Backtest Management > Read Backtest > Charts

    -
    +

    Read Backtest

    Charts

    @@ -69279,137 +70707,13 @@

    Examples

    print("List of Backtests:") print(result)
    -

    - The following example demonstrates how to read and plot chart data from a backtest through the cloud API. -

    -
    - 
    from base64 import b64encode
-from hashlib import sha256
-from time import time
-from requests import get, post
-BASE_URL = 'https://www.quantconnect.com/api/v2/'
-
-# You need to replace these with your actual credentials.
-# You can request your credentials at https://www.quantconnect.com/settings/
-# You can find our organization ID at https://www.quantconnect.com/organization/ 
-USER_ID = 0
-API_TOKEN = '____'
-ORGANIZATION_ID = '____'
-
-def get_headers():
-    # Get timestamp
-    timestamp = f'{int(time())}'
-    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
-
-    # Get hased API token
-    hashed_token = sha256(time_stamped_token).hexdigest()
-    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
-    authentication = b64encode(authentication).decode('ascii')
-
-    # Create headers dictionary.
-    return {
-        'Authorization': f'Basic {authentication}',
-        'Timestamp': timestamp
-    }
-
-# Authenticate to verify credentials
-response = post(f'{BASE_URL}/authenticate', headers = get_headers())
-print(response.json())
-
-# --------------------
-
-from time import sleep
-import matplotlib.pyplot as plt
-import matplotlib.dates as mdates
-from datetime import datetime, timedelta
-
-# The project and backtest ID of the backtest
-project_id = 12345678
-backtest_id = "..."
-
-### Read Backtest Chart
-# Define the chart name to retrieve
-chart_name = "Strategy Equity"
-# Prepare data payload to fetch chart data
-payload = {
-    "projectId": project_id,  # ID of the project
-    "backtestId": backtest_id,  # ID of the backtest
-    "name": chart_name,  # Name of the chart to retrieve
-    "count": 500,  # Number of data points to fetch
-    "start": 1717801200,  # Start time in Unix timestamp
-    "end": 1743462000  # End time in Unix timestamp
-}
-# Retry up to 10 times if the chart data is still loading
-for attempt in range(10):
-    # Send a POST request to the /backtests/chart/read endpoint
-    response = post(f'{BASE_URL}/backtests/chart/read', headers=get_headers(), json=payload)
-    # Parse the JSON response
-    result = response.json()
-    # Check if the data is still loading
-    if result.get('status') == 'loading':
-        print(f"Chart data is loading... Progress: {result['progress']}% (attempt {attempt + 1}/10)")
-        sleep(10)
-        continue
-    break
-
-# If the request was successful, extract and plot the chart data
-if result['success']:
-    chart = result['chart']
-    # Filter to series with plottable list data points
-    series_items = [(name, data) for name, data in chart['series'].items()
-                    if data['values'] and isinstance(data['values'][0], list)]
-    if not series_items:
-        print("No series data available in the chart.")
-    else:
-        # Create a subplot for each series
-        fig, axes = plt.subplots(len(series_items), 1, figsize=(12, 4 * len(series_items)), sharex=True)
-        if len(series_items) == 1:
-            axes = [axes]
-        for ax, (series_name, series_data) in zip(axes, series_items):
-            values = series_data['values']
-            # Check if the data is OHLC (5 elements: timestamp, open, high, low, close)
-            is_ohlc = len(values[0]) == 5
-            if is_ohlc:
-                # Filter out data points with None values
-                valid = [p for p in values if None not in p[1:]]
-                if not valid:
-                    ax.set_title(f"{series_name} (no valid data)")
-                    continue
-                timestamps = [datetime.utcfromtimestamp(p[0]) for p in valid]
-                dates = mdates.date2num(timestamps)
-                # Calculate bar width as 60% of the average interval between points
-                bar_width = (dates[-1] - dates[0]) / len(dates) * 0.6
-                for i, point in enumerate(valid):
-                    _, o, h, l, c = point
-                    color = 'green' if c >= o else 'red'
-                    # Draw the high-low wick
-                    ax.vlines(dates[i], l, h, color=color, linewidth=0.8)
-                    # Draw the open-close body
-                    ax.bar(dates[i], abs(c - o) or 0.01, bottom=min(o, c),
-                           width=bar_width, color=color, edgecolor=color)
-                ax.xaxis_date()
-                ax.set_ylabel("Price")
-            else:
-                # Filter out data points with None values
-                valid = [p for p in values if p[1] is not None]
-                timestamps = [datetime.utcfromtimestamp(p[0]) for p in valid]
-                y_values = [p[1] for p in valid]
-                ax.plot(timestamps, y_values)
-                ax.set_ylabel("Value")
-            ax.set_title(series_name)
-            ax.tick_params(axis='x', rotation=45)
-        fig.suptitle(chart_name, fontsize=14)
-        plt.tight_layout()
-        plt.savefig(f"{project_id}-{backtest_id}-{chart_name.replace(' ', '_')}.png")
-        plt.show()
    -

     

    API Reference > Backtest Management > Read Backtest > Orders

    -
    +

    Read Backtest

    Orders

    @@ -70921,7 +72225,7 @@

    Examples

     

    API Reference > Backtest Management > Read Backtest > Insights

    -
    +

    Read Backtest

    Insights

    @@ -71657,7 +72961,7 @@

    Examples

     

    API Reference > Backtest Management > Update Backtest

    -
    +

    Backtest Management

    Update Backtest

    @@ -72009,7 +73313,7 @@

    Examples

     

    API Reference > Backtest Management > Delete Backtest

    -
    +

    Backtest Management

    Delete Backtest

    @@ -72335,7 +73639,7 @@

    Examples

     

    API Reference > Backtest Management > List Backtests

    -
    +

    Backtest Management

    List Backtests

    @@ -73108,7 +74412,7 @@

    Examples

     

    API Reference > Live Management

    -
    +

    API Reference

    Live Management

    @@ -73124,7 +74428,7 @@

    Live Management

    -
    +

    Create Live Algorithm @@ -73135,7 +74439,7 @@

    -
    +

    Read Live Algorithm @@ -73146,7 +74450,7 @@

    -
    +

    Update Live Algorithm @@ -73157,7 +74461,7 @@

    -
    +

    List Live Algorithms @@ -73168,7 +74472,7 @@

    -
    +

    Live Commands @@ -73185,7 +74489,7 @@

     

    API Reference > Live Management > Create Live Algorithm

    -
    +

    Live Management

    Create Live Algorithm

    @@ -76804,7 +78108,7 @@

    Examples

     

    API Reference > Live Management > Read Live Algorithm

    -
    +

    Live Management

    Read Live Algorithm

    @@ -76820,7 +78124,7 @@

    Read Live Algorithm

    -
    +

    Live Algorithm Statistics @@ -76831,7 +78135,7 @@

    -
    +

    Charts @@ -76842,7 +78146,7 @@

    -
    +

    Portfolio State @@ -76853,7 +78157,7 @@

    -
    +

    Orders @@ -76864,7 +78168,7 @@

    -
    +

    Insights @@ -76875,7 +78179,7 @@

    -
    +

    Logs @@ -76892,7 +78196,7 @@

     

    API Reference > Live Management > Read Live Algorithm > Live Algorithm Statistics

    -
    +

    Read Live Algorithm

    Live Algorithm Statistics

    @@ -77786,7 +79090,7 @@

    Examples

     

    API Reference > Live Management > Read Live Algorithm > Charts

    -
    +

    Read Live Algorithm

    Charts

    @@ -78476,8 +79780,501 @@

    Examples

    print("List of Live Algorithms:") print(result)
    + + + +

     

    +

    API Reference > Live Management > Read Live Algorithm > Portfolio State

    +
    +
    +

    Read Live Algorithm

    +

    Portfolio State

    +
    +
    +

    Introduction

    + + + +

    + Read out the portfolio state of a live algorithm. The snapshot updates about every 10 minutes. +

    + + + +

    Request

    + + + +

    + Fetch the live portfolio state for the project Id provided. The + + /live/portfolio/read + + API accepts requests in the following format: +

    + + + + + + + + + + + + + + +
    + + ReadLivePortfolioRequest + + Model - Request to read the portfolio state from a live algorithm. +
    + projectId + + + integer +
    + + + required + + +
    + + + example: 23456789 + + +     +
    + Id of the project from which to read the live algorithm. +
    + Example + +
    + 
    +{
+  "projectId": 23456789
+}
    +
    +
    + + + +

    Responses

    + + + +

    + The + + /live/portfolio/read + + API provides a response in the following format: +

    +

    + 200 Success +

    + + + + + + + + + + + + + + + + + + + + + + +
    + + LivePortfolioResponse + + Model - Contains holdings and cash of the live algorithm in the request criteria. +
    + portfolio + + + Portfolio object + +
    + Portfolio object with the holdings and cash information. +
    + success + + + boolean + +
    + Indicate if the API request was successful. +
    + errors + + + string Array + +
    + List of errors with the API call. +
    + Example + +
    + 
    +{
+  "portfolio": {
+    "holdings": {
+      "a": 0,
+      "q": 0,
+      "p": 0,
+      "v": 0,
+      "u": 0,
+      "up": 0
+    },
+    "cash": {
+      "securitySymbols": [
+        "object"
+      ],
+      "symbol": "string",
+      "amount": 0,
+      "conversionRate": 0,
+      "currencySymbol": "$",
+      "valueInAccountCurrency": 0
+    }
+  },
+  "success": true,
+  "errors": [
+    "string"
+  ]
+}
    +
    +
    + + + + + + + + + + + + + + + + + + +
    + + Portfolio + + Model - Portfolio object with the holdings and cash information. +
    + holdings + + + Holding object + +
    + Dictionary of algorithm holdings information, where the key is the Symbol Id. +
    + cash + + + Cash object + +
    + Dictionary of algorithm cash currencies information, where the key is the currency ticker. +
    + Example + +
    + 
    +{
+  "holdings": {
+    "a": 0,
+    "q": 0,
+    "p": 0,
+    "v": 0,
+    "u": 0,
+    "up": 0
+  },
+  "cash": {
+    "securitySymbols": [
+      "object"
+    ],
+    "symbol": "string",
+    "amount": 0,
+    "conversionRate": 0,
+    "currencySymbol": "$",
+    "valueInAccountCurrency": 0
+  }
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + Holding + + Model - Summary of asset holding. +
    + a + + + number + +
    + Average purchase price of the holding in the currency it trades in. +
    + q + + + number + +
    + Quantity of the asset. +
    + p + + + number + +
    + Current market price of the asset in the currency it trades in. +
    + v + + + number + +
    + Current market value of the holding. +
    + u + + + number + +
    + Unrealized profit/loss of the position. +
    + up + + + number + +
    + Unrealized profit/loss percentage of the position. +
    + Example + +
    + 
    +{
+  "a": 0,
+  "q": 0,
+  "p": 0,
+  "v": 0,
+  "u": 0,
+  "up": 0
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + Cash + + Model - Represents a holding of a currency in cash. +
    + securitySymbols + + + object Array + +
    + /. +
    + symbol + + + string + +
    + Gets the symbol used to represent this cash. +
    + amount + + + number + +
    + Gets or sets the amount of cash held. +
    + conversionRate + + + number + +
    + The currency conversion rate to the account base currency. +
    + currencySymbol + + + string +
    + + + example: $ + + +     +
    + The symbol of the currency. +
    + valueInAccountCurrency + + + number + +
    + The value of the currency cash in the account base currency. +
    + Example + +
    + 
    +{
+  "securitySymbols": [
+    "object"
+  ],
+  "symbol": "string",
+  "amount": 0,
+  "conversionRate": 0,
+  "currencySymbol": "$",
+  "valueInAccountCurrency": 0
+}
    +
    +
    +

    + 401 Authentication Error +

    + + + + + + + + + + +
    + + UnauthorizedError + + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. +
    + www_authenticate + + + string + +
    + Header +
    + + + +

    Examples

    + +

    - The following example demonstrates how to read and plot chart data from a live algorithm through the cloud API. + The following example demonstates creating, reading, updating, and listing live algorithms of a project through the cloud API. 

    from base64 import b64encode
@@ -78515,98 +80312,112 @@ 
    Examples
    
 
 # --------------------
 
-from time import sleep
-import matplotlib.pyplot as plt
-import matplotlib.dates as mdates
-from datetime import datetime, timedelta
 
-# The project ID of the live algorithm
-project_id = 23456789
+### Create Live Algorithm
+# Define placeholder IDs for compilation and node (replace with actual values)
+project_id = 12345678
+compile_id = "compile_id..."
+node_id = "node_id..."
+# Prepare the data payload for creating a live algorithm with necessary details
+payload = {
+    "versionId": "-1",  # Use the latest version of the algorithm
+    "projectId": project_id,  # ID of the project to deploy as a live algorithm
+    "compileId": compile_id,  # Compilation ID for the algorithm code
+    "nodeId": node_id,  # Node ID where the algorithm will run
+    "brokerage": {  # Brokerage configuration for live trading
+        "id": "QuantConnectBrokerage",  # Brokerage identifier
+        "user": "",  # Brokerage username (replace with actual value)
+        "password": "",  # Brokerage password (replace with actual value)
+        "environment": "live-paper",  # Trading environment (live or paper)
+        "account": ""  # Brokerage account ID (replace with actual value)
+    },
+    "dataProviders": {  # Data provider configuration
+        "QuantConnectBrokerage": {
+            "id": "QuantConnectBrokerage"  # Data provider identifier
+        }
+    },
+    "parameters": {},  # Optional algorithm parameters (empty in this example)
+    "notification": {}  # Optional notification settings (empty in this example)
+}
+# Send a POST request to the /live/create endpoint to deploy the algorithm
+response = post(f'{BASE_URL}/live/create', headers=get_headers(), json=data)
+# Parse the JSON response into python managable dict from the API
+result = response.json()
+# Extract the deploy ID from the response for future operations
+deploy_id = result['deployId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Live Algorithm Created Successfully:")
+    print(result)
+
+### Read Live Algorithm Statistics
+# Prepare data payload with project and deploy IDs to fetch statistics
+payload = {
+    "projectId": project_id,  # ID of the project
+    "deployId": deploy_id  # ID of the deployed live algorithm
+}
+# Send a POST request to the /live/read endpoint to get algorithm statistics
+response = post(f'{BASE_URL}/live/read', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the statistics
+if result['success']:
+    print("Live Algorithm Statistics:")
+    print(result)
 
-### Read Live Algorithm Chart
-# Define the chart name to retrieve
-chart_name = "Strategy Equity"
-# Prepare data payload to fetch chart data
+### Liquidate Live Algorithm
+# Prepare data payload with project ID to liquidate the algorithm
 payload = {
-    "projectId": project_id,  # ID of the project that's live trading
-    "name": chart_name,  # Name of the chart to retrieve
-    "count": 500,  # Number of data points to fetch
-    "start": 1717801200,  # Start time in Unix timestamp
-    "end": 1743462000  # End time in Unix timestamp
+    "projectId": project_id  # ID of the project to liquidate
 }
-# Retry up to 10 times if the chart data is still loading
-for attempt in range(10):
-    # Send a POST request to the /live/chart/read endpoint
-    response = post(f'{BASE_URL}/live/chart/read', headers=get_headers(), json=payload)
-    # Parse the JSON response
-    result = response.json()
-    # Check if the data is still loading
-    if result.get('status') == 'loading':
-        print(f"Chart data is loading... Progress: {result['progress']}% (attempt {attempt + 1}/10)")
-        sleep(10)
-        continue
-    break
-
-# If the request was successful, extract and plot the chart data
+# Send a POST request to the /live/update/liquidate endpoint to liquidate
+response = post(f'{BASE_URL}/live/update/liquidate', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
 if result['success']:
-    chart = result['chart']
-    # Filter to series with plottable list data points
-    series_items = [(name, data) for name, data in chart['series'].items()
-                    if data['values'] and isinstance(data['values'][0], list)]
-    if not series_items:
-        print("No series data available in the chart.")
-    else:
-        # Create a subplot for each series
-        fig, axes = plt.subplots(len(series_items), 1, figsize=(12, 4 * len(series_items)), sharex=True)
-        if len(series_items) == 1:
-            axes = [axes]
-        for ax, (series_name, series_data) in zip(axes, series_items):
-            values = series_data['values']
-            # Check if the data is OHLC (5 elements: timestamp, open, high, low, close)
-            is_ohlc = len(values[0]) == 5
-            if is_ohlc:
-                # Filter out data points with None values
-                valid = [p for p in values if None not in p[1:]]
-                if not valid:
-                    ax.set_title(f"{series_name} (no valid data)")
-                    continue
-                timestamps = [datetime.utcfromtimestamp(p[0]) for p in valid]
-                dates = mdates.date2num(timestamps)
-                # Calculate bar width as 60% of the average interval between points
-                bar_width = (dates[-1] - dates[0]) / len(dates) * 0.6
-                for i, point in enumerate(valid):
-                    _, o, h, l, c = point
-                    color = 'green' if c >= o else 'red'
-                    # Draw the high-low wick
-                    ax.vlines(dates[i], l, h, color=color, linewidth=0.8)
-                    # Draw the open-close body
-                    ax.bar(dates[i], abs(c - o) or 0.01, bottom=min(o, c),
-                           width=bar_width, color=color, edgecolor=color)
-                ax.xaxis_date()
-                ax.set_ylabel("Price")
-            else:
-                # Filter out data points with None values
-                valid = [p for p in values if p[1] is not None]
-                timestamps = [datetime.utcfromtimestamp(p[0]) for p in valid]
-                y_values = [p[1] for p in valid]
-                ax.plot(timestamps, y_values)
-                ax.set_ylabel("Value")
-            ax.set_title(series_name)
-            ax.tick_params(axis='x', rotation=45)
-        fig.suptitle(chart_name, fontsize=14)
-        plt.tight_layout()
-        plt.savefig(f"{project_id}-{chart_name.replace(' ', '_')}.png")
-        plt.show()
    + print("Live Algorithm Liquidated Successfully:") + print(result) + +### Stop Live Algorithm +# Prepare data payload with project ID to stop the algorithm +payload = { + "projectId": project_id # ID of the project to stop +} +# Send a POST request to the /live/update/stop endpoint to stop the algorithm +response = post(f'{BASE_URL}/live/update/stop', headers=get_headers(), json=payload) +# Parse the JSON response into python managable dict +result = response.json() +# Check if the request was successful and print the result +if result['success']: + print("Live Algorithm Stopped Successfully:") + print(result) + +### List Live Algorithms +# Prepare data payload with filters for listing live algorithms +payload = { + "status": "Running", # Filter to show only running algorithms + "start": 1717801200, # Start time (Unix timestamp) for the list range + "end": 1743462000 # End time (Unix timestamp) for the list range +} +# Send a POST request to the /live/list endpoint to list algorithms +response = post(f'{BASE_URL}/live/list', headers=get_headers(), json=payload) +# Parse the JSON response into python managable dict +result = response.json() +# Check if the request was successful and print the list +if result['success']: + print("List of Live Algorithms:") + print(result)

     

    -

    API Reference > Live Management > Read Live Algorithm > Portfolio State

    +

    API Reference > Live Management > Read Live Algorithm > Orders

    -
    +

    Read Live Algorithm

    -

    Portfolio State

    +

    Orders

    Introduction

    @@ -78614,7 +80425,7 @@

    Introduction

    - Read out the portfolio state of a live algorithm. The snapshot updates about every 10 minutes. + Read out the orders of a live algorithm. The snapshot updates about every 10 minutes.

    @@ -78624,9 +80435,9 @@

    Request

    - Fetch the live portfolio state for the project Id provided. The + Fetch the orders of a live algorithm for the project Id and steps provided. The - /live/portfolio/read + /live/orders/read API accepts requests in the following format:

    @@ -78635,12 +80446,78 @@

    Request

    - ReadLivePortfolioRequest + ReadLiveOrdersRequest - Model - Request to read the portfolio state from a live algorithm. + Model - Request to read orders from a live algorithm.
    + algorithmId + + + string +
    + + + example: L-6e9d8a78f5af89d401f630585be90e43 + + +     +
    + Deploy Id (Algorithm Id) of the live running algorithm. +
    + start + + + integer +
    + + + required + + +
    + + + example: 0 + + +     +
    + Starting index of the orders to be fetched. +
    + end + + + integer +
    + + + required + + +
    + + + example: 100 + + +     +
    + Last index of the orders to be fetched. Note that end - start must be <= 1,000. +
    projectId @@ -78673,6 +80550,9 @@

    Request

     {
+  "algorithmId": "L-6e9d8a78f5af89d401f630585be90e43",
+  "start": 0,
+  "end": 100,
   "projectId": 23456789
 }
    @@ -78689,7 +80569,7 @@

    Responses

    The - /live/portfolio/read + /live/orders/read API provides a response in the following format:

    @@ -78700,47 +80580,305 @@

    + Orders of the live algorithm within range. +
    + + + + + + + + + +
    + + UnauthorizedError + + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. +
    + www_authenticate + - LivePortfolioResponse + string - Model - Contains holdings and cash of the live algorithm in the request criteria. +
    + Header +
    + + + + +

    Examples

    + + +

    + The following example demonstates creating, reading, updating, and listing live algorithms of a project through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+### Create Live Algorithm
+# Define placeholder IDs for compilation and node (replace with actual values)
+project_id = 12345678
+compile_id = "compile_id..."
+node_id = "node_id..."
+# Prepare the data payload for creating a live algorithm with necessary details
+payload = {
+    "versionId": "-1",  # Use the latest version of the algorithm
+    "projectId": project_id,  # ID of the project to deploy as a live algorithm
+    "compileId": compile_id,  # Compilation ID for the algorithm code
+    "nodeId": node_id,  # Node ID where the algorithm will run
+    "brokerage": {  # Brokerage configuration for live trading
+        "id": "QuantConnectBrokerage",  # Brokerage identifier
+        "user": "",  # Brokerage username (replace with actual value)
+        "password": "",  # Brokerage password (replace with actual value)
+        "environment": "live-paper",  # Trading environment (live or paper)
+        "account": ""  # Brokerage account ID (replace with actual value)
+    },
+    "dataProviders": {  # Data provider configuration
+        "QuantConnectBrokerage": {
+            "id": "QuantConnectBrokerage"  # Data provider identifier
+        }
+    },
+    "parameters": {},  # Optional algorithm parameters (empty in this example)
+    "notification": {}  # Optional notification settings (empty in this example)
+}
+# Send a POST request to the /live/create endpoint to deploy the algorithm
+response = post(f'{BASE_URL}/live/create', headers=get_headers(), json=data)
+# Parse the JSON response into python managable dict from the API
+result = response.json()
+# Extract the deploy ID from the response for future operations
+deploy_id = result['deployId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Live Algorithm Created Successfully:")
+    print(result)
+
+### Read Live Algorithm Statistics
+# Prepare data payload with project and deploy IDs to fetch statistics
+payload = {
+    "projectId": project_id,  # ID of the project
+    "deployId": deploy_id  # ID of the deployed live algorithm
+}
+# Send a POST request to the /live/read endpoint to get algorithm statistics
+response = post(f'{BASE_URL}/live/read', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the statistics
+if result['success']:
+    print("Live Algorithm Statistics:")
+    print(result)
+
+### Liquidate Live Algorithm
+# Prepare data payload with project ID to liquidate the algorithm
+payload = {
+    "projectId": project_id  # ID of the project to liquidate
+}
+# Send a POST request to the /live/update/liquidate endpoint to liquidate
+response = post(f'{BASE_URL}/live/update/liquidate', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Live Algorithm Liquidated Successfully:")
+    print(result)
+
+### Stop Live Algorithm
+# Prepare data payload with project ID to stop the algorithm
+payload = {
+    "projectId": project_id  # ID of the project to stop
+}
+# Send a POST request to the /live/update/stop endpoint to stop the algorithm
+response = post(f'{BASE_URL}/live/update/stop', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Live Algorithm Stopped Successfully:")
+    print(result)
+
+### List Live Algorithms
+# Prepare data payload with filters for listing live algorithms
+payload = {
+    "status": "Running",  # Filter to show only running algorithms
+    "start": 1717801200,  # Start time (Unix timestamp) for the list range
+    "end": 1743462000  # End time (Unix timestamp) for the list range
+}
+# Send a POST request to the /live/list endpoint to list algorithms
+response = post(f'{BASE_URL}/live/list', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Live Algorithms:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > Live Management > Read Live Algorithm > Insights

    +
    +
    +

    Read Live Algorithm

    +

    Insights

    +
    +
    +

    Introduction

    + + + +

    + Read out the insights of a live algorithm. The snapshot updates about every 10 minutes. +

    + + + +

    Request

    + + + +

    + Fetch the insights of a live algorithm for the project Id and steps provided. The + + /live/insights/read + + API accepts requests in the following format: +

    + + + + + + + + @@ -78751,68 +80889,77 @@ 

     {
-  "portfolio": {
-    "holdings": {
-      "a": 0,
-      "q": 0,
-      "p": 0,
-      "v": 0,
-      "u": 0,
-      "up": 0
-    },
-    "cash": {
-      "securitySymbols": [
-        "object"
-      ],
-      "symbol": "string",
-      "amount": 0,
-      "conversionRate": 0,
-      "currencySymbol": "$",
-      "valueInAccountCurrency": 0
-    }
-  },
-  "success": true,
-  "errors": [
-    "string"
-  ]
+  "algorithmId": "L-6e9d8a78f5af89d401f630585be90e43",
+  "start": 0,
+  "end": 100,
+  "projectId": 23456789
 }

    + + ReadLiveInsightsRequest + + Model - Request to read insights from a live algorithm.
    - portfolio + algorithmId - Portfolio object + string +
    + + + example: L-6e9d8a78f5af89d401f630585be90e43 + +
    - Portfolio object with the holdings and cash information. + Deploy Id (Algorithm Id) of the live running algorithm.
    - success + start - boolean + integer +
    + + + example: 0 + +
    - Indicate if the API request was successful. + Starting index of the insights to be fetched. Required if end > 100.
    - errors + end - string Array + integer +
    + + + required + + +
    + + + example: 100 + +
    - List of errors with the API call. + Last index of the insights to be fetched. Note that end - start must be less than 100. +
    + projectId + + + integer +
    + + + required + + +
    + + + example: 23456789 + + +     +
    + Id of the project from which to read the live algorithm.
    + + + +

    Responses

    + + + +

    + The + + /live/insights/read + + API provides a response in the following format: +

    +

    + 200 Success +

    + + + + @@ -78823,24 +80970,32 @@ 

     {
-  "holdings": {
-    "a": 0,
-    "q": 0,
-    "p": 0,
-    "v": 0,
-    "u": 0,
-    "up": 0
-  },
-  "cash": {
-    "securitySymbols": [
-      "object"
-    ],
-    "symbol": "string",
-    "amount": 0,
-    "conversionRate": 0,
-    "currencySymbol": "$",
-    "valueInAccountCurrency": 0
-  }
+  "insights": [
+    {
+      "id": "81ff7c1daf404ee39d612df321df5931",
+      "groupId": "string",
+      "sourceModel": "952d6ff7-e513-4971-aa50-cbe13bf56898",
+      "generatedTime": 1753714506,
+      "closeTime": 1753800906,
+      "symbol": "BTCUSD 2XR",
+      "ticker": "BTCUSD",
+      "type": "price",
+      "reference": 118159.19,
+      "referenceFinal": 0,
+      "direction": "down",
+      "period": 0,
+      "magnitude": 0,
+      "confidence": 0,
+      "weight": 0,
+      "scoreFinal": true,
+      "scoreDirection": 0,
+      "scoreMagnitude": 0,
+      "estimatedValue": 0,
+      "tag": "string"
+    }
+  ],
+  "length": 0,
+  "success": true
 }
    @@ -78851,190 +81006,292 @@

    + + + + + + + + + + + + + + + + -
    - Portfolio + LiveInsightsResponse - Model - Portfolio object with the holdings and cash information. + Model - Contains insights and the number of insights of the live algorithm in the request criteria.
    - holdings + insights - Holding object + Insight Array
    - Dictionary of algorithm holdings information, where the key is the Symbol Id. + Collection of insights.
    - cash + length - Cash object + integer
    - Dictionary of algorithm cash currencies information, where the key is the currency ticker. + Total number of returned insights. +
    + success + + + boolean + +
    + Indicate if the API request was successful.
    - Holding + Insight - Model - Summary of asset holding. + Model - Insight struct for emitting new prediction.
    - a + id - number + string +
    + + + example: 81ff7c1daf404ee39d612df321df5931 + +
    - Average purchase price of the holding in the currency it trades in. + Id of the insight.
    - q + groupId - number + string
    - Quantity of the asset. + Id of the group of insights.
    - p + sourceModel - number + string +
    + + + example: 952d6ff7-e513-4971-aa50-cbe13bf56898 + +
    - Current market price of the asset in the currency it trades in. + An identifier for the source model that generated this insight.
    - v + generatedTime - number + integer +
    + + + example: 1753714506 + +
    - Current market value of the holding. + Gets the Unix time this insight was generated.
    - u + closeTime + + + integer +
    + + + example: 1753800906 + + +     +
    + Gets the Unix time this insight was closed. +
    + symbol + + + string +
    + + + example: BTCUSD 2XR + + +     +
    + Gets the symbol Id this insight is for. +
    + ticker + + + string +
    + + + example: BTCUSD + + +     +
    + Gets the symbol ticker this insight is for. +
    + type + + + string Enum + +
    + Gets the type of insight. Options : ['price', 'volatility'] +
    + reference number +
    + + + example: 118159.19 + +
    - Unrealized profit/loss of the position. + Gets the initial reference value this insight is predicting against. That is, the price of the asset when the insight was created.
    - up + referenceFinal number
    - Unrealized profit/loss percentage of the position. + Gets the final reference value, used for scoring, this insight is predicting against.
    - Example + direction -
    - 
    -{
-  "a": 0,
-  "q": 0,
-  "p": 0,
-  "v": 0,
-  "u": 0,
-  "up": 0
-}
    -
    + + string Enum + +
    + Gets the predicted direction. Options : ['down', 'flat', 'up']
    - - - - - - + + + + + + + + + + + + @@ -79045,14 +81302,26 @@ 

     {
-  "securitySymbols": [
-    "object"
-  ],
-  "symbol": "string",
-  "amount": 0,
-  "conversionRate": 0,
-  "currencySymbol": "$",
-  "valueInAccountCurrency": 0
+  "id": "81ff7c1daf404ee39d612df321df5931",
+  "groupId": "string",
+  "sourceModel": "952d6ff7-e513-4971-aa50-cbe13bf56898",
+  "generatedTime": 1753714506,
+  "closeTime": 1753800906,
+  "symbol": "BTCUSD 2XR",
+  "ticker": "BTCUSD",
+  "type": "price",
+  "reference": 118159.19,
+  "referenceFinal": 0,
+  "direction": "down",
+  "period": 0,
+  "magnitude": 0,
+  "confidence": 0,
+  "weight": 0,
+  "scoreFinal": true,
+  "scoreDirection": 0,
+  "scoreMagnitude": 0,
+  "estimatedValue": 0,
+  "tag": "string"
 }
    @@ -79231,11 +81500,11 @@

    Examples

     

    -

    API Reference > Live Management > Read Live Algorithm > Orders

    +

    API Reference > Live Management > Read Live Algorithm > Logs

    -
    +

    Read Live Algorithm

    -

    Orders

    +

    Logs

    Introduction

    @@ -79243,7 +81512,7 @@

    Introduction

    - Read out the orders of a live algorithm. The snapshot updates about every 10 minutes. + Get the logs of a live algorithm. The snapshot updates about every 5 minutes.

    @@ -79253,9 +81522,9 @@

    Request

    - Fetch the orders of a live algorithm for the project Id and steps provided. The + Information about the algorithm to read live logs from. The - /live/orders/read + /live/logs/read API accepts requests in the following format:

    @@ -79264,33 +81533,33 @@

    Request

    + + + + + + + + @@ -79368,10 +81661,12 @@

    Request

     {
+  "format": "json",
+  "projectId": 23456789,
   "algorithmId": "L-6e9d8a78f5af89d401f630585be90e43",
-  "start": 0,
-  "end": 100,
-  "projectId": 23456789
+  "startLine": 0,
+  "endLine": 0,
+  "deploymentLogs": true
 }
    @@ -79387,7 +81682,7 @@

    Responses

    The - /live/orders/read + /live/logs/read API provides a response in the following format:

    @@ -79398,37 +81693,121 @@

    -

    - 401 Authentication Error -

    -
    - - Cash - - Model - Represents a holding of a currency in cash. -
    - securitySymbols + period - object Array + integer
    - /. + Gets the period, in seconds, over which this insight is expected to come to fruition.
    - symbol + magnitude - string + number
    - Gets the symbol used to represent this cash. + Gets the predicted percent change in the insight type (price/volatility). This value can be null.
    - amount + confidence number
    - Gets or sets the amount of cash held. + Gets the confidence in this insight. This value can be null.
    - conversionRate + weight number
    - The currency conversion rate to the account base currency. + Gets the portfolio weight of this insight. This value can be null.
    - currencySymbol + scoreFinal - string -
    - - - example: $ - - + boolean
    - The symbol of the currency. + Gets whether or not this is the insight's final score.
    - valueInAccountCurrency + scoreDirection number
    - The value of the currency cash in the account base currency. + Gets the direction score. +
    + scoreMagnitude + + + number + +
    + Gets the magnitude score. +
    + estimatedValue + + + number + +
    + Gets the estimated value of this insight in the account currency. +
    + tag + + + string + +
    + The insight's tag containing additional information.
    - ReadLiveOrdersRequest + ReadLiveLogsRequest - Model - Request to read orders from a live algorithm. + Model - Request to read the logs of a specific algorithm.
    - algorithmId + format - string + object
    - example: L-6e9d8a78f5af89d401f630585be90e43 + example: json
    - Deploy Id (Algorithm Id) of the live running algorithm. + Format of the log results.
    - start + projectId @@ -79304,21 +81573,21 @@

    Request


    - example: 0 + example: 23456789
    - Starting index of the orders to be fetched. + Id of the project that contains the live running algorithm.
    - end + algorithmId - integer + string
    @@ -79328,17 +81597,17 @@

    Request


    - example: 100 + example: L-6e9d8a78f5af89d401f630585be90e43
    - Last index of the orders to be fetched. Note that end - start must be <= 1,000. + Deploy Id (Algorithm Id) of the live running algorithm.
    - projectId + startLine @@ -79349,15 +81618,39 @@

    Request

    required +     +
    + Start line (inclusive) of logs to read. The lines numbers start at 0. +
    + endLine + + + integer
    - example: 23456789 + required
    - Id of the project from which to read the live algorithm. + End line (exclusive) of logs to read, where endLine - startLine <= 250. +
    + deploymentLogs + + + boolean + +
    + Indicates if only the given deployment logs should be included in the response.
    - Orders of the live algorithm within range. + + ReadLiveLogsResponse + + Model - Logs from a live algorithm.
    - - - - - + + + + + + + + + + + + + + + + + + + + + + + + +
    - - UnauthorizedError - - Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. -
    + logs + + + string Array + +
    + List of logs from the live algorithm. +
    + length + + + integer + +
    + Total amount of rows in the logs across all live deployments for this project. +
    + deploymentOffset + + + integer + +
    + Number of log rows before the given deployment (the `algorithmId` in the request). +
    + success + + + boolean + +
    + Indicate if the API request was successful. +
    + errors + + + string Array + +
    + List of errors with the API call. +
    + Example + +
    + 
    +{
+  "logs": [
+    "string"
+  ],
+  "length": 0,
+  "deploymentOffset": 0,
+  "success": true,
+  "errors": [
+    "string"
+  ]
+}
    +
    +
    +

    + 401 Authentication Error +

    + + - - -
    - www_authenticate - + - string + UnauthorizedError -
    - Header - -
    + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. + + + + + + www_authenticate + + + + string + +
    + Header + + @@ -79576,11 +81955,55 @@

    Examples

     

    -

    API Reference > Live Management > Read Live Algorithm > Insights

    +

    API Reference > Live Management > Update Live Algorithm

    -
    -

    Read Live Algorithm

    -

    Insights

    +
    +

    Live Management

    +

    Update Live Algorithm

    +
    +
    + + +
    +
    +

    + The QuantConnect REST API lets you update your live algorithms on our cloud servers through URL endpoints. +

    +
    +
    +
    + +
    +
    +

    + Liquidate Live Portfolio +

    +
    +
    +     +
    +
    + +
    +
    +

    + Stop Live Algorithm +

    +
    +
    +     +
    +
    +
    + + + +

     

    +

    API Reference > Live Management > Update Live Algorithm > Liquidate Live Portfolio

    +
    +
    +

    Update Live Algorithm

    +

    Liquidate Live Portfolio

    Introduction

    @@ -79588,7 +82011,7 @@

    Introduction

    - Read out the insights of a live algorithm. The snapshot updates about every 10 minutes. + Liquidate a live algorithm from the specified project Id.

    @@ -79598,9 +82021,9 @@

    Request

    - Fetch the insights of a live algorithm for the project Id and steps provided. The + Information about the live algorithm to liquidate. The - /live/insights/read + /live/update/liquidate API accepts requests in the following format:

    @@ -79609,72 +82032,12 @@

    Request

    - ReadLiveInsightsRequest + LiquidateLiveAlgorithmRequest - Model - Request to read insights from a live algorithm. + Model - Request to liquidate a live algorithm. - - - algorithmId - - - - string -
    - - - example: L-6e9d8a78f5af89d401f630585be90e43 - - -     -
    - Deploy Id (Algorithm Id) of the live running algorithm. - - - - - start - - - - integer -
    - - - example: 0 - - -     -
    - Starting index of the insights to be fetched. Required if end > 100. - - - - - end - - - - integer -
    - - - required - - -
    - - - example: 100 - - -     -
    - Last index of the insights to be fetched. Note that end - start must be less than 100. - - projectId @@ -79696,7 +82059,7 @@

    Request


    - Id of the project from which to read the live algorithm. + Project Id for the live instance to liquidate. @@ -79707,9 +82070,6 @@

    Request

     {
-  "algorithmId": "L-6e9d8a78f5af89d401f630585be90e43",
-  "start": 0,
-  "end": 100,
   "projectId": 23456789
 }
    @@ -79726,7 +82086,7 @@

    Responses

    The - /live/insights/read + /live/update/liquidate API provides a response in the following format:

    @@ -79738,46 +82098,34 @@

    - LiveInsightsResponse + RestResponse - Model - Contains insights and the number of insights of the live algorithm in the request criteria. + Model - Base API response class for the QuantConnect API. - insights - - - - Insight Array - -
    - Collection of insights. - - - - - length + success - integer + boolean
    - Total number of returned insights. + Indicate if the API request was successful. - success + errors - boolean + string Array
    - Indicate if the API request was successful. + List of errors with the API call. @@ -79788,358 +82136,329 @@ 

     {
-  "insights": [
-    {
-      "id": "81ff7c1daf404ee39d612df321df5931",
-      "groupId": "string",
-      "sourceModel": "952d6ff7-e513-4971-aa50-cbe13bf56898",
-      "generatedTime": 1753714506,
-      "closeTime": 1753800906,
-      "symbol": "BTCUSD 2XR",
-      "ticker": "BTCUSD",
-      "type": "price",
-      "reference": 118159.19,
-      "referenceFinal": 0,
-      "direction": "down",
-      "period": 0,
-      "magnitude": 0,
-      "confidence": 0,
-      "weight": 0,
-      "scoreFinal": true,
-      "scoreDirection": 0,
-      "scoreMagnitude": 0,
-      "estimatedValue": 0,
-      "tag": "string"
-    }
-  ],
-  "length": 0,
-  "success": true
+  "success": true,
+  "errors": [
+    "string"
+  ]
 }
    +

    + 401 Authentication Error +

    - - - - +
    - Insight + UnauthorizedError - Model - Insight struct for emitting new prediction. + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash.
    - id - - - string -
    - - - example: 81ff7c1daf404ee39d612df321df5931 - - -     -
    - Id of the insight. -
    - groupId + www_authenticate string
    - Id of the group of insights. + Header
    + + + +

    Examples

    + + +

    + The following example demonstates creating, reading, updating, and listing live algorithms of a project through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+### Create Live Algorithm
+# Define placeholder IDs for compilation and node (replace with actual values)
+project_id = 12345678
+compile_id = "compile_id..."
+node_id = "node_id..."
+# Prepare the data payload for creating a live algorithm with necessary details
+payload = {
+    "versionId": "-1",  # Use the latest version of the algorithm
+    "projectId": project_id,  # ID of the project to deploy as a live algorithm
+    "compileId": compile_id,  # Compilation ID for the algorithm code
+    "nodeId": node_id,  # Node ID where the algorithm will run
+    "brokerage": {  # Brokerage configuration for live trading
+        "id": "QuantConnectBrokerage",  # Brokerage identifier
+        "user": "",  # Brokerage username (replace with actual value)
+        "password": "",  # Brokerage password (replace with actual value)
+        "environment": "live-paper",  # Trading environment (live or paper)
+        "account": ""  # Brokerage account ID (replace with actual value)
+    },
+    "dataProviders": {  # Data provider configuration
+        "QuantConnectBrokerage": {
+            "id": "QuantConnectBrokerage"  # Data provider identifier
+        }
+    },
+    "parameters": {},  # Optional algorithm parameters (empty in this example)
+    "notification": {}  # Optional notification settings (empty in this example)
+}
+# Send a POST request to the /live/create endpoint to deploy the algorithm
+response = post(f'{BASE_URL}/live/create', headers=get_headers(), json=data)
+# Parse the JSON response into python managable dict from the API
+result = response.json()
+# Extract the deploy ID from the response for future operations
+deploy_id = result['deployId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Live Algorithm Created Successfully:")
+    print(result)
+
+### Read Live Algorithm Statistics
+# Prepare data payload with project and deploy IDs to fetch statistics
+payload = {
+    "projectId": project_id,  # ID of the project
+    "deployId": deploy_id  # ID of the deployed live algorithm
+}
+# Send a POST request to the /live/read endpoint to get algorithm statistics
+response = post(f'{BASE_URL}/live/read', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the statistics
+if result['success']:
+    print("Live Algorithm Statistics:")
+    print(result)
+
+### Liquidate Live Algorithm
+# Prepare data payload with project ID to liquidate the algorithm
+payload = {
+    "projectId": project_id  # ID of the project to liquidate
+}
+# Send a POST request to the /live/update/liquidate endpoint to liquidate
+response = post(f'{BASE_URL}/live/update/liquidate', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Live Algorithm Liquidated Successfully:")
+    print(result)
+
+### Stop Live Algorithm
+# Prepare data payload with project ID to stop the algorithm
+payload = {
+    "projectId": project_id  # ID of the project to stop
+}
+# Send a POST request to the /live/update/stop endpoint to stop the algorithm
+response = post(f'{BASE_URL}/live/update/stop', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Live Algorithm Stopped Successfully:")
+    print(result)
+
+### List Live Algorithms
+# Prepare data payload with filters for listing live algorithms
+payload = {
+    "status": "Running",  # Filter to show only running algorithms
+    "start": 1717801200,  # Start time (Unix timestamp) for the list range
+    "end": 1743462000  # End time (Unix timestamp) for the list range
+}
+# Send a POST request to the /live/list endpoint to list algorithms
+response = post(f'{BASE_URL}/live/list', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Live Algorithms:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > Live Management > Update Live Algorithm > Stop Live Algorithm

    +
    +
    +

    Update Live Algorithm

    +

    Stop Live Algorithm

    +
    +
    +

    Introduction

    + + + +

    + Stop a live algorithm from the specified project Id. +

    + + + +

    Request

    + + + +

    + Information about the project to delete. The + + /live/update/stop + + API accepts requests in the following format: +

    + + + + + + - - - - +
    + + StopLiveAlgorithmRequest + + Model - Request to stop a live algorithm. +
    - sourceModel + projectId - string + integer
    - example: 952d6ff7-e513-4971-aa50-cbe13bf56898 + required -     -
    - An identifier for the source model that generated this insight. -
    - generatedTime - - - integer
    - example: 1753714506 + example: 23456789
    - Gets the Unix time this insight was generated. + Id of the project to stop trading live.
    - closeTime + Example - - integer -
    - - - example: 1753800906 - - -     -
    - Gets the Unix time this insight was closed. +
    + 
    +{
+  "projectId": 23456789
+}
    +
    + + + +

    Responses

    + + + +

    + The + + /live/update/stop + + API provides a response in the following format: +

    +

    + 200 Success +

    + + + + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - @@ -80318,11 +82637,11 @@

    Examples

     

    -

    API Reference > Live Management > Read Live Algorithm > Logs

    +

    API Reference > Live Management > List Live Algorithms

    -
    -

    Read Live Algorithm

    -

    Logs

    +
    +

    Live Management

    +

    List Live Algorithms

    Introduction

    @@ -80330,7 +82649,7 @@

    Introduction

    - Get the logs of a live algorithm. The snapshot updates about every 5 minutes. + List all your past and current live trading deployments.

    @@ -80340,9 +82659,9 @@

    Request

    - Information about the algorithm to read live logs from. The + Request body to obtain a list of live running algorithms. The - /live/logs/read + /live/list API accepts requests in the following format:

    @@ -80351,124 +82670,119 @@

    Request

    +
    + + RestResponse + + Model - Base API response class for the QuantConnect API. +
    - symbol + success - string -
    - - - example: BTCUSD 2XR - - + boolean
    - Gets the symbol Id this insight is for. + Indicate if the API request was successful.
    - ticker + errors - string -
    - - - example: BTCUSD - - + string Array
    - Gets the symbol ticker this insight is for. + List of errors with the API call.
    - type - - - string Enum - -
    - Gets the type of insight. Options : ['price', 'volatility'] -
    - reference - - - number -
    - - - example: 118159.19 - - -     -
    - Gets the initial reference value this insight is predicting against. That is, the price of the asset when the insight was created. -
    - referenceFinal - - - number - -
    - Gets the final reference value, used for scoring, this insight is predicting against. -
    - direction - - - string Enum - -
    - Gets the predicted direction. Options : ['down', 'flat', 'up'] -
    - period - - - integer - -
    - Gets the period, in seconds, over which this insight is expected to come to fruition. -
    - magnitude - - - number - -
    - Gets the predicted percent change in the insight type (price/volatility). This value can be null. -
    - confidence - - - number - -
    - Gets the confidence in this insight. This value can be null. -
    - weight - - - number - -
    - Gets the portfolio weight of this insight. This value can be null. -
    - scoreFinal - - - boolean - -
    - Gets whether or not this is the insight's final score. -
    - scoreDirection - - - number - -
    - Gets the direction score. -
    - scoreMagnitude - - - number - -
    - Gets the magnitude score. -
    - estimatedValue - - - number - -
    - Gets the estimated value of this insight in the account currency. -
    - tag - - - string - -
    - The insight's tag containing additional information. -
    - Example + Example 
     {
-  "id": "81ff7c1daf404ee39d612df321df5931",
-  "groupId": "string",
-  "sourceModel": "952d6ff7-e513-4971-aa50-cbe13bf56898",
-  "generatedTime": 1753714506,
-  "closeTime": 1753800906,
-  "symbol": "BTCUSD 2XR",
-  "ticker": "BTCUSD",
-  "type": "price",
-  "reference": 118159.19,
-  "referenceFinal": 0,
-  "direction": "down",
-  "period": 0,
-  "magnitude": 0,
-  "confidence": 0,
-  "weight": 0,
-  "scoreFinal": true,
-  "scoreDirection": 0,
-  "scoreMagnitude": 0,
-  "estimatedValue": 0,
-  "tag": "string"
+  "success": true,
+  "errors": [
+    "string"
+  ]
 }
    - ReadLiveLogsRequest + ListLiveAlgorithmsRequest - Model - Request to read the logs of a specific algorithm. + Model - Request for a list of past and present live deployments.
    - format + projectId - object + integer
    - example: json + example: 23456789
    - Format of the log results. + Id of the project to include in response. If you omit this property, the response includes all your projects.
    - projectId + status - integer -
    - - - required - - -
    - - - example: 23456789 - - + string Enum
    - Id of the project that contains the live running algorithm. + Status of the live deployments to include in the response. If you omit this property, the response includes deployments with any status. Options : ['Running', 'Stopped', 'RuntimeError', 'Liquidated']
    - algorithmId + Example - - string -
    - - - required - - -
    - - - example: L-6e9d8a78f5af89d401f630585be90e43 - - -     -
    - Deploy Id (Algorithm Id) of the live running algorithm. +
    + 
    +{
+  "projectId": 23456789,
+  "status": "Running"
+}
    +
    + + + +

    Responses

    + + + +

    + The + + /live/list + + API provides a response in the following format: +

    +

    + 200 Success +

    + + + + + + @@ -80479,103 +82793,186 @@

    Request

     {
-  "format": "json",
-  "projectId": 23456789,
-  "algorithmId": "L-6e9d8a78f5af89d401f630585be90e43",
-  "startLine": 0,
-  "endLine": 0,
-  "deploymentLogs": true
+  "live": [
+    {
+      "projectId": 0,
+      "deployId": "string",
+      "status": "string",
+      "launched": "2021-11-26T15:18:27.693Z",
+      "stopped": "2021-11-26T15:18:27.693Z",
+      "brokerage": "string",
+      "subscription": "string",
+      "equity": 0,
+      "environment": "string",
+      "description": "string",
+      "error": "string",
+      "leagues": [
+        "string"
+      ]
+    }
+  ],
+  "success": true,
+  "errors": [
+    "string"
+  ]
 }
    + + LiveAlgorithmListResponse + + Model - List of the live algorithms running which match the requested status. +
    - startLine + live - integer -
    - - - required - - + LiveAlgorithmSummary Array
    - Start line (inclusive) of logs to read. The lines numbers start at 0. + Live algorithms that pass the filters in the request.
    - endLine + success - integer -
    - - - required - - + boolean
    - End line (exclusive) of logs to read, where endLine - startLine <= 250. + Indicate if the API request was successful.
    - deploymentLogs + errors - boolean + string Array
    - Indicates if only the given deployment logs should be included in the response. + List of errors with the API call.
    - - - -

    Responses

    - - - -

    - The - - /live/logs/read - - API provides a response in the following format: -

    -

    - 200 Success -

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -80586,13 +82983,18 @@ 

     {
-  "logs": [
-    "string"
-  ],
-  "length": 0,
-  "deploymentOffset": 0,
-  "success": true,
-  "errors": [
+  "projectId": 0,
+  "deployId": "string",
+  "status": "string",
+  "launched": "2021-11-26T15:18:27.693Z",
+  "stopped": "2021-11-26T15:18:27.693Z",
+  "brokerage": "string",
+  "subscription": "string",
+  "equity": 0,
+  "environment": "string",
+  "description": "string",
+  "error": "string",
+  "leagues": [
     "string"
   ]
 }
    @@ -80773,11 +83175,11 @@

    Examples

     

    -

    API Reference > Live Management > Update Live Algorithm

    +

    API Reference > Live Management > Live Commands

    -
    +

    Live Management

    -

    Update Live Algorithm

    +

    Live Commands

    @@ -80785,27 +83187,27 @@

    Update Live Algorithm

    - The QuantConnect REST API lets you update your live algorithms on our cloud servers through URL endpoints. + The QuantConnect REST API send commands to live algorithms on our cloud servers through URL endpoints.

    - -
    + +

    - Liquidate Live Portfolio + Create Live Command

    - -
    + +

    - Stop Live Algorithm + Broadcast Live Command

    @@ -80817,11 +83219,11 @@

     

    -

    API Reference > Live Management > Update Live Algorithm > Liquidate Live Portfolio

    +

    API Reference > Live Management > Live Commands > Create Live Command

    -
    -

    Update Live Algorithm

    -

    Liquidate Live Portfolio

    +
    +

    Live Commands

    +

    Create Live Command

    Introduction

    @@ -80829,7 +83231,7 @@

    Introduction

    - Liquidate a live algorithm from the specified project Id. + Send a live command to a live trading algorithm.

    @@ -80839,9 +83241,9 @@

    Request

    - Information about the live algorithm to liquidate. The + Sends a command to a live deployment to trigger an action such as placing orders. The - /live/update/liquidate + /live/commands/create API accepts requests in the following format:

    @@ -80850,9 +83252,9 @@

    Request

    @@ -80877,7 +83279,31 @@

    Request


    - Project Id for the live instance to liquidate. + Project for the live instance we want to run the command against. + + +     + + @@ -80888,7 +83314,19 @@

    Request

     {
-  "projectId": 23456789
+  "projectId": 23456789,
+  "command": {
+    "$type": "OrderCommand",
+    "symbol": {
+      "id": "BTCUSD 2XR",
+      "value": "BTCUSD"
+    },
+    "order_type": "market",
+    "quantity": "0.1",
+    "limit_price": 0,
+    "stop_price": 0,
+    "tag": ""
+  }
 }
    @@ -80904,7 +83342,7 @@

    Responses

    The - /live/update/liquidate + /live/commands/create API provides a response in the following format:

    @@ -81136,11 +83574,11 @@

    Examples

     

    -

    API Reference > Live Management > Update Live Algorithm > Stop Live Algorithm

    +

    API Reference > Live Management > Live Commands > Broadcast Live Command

    -
    -

    Update Live Algorithm

    -

    Stop Live Algorithm

    +
    +

    Live Commands

    +

    Broadcast Live Command

    Introduction

    @@ -81148,7 +83586,7 @@

    Introduction

    - Stop a live algorithm from the specified project Id. + Broadcast a live command to all live algorithms in the organization.

    @@ -81158,9 +83596,9 @@

    Request

    - Information about the project to delete. The + Broadcasts a command to all live deployments in the organization. The - /live/update/stop + /live/commands/broadcast API accepts requests in the following format:

    @@ -81169,19 +83607,19 @@

    Request

     + + + + + + + + @@ -81207,7 +83687,20 @@

    Request

     {
-  "projectId": 23456789
+  "organizationId": "5cad178b20a1d52567b534553413b691",
+  "excludeProjectId": 23456789,
+  "command": {
+    "$type": "OrderCommand",
+    "symbol": {
+      "id": "BTCUSD 2XR",
+      "value": "BTCUSD"
+    },
+    "order_type": "market",
+    "quantity": "0.1",
+    "limit_price": 0,
+    "stop_price": 0,
+    "tag": ""
+  }
 }
    @@ -81223,7 +83716,7 @@

    Responses

    The - /live/update/stop + /live/commands/broadcast API provides a response in the following format:

    @@ -81455,11 +83948,18 @@

    Examples

     

    -

    API Reference > Live Management > List Live Algorithms

    +

    API Reference > Optimization Management

    -
    -

    Live Management

    -

    List Live Algorithms

    +
    +

    API Reference

    +

    Optimization Management

    +
    +
    +

    API Reference > Optimization Management > Create Optimization

    +
    +
    +

    Optimization Management

    +

    Create Optimization

    Introduction

    @@ -81467,7 +83967,7 @@

    Introduction

    - List all your past and current live trading deployments. + Create an optimization with the specified parameters.

    @@ -81477,9 +83977,9 @@

    Request

    - Request body to obtain a list of live running algorithms. The + Project, compile, and optimization parameters for launching an optimization job. The - /live/list + /optimizations/create API accepts requests in the following format:

    @@ -81488,9 +83988,9 @@

    Request

     @@ -81502,6 +84002,12 @@

    Request

    integer
    + + + required + + +
    example: 23456789 @@ -81509,288 +84015,335 @@

    Request
    - Id of the project to include in response. If you omit this property, the response includes all your projects. + Id of the project to optimize.     - - - - -
    - ReadLiveLogsResponse + LiveAlgorithmSummary - Model - Logs from a live algorithm. + Model - Summary of the live algorithm.
    - logs + projectId - string Array + integer
    - List of logs from the live algorithm. + Id of the project.
    - length + deployId - integer + string
    - Total amount of rows in the logs across all live deployments for this project. + Id of the live deployment.
    - deploymentOffset + status - integer + string
    - Number of log rows before the given deployment (the `algorithmId` in the request). + The current status of the deployment.
    - success + launched - boolean + string($date-time)
    - Indicate if the API request was successful. + The date and time when the deployment was launched.
    - errors + stopped + + + string($date-time) + +
    + The date and time when the deployment was stopped. +
    + brokerage + + + string + +
    + The brokerage used for the deployment. +
    + subscription + + + string + +
    + The chart to which you're subscribed. +
    + equity + + + number + +
    + The equity value associated with the deployment. +
    + environment + + + string + +
    + The environment in which the deployment is running. +
    + description + + + string + +
    + The project description. +
    + error + + + string + +
    + The error message if there was a runtime error. +
    + leagues string Array
    - List of errors with the API call. + Quant Leagues the algorithm is enrolled in.
    - LiquidateLiveAlgorithmRequest + CreateLiveCommandRequest - Model - Request to liquidate a live algorithm. + Model - Request to create a live command.
    + command + + + object +
    + + + required + + +
    + + + example: {'$type': 'OrderCommand', 'symbol': {'id': 'BTCUSD 2XR', 'value': 'BTCUSD'}, 'order_type': 'market', 'quantity': '0.1', 'limit_price': 0, 'stop_price': 0, 'tag': ''} + + +     +
    + The command to run.
    - StopLiveAlgorithmRequest + BroadcastLiveCommandRequest - Model - Request to stop a live algorithm. + Model - Request to create a live command.
    - projectId + organizationId - integer + string
    @@ -81189,6 +83627,24 @@

    Request
    + + + example: 5cad178b20a1d52567b534553413b691 + + +     +
    + Organization Id of the projects we would like to broadcast the command to. +
    + excludeProjectId + + + integer +
    example: 23456789 @@ -81196,7 +83652,31 @@

    Request
    - Id of the project to stop trading live. + Project for the live instance we want to exclude from the broadcast list. If null, all projects will be included. +
    + command + + + object +
    + + + required + + +
    + + + example: {'$type': 'OrderCommand', 'symbol': {'id': 'BTCUSD 2XR', 'value': 'BTCUSD'}, 'order_type': 'market', 'quantity': '0.1', 'limit_price': 0, 'stop_price': 0, 'tag': ''} + + +     +
    + The command to run.
    - ListLiveAlgorithmsRequest + CreateOptimizationRequest - Model - Request for a list of past and present live deployments. + Model - Request to create an optimization job.
    - status + name - string Enum + string +
    + + + required + + +
    + + + example: Mia First Optimization Job + +
    - Status of the live deployments to include in the response. If you omit this property, the response includes deployments with any status. Options : ['Running', 'Stopped', 'RuntimeError', 'Liquidated'] -
    - Example - -
    - 
    -{
-  "projectId": 23456789,
-  "status": "Running"
-}
    -
    + Name of the optimization.
    - - - -

    Responses

    - - - -

    - The - - /live/list - - API provides a response in the following format: -

    -

    - 200 Success -

    - - - - - - -
    - - LiveAlgorithmListResponse - - Model - List of the live algorithms running which match the requested status. -
    - live + target - LiveAlgorithmSummary Array + string Enum
    - Live algorithms that pass the filters in the request. + Target statistic of the optimization to minimize or maximize. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown']
    - success + targetTo - boolean + string Enum
    - Indicate if the API request was successful. + Target extremum of the optimization. Options : ['min', 'max']
    - errors + targetValue - string Array + number +
    + + + example: 1 + +
    - List of errors with the API call. + Desired value for the optimization target statistic.
    - Example + strategy -
    - 
    -{
-  "live": [
-    {
-      "projectId": 0,
-      "deployId": "string",
-      "status": "string",
-      "launched": "2021-11-26T15:18:27.693Z",
-      "stopped": "2021-11-26T15:18:27.693Z",
-      "brokerage": "string",
-      "subscription": "string",
-      "equity": 0,
-      "environment": "string",
-      "description": "string",
-      "error": "string",
-      "leagues": [
-        "string"
-      ]
-    }
-  ],
-  "success": true,
-  "errors": [
-    "string"
-  ]
-}
    -
    + + string Enum + +
    + Optimization strategy. Options : ['QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy']
    - - - - - - +
    - - LiveAlgorithmSummary - - Model - Summary of the live algorithm. -
    - projectId + compileId - integer + string +
    + + + required + + +
    + + + example: 5d1f2cba3a0ec7407c566614300502b5-173e0419674daf4144ce7c9931155ca8 + +
    - Id of the project. + Optimization compile Id.
    - deployId + parameters - string + OptimizationParameter Array +
    + + + required + +
    - Id of the live deployment. + Optimization parameters.
    - status + constraints - string + OptimizationConstraint Array
    - The current status of the deployment. + Optimization constraints.
    - launched + estimatedCost - string($date-time) + number +
    + + + required + + +
    + + + example: 10 + +
    - The date and time when the deployment was launched. + Estimated cost for optimization.
    - stopped + nodeType - string($date-time) + string Enum
    - The date and time when the deployment was stopped. + Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16']
    - brokerage + parallelNodes - string + integer +
    + + + required + + +
    + + + example: 4 + +
    - The brokerage used for the deployment. + Number of parallel nodes for optimization.
    - subscription + Example - - string - -
    - The chart to which you're subscribed. +
    + 
    +{
+  "projectId": 23456789,
+  "name": "Mia First Optimization Job",
+  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+  "targetTo": "min",
+  "targetValue": 1,
+  "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",
+  "compileId": "5d1f2cba3a0ec7407c566614300502b5-173e0419674daf4144ce7c9931155ca8",
+  "parameters": [
+    {
+      "name": "rsi_period",
+      "min": 10,
+      "max": 20,
+      "step": 1,
+      "minStep": 1
+    }
+  ],
+  "constraints": [
+    {
+      "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+      "operator": "LessOrEqual",
+      "targetValue": 1
+    }
+  ],
+  "estimatedCost": 10,
+  "nodeType": "O2-8",
+  "parallelNodes": 4
+}
    +
    + + + + + + +
    + + OptimizationTargetStatistic + + Model +
    - equity + OptimizationNodeType - number + string Enum
    - The equity value associated with the deployment. + /. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown']
    - environment + Example - - string - -
    - The environment in which the deployment is running. +
    + 
    +{
+  "OptimizationNodeType": "TotalPerformance.PortfolioStatistics.SharpeRatio"
+}
    +
    + + + + + + +
    + + OptimizationTargetTo + + Model +
    - description + OptimizationNodeType - string + string Enum
    - The project description. + /. Options : ['min', 'max']
    - error + Example - - string - -
    - The error message if there was a runtime error. +
    + 
    +{
+  "OptimizationNodeType": "min"
+}
    +
    + + + + + + @@ -81801,50 +84354,3598 @@ 

     {
-  "projectId": 0,
-  "deployId": "string",
-  "status": "string",
-  "launched": "2021-11-26T15:18:27.693Z",
-  "stopped": "2021-11-26T15:18:27.693Z",
-  "brokerage": "string",
-  "subscription": "string",
-  "equity": 0,
-  "environment": "string",
-  "description": "string",
-  "error": "string",
-  "leagues": [
-    "string"
-  ]
+  "OptimizationNodeType": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy"
 }

    + + OptimizationStrategy + + Model +
    - leagues + OptimizationNodeType - string Array + string Enum
    - Quant Leagues the algorithm is enrolled in. + /. Options : ['QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy']
    -

    - 401 Authentication Error -

    +
    + + + required + + +
    + + + example: rsi_period + + + +
    + Name of optimization parameter. + + + + + + + + + + + + + + + + + + + + + + +
    - UnauthorizedError + OptimizationParameter - Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. + Model
    - www_authenticate + name string - -
    - Header -
    + min + + + number +
    + + + required + + +
    + + + example: 10 + + +     +
    + Minimum value of optimization parameter, applicable for boundary conditions. +
    + max + + + number +
    + + + required + + +
    + + + example: 20 + + +     +
    + Maximum value of optimization parameter, applicable for boundary conditions. +
    + step + + + number +
    + + + required + + +
    + + + example: 1 + + +     +
    + Movement, should be positive. +
    + minStep + + + number +
    + + + example: 1 + + +     +
    + Minimal possible movement for current parameter, should be positive. Used by + + Strategies.EulerSearchOptimizationStrategy + + to determine when this parameter can no longer be optimized. +
    + Example + +
    + 
    +{
+  "name": "rsi_period",
+  "min": 10,
+  "max": 20,
+  "step": 1,
+  "minStep": 1
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + +
    + + OptimizationConstraint + + Model - Backtests in the optimization job that don't respect this constraint are excluded from the optimization result. +
    + target + + + string Enum + +
    + The target statistic to track. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] +
    + operator + + + string Enum +
    + + + required + + +     +
    + The target comparison operation. Options : ['LessOrEqual', 'Less', 'GreaterOrEqual', 'Greater', 'NotEqual', 'Equals'] +
    + targetValue + + + number +
    + + + required + + +
    + + + example: 1 + + +     +
    + The threshold value for the target constraint. +
    + Example + +
    + 
    +{
+  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+  "operator": "LessOrEqual",
+  "targetValue": 1
+}
    +
    +
    + + + + + + + + + + + + + + +
    + + OptimizationNodeType + + Model - Optimization node types available in QuantConnect Cloud. +
    + OptimizationNodeType + + + string Enum + +
    + Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] +
    + Example + +
    + 
    +{
+  "OptimizationNodeType": "O2-8"
+}
    +
    +
    + + + +

    Responses

    + + + +

    + The + + /optimizations/create + + API provides a response in the following format: +

    +

    + 200 Success +

    + + + + + + + + + + + + + + + + + + + + + + +
    + + ListOptimizationResponse + + Model - Response received when creating an optimization or listing optimizations of a project. +
    + optimizations + + + CreateOptimizationResponse Array + +
    + Collection of summarized optimization objects. +
    + success + + + boolean + +
    + Indicate if the API request was successful. +
    + errors + + + string Array + +
    + List of errors with the API call. +
    + Example + +
    + 
    +{
+  "optimizations": [
+    {
+      "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
+      "projectId": 23456789,
+      "name": "string",
+      "status": ,
+      "nodeType": "O2-8",
+      "extremum": "min",
+      "criterion": {
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "extremum": "min",
+        "targetValue": 1
+      },
+      "created": "2021-11-26T15:18:27.693Z",
+      "psr": 0,
+      "sharpeRatio": 0,
+      "trades": 0,
+      "cloneId": 0,
+      "outOfSampleDays": 0,
+      "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z",
+      "parameters": [
+        {
+          "name": "rsi_period",
+          "min": 10,
+          "max": 20,
+          "step": 1,
+          "minStep": 1
+        }
+      ]
+    }
+  ],
+  "success": true,
+  "errors": [
+    "string"
+  ]
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + CreateOptimizationResponse + + Model - Response received when launching an optimization job or listing all the optimization jobs of a project. +
    + optimizationId + + + string +
    + + + example: O-401d3d40b5a0e9f8c46c954a303f3ddd + + +     +
    + Id of the optimization job. +
    + projectId + + + integer +
    + + + example: 23456789 + + +     +
    + Id of the project the optimization belongs to. +
    + name + + + string + +
    + Name of the optimization. +
    + status + + + OptimizationStatus object + +
    + Status of the optimization. +
    + nodeType + + + string Enum + +
    + Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] +
    + extremum + + + string Enum + +
    + Defines the direction of optimization. Options : ['min', 'max'] +
    + criterion + + + OptimizationTarget object + +
    + Optimization statistical target. +
    + created + + + string($date-time) + +
    + Date and time of when this optimization was created. +
    + psr + + + number + +
    + Probabilistic Sharpe ratio statistic. +
    + sharpeRatio + + + number + +
    + Sharpe ratio statistic. +
    + trades + + + integer + +
    + Number of trades in the best backtest (based on the criterion) of the optimziation. +
    + cloneId + + + integer + +
    + Id of project where this current project was originally cloned. +
    + outOfSampleDays + + + integer + +
    + Number out-of-sample days. +
    + outOfSampleMaxEndDate + + + string($date-time) + +
    + End date of out-of-sample data. +
    + parameters + + + OptimizationParameter Array + +
    + Parameters used in this optimization. +
    + Example + +
    + 
    +{
+  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
+  "projectId": 23456789,
+  "name": "string",
+  "status": ,
+  "nodeType": "O2-8",
+  "extremum": "min",
+  "criterion": {
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+    "extremum": "min",
+    "targetValue": 1
+  },
+  "created": "2021-11-26T15:18:27.693Z",
+  "psr": 0,
+  "sharpeRatio": 0,
+  "trades": 0,
+  "cloneId": 0,
+  "outOfSampleDays": 0,
+  "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z",
+  "parameters": [
+    {
+      "name": "rsi_period",
+      "min": 10,
+      "max": 20,
+      "step": 1,
+      "minStep": 1
+    }
+  ]
+}
    +
    +
    + + + + + + + + + + + + + + +
    + + OptimizationNodeType + + Model - Optimization node types available in QuantConnect Cloud. +
    + OptimizationParameter + + + string Enum + +
    + Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] +
    + Example + +
    + 
    +{
+  "OptimizationParameter": "O2-8"
+}
    +
    +
    + + + + + + + + + + + + + + +
    + + OptimizationTargetTo + + Model +
    + OptimizationParameter + + + string Enum + +
    + /. Options : ['min', 'max'] +
    + Example + +
    + 
    +{
+  "OptimizationParameter": "min"
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + +
    + + OptimizationTarget + + Model +
    + target + + + string Enum + +
    + The target statistic to track. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] +
    + extremum + + + string Enum + +
    + Defines the direction of optimization. Options : ['min', 'max'] +
    + targetValue + + + number +
    + + + example: 1 + + +     +
    + Desired value for the optimization target statistic. +
    + Example + +
    + 
    +{
+  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+  "extremum": "min",
+  "targetValue": 1
+}
    +
    +
    + + + + + + + + + + + + + + +
    + + OptimizationTargetStatistic + + Model +
    + OptimizationParameter + + + string Enum + +
    + /. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] +
    + Example + +
    + 
    +{
+  "OptimizationParameter": "TotalPerformance.PortfolioStatistics.SharpeRatio"
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + OptimizationParameter + + Model +
    + name + + + string +
    + + + required + + +
    + + + example: rsi_period + + +     +
    + Name of optimization parameter. +
    + min + + + number +
    + + + required + + +
    + + + example: 10 + + +     +
    + Minimum value of optimization parameter, applicable for boundary conditions. +
    + max + + + number +
    + + + required + + +
    + + + example: 20 + + +     +
    + Maximum value of optimization parameter, applicable for boundary conditions. +
    + step + + + number +
    + + + required + + +
    + + + example: 1 + + +     +
    + Movement, should be positive. +
    + minStep + + + number +
    + + + example: 1 + + +     +
    + Minimal possible movement for current parameter, should be positive. Used by + + Strategies.EulerSearchOptimizationStrategy + + to determine when this parameter can no longer be optimized. +
    + Example + +
    + 
    +{
+  "name": "rsi_period",
+  "min": 10,
+  "max": 20,
+  "step": 1,
+  "minStep": 1
+}
    +
    +
    +

    + 401 Authentication Error +

    + + + + + + + + + + +
    + + UnauthorizedError + + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. +
    + www_authenticate + + + string + +
    + Header +
    + + + +

    Examples

    + + +

    + The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# The project ID of the project to manage an optimization job
+project_id = 12345678
+
+### Estimate Optimization Cost
+# Send a POST request to the /optimizations/estimate endpoint to estimate cost
+response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
+    "targetTo": "max",  # Direction to optimize (maximize)
+    "targetValue": None,  # Specific target value (None for max/min)
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
+    "compileId": compile_id,  # Compilation ID for the optimization
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value for first parameter
+    "parameters[0][max]": 200,  # Maximum value for first parameter
+    "parameters[0][step]": 50,  # Step size for first parameter
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value for second parameter
+    "parameters[1][max]": 300,  # Maximum value for second parameter
+    "parameters[1][step]": 50,  # Step size for second parameter
+    "constraints": [{  # Constraints for the optimization
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }]
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the estimated cost
+if result['success']:
+    print("Optimization Cost Estimated Successfully:")
+    print(result)
+
+### Create Optimization
+# Send a POST request to the /optimizations/create endpoint to create an optimization
+response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
+    "targetTo": "max",  # Direction to optimize
+    "targetValue": None,  # Specific target value
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
+    "compileId": compile_id,  # Compilation ID
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value
+    "parameters[0][max]": 200,  # Maximum value
+    "parameters[0][step]": 50,  # Step size
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value
+    "parameters[1][max]": 300,  # Maximum value
+    "parameters[1][step]": 50,  # Step size
+    "constraints": [{  # Constraints
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }],
+    "estimatedCost": 10,  # Estimated cost of optimization
+    "nodeType": "O2-8",  # Node type for optimization
+    "parallelNodes": 4  # Number of parallel nodes
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the optimization ID from the response
+optimization_id = result['optimizations'][0]['optimizationId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Created Successfully:")
+    print(result)
+
+### Update Optimization
+# Send a POST request to the /optimizations/update endpoint to update the optimization
+response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
+    "optimizationId": optimization_id,  # ID of the optimization to update
+    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Updated Successfully:")
+    print(result)
+
+### Read Optimization
+# Prepare data payload to read optimization details
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to read
+}
+# Send a POST request to the /optimizations/read endpoint to get details
+response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the details
+if result['success']:
+    print("Optimization Details:")
+    print(result)
+
+### Abort Optimization
+# Prepare data payload to abort the optimization
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to abort
+}
+# Send a POST request to the /optimizations/abort endpoint to abort
+response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Aborted Successfully:")
+    print(result)
+
+### Delete Optimization
+# Prepare data payload to delete the optimization
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to delete
+}
+# Send a POST request to the /optimizations/delete endpoint to delete
+response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Deleted Successfully:")
+    print(result)
+
+### List Optimizations
+# Prepare data payload to list optimizations
+payload = {
+    "projectId": project_id  # ID of the project
+}
+# Send a POST request to the /optimizations/list endpoint to list optimizations
+response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Optimizations:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > Optimization Management > Read Optimization

    +
    +
    +

    Optimization Management

    +

    Read Optimization

    +
    +
    +

    Introduction

    + + + +

    + Read an optimization. +

    + + + +

    Request

    + + + +

    + Id of the optimization to read. The + + /optimizations/read + + API accepts requests in the following format: +

    + + + + + + + + + + + + + + +
    + + ReadOptimizationRequest + + Model - Request to read an optimization from a project. +
    + optimizationId + + + string +
    + + + required + + +
    + + + example: O-401d3d40b5a0e9f8c46c954a303f3ddd + + +     +
    + Id of the optimization to read. +
    + Example + +
    + 
    +{
+  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd"
+}
    +
    +
    + + + +

    Responses

    + + + +

    + The + + /optimizations/read + + API provides a response in the following format: +

    +

    + 200 Success +

    + + + + + + + + + + + + + + + + + + + + + + +
    + + ReadOptimizationResponse + + Model - Response received when reading an optimization. +
    + optimization + + + Optimization object + +
    + Response received when reading an optimization job. +
    + success + + + boolean + +
    + Indicate if the API request was successful. +
    + errors + + + string Array + +
    + List of errors with the API call. +
    + Example + +
    + 
    +{
+  "optimization": {
+    "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
+    "snapshotId": 24013333,
+    "projectId": 23456789,
+    "name": "string",
+    "extremum": "min",
+    "status": ,
+    "nodeType": "O2-8",
+    "parallelNodes": 4,
+    "criterion": {
+      "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+      "extremum": "min",
+      "targetValue": 1
+    },
+    "runtimeStatistics": {
+      "Completed": "string",
+      "Failed": "string",
+      "Running": "string",
+      "In Queue": "string",
+      "Average Length": "2021-11-26T15:18:27.693Z",
+      "Total Runtime": "2021-11-26T15:18:27.693Z",
+      "Total": "string",
+      "Consumed": "string"
+    },
+    "constraints": [
+      {
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "LessOrEqual",
+        "targetValue": 1
+      }
+    ],
+    "parameters": [
+      {
+        "name": "rsi_period",
+        "min": 10,
+        "max": 20,
+        "step": 1,
+        "minStep": 1
+      }
+    ],
+    "backtests": {
+      "name": "string",
+      "id": "string",
+      "progress": 0,
+      "exitCode": 0,
+      "statistics": [
+        "number"
+      ],
+      "parameterSet": ,
+      "equity": [
+        "array"
+      ],
+      "startDate": "2021-11-26T15:18:27.693Z",
+      "endDate": "2021-11-26T15:18:27.693Z",
+      "outOfSampleDays": 0,
+      "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z"
+    },
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",
+    "requested": "2021-11-26T15:18:27.693Z",
+    "optimizationTarget": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+    "targetValue": 1,
+    "gridLayout": [
+      {
+        "chartName": "string",
+        "width": 0,
+        "height": 0,
+        "row": 0,
+        "column": 0,
+        "sort": 0,
+        "definition": [
+          "string"
+        ]
+      }
+    ],
+    "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z",
+    "outOfSampleDays": 0,
+    "created": "2021-11-26T15:18:27.693Z",
+    "psr": 0,
+    "sharpeRatio": 0,
+    "trades": 0,
+    "cloneId": 0
+  },
+  "success": true,
+  "errors": [
+    "string"
+  ]
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + Optimization + + Model - Response received when reading an optimization job. +
    + optimizationId + + + string +
    + + + required + + +
    + + + example: O-401d3d40b5a0e9f8c46c954a303f3ddd + + +     +
    + Id of the optimization. +
    + snapshotId + + + integer +
    + + + required + + +
    + + + example: 24013333 + + +     +
    + Snapshot Id of this optimization. +
    + projectId + + + integer +
    + + + required + + +
    + + + example: 23456789 + + +     +
    + Id of the project the optimization belongs to. +
    + name + + + string +
    + + + required + + +     +
    + Name of the optimization. +
    + extremum + + + string Enum + +
    + Defines the direction of optimization. Options : ['min', 'max'] +
    + status + + + OptimizationStatus object +
    + + + required + + +     +
    + Status of the optimization. +
    + nodeType + + + string Enum + +
    + Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] +
    + parallelNodes + + + integer +
    + + + example: 4 + + +     +
    + Number of parallel nodes for the optimization. +
    + criterion + + + OptimizationTarget object +
    + + + required + + +     +
    + Optimization statistical target. +
    + runtimeStatistics + + + OptimizationRuntimeStatistics object + +
    + Dictionary representing a runtime banner/updating statistics for the optimization. +
    + constraints + + + OptimizationConstraint Array + +
    + Optimization constraints. +
    + parameters + + + OptimizationParameter Array +
    + + + required + + +     +
    + Optimization parameters. +
    + backtests + + + OptimizationBacktest object + +
    + Dictionary of optimization backtests. +
    + strategy + + + string Enum + +
    + Optimization strategy. Options : ['QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy'] +
    + requested + + + string($date-time) + +
    + Optimization requested date and time. +
    + optimizationTarget + + + string Enum + +
    + Statistic to be optimized. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] +
    + targetValue + + + number +
    + + + example: 1 + + +     +
    + Desired value for the optimization target statistic. +
    + gridLayout + + + GridChart Array + +
    + List with grid charts representing the grid layout. +
    + outOfSampleMaxEndDate + + + string($date-time) +
    + + + required + + +     +
    + End date of out of sample data. +
    + outOfSampleDays + + + integer +
    + + + required + + +     +
    + Number of days of out of sample days. +
    + created + + + string($date-time) +
    + + + required + + +     +
    + Date and time of when this optimization was created. +
    + psr + + + number +
    + + + required + + +     +
    + Probabilistic Sharpe ratio statistic. +
    + sharpeRatio + + + number +
    + + + required + + +     +
    + Sharpe ratio statistic. +
    + trades + + + integer +
    + + + required + + +     +
    + Number of trades in the best backtest (based on the criterion) of the optimziation. +
    + cloneId + + + integer +
    + + + required + + +     +
    + Id of project where this current project was originally cloned. +
    + Example + +
    + 
    +{
+  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
+  "snapshotId": 24013333,
+  "projectId": 23456789,
+  "name": "string",
+  "extremum": "min",
+  "status": ,
+  "nodeType": "O2-8",
+  "parallelNodes": 4,
+  "criterion": {
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+    "extremum": "min",
+    "targetValue": 1
+  },
+  "runtimeStatistics": {
+    "Completed": "string",
+    "Failed": "string",
+    "Running": "string",
+    "In Queue": "string",
+    "Average Length": "2021-11-26T15:18:27.693Z",
+    "Total Runtime": "2021-11-26T15:18:27.693Z",
+    "Total": "string",
+    "Consumed": "string"
+  },
+  "constraints": [
+    {
+      "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+      "operator": "LessOrEqual",
+      "targetValue": 1
+    }
+  ],
+  "parameters": [
+    {
+      "name": "rsi_period",
+      "min": 10,
+      "max": 20,
+      "step": 1,
+      "minStep": 1
+    }
+  ],
+  "backtests": {
+    "name": "string",
+    "id": "string",
+    "progress": 0,
+    "exitCode": 0,
+    "statistics": [
+      "number"
+    ],
+    "parameterSet": ,
+    "equity": [
+      "array"
+    ],
+    "startDate": "2021-11-26T15:18:27.693Z",
+    "endDate": "2021-11-26T15:18:27.693Z",
+    "outOfSampleDays": 0,
+    "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z"
+  },
+  "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",
+  "requested": "2021-11-26T15:18:27.693Z",
+  "optimizationTarget": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+  "targetValue": 1,
+  "gridLayout": [
+    {
+      "chartName": "string",
+      "width": 0,
+      "height": 0,
+      "row": 0,
+      "column": 0,
+      "sort": 0,
+      "definition": [
+        "string"
+      ]
+    }
+  ],
+  "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z",
+  "outOfSampleDays": 0,
+  "created": "2021-11-26T15:18:27.693Z",
+  "psr": 0,
+  "sharpeRatio": 0,
+  "trades": 0,
+  "cloneId": 0
+}
    +
    +
    + + + + + + + + + + + + + + +
    + + OptimizationTargetTo + + Model +
    + GridChart + + + string Enum + +
    + /. Options : ['min', 'max'] +
    + Example + +
    + 
    +{
+  "GridChart": "min"
+}
    +
    +
    + + + + + + + + + + + + + + +
    + + OptimizationNodeType + + Model - Optimization node types available in QuantConnect Cloud. +
    + GridChart + + + string Enum + +
    + Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] +
    + Example + +
    + 
    +{
+  "GridChart": "O2-8"
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + +
    + + OptimizationTarget + + Model +
    + target + + + string Enum + +
    + The target statistic to track. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] +
    + extremum + + + string Enum + +
    + Defines the direction of optimization. Options : ['min', 'max'] +
    + targetValue + + + number +
    + + + example: 1 + + +     +
    + Desired value for the optimization target statistic. +
    + Example + +
    + 
    +{
+  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+  "extremum": "min",
+  "targetValue": 1
+}
    +
    +
    + + + + + + + + + + + + + + +
    + + OptimizationTargetStatistic + + Model +
    + GridChart + + + string Enum + +
    + /. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] +
    + Example + +
    + 
    +{
+  "GridChart": "TotalPerformance.PortfolioStatistics.SharpeRatio"
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + OptimizationRuntimeStatistics + + Model +
    + Completed + + + string + +
    + Number of completed backtests in the optimization job. +
    + Failed + + + string + +
    + Number of failed backtests in the optimization job. +
    + Running + + + string + +
    + Number of running backtests in the optimization job. +
    + In Queue + + + string + +
    + Number of backtests waiting in-queue in the optimization job. +
    + Average Length + + + string($time) + +
    + The average time of each backtest in the optimization job (HH:MM:SS). +
    + Total Runtime + + + string($time) + +
    + The total runtime of the optimization (HH:MM:SS). +
    + Total + + + string + +
    + Number of backtests in the optimization job. +
    + Consumed + + + string + +
    + Amount of QuantConnect Credit consumed by the optimization job. +
    + Example + +
    + 
    +{
+  "Completed": "string",
+  "Failed": "string",
+  "Running": "string",
+  "In Queue": "string",
+  "Average Length": "2021-11-26T15:18:27.693Z",
+  "Total Runtime": "2021-11-26T15:18:27.693Z",
+  "Total": "string",
+  "Consumed": "string"
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + +
    + + OptimizationConstraint + + Model - Backtests in the optimization job that don't respect this constraint are excluded from the optimization result. +
    + target + + + string Enum + +
    + The target statistic to track. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] +
    + operator + + + string Enum +
    + + + required + + +     +
    + The target comparison operation. Options : ['LessOrEqual', 'Less', 'GreaterOrEqual', 'Greater', 'NotEqual', 'Equals'] +
    + targetValue + + + number +
    + + + required + + +
    + + + example: 1 + + +     +
    + The threshold value for the target constraint. +
    + Example + +
    + 
    +{
+  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+  "operator": "LessOrEqual",
+  "targetValue": 1
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + OptimizationParameter + + Model +
    + name + + + string +
    + + + required + + +
    + + + example: rsi_period + + +     +
    + Name of optimization parameter. +
    + min + + + number +
    + + + required + + +
    + + + example: 10 + + +     +
    + Minimum value of optimization parameter, applicable for boundary conditions. +
    + max + + + number +
    + + + required + + +
    + + + example: 20 + + +     +
    + Maximum value of optimization parameter, applicable for boundary conditions. +
    + step + + + number +
    + + + required + + +
    + + + example: 1 + + +     +
    + Movement, should be positive. +
    + minStep + + + number +
    + + + example: 1 + + +     +
    + Minimal possible movement for current parameter, should be positive. Used by + + Strategies.EulerSearchOptimizationStrategy + + to determine when this parameter can no longer be optimized. +
    + Example + +
    + 
    +{
+  "name": "rsi_period",
+  "min": 10,
+  "max": 20,
+  "step": 1,
+  "minStep": 1
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + OptimizationBacktest + + Model +
    + name + + + string + +
    + The backtest name. +
    + id + + + string + +
    + Id of the backtest. +
    + progress + + + number + +
    + Progress of the backtest as a percentage from 0-1 based on the days lapsed from start-finish. +
    + exitCode + + + integer + +
    + The exit code of this backtest. +
    + statistics + + + number Array + +
    + The backtest statistics results. [alpha, annual standard deviation, annual variance, average loss (%), average win (%), beta, compounding annual return (%), drawdown (%), estimated strategy capacity, expectancy, information ratio, loss rate (%), net profit (%), probabilistic sharpe ratio, profit-loss ratio, sharpe ratio, total fees, total orders, tracking error, treynor ratio, win rate (%)]. +
    + parameterSet + + + ParameterSet object + +
    + Parameters used in the backtest. +
    + equity + + + array Array + +
    + The backtest equity chart series. +
    + startDate + + + string($date-time) + +
    + The backtest start date. +
    + endDate + + + string($date-time) + +
    + The backtest end date. +
    + outOfSampleDays + + + integer + +
    + The backtest out-of-sample day count. +
    + outOfSampleMaxEndDate + + + string($date-time) + +
    + End date of out-of-sample data. +
    + Example + +
    + 
    +{
+  "name": "string",
+  "id": "string",
+  "progress": 0,
+  "exitCode": 0,
+  "statistics": [
+    "number"
+  ],
+  "parameterSet": ,
+  "equity": [
+    "array"
+  ],
+  "startDate": "2021-11-26T15:18:27.693Z",
+  "endDate": "2021-11-26T15:18:27.693Z",
+  "outOfSampleDays": 0,
+  "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z"
+}
    +
    +
    + + + + + + + + + + + + + + +
    + + OptimizationStrategy + + Model +
    + value + + + string Enum + +
    + /. Options : ['QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy'] +
    + Example + +
    + 
    +{
+  "value": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy"
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + GridChart + + Model - The chart display properties. +
    + chartName + + + string +
    + + + required + + +     +
    + The chart name. +
    + width + + + integer +
    + + + required + + +     +
    + Width of the chart. +
    + height + + + integer +
    + + + required + + +     +
    + Height of the chart. +
    + row + + + integer +
    + + + required + + +     +
    + Number of rows of the chart. +
    + column + + + integer +
    + + + required + + +     +
    + Number of columns of the chart. +
    + sort + + + integer + +
    + Sort of the chart. +
    + definition + + + string Array + +
    + The chart definition labels. +
    + Example + +
    + 
    +{
+  "chartName": "string",
+  "width": 0,
+  "height": 0,
+  "row": 0,
+  "column": 0,
+  "sort": 0,
+  "definition": [
+    "string"
+  ]
+}
    +
    +
    +

    + 401 Authentication Error +

    + + + + + + + + + + +
    + + UnauthorizedError + + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. +
    + www_authenticate + + + string + +
    + Header +
    + + + +

    Examples

    + + +

    + The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# The project ID of the project to manage an optimization job
+project_id = 12345678
+
+### Estimate Optimization Cost
+# Send a POST request to the /optimizations/estimate endpoint to estimate cost
+response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
+    "targetTo": "max",  # Direction to optimize (maximize)
+    "targetValue": None,  # Specific target value (None for max/min)
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
+    "compileId": compile_id,  # Compilation ID for the optimization
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value for first parameter
+    "parameters[0][max]": 200,  # Maximum value for first parameter
+    "parameters[0][step]": 50,  # Step size for first parameter
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value for second parameter
+    "parameters[1][max]": 300,  # Maximum value for second parameter
+    "parameters[1][step]": 50,  # Step size for second parameter
+    "constraints": [{  # Constraints for the optimization
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }]
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the estimated cost
+if result['success']:
+    print("Optimization Cost Estimated Successfully:")
+    print(result)
+
+### Create Optimization
+# Send a POST request to the /optimizations/create endpoint to create an optimization
+response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
+    "targetTo": "max",  # Direction to optimize
+    "targetValue": None,  # Specific target value
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
+    "compileId": compile_id,  # Compilation ID
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value
+    "parameters[0][max]": 200,  # Maximum value
+    "parameters[0][step]": 50,  # Step size
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value
+    "parameters[1][max]": 300,  # Maximum value
+    "parameters[1][step]": 50,  # Step size
+    "constraints": [{  # Constraints
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }],
+    "estimatedCost": 10,  # Estimated cost of optimization
+    "nodeType": "O2-8",  # Node type for optimization
+    "parallelNodes": 4  # Number of parallel nodes
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the optimization ID from the response
+optimization_id = result['optimizations'][0]['optimizationId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Created Successfully:")
+    print(result)
+
+### Update Optimization
+# Send a POST request to the /optimizations/update endpoint to update the optimization
+response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
+    "optimizationId": optimization_id,  # ID of the optimization to update
+    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Updated Successfully:")
+    print(result)
+
+### Read Optimization
+# Prepare data payload to read optimization details
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to read
+}
+# Send a POST request to the /optimizations/read endpoint to get details
+response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the details
+if result['success']:
+    print("Optimization Details:")
+    print(result)
+
+### Abort Optimization
+# Prepare data payload to abort the optimization
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to abort
+}
+# Send a POST request to the /optimizations/abort endpoint to abort
+response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Aborted Successfully:")
+    print(result)
+
+### Delete Optimization
+# Prepare data payload to delete the optimization
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to delete
+}
+# Send a POST request to the /optimizations/delete endpoint to delete
+response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Deleted Successfully:")
+    print(result)
+
+### List Optimizations
+# Prepare data payload to list optimizations
+payload = {
+    "projectId": project_id  # ID of the project
+}
+# Send a POST request to the /optimizations/list endpoint to list optimizations
+response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Optimizations:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > Optimization Management > Update Optimization

    +
    +
    +

    Optimization Management

    +

    Update Optimization

    +
    +
    +

    Introduction

    + + + +

    + Update the name of an optimization. +

    + + + +

    Request

    + + + +

    + The + + /optimizations/update + + API accepts requests in the following format: +

    + + + + + + + + + + + + + + + + + + +
    + + UpdateOptimizationRequest + + Model - Update the name of an optimization. +
    + optimizationId + + + string +
    + + + required + + +
    + + + example: O-401d3d40b5a0e9f8c46c954a303f3ddd + + +     +
    + Id of the optimization to update. +
    + name + + + string +
    + + + required + + +
    + + + example: New Optimization Name + + +     +
    + Name to assign to the optimization. +
    + Example + +
    + 
    +{
+  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
+  "name": "New Optimization Name"
+}
    +
    +
    + + + +

    Responses

    + + + +

    + The + + /optimizations/update + + API provides a response in the following format: +

    +

    + 200 Success +

    + + + + + + + + + + + + + + + + + + +
    + + RestResponse + + Model - Base API response class for the QuantConnect API. +
    + success + + + boolean + +
    + Indicate if the API request was successful. +
    + errors + + + string Array + +
    + List of errors with the API call. +
    + Example + +
    + 
    +{
+  "success": true,
+  "errors": [
+    "string"
+  ]
+}
    +
    +
    +

    + 401 Authentication Error +

    + + + + + + + + + + +
    + + UnauthorizedError + + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. +
    + www_authenticate + + + string + +
    + Header +
    + + + +

    Examples

    + + +

    + The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# The project ID of the project to manage an optimization job
+project_id = 12345678
+
+### Estimate Optimization Cost
+# Send a POST request to the /optimizations/estimate endpoint to estimate cost
+response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
+    "targetTo": "max",  # Direction to optimize (maximize)
+    "targetValue": None,  # Specific target value (None for max/min)
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
+    "compileId": compile_id,  # Compilation ID for the optimization
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value for first parameter
+    "parameters[0][max]": 200,  # Maximum value for first parameter
+    "parameters[0][step]": 50,  # Step size for first parameter
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value for second parameter
+    "parameters[1][max]": 300,  # Maximum value for second parameter
+    "parameters[1][step]": 50,  # Step size for second parameter
+    "constraints": [{  # Constraints for the optimization
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }]
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the estimated cost
+if result['success']:
+    print("Optimization Cost Estimated Successfully:")
+    print(result)
+
+### Create Optimization
+# Send a POST request to the /optimizations/create endpoint to create an optimization
+response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
+    "targetTo": "max",  # Direction to optimize
+    "targetValue": None,  # Specific target value
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
+    "compileId": compile_id,  # Compilation ID
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value
+    "parameters[0][max]": 200,  # Maximum value
+    "parameters[0][step]": 50,  # Step size
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value
+    "parameters[1][max]": 300,  # Maximum value
+    "parameters[1][step]": 50,  # Step size
+    "constraints": [{  # Constraints
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }],
+    "estimatedCost": 10,  # Estimated cost of optimization
+    "nodeType": "O2-8",  # Node type for optimization
+    "parallelNodes": 4  # Number of parallel nodes
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the optimization ID from the response
+optimization_id = result['optimizations'][0]['optimizationId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Created Successfully:")
+    print(result)
+
+### Update Optimization
+# Send a POST request to the /optimizations/update endpoint to update the optimization
+response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
+    "optimizationId": optimization_id,  # ID of the optimization to update
+    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Updated Successfully:")
+    print(result)
+
+### Read Optimization
+# Prepare data payload to read optimization details
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to read
+}
+# Send a POST request to the /optimizations/read endpoint to get details
+response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the details
+if result['success']:
+    print("Optimization Details:")
+    print(result)
+
+### Abort Optimization
+# Prepare data payload to abort the optimization
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to abort
+}
+# Send a POST request to the /optimizations/abort endpoint to abort
+response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Aborted Successfully:")
+    print(result)
+
+### Delete Optimization
+# Prepare data payload to delete the optimization
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to delete
+}
+# Send a POST request to the /optimizations/delete endpoint to delete
+response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Deleted Successfully:")
+    print(result)
+
+### List Optimizations
+# Prepare data payload to list optimizations
+payload = {
+    "projectId": project_id  # ID of the project
+}
+# Send a POST request to the /optimizations/list endpoint to list optimizations
+response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Optimizations:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > Optimization Management > Delete Optimization

    +
    +
    +

    Optimization Management

    +

    Delete Optimization

    +
    +
    +

    Introduction

    + + + +

    + Delete an optimization. +

    + + + +

    Request

    + + + +

    + The + + /optimizations/delete + + API accepts requests in the following format: +

    + + + + + + + + + + + + + + +
    + + DeleteOptimizationRequest + + Model - Delete an optimization. +
    + optimizationId + + + string +
    + + + required + + +
    + + + example: O-401d3d40b5a0e9f8c46c954a303f3ddd + + +     +
    + Id of the optimization to delete. +
    + Example + +
    + 
    +{
+  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd"
+}
    +
    +
    + + + +

    Responses

    + + + +

    + The + + /optimizations/delete + + API provides a response in the following format: +

    +

    + 200 Success +

    + + + + + + + + + + + + + + + + + + +
    + + RestResponse + + Model - Base API response class for the QuantConnect API. +
    + success + + + boolean + +
    + Indicate if the API request was successful. +
    + errors + + + string Array + +
    + List of errors with the API call. +
    + Example + +
    + 
    +{
+  "success": true,
+  "errors": [
+    "string"
+  ]
+}
    +
    +
    +

    + 401 Authentication Error +

    + + + + + + + + +
    + + UnauthorizedError + + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. +
    + www_authenticate + + + string + +
    + Header +
    @@ -81854,7 +87955,369 @@

    Examples

    - The following example demonstates creating, reading, updating, and listing live algorithms of a project through the cloud API. + The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# The project ID of the project to manage an optimization job
+project_id = 12345678
+
+### Estimate Optimization Cost
+# Send a POST request to the /optimizations/estimate endpoint to estimate cost
+response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
+    "targetTo": "max",  # Direction to optimize (maximize)
+    "targetValue": None,  # Specific target value (None for max/min)
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
+    "compileId": compile_id,  # Compilation ID for the optimization
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value for first parameter
+    "parameters[0][max]": 200,  # Maximum value for first parameter
+    "parameters[0][step]": 50,  # Step size for first parameter
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value for second parameter
+    "parameters[1][max]": 300,  # Maximum value for second parameter
+    "parameters[1][step]": 50,  # Step size for second parameter
+    "constraints": [{  # Constraints for the optimization
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }]
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the estimated cost
+if result['success']:
+    print("Optimization Cost Estimated Successfully:")
+    print(result)
+
+### Create Optimization
+# Send a POST request to the /optimizations/create endpoint to create an optimization
+response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
+    "targetTo": "max",  # Direction to optimize
+    "targetValue": None,  # Specific target value
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
+    "compileId": compile_id,  # Compilation ID
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value
+    "parameters[0][max]": 200,  # Maximum value
+    "parameters[0][step]": 50,  # Step size
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value
+    "parameters[1][max]": 300,  # Maximum value
+    "parameters[1][step]": 50,  # Step size
+    "constraints": [{  # Constraints
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }],
+    "estimatedCost": 10,  # Estimated cost of optimization
+    "nodeType": "O2-8",  # Node type for optimization
+    "parallelNodes": 4  # Number of parallel nodes
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the optimization ID from the response
+optimization_id = result['optimizations'][0]['optimizationId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Created Successfully:")
+    print(result)
+
+### Update Optimization
+# Send a POST request to the /optimizations/update endpoint to update the optimization
+response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
+    "optimizationId": optimization_id,  # ID of the optimization to update
+    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Updated Successfully:")
+    print(result)
+
+### Read Optimization
+# Prepare data payload to read optimization details
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to read
+}
+# Send a POST request to the /optimizations/read endpoint to get details
+response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the details
+if result['success']:
+    print("Optimization Details:")
+    print(result)
+
+### Abort Optimization
+# Prepare data payload to abort the optimization
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to abort
+}
+# Send a POST request to the /optimizations/abort endpoint to abort
+response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Aborted Successfully:")
+    print(result)
+
+### Delete Optimization
+# Prepare data payload to delete the optimization
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to delete
+}
+# Send a POST request to the /optimizations/delete endpoint to delete
+response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Deleted Successfully:")
+    print(result)
+
+### List Optimizations
+# Prepare data payload to list optimizations
+payload = {
+    "projectId": project_id  # ID of the project
+}
+# Send a POST request to the /optimizations/list endpoint to list optimizations
+response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Optimizations:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > Optimization Management > Abort Optimization

    +
    +
    +

    Optimization Management

    +

    Abort Optimization

    +
    +
    +

    Introduction

    + + + +

    + Abort an optimization. +

    + + + +

    Request

    + + + +

    + The + + /optimizations/abort + + API accepts requests in the following format: +

    + + + + + + + + + + + + + + +
    + + AbortOptimizationRequest + + Model - Abort an optimization. +
    + optimizationId + + + string +
    + + + required + + +
    + + + example: O-401d3d40b5a0e9f8c46c954a303f3ddd + + +     +
    + Id of the optimization to abort. +
    + Example + +
    + 
    +{
+  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd"
+}
    +
    +
    + + + +

    Responses

    + + + +

    + The + + /optimizations/abort + + API provides a response in the following format: +

    +

    + 200 Success +

    + + + + + + + + + + + + + + + + + + +
    + + RestResponse + + Model - Base API response class for the QuantConnect API. +
    + success + + + boolean + +
    + Indicate if the API request was successful. +
    + errors + + + string Array + +
    + List of errors with the API call. +
    + Example + +
    + 
    +{
+  "success": true,
+  "errors": [
+    "string"
+  ]
+}
    +
    +
    +

    + 401 Authentication Error +

    + + + + + + + + + + +
    + + UnauthorizedError + + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. +
    + www_authenticate + + + string + +
    + Header +
    + + + +

    Examples

    + + +

    + The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. 

    from base64 import b64encode
@@ -81893,235 +88356,639 @@ 
    Examples
    
 # --------------------
 
 
-### Create Live Algorithm
-# Define placeholder IDs for compilation and node (replace with actual values)
-project_id = 12345678
-compile_id = "compile_id..."
-node_id = "node_id..."
-# Prepare the data payload for creating a live algorithm with necessary details
-payload = {
-    "versionId": "-1",  # Use the latest version of the algorithm
-    "projectId": project_id,  # ID of the project to deploy as a live algorithm
-    "compileId": compile_id,  # Compilation ID for the algorithm code
-    "nodeId": node_id,  # Node ID where the algorithm will run
-    "brokerage": {  # Brokerage configuration for live trading
-        "id": "QuantConnectBrokerage",  # Brokerage identifier
-        "user": "",  # Brokerage username (replace with actual value)
-        "password": "",  # Brokerage password (replace with actual value)
-        "environment": "live-paper",  # Trading environment (live or paper)
-        "account": ""  # Brokerage account ID (replace with actual value)
-    },
-    "dataProviders": {  # Data provider configuration
-        "QuantConnectBrokerage": {
-            "id": "QuantConnectBrokerage"  # Data provider identifier
-        }
-    },
-    "parameters": {},  # Optional algorithm parameters (empty in this example)
-    "notification": {}  # Optional notification settings (empty in this example)
-}
-# Send a POST request to the /live/create endpoint to deploy the algorithm
-response = post(f'{BASE_URL}/live/create', headers=get_headers(), json=data)
-# Parse the JSON response into python managable dict from the API
+# The project ID of the project to manage an optimization job
+project_id = 12345678
+
+### Estimate Optimization Cost
+# Send a POST request to the /optimizations/estimate endpoint to estimate cost
+response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
+    "targetTo": "max",  # Direction to optimize (maximize)
+    "targetValue": None,  # Specific target value (None for max/min)
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
+    "compileId": compile_id,  # Compilation ID for the optimization
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value for first parameter
+    "parameters[0][max]": 200,  # Maximum value for first parameter
+    "parameters[0][step]": 50,  # Step size for first parameter
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value for second parameter
+    "parameters[1][max]": 300,  # Maximum value for second parameter
+    "parameters[1][step]": 50,  # Step size for second parameter
+    "constraints": [{  # Constraints for the optimization
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }]
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the estimated cost
+if result['success']:
+    print("Optimization Cost Estimated Successfully:")
+    print(result)
+
+### Create Optimization
+# Send a POST request to the /optimizations/create endpoint to create an optimization
+response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
+    "targetTo": "max",  # Direction to optimize
+    "targetValue": None,  # Specific target value
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
+    "compileId": compile_id,  # Compilation ID
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value
+    "parameters[0][max]": 200,  # Maximum value
+    "parameters[0][step]": 50,  # Step size
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value
+    "parameters[1][max]": 300,  # Maximum value
+    "parameters[1][step]": 50,  # Step size
+    "constraints": [{  # Constraints
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }],
+    "estimatedCost": 10,  # Estimated cost of optimization
+    "nodeType": "O2-8",  # Node type for optimization
+    "parallelNodes": 4  # Number of parallel nodes
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the optimization ID from the response
+optimization_id = result['optimizations'][0]['optimizationId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Created Successfully:")
+    print(result)
+
+### Update Optimization
+# Send a POST request to the /optimizations/update endpoint to update the optimization
+response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
+    "optimizationId": optimization_id,  # ID of the optimization to update
+    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
+})
+# Parse the JSON response into python managable dict
 result = response.json()
-# Extract the deploy ID from the response for future operations
-deploy_id = result['deployId']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Live Algorithm Created Successfully:")
+    print("Optimization Updated Successfully:")
     print(result)
 
-### Read Live Algorithm Statistics
-# Prepare data payload with project and deploy IDs to fetch statistics
+### Read Optimization
+# Prepare data payload to read optimization details
 payload = {
-    "projectId": project_id,  # ID of the project
-    "deployId": deploy_id  # ID of the deployed live algorithm
+    "optimizationId": optimization_id  # ID of the optimization to read
 }
-# Send a POST request to the /live/read endpoint to get algorithm statistics
-response = post(f'{BASE_URL}/live/read', headers=get_headers(), json=payload)
+# Send a POST request to the /optimizations/read endpoint to get details
+response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the statistics
+# Check if the request was successful and print the details
 if result['success']:
-    print("Live Algorithm Statistics:")
+    print("Optimization Details:")
     print(result)
 
-### Liquidate Live Algorithm
-# Prepare data payload with project ID to liquidate the algorithm
+### Abort Optimization
+# Prepare data payload to abort the optimization
 payload = {
-    "projectId": project_id  # ID of the project to liquidate
+    "optimizationId": optimization_id  # ID of the optimization to abort
 }
-# Send a POST request to the /live/update/liquidate endpoint to liquidate
-response = post(f'{BASE_URL}/live/update/liquidate', headers=get_headers(), json=payload)
+# Send a POST request to the /optimizations/abort endpoint to abort
+response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the result
 if result['success']:
-    print("Live Algorithm Liquidated Successfully:")
+    print("Optimization Aborted Successfully:")
     print(result)
 
-### Stop Live Algorithm
-# Prepare data payload with project ID to stop the algorithm
+### Delete Optimization
+# Prepare data payload to delete the optimization
 payload = {
-    "projectId": project_id  # ID of the project to stop
+    "optimizationId": optimization_id  # ID of the optimization to delete
 }
-# Send a POST request to the /live/update/stop endpoint to stop the algorithm
-response = post(f'{BASE_URL}/live/update/stop', headers=get_headers(), json=payload)
+# Send a POST request to the /optimizations/delete endpoint to delete
+response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the result
 if result['success']:
-    print("Live Algorithm Stopped Successfully:")
+    print("Optimization Deleted Successfully:")
     print(result)
 
-### List Live Algorithms
-# Prepare data payload with filters for listing live algorithms
+### List Optimizations
+# Prepare data payload to list optimizations
 payload = {
-    "status": "Running",  # Filter to show only running algorithms
-    "start": 1717801200,  # Start time (Unix timestamp) for the list range
-    "end": 1743462000  # End time (Unix timestamp) for the list range
+    "projectId": project_id  # ID of the project
 }
-# Send a POST request to the /live/list endpoint to list algorithms
-response = post(f'{BASE_URL}/live/list', headers=get_headers(), json=payload)
+# Send a POST request to the /optimizations/list endpoint to list optimizations
+response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the list
 if result['success']:
-    print("List of Live Algorithms:")
+    print("List of Optimizations:")
     print(result)

     

    -

    API Reference > Live Management > Live Commands

    +

    API Reference > Optimization Management > List Optimization

    -
    -

    Live Management

    -

    Live Commands

    +
    +

    Optimization Management

    +

    List Optimization

    +

    Introduction

    + -
    -
    -

    - The QuantConnect REST API send commands to live algorithms on our cloud servers through URL endpoints. -

    -
    -
    -
    - -
    -
    -

    - Create Live Command -

    -
    -
    -     -
    -
    - -
    -
    -

    - Broadcast Live Command -

    -
    -
    -     -
    -
    -
    +

    + List all the optimizations for a project. +

    -

     

    -

    API Reference > Live Management > Live Commands > Create Live Command

    -
    -
    -

    Live Commands

    -

    Create Live Command

    -
    -
    -

    Introduction

    +

    Request

    - Send a live command to a live trading algorithm. + If of the Project to get a list of optimizations for. The + + /optimizations/list + + API accepts requests in the following format:

    + + + + + + + + + + + + + + +
    + + ListOptimizationRequest + + Model - Id of the Project to get a list of optimizations for. +
    + projectId + + + integer +
    + + + required + + +
    + + + example: 23456789 + + +     +
    + Id of the Project to get a list of optimizations for. +
    + Example + +
    + 
    +{
+  "projectId": 23456789
+}
    +
    +
    -

    Request

    +

    Responses

    - Sends a command to a live deployment to trigger an action such as placing orders. The + The - /live/commands/create + /optimizations/list - API accepts requests in the following format: + API provides a response in the following format:

    +

    + 200 Success +

    + + + + + + + + + + + + + + + + + + + + + + +
    + + ListOptimizationResponse + + Model - Response received when creating an optimization or listing optimizations of a project. +
    + optimizations + + + CreateOptimizationResponse Array + +
    + Collection of summarized optimization objects. +
    + success + + + boolean + +
    + Indicate if the API request was successful. +
    + errors + + + string Array + +
    + List of errors with the API call. +
    + Example + +
    + 
    +{
+  "optimizations": [
+    {
+      "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
+      "projectId": 23456789,
+      "name": "string",
+      "status": ,
+      "nodeType": "O2-8",
+      "extremum": "min",
+      "criterion": {
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "extremum": "min",
+        "targetValue": 1
+      },
+      "created": "2021-11-26T15:18:27.693Z",
+      "psr": 0,
+      "sharpeRatio": 0,
+      "trades": 0,
+      "cloneId": 0,
+      "outOfSampleDays": 0,
+      "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z",
+      "parameters": [
+        {
+          "name": "rsi_period",
+          "min": 10,
+          "max": 20,
+          "step": 1,
+          "minStep": 1
+        }
+      ]
+    }
+  ],
+  "success": true,
+  "errors": [
+    "string"
+  ]
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + CreateOptimizationResponse + + Model - Response received when launching an optimization job or listing all the optimization jobs of a project. +
    + optimizationId + + + string +
    + + + example: O-401d3d40b5a0e9f8c46c954a303f3ddd + + +     +
    + Id of the optimization job. +
    + projectId + + + integer +
    + + + example: 23456789 + + +     +
    + Id of the project the optimization belongs to. +
    + name + + + string + +
    + Name of the optimization. +
    + status + + + OptimizationStatus object + +
    + Status of the optimization. +
    + nodeType + + + string Enum + +
    + Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] +
    + extremum + + + string Enum + +
    + Defines the direction of optimization. Options : ['min', 'max'] +
    + criterion + + + OptimizationTarget object + +
    + Optimization statistical target. +
    + created + + + string($date-time) + +
    + Date and time of when this optimization was created. +
    + psr + + + number + +
    + Probabilistic Sharpe ratio statistic. +
    + sharpeRatio + + + number + +
    + Sharpe ratio statistic. +
    + trades + + + integer + +
    + Number of trades in the best backtest (based on the criterion) of the optimziation. +
    + cloneId + + + integer + +
    + Id of project where this current project was originally cloned. +
    + outOfSampleDays + + + integer + +
    + Number out-of-sample days. +
    + outOfSampleMaxEndDate + + + string($date-time) + +
    + End date of out-of-sample data. +
    + parameters + + + OptimizationParameter Array + +
    + Parameters used in this optimization. +
    + Example + +
    + 
    +{
+  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
+  "projectId": 23456789,
+  "name": "string",
+  "status": ,
+  "nodeType": "O2-8",
+  "extremum": "min",
+  "criterion": {
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+    "extremum": "min",
+    "targetValue": 1
+  },
+  "created": "2021-11-26T15:18:27.693Z",
+  "psr": 0,
+  "sharpeRatio": 0,
+  "trades": 0,
+  "cloneId": 0,
+  "outOfSampleDays": 0,
+  "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z",
+  "parameters": [
+    {
+      "name": "rsi_period",
+      "min": 10,
+      "max": 20,
+      "step": 1,
+      "minStep": 1
+    }
+  ]
+}
    +
    +
    + + +
    - CreateLiveCommandRequest + OptimizationNodeType - Model - Request to create a live command. + Model - Optimization node types available in QuantConnect Cloud.
    - projectId + OptimizationParameter - integer -
    - - - required - - -
    - - - example: 23456789 - - + string Enum
    - Project for the live instance we want to run the command against. + Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16']
    - command + Example + +
    + 
    +{
+  "OptimizationParameter": "O2-8"
+}
    +
    +
    + + + + + + + + @@ -82132,74 +88999,63 @@

    Request

     {
-  "projectId": 23456789,
-  "command": {
-    "$type": "OrderCommand",
-    "symbol": {
-        "id": "BTCUSD 2XR",
-        "value": "BTCUSD"
-    },
-    "order_type": "market",
-    "quantity": "0.1",
-    "limit_price": 0,
-    "stop_price": 0,
-    "tag": ""
-}
+  "OptimizationParameter": "min"
 }
    + + OptimizationTargetTo + + Model +
    + OptimizationParameter - object -
    - - - required - - -
    - - - example: {'$type': 'OrderCommand', 'symbol': {'id': 'BTCUSD 2XR', 'value': 'BTCUSD'}, 'order_type': 'market', 'quantity': '0.1', 'limit_price': 0, 'stop_price': 0, 'tag': ''} - - + string Enum
    - The command to run. + /. Options : ['min', 'max']
    - - - -

    Responses

    - - - -

    - The - - /live/commands/create - - API provides a response in the following format: -

    -

    - 200 Success -

    + + + + @@ -82210,230 +89066,65 @@ 

     {
-  "success": true,
-  "errors": [
-    "string"
-  ]
+  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+  "extremum": "min",
+  "targetValue": 1
 }

    - RestResponse + OptimizationTarget - Model - Base API response class for the QuantConnect API. + Model
    - success + target - boolean + string Enum
    - Indicate if the API request was successful. + The target statistic to track. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown']
    - errors + extremum - string Array + string Enum
    - List of errors with the API call. + Defines the direction of optimization. Options : ['min', 'max'] +
    + targetValue + + + number +
    + + + example: 1 + + +     +
    + Desired value for the optimization target statistic.
    -

    - 401 Authentication Error -

    + + + +
    - UnauthorizedError + OptimizationTargetStatistic - Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. + Model
    - www_authenticate + OptimizationParameter - string + string Enum
    - Header + /. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] +
    + Example + +
    + 
    +{
+  "OptimizationParameter": "TotalPerformance.PortfolioStatistics.SharpeRatio"
+}
    +
    - - - -

    Examples

    - - -

    - The following example demonstates creating, reading, updating, and listing live algorithms of a project through the cloud API. -

    -
    - 
    from base64 import b64encode
-from hashlib import sha256
-from time import time
-from requests import get, post
-BASE_URL = 'https://www.quantconnect.com/api/v2/'
-
-# You need to replace these with your actual credentials.
-# You can request your credentials at https://www.quantconnect.com/settings/
-# You can find our organization ID at https://www.quantconnect.com/organization/ 
-USER_ID = 0
-API_TOKEN = '____'
-ORGANIZATION_ID = '____'
-
-def get_headers():
-    # Get timestamp
-    timestamp = f'{int(time())}'
-    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
-
-    # Get hased API token
-    hashed_token = sha256(time_stamped_token).hexdigest()
-    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
-    authentication = b64encode(authentication).decode('ascii')
-
-    # Create headers dictionary.
-    return {
-        'Authorization': f'Basic {authentication}',
-        'Timestamp': timestamp
-    }
-
-# Authenticate to verify credentials
-response = post(f'{BASE_URL}/authenticate', headers = get_headers())
-print(response.json())
-
-# --------------------
-
-
-### Create Live Algorithm
-# Define placeholder IDs for compilation and node (replace with actual values)
-project_id = 12345678
-compile_id = "compile_id..."
-node_id = "node_id..."
-# Prepare the data payload for creating a live algorithm with necessary details
-payload = {
-    "versionId": "-1",  # Use the latest version of the algorithm
-    "projectId": project_id,  # ID of the project to deploy as a live algorithm
-    "compileId": compile_id,  # Compilation ID for the algorithm code
-    "nodeId": node_id,  # Node ID where the algorithm will run
-    "brokerage": {  # Brokerage configuration for live trading
-        "id": "QuantConnectBrokerage",  # Brokerage identifier
-        "user": "",  # Brokerage username (replace with actual value)
-        "password": "",  # Brokerage password (replace with actual value)
-        "environment": "live-paper",  # Trading environment (live or paper)
-        "account": ""  # Brokerage account ID (replace with actual value)
-    },
-    "dataProviders": {  # Data provider configuration
-        "QuantConnectBrokerage": {
-            "id": "QuantConnectBrokerage"  # Data provider identifier
-        }
-    },
-    "parameters": {},  # Optional algorithm parameters (empty in this example)
-    "notification": {}  # Optional notification settings (empty in this example)
-}
-# Send a POST request to the /live/create endpoint to deploy the algorithm
-response = post(f'{BASE_URL}/live/create', headers=get_headers(), json=data)
-# Parse the JSON response into python managable dict from the API
-result = response.json()
-# Extract the deploy ID from the response for future operations
-deploy_id = result['deployId']
-# Check if the request was successful and print the result
-if result['success']:
-    print("Live Algorithm Created Successfully:")
-    print(result)
-
-### Read Live Algorithm Statistics
-# Prepare data payload with project and deploy IDs to fetch statistics
-payload = {
-    "projectId": project_id,  # ID of the project
-    "deployId": deploy_id  # ID of the deployed live algorithm
-}
-# Send a POST request to the /live/read endpoint to get algorithm statistics
-response = post(f'{BASE_URL}/live/read', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the statistics
-if result['success']:
-    print("Live Algorithm Statistics:")
-    print(result)
-
-### Liquidate Live Algorithm
-# Prepare data payload with project ID to liquidate the algorithm
-payload = {
-    "projectId": project_id  # ID of the project to liquidate
-}
-# Send a POST request to the /live/update/liquidate endpoint to liquidate
-response = post(f'{BASE_URL}/live/update/liquidate', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Live Algorithm Liquidated Successfully:")
-    print(result)
-
-### Stop Live Algorithm
-# Prepare data payload with project ID to stop the algorithm
-payload = {
-    "projectId": project_id  # ID of the project to stop
-}
-# Send a POST request to the /live/update/stop endpoint to stop the algorithm
-response = post(f'{BASE_URL}/live/update/stop', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Live Algorithm Stopped Successfully:")
-    print(result)
-
-### List Live Algorithms
-# Prepare data payload with filters for listing live algorithms
-payload = {
-    "status": "Running",  # Filter to show only running algorithms
-    "start": 1717801200,  # Start time (Unix timestamp) for the list range
-    "end": 1743462000  # End time (Unix timestamp) for the list range
-}
-# Send a POST request to the /live/list endpoint to list algorithms
-response = post(f'{BASE_URL}/live/list', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the list
-if result['success']:
-    print("List of Live Algorithms:")
-    print(result)
    -
    - - - -

     

    -

    API Reference > Live Management > Live Commands > Broadcast Live Command

    -
    -
    -

    Live Commands

    -

    Broadcast Live Command

    -
    -
    -

    Introduction

    - - - -

    - Broadcast a live command to all live algorithms in the organization. -

    - - - -

    Request

    - - - -

    - Broadcasts a command to all live deployments in the organization. The - - /live/commands/broadcast - - API accepts requests in the following format: -

    - - - - - -
    - BroadcastLiveCommandRequest + OptimizationParameter - Model - Request to create a live command. + Model
    - organizationId + name @@ -82447,39 +89138,45 @@

    Request


    - example: 5cad178b20a1d52567b534553413b691 + example: rsi_period
    - Organization Id of the projects we would like to broadcast the command to. + Name of optimization parameter.
    - excludeProjectId + min - integer + number
    - example: 23456789 + required + + +
    + + + example: 10
    - Project for the live instance we want to exclude from the broadcast list. If null, all projects will be included. + Minimum value of optimization parameter, applicable for boundary conditions.
    - command + max - object + number
    @@ -82489,91 +89186,58 @@

    Request


    - example: {'$type': 'OrderCommand', 'symbol': {'id': 'BTCUSD 2XR', 'value': 'BTCUSD'}, 'order_type': 'market', 'quantity': '0.1', 'limit_price': 0, 'stop_price': 0, 'tag': ''} + example: 20 - -     -
    - The command to run. -
    - Example - -
    - 
    -{
-  "organizationId": "5cad178b20a1d52567b534553413b691",
-  "excludeProjectId": 23456789,
-  "command": {
-    "$type": "OrderCommand",
-    "symbol": {
-        "id": "BTCUSD 2XR",
-        "value": "BTCUSD"
-    },
-    "order_type": "market",
-    "quantity": "0.1",
-    "limit_price": 0,
-    "stop_price": 0,
-    "tag": ""
-}
-}
    -
    -
    - - - -

    Responses

    - - - -

    - The - - /live/commands/broadcast - - API provides a response in the following format: -

    -

    - 200 Success -

    - - - - - - + + +
    + Maximum value of optimization parameter, applicable for boundary conditions. + + @@ -82584,10 +89248,11 @@ 

     {
-  "success": true,
-  "errors": [
-    "string"
-  ]
+  "name": "rsi_period",
+  "min": 10,
+  "max": 20,
+  "step": 1,
+  "minStep": 1
 }
    @@ -82627,7 +89292,7 @@

    Examples

    - The following example demonstates creating, reading, updating, and listing live algorithms of a project through the cloud API. + The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. 

    from base64 import b64encode
@@ -82666,118 +89331,154 @@ 
    Examples
    
 # --------------------
 
 
-### Create Live Algorithm
-# Define placeholder IDs for compilation and node (replace with actual values)
+# The project ID of the project to manage an optimization job
 project_id = 12345678
-compile_id = "compile_id..."
-node_id = "node_id..."
-# Prepare the data payload for creating a live algorithm with necessary details
-payload = {
-    "versionId": "-1",  # Use the latest version of the algorithm
-    "projectId": project_id,  # ID of the project to deploy as a live algorithm
-    "compileId": compile_id,  # Compilation ID for the algorithm code
-    "nodeId": node_id,  # Node ID where the algorithm will run
-    "brokerage": {  # Brokerage configuration for live trading
-        "id": "QuantConnectBrokerage",  # Brokerage identifier
-        "user": "",  # Brokerage username (replace with actual value)
-        "password": "",  # Brokerage password (replace with actual value)
-        "environment": "live-paper",  # Trading environment (live or paper)
-        "account": ""  # Brokerage account ID (replace with actual value)
-    },
-    "dataProviders": {  # Data provider configuration
-        "QuantConnectBrokerage": {
-            "id": "QuantConnectBrokerage"  # Data provider identifier
-        }
-    },
-    "parameters": {},  # Optional algorithm parameters (empty in this example)
-    "notification": {}  # Optional notification settings (empty in this example)
-}
-# Send a POST request to the /live/create endpoint to deploy the algorithm
-response = post(f'{BASE_URL}/live/create', headers=get_headers(), json=data)
-# Parse the JSON response into python managable dict from the API
+
+### Estimate Optimization Cost
+# Send a POST request to the /optimizations/estimate endpoint to estimate cost
+response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
+    "targetTo": "max",  # Direction to optimize (maximize)
+    "targetValue": None,  # Specific target value (None for max/min)
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
+    "compileId": compile_id,  # Compilation ID for the optimization
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value for first parameter
+    "parameters[0][max]": 200,  # Maximum value for first parameter
+    "parameters[0][step]": 50,  # Step size for first parameter
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value for second parameter
+    "parameters[1][max]": 300,  # Maximum value for second parameter
+    "parameters[1][step]": 50,  # Step size for second parameter
+    "constraints": [{  # Constraints for the optimization
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }]
+})
+# Parse the JSON response into python managable dict
 result = response.json()
-# Extract the deploy ID from the response for future operations
-deploy_id = result['deployId']
+# Check if the request was successful and print the estimated cost
+if result['success']:
+    print("Optimization Cost Estimated Successfully:")
+    print(result)
+
+### Create Optimization
+# Send a POST request to the /optimizations/create endpoint to create an optimization
+response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
+    "targetTo": "max",  # Direction to optimize
+    "targetValue": None,  # Specific target value
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
+    "compileId": compile_id,  # Compilation ID
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value
+    "parameters[0][max]": 200,  # Maximum value
+    "parameters[0][step]": 50,  # Step size
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value
+    "parameters[1][max]": 300,  # Maximum value
+    "parameters[1][step]": 50,  # Step size
+    "constraints": [{  # Constraints
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }],
+    "estimatedCost": 10,  # Estimated cost of optimization
+    "nodeType": "O2-8",  # Node type for optimization
+    "parallelNodes": 4  # Number of parallel nodes
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the optimization ID from the response
+optimization_id = result['optimizations'][0]['optimizationId']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Live Algorithm Created Successfully:")
+    print("Optimization Created Successfully:")
     print(result)
 
-### Read Live Algorithm Statistics
-# Prepare data payload with project and deploy IDs to fetch statistics
+### Update Optimization
+# Send a POST request to the /optimizations/update endpoint to update the optimization
+response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
+    "optimizationId": optimization_id,  # ID of the optimization to update
+    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Updated Successfully:")
+    print(result)
+
+### Read Optimization
+# Prepare data payload to read optimization details
 payload = {
-    "projectId": project_id,  # ID of the project
-    "deployId": deploy_id  # ID of the deployed live algorithm
+    "optimizationId": optimization_id  # ID of the optimization to read
 }
-# Send a POST request to the /live/read endpoint to get algorithm statistics
-response = post(f'{BASE_URL}/live/read', headers=get_headers(), json=payload)
+# Send a POST request to the /optimizations/read endpoint to get details
+response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the statistics
+# Check if the request was successful and print the details
 if result['success']:
-    print("Live Algorithm Statistics:")
+    print("Optimization Details:")
     print(result)
 
-### Liquidate Live Algorithm
-# Prepare data payload with project ID to liquidate the algorithm
+### Abort Optimization
+# Prepare data payload to abort the optimization
 payload = {
-    "projectId": project_id  # ID of the project to liquidate
+    "optimizationId": optimization_id  # ID of the optimization to abort
 }
-# Send a POST request to the /live/update/liquidate endpoint to liquidate
-response = post(f'{BASE_URL}/live/update/liquidate', headers=get_headers(), json=payload)
+# Send a POST request to the /optimizations/abort endpoint to abort
+response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the result
 if result['success']:
-    print("Live Algorithm Liquidated Successfully:")
+    print("Optimization Aborted Successfully:")
     print(result)
 
-### Stop Live Algorithm
-# Prepare data payload with project ID to stop the algorithm
+### Delete Optimization
+# Prepare data payload to delete the optimization
 payload = {
-    "projectId": project_id  # ID of the project to stop
+    "optimizationId": optimization_id  # ID of the optimization to delete
 }
-# Send a POST request to the /live/update/stop endpoint to stop the algorithm
-response = post(f'{BASE_URL}/live/update/stop', headers=get_headers(), json=payload)
+# Send a POST request to the /optimizations/delete endpoint to delete
+response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the result
 if result['success']:
-    print("Live Algorithm Stopped Successfully:")
+    print("Optimization Deleted Successfully:")
     print(result)
 
-### List Live Algorithms
-# Prepare data payload with filters for listing live algorithms
+### List Optimizations
+# Prepare data payload to list optimizations
 payload = {
-    "status": "Running",  # Filter to show only running algorithms
-    "start": 1717801200,  # Start time (Unix timestamp) for the list range
-    "end": 1743462000  # End time (Unix timestamp) for the list range
+    "projectId": project_id  # ID of the project
 }
-# Send a POST request to the /live/list endpoint to list algorithms
-response = post(f'{BASE_URL}/live/list', headers=get_headers(), json=payload)
+# Send a POST request to the /optimizations/list endpoint to list optimizations
+response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the list
 if result['success']:
-    print("List of Live Algorithms:")
+    print("List of Optimizations:")
     print(result)

     

    -

    API Reference > Optimization Management

    -
    -
    -

    API Reference

    -

    Optimization Management

    -
    -
    -

    API Reference > Optimization Management > Create Optimization

    +

    API Reference > Optimization Management > Estimate Optimization Cost

    -
    +

    Optimization Management

    -

    Create Optimization

    +

    Estimate Optimization Cost

    Introduction

    @@ -82785,7 +89486,7 @@

    Introduction

    - Create an optimization with the specified parameters. + Estimate the execution time of an optimization with the specified parameters.

    @@ -82795,9 +89496,9 @@

    Request

    - Project, compile, and optimization parameters for launching an optimization job. The + Project, compile, and optimization parameters for estimating the execution time of an optimization job. The - /optimizations/create + /optimizations/estimate API accepts requests in the following format:

    @@ -82806,9 +89507,9 @@

    Request

    @@ -82918,6 +89619,212 @@

    Request

    + + + + + + + + + + + + + + +
    - - RestResponse - - Model - Base API response class for the QuantConnect API. -
    - success + step - boolean + number +
    + + + required + + +
    + + + example: 1 + +
    - Indicate if the API request was successful. + Movement, should be positive.
    - errors + minStep - string Array + number +
    + + + example: 1 + +
    - List of errors with the API call. + Minimal possible movement for current parameter, should be positive. Used by + + Strategies.EulerSearchOptimizationStrategy + + to determine when this parameter can no longer be optimized.
    - CreateOptimizationRequest + EstimateOptimizationRequest - Model - Request to create an optimization job. + Model - Request to estimate the cost of an optimization job.
    compileId + + string +
    + + + example: 5d1f2cba3a0ec7407c566614300502b5-173e0419674daf4144ce7c9931155ca8 + + +     +
    + Optimization compile Id. +
    + parameters + + + OptimizationParameter Array +
    + + + required + + +     +
    + Optimization parameters. +
    + constraints + + + OptimizationConstraint Array + +
    + Optimization constraints. +
    + Example + +
    + 
    +{
+  "projectId": 23456789,
+  "name": "Mia First Optimization Job",
+  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+  "targetTo": "min",
+  "targetValue": 1,
+  "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",
+  "compileId": "5d1f2cba3a0ec7407c566614300502b5-173e0419674daf4144ce7c9931155ca8",
+  "parameters": [
+    {
+      "name": "rsi_period",
+      "min": 10,
+      "max": 20,
+      "step": 1,
+      "minStep": 1
+    }
+  ],
+  "constraints": [
+    {
+      "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+      "operator": "LessOrEqual",
+      "targetValue": 1
+    }
+  ]
+}
    +
    +
    + + + + + + + + + + + + + + +
    + + OptimizationTargetStatistic + + Model +
    + OptimizationConstraint + + + string Enum + +
    + /. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] +
    + Example + +
    + 
    +{
+  "OptimizationConstraint": "TotalPerformance.PortfolioStatistics.SharpeRatio"
+}
    +
    +
    + + + + + + + + + + + + + + +
    + + OptimizationTargetTo + + Model +
    + OptimizationConstraint + + + string Enum + +
    + /. Options : ['min', 'max'] +
    + Example + +
    + 
    +{
+  "OptimizationConstraint": "min"
+}
    +
    +
    + + + + + + + + + + + + + + +
    + + OptimizationStrategy + + Model +
    + OptimizationConstraint + + + string Enum + +
    + /. Options : ['QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy'] +
    + Example + +
    + 
    +{
+  "OptimizationConstraint": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy"
+}
    +
    +
    + + + + + + + + + + + + + + + + + + + + +
    + + OptimizationParameter + + Model +
    + name + string @@ -82930,87 +89837,174 @@

    Request


    - example: 5d1f2cba3a0ec7407c566614300502b5-173e0419674daf4144ce7c9931155ca8 + example: rsi_period + + +     +
    + Name of optimization parameter. +
    + min + + + number +
    + + + required + + +
    + + + example: 10 + + +     +
    + Minimum value of optimization parameter, applicable for boundary conditions. +
    + max + + + number +
    + + + required + + +
    + + + example: 20 + + +     +
    + Maximum value of optimization parameter, applicable for boundary conditions. +
    + step + + + number +
    + + + required + + +
    + + + example: 1
    - Optimization compile Id. + Movement, should be positive.
    - parameters + minStep - OptimizationParameter Array + number
    - required + example: 1
    - Optimization parameters. + Minimal possible movement for current parameter, should be positive. Used by + + Strategies.EulerSearchOptimizationStrategy + + to determine when this parameter can no longer be optimized.
    - constraints + Example - - OptimizationConstraint Array - -
    - Optimization constraints. +
    + 
    +{
+  "name": "rsi_period",
+  "min": 10,
+  "max": 20,
+  "step": 1,
+  "minStep": 1
+}
    +
    + + + + + + @@ -83036,95 +90030,76 @@

    Request

     {
-  "projectId": 23456789,
-  "name": "Mia First Optimization Job",
   "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-  "targetTo": "min",
-  "targetValue": 1,
-  "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",
-  "compileId": "5d1f2cba3a0ec7407c566614300502b5-173e0419674daf4144ce7c9931155ca8",
-  "parameters": [
-    {
-      "name": "rsi_period",
-      "min": 10,
-      "max": 20,
-      "step": 1,
-      "minStep": 1
-    }
-  ],
-  "constraints": [
-    {
-      "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-      "operator": "LessOrEqual",
-      "targetValue": 1
-    }
-  ],
-  "estimatedCost": 10,
-  "nodeType": "O2-8",
-  "parallelNodes": 4
+  "operator": "LessOrEqual",
+  "targetValue": 1
 }
    + + OptimizationConstraint + + Model - Backtests in the optimization job that don't respect this constraint are excluded from the optimization result. +
    - estimatedCost + target - number -
    - - - required - - -
    - - - example: 10 - - + string Enum
    - Estimated cost for optimization. + The target statistic to track. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown']
    - nodeType + operator string Enum +
    + + + required + +
    - Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] + The target comparison operation. Options : ['LessOrEqual', 'Less', 'GreaterOrEqual', 'Greater', 'NotEqual', 'Equals']
    - parallelNodes + targetValue - integer + number
    @@ -83020,12 +90014,12 @@

    Request


    - example: 4 + example: 1
    - Number of parallel nodes for optimization. + The threshold value for the target constraint.
    + + + +

    Responses

    + + + +

    + The + + /optimizations/estimate + + API provides a response in the following format: +

    +

    + 200 Success +

    -
    - OptimizationTargetStatistic + EstimateOptimizationResponse - Model + Model - Response received when estimating the cost of an optimization.
    - OptimizationNodeType + estimate - string Enum + Estimate object
    - /. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] + Response received when estimating the time to run an optimization job.
    - Example + success -
    - 
    -{
-  "OptimizationNodeType": "TotalPerformance.PortfolioStatistics.SharpeRatio"
-}
    -
    + + boolean + +
    + Indicate if the API request was successful.
    - - - - - - @@ -83135,7 +90110,15 @@

    Request

     {
-  "OptimizationNodeType": "min"
+  "estimate": {
+    "estimateId": "string",
+    "time": 60,
+    "balance": 10
+  },
+  "success": true,
+  "errors": [
+    "string"
+  ]
 }
    @@ -83146,22 +90129,58 @@

    Request

    + + + + + + + + @@ -83172,78 +90191,290 @@

    Request

     {
-  "OptimizationNodeType": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy"
+  "estimateId": "string",
+  "time": 60,
+  "balance": 10
 }
    - - OptimizationTargetTo - - Model -
    - OptimizationNodeType + errors - string Enum + string Array
    - /. Options : ['min', 'max'] + List of errors with the API call.
    - OptimizationStrategy + Estimate - Model + Model - Response received when estimating the time to run an optimization job.
    - OptimizationNodeType + estimateId - string Enum + string
    - /. Options : ['QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy'] + Estimate Id. +
    + time + + + integer +
    + + + example: 60 + + +     +
    + Estimated time in seconds to run the optimization job. +
    + balance + + + integer +
    + + + example: 10 + + +     +
    + The organization's QCC balance.
    +

    + 401 Authentication Error +

    - - - - +
    - OptimizationParameter + UnauthorizedError - Model + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash.
    - name + www_authenticate string -
    - - - required - - -
    - - - example: rsi_period - - -     -
    - Name of optimization parameter. -
    - min - - - number -
    - - - required - - -
    - - - example: 10 - -
    - Minimum value of optimization parameter, applicable for boundary conditions. + Header
    + + + +

    Examples

    + + +

    + The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# The project ID of the project to manage an optimization job
+project_id = 12345678
+
+### Estimate Optimization Cost
+# Send a POST request to the /optimizations/estimate endpoint to estimate cost
+response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
+    "targetTo": "max",  # Direction to optimize (maximize)
+    "targetValue": None,  # Specific target value (None for max/min)
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
+    "compileId": compile_id,  # Compilation ID for the optimization
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value for first parameter
+    "parameters[0][max]": 200,  # Maximum value for first parameter
+    "parameters[0][step]": 50,  # Step size for first parameter
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value for second parameter
+    "parameters[1][max]": 300,  # Maximum value for second parameter
+    "parameters[1][step]": 50,  # Step size for second parameter
+    "constraints": [{  # Constraints for the optimization
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }]
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the estimated cost
+if result['success']:
+    print("Optimization Cost Estimated Successfully:")
+    print(result)
+
+### Create Optimization
+# Send a POST request to the /optimizations/create endpoint to create an optimization
+response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
+    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
+    "targetTo": "max",  # Direction to optimize
+    "targetValue": None,  # Specific target value
+    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
+    "compileId": compile_id,  # Compilation ID
+    "parameters[0][key]": "ema_fast",  # First parameter key
+    "parameters[0][min]": 100,  # Minimum value
+    "parameters[0][max]": 200,  # Maximum value
+    "parameters[0][step]": 50,  # Step size
+    "parameters[1][key]": "ema_slow",  # Second parameter key
+    "parameters[1][min]": 200,  # Minimum value
+    "parameters[1][max]": 300,  # Maximum value
+    "parameters[1][step]": 50,  # Step size
+    "constraints": [{  # Constraints
+        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
+        "operator": "greater",
+        "target-value": 1
+    }],
+    "estimatedCost": 10,  # Estimated cost of optimization
+    "nodeType": "O2-8",  # Node type for optimization
+    "parallelNodes": 4  # Number of parallel nodes
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the optimization ID from the response
+optimization_id = result['optimizations'][0]['optimizationId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Created Successfully:")
+    print(result)
+
+### Update Optimization
+# Send a POST request to the /optimizations/update endpoint to update the optimization
+response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
+    "optimizationId": optimization_id,  # ID of the optimization to update
+    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Updated Successfully:")
+    print(result)
+
+### Read Optimization
+# Prepare data payload to read optimization details
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to read
+}
+# Send a POST request to the /optimizations/read endpoint to get details
+response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the details
+if result['success']:
+    print("Optimization Details:")
+    print(result)
+
+### Abort Optimization
+# Prepare data payload to abort the optimization
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to abort
+}
+# Send a POST request to the /optimizations/abort endpoint to abort
+response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Aborted Successfully:")
+    print(result)
+
+### Delete Optimization
+# Prepare data payload to delete the optimization
+payload = {
+    "optimizationId": optimization_id  # ID of the optimization to delete
+}
+# Send a POST request to the /optimizations/delete endpoint to delete
+response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Optimization Deleted Successfully:")
+    print(result)
+
+### List Optimizations
+# Prepare data payload to list optimizations
+payload = {
+    "projectId": project_id  # ID of the project
+}
+# Send a POST request to the /optimizations/list endpoint to list optimizations
+response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Optimizations:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > Object Store Management

    +
    +
    +

    API Reference

    +

    Object Store Management

    +
    +
    +

    API Reference > Object Store Management > Upload Object Store Files

    +
    +
    +

    Object Store Management

    +

    Upload Object Store Files

    +
    +
    +

    Introduction

    + + + +

    + Upload files to the Object Store. +

    + + + +

    Request

    + + + +

    + Upload files to the Object Store. The + + /object/set + + API accepts requests in the following format: +

    +

    + The + + /object/set + + API requires a file request in the following format: +

    + + + + + + @@ -83315,79 +90542,64 @@

    Request

     {
-  "name": "rsi_period",
-  "min": 10,
-  "max": 20,
-  "step": 1,
-  "minStep": 1
+  "organizationId": "5cad178b20a1d52567b534553413b691",
+  "key": "key1",
+  "objectData": b"Hello, world!"
 }
    + + ObjectStoreBinaryFile + + Model - Represents a binary file we we'd like to upload the file to upload to the Object Store. +
    - max + organizationId - number + string
    @@ -83253,21 +90484,21 @@

    Request


    - example: 20 + example: 5cad178b20a1d52567b534553413b691
    - Maximum value of optimization parameter, applicable for boundary conditions. + Orgainization ID.
    - step + key - number + string
    @@ -83277,34 +90508,30 @@

    Request


    - example: 1 + example: key1
    - Movement, should be positive. + Unique key to access the object in Object Store.
    - minStep + objectData - number + binary
    - example: 1 + required
    - Minimal possible movement for current parameter, should be positive. Used by - - Strategies.EulerSearchOptimizationStrategy - - to determine when this parameter can no longer be optimized. + Object data to be stored.
    + + + +

    Responses

    + + + +

    + The + + /object/set + + API provides a response in the following format: +

    +

    + 200 Success +

    - - - - @@ -83398,35 +90610,254 @@

    Request

     {
-  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-  "operator": "LessOrEqual",
-  "targetValue": 1
+  "success": true,
+  "errors": [
+    "string"
+  ]
 }
    - OptimizationConstraint + RestResponse - Model - Backtests in the optimization job that don't respect this constraint are excluded from the optimization result. + Model - Base API response class for the QuantConnect API.
    - target - - - string Enum - -
    - The target statistic to track. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] -
    - operator + success - string Enum -
    - - - required - - + boolean
    - The target comparison operation. Options : ['LessOrEqual', 'Less', 'GreaterOrEqual', 'Greater', 'NotEqual', 'Equals'] + Indicate if the API request was successful.
    - targetValue + errors - number -
    - - - required - - -
    - - - example: 1 - - + string Array
    - The threshold value for the target constraint. + List of errors with the API call.
    +

    + 401 Authentication Error +

    + +
    - OptimizationNodeType + UnauthorizedError - Model - Optimization node types available in QuantConnect Cloud. + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash.
    - OptimizationNodeType + www_authenticate - string Enum + string
    - Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] + Header +
    + + + +

    Examples

    + + +

    + The following example demonstates uploading, getting, deleting, and listing Object Store objects through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# The key of the object wishes to manipulate
+key = "..."
+
+### Upload Object Store File
+# Send a POST request to the /object/set endpoint to upload a file
+response = post(f'{BASE_URL}/object/set', headers=get_headers(), 
+                data={"organizationId": ORGANIZATION_ID, "key": key},  # Organization ID and key for the object
+                files={"objectData": b"Hello, world!"})  # File content as bytes
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Object Store File Uploaded Successfully:")
+    print(result)
+
+### Get Object Store Metadata
+# Prepare data payload to get object metadata
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "key": key  # Key of the object to get metadata for
+}
+# Send a POST request to the /object/properties endpoint to get metadata
+response = post(f'{BASE_URL}/object/properties', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the metadata
+if result['success']:
+    print("Object Store Metadata:")
+    print(result)
+
+### Get Object Store File
+# Prepare data payload to retrieve the object
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "keys": [key]  # List of keys to retrieve (single key in this case)
+}
+# Send a POST request to the /object/get endpoint to get the object
+response = post(f'{BASE_URL}/object/get', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the file content
+if result['success']:
+    print("Object Store File Content:")
+    print(result)
+
+### Delete Object Store File
+# Prepare data payload to delete the object
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "key": key  # Key of the object to delete
+}
+# Send a POST request to the /object/delete endpoint to delete the object
+response = post(f'{BASE_URL}/object/delete', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Object Store File Deleted Successfully:")
+    print(result)
+
+### List Object Store Files
+# Define an empty path to list all objects (replace with specific path if needed)
+path = ""
+# Prepare data payload to list objects
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "path": path  # Path to list objects from
+}
+# Send a POST request to the /object/list endpoint to list objects
+response = post(f'{BASE_URL}/object/list', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Object Store Files:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > Object Store Management > Get Object Store Metadata

    +
    +
    +

    Object Store Management

    +

    Get Object Store Metadata

    +
    +
    +

    Introduction

    + + + +

    + Get Object Store properties of a specific organization and key. It doesn't work if the key is a directory in the Object Store. +

    + + + +

    Request

    + + + +

    + Get Object Store properties of a specific organization and key. The + + /object/properties + + API accepts requests in the following format: +

    + + + + + + + + + + + + + @@ -83437,7 +90868,8 @@

    Request

     {
-  "OptimizationNodeType": "O2-8"
+  "organizationId": "5cad178b20a1d52567b534553413b691",
+  "key": "key1"
 }
    @@ -83453,7 +90885,7 @@

    Responses

    The - /optimizations/create + /object/properties API provides a response in the following format:

    @@ -83465,22 +90897,22 @@

    @@ -83515,37 +90947,15 @@ 

     {
-  "optimizations": [
-    {
-      "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
-      "projectId": 23456789,
-      "name": "string",
-      "status": ,
-      "nodeType": "O2-8",
-      "extremum": "min",
-      "criterion": {
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "extremum": "min",
-        "targetValue": 1
-      },
-      "created": "2021-11-26T15:18:27.693Z",
-      "psr": 0,
-      "sharpeRatio": 0,
-      "trades": 0,
-      "cloneId": 0,
-      "outOfSampleDays": 0,
-      "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z",
-      "parameters": [
-        {
-          "name": "rsi_period",
-          "min": 10,
-          "max": 20,
-          "step": 1,
-          "minStep": 1
-        }
-      ]
-    }
-  ],
+  "metadata": {
+    "key": "string",
+    "modified": "2021-11-26T15:18:27.693Z",
+    "created": "2021-11-26T15:18:27.693Z",
+    "size": 24,
+    "md5": "string",
+    "mime": "string",
+    "preview": "string"
+  },
   "success": true,
   "errors": [
     "string"
@@ -83560,106 +90970,34 @@

    - - - - - - - - - - - - - - - - - - - - @@ -83671,91 +91009,61 @@

    string($date-time)
    - Date and time of when this optimization was created. - -

    - - - - - - - - - - - @@ -83766,163 +91074,283 @@ 

     {
-  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
-  "projectId": 23456789,
-  "name": "string",
-  "status": ,
-  "nodeType": "O2-8",
-  "extremum": "min",
-  "criterion": {
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-    "extremum": "min",
-    "targetValue": 1
-  },
+  "key": "string",
+  "modified": "2021-11-26T15:18:27.693Z",
   "created": "2021-11-26T15:18:27.693Z",
-  "psr": 0,
-  "sharpeRatio": 0,
-  "trades": 0,
-  "cloneId": 0,
-  "outOfSampleDays": 0,
-  "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z",
-  "parameters": [
-    {
-      "name": "rsi_period",
-      "min": 10,
-      "max": 20,
-      "step": 1,
-      "minStep": 1
-    }
-  ]
+  "size": 24,
+  "md5": "string",
+  "mime": "string",
+  "preview": "string"
 }

    + + GetObjectStorePropertiesRequest + + Model - Request to get Object Store properties of a specific organization and key. +
    + organizationId + + + string +
    + + + required + + +
    + + + example: 5cad178b20a1d52567b534553413b691 + + +     +
    + Id of the organization that owns the Object Store. +
    + key + + + string +
    + + + required + + +
    + + + example: key1 + + +     +
    + Key in the Object Store.
    - ListOptimizationResponse + GetObjectStorePropertiesResponse - Model - Response received when creating an optimization or listing optimizations of a project. + Model - Response received when fetching Object Store file properties.
    - optimizations + metadata - CreateOptimizationResponse Array + ObjectStoreProperties object
    - Collection of summarized optimization objects. + Object Store file properties.
    - CreateOptimizationResponse + ObjectStoreProperties - Model - Response received when launching an optimization job or listing all the optimization jobs of a project. + Model - Object Store file properties.
    - optimizationId - - - string -
    - - - example: O-401d3d40b5a0e9f8c46c954a303f3ddd - - -     -
    - Id of the optimization job. -
    - projectId - - - integer -
    - - - example: 23456789 - - -     -
    - Id of the project the optimization belongs to. -
    - name + key string
    - Name of the optimization. -
    - status - - - OptimizationStatus object - -
    - Status of the optimization. -
    - nodeType - - - string Enum - -
    - Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] -
    - extremum - - - string Enum - -
    - Defines the direction of optimization. Options : ['min', 'max'] + Object Store key.
    - criterion + modified - OptimizationTarget object + string($date-time)
    - Optimization statistical target. + Last time it was modified.
    - psr - - - number - -
    - Probabilistic Sharpe ratio statistic. -
    - sharpeRatio - - - number - -
    - Sharpe ratio statistic. -
    - trades - - - integer - -
    - Number of trades in the best backtest (based on the criterion) of the optimziation. + Date this project was created.
    - cloneId + size - integer + number +
    + + + example: 24 + +
    - Id of project where this current project was originally cloned. + Object Store file size.
    - outOfSampleDays + md5 - integer + string
    - Number out-of-sample days. + MD5 (hashing algorithm) hash authentication code.
    - outOfSampleMaxEndDate + mime - string($date-time) + string
    - End date of out-of-sample data. + MIME type.
    - parameters + preview - OptimizationParameter Array + string
    - Parameters used in this optimization. + Preview of the Object Store file content.
    +

    + 401 Authentication Error +

    - - - -
    - OptimizationNodeType + UnauthorizedError - Model - Optimization node types available in QuantConnect Cloud. + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash.
    - OptimizationParameter + www_authenticate - string Enum + string
    - Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] -
    - Example - -
    - 
    -{
-  "OptimizationParameter": "O2-8"
-}
    -
    + Header
    + + + +

    Examples

    + + +

    + The following example demonstates uploading, getting, deleting, and listing Object Store objects through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# The key of the object wishes to manipulate
+key = "..."
+
+### Upload Object Store File
+# Send a POST request to the /object/set endpoint to upload a file
+response = post(f'{BASE_URL}/object/set', headers=get_headers(), 
+                data={"organizationId": ORGANIZATION_ID, "key": key},  # Organization ID and key for the object
+                files={"objectData": b"Hello, world!"})  # File content as bytes
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Object Store File Uploaded Successfully:")
+    print(result)
+
+### Get Object Store Metadata
+# Prepare data payload to get object metadata
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "key": key  # Key of the object to get metadata for
+}
+# Send a POST request to the /object/properties endpoint to get metadata
+response = post(f'{BASE_URL}/object/properties', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the metadata
+if result['success']:
+    print("Object Store Metadata:")
+    print(result)
+
+### Get Object Store File
+# Prepare data payload to retrieve the object
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "keys": [key]  # List of keys to retrieve (single key in this case)
+}
+# Send a POST request to the /object/get endpoint to get the object
+response = post(f'{BASE_URL}/object/get', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the file content
+if result['success']:
+    print("Object Store File Content:")
+    print(result)
+
+### Delete Object Store File
+# Prepare data payload to delete the object
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "key": key  # Key of the object to delete
+}
+# Send a POST request to the /object/delete endpoint to delete the object
+response = post(f'{BASE_URL}/object/delete', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Object Store File Deleted Successfully:")
+    print(result)
+
+### List Object Store Files
+# Define an empty path to list all objects (replace with specific path if needed)
+path = ""
+# Prepare data payload to list objects
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "path": path  # Path to list objects from
+}
+# Send a POST request to the /object/list endpoint to list objects
+response = post(f'{BASE_URL}/object/list', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Object Store Files:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > Object Store Management > Get Object Store File

    +
    +
    +

    Object Store Management

    +

    Get Object Store File

    +
    +
    +

    Introduction

    + + + +

    + Get Object Store file of a specific organization and key. +

    + + + +

    Request

    + + + +

    + Get Object Store files of a specific organization and key. The + + /object/get + + API accepts requests in the following format: +

    + + + + + + +
    + Get Object Store files of a specific organization and key. +
    + + + +

    Responses

    + + + +

    + The + + /object/get + + API provides a response in the following format: +

    +

    + 200 Success +

    - - - - -
    - OptimizationTargetTo + GetObjectStoreResponse - Model + Model - Response received when fetching an Object Store file.
    - OptimizationParameter + jobId - string Enum + string
    - /. Options : ['min', 'max'] -
    - Example - -
    - 
    -{
-  "OptimizationParameter": "min"
-}
    -
    + Id of the job, which you can use to request a download URL.
    - - - - - - @@ -83933,65 +91361,213 @@ 

     {
-  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-  "extremum": "min",
-  "targetValue": 1
+  "jobId": "string",
+  "url": "string",
+  "success": true,
+  "errors": [
+    "string"
+  ]
 }

    - - OptimizationTarget - - Model -
    - target + url - string Enum + string
    - The target statistic to track. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] + The URL to download the object. This can also be null. To download the object, paste the full URL (including the URL parameters) into a browser.
    - extremum + success - string Enum + boolean
    - Defines the direction of optimization. Options : ['min', 'max'] + Indicate if the API request was successful.
    - targetValue + errors - number -
    - - - example: 1 - - + string Array
    - Desired value for the optimization target statistic. + List of errors with the API call.
    +

    + 401 Authentication Error +

    - - - -
    - OptimizationTargetStatistic + UnauthorizedError - Model + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash.
    - OptimizationParameter + www_authenticate - string Enum + string
    - /. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] -
    - Example - -
    - 
    -{
-  "OptimizationParameter": "TotalPerformance.PortfolioStatistics.SharpeRatio"
-}
    -
    + Header
    + + + +

    Examples

    + + +

    + The following example demonstates uploading, getting, deleting, and listing Object Store objects through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# The key of the object wishes to manipulate
+key = "..."
+
+### Upload Object Store File
+# Send a POST request to the /object/set endpoint to upload a file
+response = post(f'{BASE_URL}/object/set', headers=get_headers(), 
+                data={"organizationId": ORGANIZATION_ID, "key": key},  # Organization ID and key for the object
+                files={"objectData": b"Hello, world!"})  # File content as bytes
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Object Store File Uploaded Successfully:")
+    print(result)
+
+### Get Object Store Metadata
+# Prepare data payload to get object metadata
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "key": key  # Key of the object to get metadata for
+}
+# Send a POST request to the /object/properties endpoint to get metadata
+response = post(f'{BASE_URL}/object/properties', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the metadata
+if result['success']:
+    print("Object Store Metadata:")
+    print(result)
+
+### Get Object Store File
+# Prepare data payload to retrieve the object
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "keys": [key]  # List of keys to retrieve (single key in this case)
+}
+# Send a POST request to the /object/get endpoint to get the object
+response = post(f'{BASE_URL}/object/get', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the file content
+if result['success']:
+    print("Object Store File Content:")
+    print(result)
+
+### Delete Object Store File
+# Prepare data payload to delete the object
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "key": key  # Key of the object to delete
+}
+# Send a POST request to the /object/delete endpoint to delete the object
+response = post(f'{BASE_URL}/object/delete', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Object Store File Deleted Successfully:")
+    print(result)
+
+### List Object Store Files
+# Define an empty path to list all objects (replace with specific path if needed)
+path = ""
+# Prepare data payload to list objects
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "path": path  # Path to list objects from
+}
+# Send a POST request to the /object/list endpoint to list objects
+response = post(f'{BASE_URL}/object/list', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Object Store Files:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > Object Store Management > Delete Object Store File

    +
    +
    +

    Object Store Management

    +

    Delete Object Store File

    +
    +
    +

    Introduction

    + + + +

    + Delete the Object Store file of a specific organization and key. +

    + + + +

    Request

    + + + +

    + Delete the Object Store file of a specific organization and key. The + + /object/delete + + API accepts requests in the following format: +

    +
    - OptimizationParameter + DeleteObjectStoreRequest - Model + Model - Request to delete an object in the Object Store for a specific organization and key.
    - name + organizationId @@ -84005,21 +91581,21 @@


    - example: rsi_period + example: 5cad178b20a1d52567b534553413b691
    - Name of optimization parameter. + Id of the organization that owns the Object Store.

    - min + key - number + string
    @@ -84029,82 +91605,79 @@


    - example: 10 + example: key1
    - Minimum value of optimization parameter, applicable for boundary conditions. + Key of the Object Store file to delete.

    - max + Example - - number -
    - - - required - - -
    - - - example: 20 - - -     -
    - Maximum value of optimization parameter, applicable for boundary conditions. +
    + 
    +{
+  "organizationId": "5cad178b20a1d52567b534553413b691",
+  "key": "key1"
+}
    +
    + + + +

    Responses

    + + + +

    + The + + /object/delete + + API provides a response in the following format: +

    +

    + 200 Success +

    + + + + + + @@ -84115,11 +91688,10 @@ 

     {
-  "name": "rsi_period",
-  "min": 10,
-  "max": 20,
-  "step": 1,
-  "minStep": 1
+  "success": true,
+  "errors": [
+    "string"
+  ]
 }
    @@ -84159,7 +91731,7 @@

    Examples

    - The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. + The following example demonstates uploading, getting, deleting, and listing Object Store objects through the cloud API. 

    from base64 import b64encode
@@ -84198,154 +91770,92 @@ 
    Examples
    
 # --------------------
 
 
-# The project ID of the project to manage an optimization job
-project_id = 12345678
-
-### Estimate Optimization Cost
-# Send a POST request to the /optimizations/estimate endpoint to estimate cost
-response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
-    "targetTo": "max",  # Direction to optimize (maximize)
-    "targetValue": None,  # Specific target value (None for max/min)
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
-    "compileId": compile_id,  # Compilation ID for the optimization
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value for first parameter
-    "parameters[0][max]": 200,  # Maximum value for first parameter
-    "parameters[0][step]": 50,  # Step size for first parameter
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value for second parameter
-    "parameters[1][max]": 300,  # Maximum value for second parameter
-    "parameters[1][step]": 50,  # Step size for second parameter
-    "constraints": [{  # Constraints for the optimization
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }]
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the estimated cost
-if result['success']:
-    print("Optimization Cost Estimated Successfully:")
-    print(result)
-
-### Create Optimization
-# Send a POST request to the /optimizations/create endpoint to create an optimization
-response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
-    "targetTo": "max",  # Direction to optimize
-    "targetValue": None,  # Specific target value
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
-    "compileId": compile_id,  # Compilation ID
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value
-    "parameters[0][max]": 200,  # Maximum value
-    "parameters[0][step]": 50,  # Step size
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value
-    "parameters[1][max]": 300,  # Maximum value
-    "parameters[1][step]": 50,  # Step size
-    "constraints": [{  # Constraints
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }],
-    "estimatedCost": 10,  # Estimated cost of optimization
-    "nodeType": "O2-8",  # Node type for optimization
-    "parallelNodes": 4  # Number of parallel nodes
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Extract the optimization ID from the response
-optimization_id = result['optimizations'][0]['optimizationId']
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Created Successfully:")
-    print(result)
+# The key of the object wishes to manipulate
+key = "..."
 
-### Update Optimization
-# Send a POST request to the /optimizations/update endpoint to update the optimization
-response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
-    "optimizationId": optimization_id,  # ID of the optimization to update
-    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
-})
+### Upload Object Store File
+# Send a POST request to the /object/set endpoint to upload a file
+response = post(f'{BASE_URL}/object/set', headers=get_headers(), 
+                data={"organizationId": ORGANIZATION_ID, "key": key},  # Organization ID and key for the object
+                files={"objectData": b"Hello, world!"})  # File content as bytes
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the result
 if result['success']:
-    print("Optimization Updated Successfully:")
+    print("Object Store File Uploaded Successfully:")
     print(result)
 
-### Read Optimization
-# Prepare data payload to read optimization details
+### Get Object Store Metadata
+# Prepare data payload to get object metadata
 payload = {
-    "optimizationId": optimization_id  # ID of the optimization to read
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "key": key  # Key of the object to get metadata for
 }
-# Send a POST request to the /optimizations/read endpoint to get details
-response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
+# Send a POST request to the /object/properties endpoint to get metadata
+response = post(f'{BASE_URL}/object/properties', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the details
+# Check if the request was successful and print the metadata
 if result['success']:
-    print("Optimization Details:")
+    print("Object Store Metadata:")
     print(result)
 
-### Abort Optimization
-# Prepare data payload to abort the optimization
+### Get Object Store File
+# Prepare data payload to retrieve the object
 payload = {
-    "optimizationId": optimization_id  # ID of the optimization to abort
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "keys": [key]  # List of keys to retrieve (single key in this case)
 }
-# Send a POST request to the /optimizations/abort endpoint to abort
-response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
+# Send a POST request to the /object/get endpoint to get the object
+response = post(f'{BASE_URL}/object/get', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the result
+# Check if the request was successful and print the file content
 if result['success']:
-    print("Optimization Aborted Successfully:")
+    print("Object Store File Content:")
     print(result)
 
-### Delete Optimization
-# Prepare data payload to delete the optimization
+### Delete Object Store File
+# Prepare data payload to delete the object
 payload = {
-    "optimizationId": optimization_id  # ID of the optimization to delete
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "key": key  # Key of the object to delete
 }
-# Send a POST request to the /optimizations/delete endpoint to delete
-response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
+# Send a POST request to the /object/delete endpoint to delete the object
+response = post(f'{BASE_URL}/object/delete', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the result
 if result['success']:
-    print("Optimization Deleted Successfully:")
+    print("Object Store File Deleted Successfully:")
     print(result)
 
-### List Optimizations
-# Prepare data payload to list optimizations
+### List Object Store Files
+# Define an empty path to list all objects (replace with specific path if needed)
+path = ""
+# Prepare data payload to list objects
 payload = {
-    "projectId": project_id  # ID of the project
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "path": path  # Path to list objects from
 }
-# Send a POST request to the /optimizations/list endpoint to list optimizations
-response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
+# Send a POST request to the /object/list endpoint to list objects
+response = post(f'{BASE_URL}/object/list', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the list
 if result['success']:
-    print("List of Optimizations:")
+    print("List of Object Store Files:")
     print(result)

     

    -

    API Reference > Optimization Management > Read Optimization

    +

    API Reference > Object Store Management > List Object Store Files

    -
    -

    Optimization Management

    -

    Read Optimization

    +
    +

    Object Store Management

    +

    List Object Store Files

    Introduction

    @@ -84353,7 +91863,7 @@

    Introduction

    - Read an optimization. + List the Object Store files under a specific directory in an organization.

    @@ -84363,9 +91873,9 @@

    Request

    - Id of the optimization to read. The + List the Object Store files under a specific directory in an organization. The - /optimizations/read + /object/list API accepts requests in the following format:

    @@ -84374,15 +91884,15 @@

    Request

    + + + + @@ -84412,7 +91940,8 @@

    Request

     {
-  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd"
+  "organizationId": "5cad178b20a1d52567b534553413b691",
+  "path": "/"
 }
    @@ -84428,487 +91957,263 @@

    Responses

    The - /optimizations/read + /object/list API provides a response in the following format:

    -

    - 200 Success -

    -
    + + RestResponse + + Model - Base API response class for the QuantConnect API. +
    - step + success - number -
    - - - required - - -
    - - - example: 1 - - + boolean
    - Movement, should be positive. + Indicate if the API request was successful.
    - minStep + errors - number -
    - - - example: 1 - - + string Array
    - Minimal possible movement for current parameter, should be positive. Used by - - Strategies.EulerSearchOptimizationStrategy - - to determine when this parameter can no longer be optimized. + List of errors with the API call.
    - ReadOptimizationRequest + ListObjectStoreRequest - Model - Request to read an optimization from a project. + Model - Request to list the Object Store files under a specific directory in an organization.
    - optimizationId + organizationId @@ -84396,12 +91906,30 @@

    Request


    - example: O-401d3d40b5a0e9f8c46c954a303f3ddd + example: 5cad178b20a1d52567b534553413b691
    - Id of the optimization to read. + Id of the organization to list the Object Store files from. +
    + path + + + string +
    + + + example: / + + +     +
    + Path to a directory in the Object Store.
    - - - - - - - - - - - - - - - - - - - - - -
    - - ReadOptimizationResponse - - Model - Response received when reading an optimization. -
    - optimization - - - Optimization object - -
    - Response received when reading an optimization job. -
    - success - - - boolean - -
    - Indicate if the API request was successful. -
    - errors - - - string Array - -
    - List of errors with the API call. -
    - Example - -
    - 
    -{
-  "optimization": {
-    "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
-    "snapshotId": 24013333,
-    "projectId": 23456789,
-    "name": "string",
-    "extremum": "min",
-    "status": ,
-    "nodeType": "O2-8",
-    "parallelNodes": 4,
-    "criterion": {
-      "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-      "extremum": "min",
-      "targetValue": 1
-    },
-    "runtimeStatistics": {
-      "Completed": "string",
-      "Failed": "string",
-      "Running": "string",
-      "In Queue": "string",
-      "Average Length": "2021-11-26T15:18:27.693Z",
-      "Total Runtime": "2021-11-26T15:18:27.693Z",
-      "Total": "string",
-      "Consumed": "string"
-    },
-    "constraints": [
-      {
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "LessOrEqual",
-        "targetValue": 1
-      }
-    ],
-    "parameters": [
-      {
-        "name": "rsi_period",
-        "min": 10,
-        "max": 20,
-        "step": 1,
-        "minStep": 1
-      }
-    ],
-    "backtests": {
-      "name": "string",
-      "id": "string",
-      "progress": 0,
-      "exitCode": 0,
-      "statistics": [
-        "number"
-      ],
-      "parameterSet": ,
-      "equity": [
-        "array"
-      ],
-      "startDate": "2021-11-26T15:18:27.693Z",
-      "endDate": "2021-11-26T15:18:27.693Z",
-      "outOfSampleDays": 0,
-      "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z"
-    },
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",
-    "requested": "2021-11-26T15:18:27.693Z",
-    "optimizationTarget": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-    "targetValue": 1,
-    "gridLayout": [
-      {
-        "chartName": "string",
-        "width": 0,
-        "height": 0,
-        "row": 0,
-        "column": 0,
-        "sort": 0,
-        "definition": [
-          "string"
-        ]
-      }
-    ],
-    "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z",
-    "outOfSampleDays": 0
-  },
-  "success": true,
-  "errors": [
-    "string"
-  ]
-}
    -
    -
    - - - - - - - - - - - - - - +

    + 200 Success +

    +
    - - Optimization - - Model - Response received when reading an optimization job. -
    - optimizationId - - - string -
    - - - required - - -
    - - - example: O-401d3d40b5a0e9f8c46c954a303f3ddd - - -     -
    - Id of the optimization. -
    - snapshotId - - - integer -
    - - - required - - -
    - - - example: 24013333 - - -     -
    - Snapshot Id of this optimization. -
    + + + + + +
    + + ListObjectStoreResponse + + Model - Response received containing a list of stored objects metadata, as well as the total size of all of them. +
    - projectId + path - integer -
    - - - required - - + string
    - example: 23456789 + example: Mia
    - Id of the project the optimization belongs to. + Path to the directory in the Object Store.
    - name + objects - string -
    - - - required - - + ObjectStoreSummary Array
    - Name of the optimization. + List of directories and files stored in the directory at the given path. If the path contains directories, this list of objects doesn't contain the children of those directories.
    - extremum + page - string Enum + integer
    - Defines the direction of optimization. Options : ['min', 'max'] + The current page number in the paginated response.
    - status + totalPages - OptimizationStatus object -
    - - - required - - + integer
    - Status of the optimization. + The total number of pages in the paginated response.
    - nodeType + objectStorageUsed - string Enum + integer
    - Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] + Size of all objects stored in bytes.
    - parallelNodes + objectStorageUsedHuman - integer + string
    - example: 4 + example: 2.27 GB
    - Number of parallel nodes for the optimization. + Size of all the objects stored in human-readable format.
    - criterion + success - OptimizationTarget object -
    - - - required - - + boolean
    - Optimization statistical target. + Indicate if the API request was successful.
    - runtimeStatistics + errors - OptimizationRuntimeStatistics object + string Array
    - Dictionary representing a runtime banner/updating statistics for the optimization. + List of errors with the API call.
    - constraints + Example - - OptimizationConstraint Array - -
    - Optimization constraints. +
    + 
    +{
+  "path": "Mia",
+  "objects": [
+    {
+      "key": "Mia/Test",
+      "name": "string",
+      "modified": "2021-11-26T15:18:27.693Z",
+      "mime": "application/json",
+      "folder": true,
+      "size": 13
+    }
+  ],
+  "page": 0,
+  "totalPages": 0,
+  "objectStorageUsed": 0,
+  "objectStorageUsedHuman": "2.27 GB",
+  "success": true,
+  "errors": [
+    "string"
+  ]
+}
    +
    + + + + + + - - - - - - - - - - - - @@ -84919,107 +92224,270 @@ 

     {
-  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
-  "snapshotId": 24013333,
-  "projectId": 23456789,
+  "key": "Mia/Test",
   "name": "string",
-  "extremum": "min",
-  "status": ,
-  "nodeType": "O2-8",
-  "parallelNodes": 4,
-  "criterion": {
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-    "extremum": "min",
-    "targetValue": 1
-  },
-  "runtimeStatistics": {
-    "Completed": "string",
-    "Failed": "string",
-    "Running": "string",
-    "In Queue": "string",
-    "Average Length": "2021-11-26T15:18:27.693Z",
-    "Total Runtime": "2021-11-26T15:18:27.693Z",
-    "Total": "string",
-    "Consumed": "string"
-  },
-  "constraints": [
-    {
-      "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-      "operator": "LessOrEqual",
-      "targetValue": 1
-    }
-  ],
-  "parameters": [
-    {
-      "name": "rsi_period",
-      "min": 10,
-      "max": 20,
-      "step": 1,
-      "minStep": 1
-    }
-  ],
-  "backtests": {
-    "name": "string",
-    "id": "string",
-    "progress": 0,
-    "exitCode": 0,
-    "statistics": [
-      "number"
-    ],
-    "parameterSet": ,
-    "equity": [
-      "array"
-    ],
-    "startDate": "2021-11-26T15:18:27.693Z",
-    "endDate": "2021-11-26T15:18:27.693Z",
-    "outOfSampleDays": 0,
-    "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z"
-  },
-  "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",
-  "requested": "2021-11-26T15:18:27.693Z",
-  "optimizationTarget": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-  "targetValue": 1,
-  "gridLayout": [
-    {
-      "chartName": "string",
-      "width": 0,
-      "height": 0,
-      "row": 0,
-      "column": 0,
-      "sort": 0,
-      "definition": [
-        "string"
-      ]
-    }
-  ],
-  "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z",
-  "outOfSampleDays": 0
+  "modified": "2021-11-26T15:18:27.693Z",
+  "mime": "application/json",
+  "folder": true,
+  "size": 13
 }

    + + ObjectStoreSummary + + Model - Summary information of the Object Store. +
    - parameters + key - OptimizationParameter Array + string
    - required + example: Mia/Test
    - Optimization parameters. -
    - backtests - - - OptimizationBacktest object - -
    - Dictionary of optimization backtests. + Object Store key.
    - strategy + name - string Enum + string
    - Optimization strategy. Options : ['QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy'] + File or folder name.
    - requested + modified string($date-time)
    - Optimization requested date and time. -
    - optimizationTarget - - - string Enum - -
    - Statistic to be optimized. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] + Last time it was modified.
    - targetValue + mime - number + string
    - example: 1 + example: application/json
    - Desired value for the optimization target statistic. -
    - gridLayout - - - GridChart Array - -
    - List with grid charts representing the grid layout. + MIME type.
    - outOfSampleMaxEndDate + folder - string($date-time) -
    - - - required - - + boolean
    - End date of out of sample data. + True if it is a folder, false otherwise.
    - outOfSampleDays + size - integer + number
    - required + example: 13
    - Number of days of out of sample days. + Object Store file size.
    +

    + 401 Authentication Error +

    + + + + + + +
    - OptimizationTargetTo + UnauthorizedError - Model + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. +
    + www_authenticate + + + string + +
    + Header +
    + + + +

    Examples

    + + +

    + The following example demonstates uploading, getting, deleting, and listing Object Store objects through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+# The key of the object wishes to manipulate
+key = "..."
+
+### Upload Object Store File
+# Send a POST request to the /object/set endpoint to upload a file
+response = post(f'{BASE_URL}/object/set', headers=get_headers(), 
+                data={"organizationId": ORGANIZATION_ID, "key": key},  # Organization ID and key for the object
+                files={"objectData": b"Hello, world!"})  # File content as bytes
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Object Store File Uploaded Successfully:")
+    print(result)
+
+### Get Object Store Metadata
+# Prepare data payload to get object metadata
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "key": key  # Key of the object to get metadata for
+}
+# Send a POST request to the /object/properties endpoint to get metadata
+response = post(f'{BASE_URL}/object/properties', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the metadata
+if result['success']:
+    print("Object Store Metadata:")
+    print(result)
+
+### Get Object Store File
+# Prepare data payload to retrieve the object
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "keys": [key]  # List of keys to retrieve (single key in this case)
+}
+# Send a POST request to the /object/get endpoint to get the object
+response = post(f'{BASE_URL}/object/get', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the file content
+if result['success']:
+    print("Object Store File Content:")
+    print(result)
+
+### Delete Object Store File
+# Prepare data payload to delete the object
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "key": key  # Key of the object to delete
+}
+# Send a POST request to the /object/delete endpoint to delete the object
+response = post(f'{BASE_URL}/object/delete', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Object Store File Deleted Successfully:")
+    print(result)
+
+### List Object Store Files
+# Define an empty path to list all objects (replace with specific path if needed)
+path = ""
+# Prepare data payload to list objects
+payload = {
+    "organizationId": ORGANIZATION_ID,  # ID of the organization
+    "path": path  # Path to list objects from
+}
+# Send a POST request to the /object/list endpoint to list objects
+response = post(f'{BASE_URL}/object/list', headers=get_headers(), json=payload)
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Object Store Files:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > AI Assistance

    +
    +
    +

    API Reference

    +

    AI Assistance

    +
    +
    +

    API Reference > AI Assistance > Tools

    +
    +
    +

    AI Assistance

    +

    Tools

    +
    +
    +

    API Reference > AI Assistance > Tools > Backtest Initialization

    +
    +
    +

    Tools

    +

    Backtest Initialization

    +
    +
    +

    Introduction

    + + + +

    + Run a backtest for a few seconds to initialize the algorithm and get initialization errors if any. +

    + + + +

    Request

    + + + +

    + Run a backtest for a few seconds to initialize the algorithm and get initialization errors if any. The + + /ai/tools/backtest-init + + API accepts requests in the following format: +

    + + + + + + + + @@ -85030,7 +92498,9 @@ 

     {
-  "GridChart": "min"
+  "language": "Py",
+  "files": "[{"name": "file.py", "content": "fileContent"}]"
+  ]
 }
    @@ -85041,22 +92511,40 @@

    + + + + @@ -85067,63 +92555,99 @@ 

     {
-  "GridChart": "O2-8"
+  "name": "main.py",
+  "content": "string"
 }

    + + BasicFilesRequest + + Model - Request to process files.
    - GridChart + language + + + string Enum +
    + + + required + + +
    + + + example: Py + + +     +
    + Programming language. Options : ['C#', 'Py'] +
    + files - string Enum + File Array +
    + + + required + + +
    + + + example: [{"name": "file.py", "content": "fileContent"}] + +
    - /. Options : ['min', 'max'] + Files to process.
    - OptimizationNodeType + File - Model - Optimization node types available in QuantConnect Cloud. + Model - File for a AI.
    - GridChart + name - string Enum + string +
    + + + example: main.py + +
    - Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] + Name of a file. +
    + content + + + string + +
    + Contents of the file.
    + + + +

    Responses

    + + + +

    + The + + /ai/tools/backtest-init + + API provides a response in the following format: +

    +

    + 200 Success +

    + + + + @@ -85134,35 +92658,222 @@ 

     {
-  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-  "extremum": "min",
-  "targetValue": 1
+  "state": "End",
+  "version": 2.0,
+  "payload": "string",
+  "payloadType": "String"
 }

    - OptimizationTarget + BacktestInitResponse - Model + Model - Response to a backtest initialization request.
    - target + state string Enum
    - The target statistic to track. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] + State of the backtest. Options : ['End', 'Error']
    - extremum + version - string Enum + number +
    + + + example: 2.0 + +
    - Defines the direction of optimization. Options : ['min', 'max'] + Version of the response.
    - targetValue + payload - number + string + +
    + Information about the backtest initialization. +
    + payloadType + + + string
    - example: 1 + example: String
    - Desired value for the optimization target statistic. + Type of the payload.
    +

    + 401 Authentication Error +

    + + +
    - OptimizationTargetStatistic + UnauthorizedError - Model + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash.
    - GridChart + www_authenticate + + + string + +
    + Header +
    + + + +

    Examples

    + + +

    + The following example demonstates initializing a backtest for a specific algorithm through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+### Initialize Backtest
+# Send a POST request to the /ai/tools/backtest-init endpoint to initialize a backtest
+response = post(f'{BASE_URL}/ai/tools/backtest-init', headers=get_headers(), json={
+    "language": "Python",  # Programming language of the algorithm
+    "files": [  # List of files for the backtest
+        {
+            "name": "utils.py",  # Name of the file
+            "content": '''
+# region imports
+from AlgorithmImports import *
+# endregion
+
+class Project(QCAlgorithm):
+
+    def Initialize(self):
+        self.AddEquity("SPY", Resolution.Minute)
+'''  # Content of the file (Python code)
+        }
+    ]
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the backtest initialization result
+if result['success']:
+    print("Backtest Initialized Successfully:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > AI Assistance > Tools > Code Completion

    +
    +
    +

    Tools

    +

    Code Completion

    +
    +
    +

    Introduction

    + + + +

    + Show the code completion for a specific text input. +

    + + + +

    Request

    + + + +

    + Show the code completion for a specific text input. The + + /ai/tools/complete + + API accepts requests in the following format: +

    + + + + + + + + + + + + + + + + @@ -85173,117 +92884,292 @@ 

     {
-  "GridChart": "TotalPerformance.PortfolioStatistics.SharpeRatio"
+  "language": "Py",
+  "sentence": "self.add_eq",
+  "responseSizeLimit": 10
 }

    + + CodeCompletionRequest + + Model - Request to show code completion for a specific text input. +
    + language string Enum +
    + + + required + + +
    + + + example: Py + +
    - /. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] + Programming language for the code completion. Options : ['C#', 'Py'] +
    + sentence + + + string +
    + + + required + + +
    + + + example: self.add_eq + + +     +
    + Sentence to complete. +
    + responseSizeLimit + + + integer +
    + + + example: 10 + + +     +
    + Maximum size of the responses.
    + + + +

    Responses

    + + + +

    + The + + /ai/tools/complete + + API provides a response in the following format: +

    +

    + 200 Success +

    +
    - OptimizationRuntimeStatistics + CodeCompletionResponse - Model + Model - Response to a code completion request.
    - Completed + state - string + string Enum
    - Number of completed backtests in the optimization job. + State of the code completion. Options : ['End']
    - Failed + version - string + number +
    + + + example: 2.0 + +
    - Number of failed backtests in the optimization job. + Version of the response.
    - Running + payload - string + string Array
    - Number of running backtests in the optimization job. + Code completion suggestions.
    - In Queue + payloadType string +
    + + + example: StringArray + +
    - Number of backtests waiting in-queue in the optimization job. + Type of the payload.
    - Average Length + Example - - string($time) - -
    - The average time of each backtest in the optimization job (HH:MM:SS). +
    + 
    +{
+  "state": "End",
+  "version": 2.0,
+  "payload": [
+    "string"
+  ],
+  "payloadType": "StringArray"
+  ]
+}
    +
    +

    + 401 Authentication Error +

    + + + + + + +
    + + UnauthorizedError + + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. +
    - Total Runtime + www_authenticate - string($time) + string
    - The total runtime of the optimization (HH:MM:SS). + Header
    + + + +

    Examples

    + + +

    + The following example demonstates code completion for a specific algorithm attribute through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+### Code Completion
+# Send a POST request to the /ai/tools/complete endpoint to get code completion
+response = post(f'{BASE_URL}/ai/tools/complete', headers=get_headers(), json={
+    "language": "Python",  # Programming language for code completion
+    "sentence": "self.add_equity('AAPL",  # Partial code to complete
+    "responseSizeLimit": 30  # Maximum size of the completion response
+})
+# Parse the JSON response
+result = response.json()
+# Check if the request was successful and print the code completion results
+if result['success']:
+    print("Code Completion Results:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > AI Assistance > Tools > Error Enhancement

    +
    +
    +

    Tools

    +

    Error Enhancement

    +
    +
    +

    Introduction

    + + + +

    + Show additional context and suggestions for error messages. +

    + + + +

    Request

    + + + +

    + Show additional context and suggestions for error messages. The + + /ai/tools/error-enhance + + API accepts requests in the following format: +

    + + + + + + @@ -85294,14 +93180,11 @@ 

     {
-  "Completed": "string",
-  "Failed": "string",
-  "Running": "string",
-  "In Queue": "string",
-  "Average Length": "2021-11-26T15:18:27.693Z",
-  "Total Runtime": "2021-11-26T15:18:27.693Z",
-  "Total": "string",
-  "Consumed": "string"
+  "language": "Py",
+  "error": {
+    "message": "string",
+    "stacktrace": "string"
+  }
 }
    @@ -85312,31 +93195,19 @@

    - - - - @@ -85380,28 +93239,44 @@ 

     {
-  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-  "operator": "LessOrEqual",
-  "targetValue": 1
+  "message": "string",
+  "stacktrace": "string"
 }

    + + ErrorEnhanceRequest + + Model - Request to show additional context and suggestions for error messages. +
    - Total + language - string + string Enum +
    + + + required + + +
    + + + example: Py + +
    - Number of backtests in the optimization job. + Programming language for the code completion. Options : ['C#', 'Py']
    - Consumed + error - string + Error object +
    + + + required + +
    - Amount of QuantConnect Credit consumed by the optimization job. + Error information.
    - OptimizationConstraint + Error - Model - Backtests in the optimization job that don't respect this constraint are excluded from the optimization result. + Model - Error information.
    - target - - - string Enum - -
    - The target statistic to track. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] -
    - operator + message - string Enum + string
    @@ -85345,31 +93216,19 @@


    - The target comparison operation. Options : ['LessOrEqual', 'Less', 'GreaterOrEqual', 'Greater', 'NotEqual', 'Equals'] + Error message.

    - targetValue + stacktrace - number -
    - - - required - - -
    - - - example: 1 - - + string
    - The threshold value for the target constraint. + Stack trace of the error.
    + + + +

    Responses

    + + + +

    + The + + /ai/tools/error-enhance + + API provides a response in the following format: +

    +

    + 200 Success +

    + + +
    - OptimizationParameter + ErrorEnhanceResponse - Model + Model - Response to error enhancement request.
    - name + state @@ -85409,23 +93284,17 @@


    - required - - -
    - - - example: rsi_period + example: End
    - Name of optimization parameter. + State of the code completion.

    - min + version @@ -85433,88 +93302,219 @@


    - required - - -
    - - - example: 10 + example: 2.0
    - Minimum value of optimization parameter, applicable for boundary conditions. + Version of the response.

    - max + payload - number -
    - - - required - - -
    - - - example: 20 - - + string
    - Maximum value of optimization parameter, applicable for boundary conditions. + Error message suggestions.
    - step + payloadType - number -
    - - - required - - + string
    - example: 1 + example: String
    - Movement, should be positive. + Type of the payload.
    - minStep + Example + +
    + 
    +{
+  "state": "End",
+  "version": 2.0,
+  "payload": "string",
+  "payloadType": "String"
+}
    +
    +
    +

    + 401 Authentication Error +

    + + + + + + + + + +
    + + UnauthorizedError + + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. +
    + www_authenticate - number + string + +
    + Header +
    + + + +

    Examples

    + + +

    + The following example demonstates how to get additional context and suggestions for error messages through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+### Error Enhancement
+# Send a POST request to the /ai/tools/error-enhance endpoint to enhance error message
+response = post(f'{BASE_URL}/ai/tools/error-enhance/', headers=get_headers(), json={
+    "language": "Python",  # Programming language of the code
+    "error": {  # Error details
+        "message": '''  at initialize
+        self._option = self.add_index_option("SPX", Resolution.MINUTE, "SPXW").symbol
+                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    in c1afe80a-e056-4841-b0c1-d9c562cf2bd8.py: line 15
+    The specified market wasn't found in the markets lookup. Requested: spxw. You can add markets by calling QuantConnect.Market.Add(string,int) (Parameter 'market')''',  # Error message
+        "stacktrace": ""  # Stacktrace (empty in this case)
+    }
+})
+# Parse the JSON response
+result = response.json()
+# Check if the request was successful and print the enhanced error details
+if result['success']:
+    print("Enhanced Error Details:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > AI Assistance > Tools > PEP8 Conversion

    +
    +
    +

    Tools

    +

    PEP8 Conversion

    +
    +
    +

    Introduction

    + + + +

    + Update Python code to follow PEP8 style. +

    + + + +

    Request

    + + + +

    + Update Python code to follow PEP8 style. The + + /ai/tools/pep8-convert + + API accepts requests in the following format: +

    + + + + + + + + + @@ -85525,11 +93525,8 @@ 

     {
-  "name": "rsi_period",
-  "min": 10,
-  "max": 20,
-  "step": 1,
-  "minStep": 1
+  "files": "[{"name": "file.py", "content": "fileContent"}]"
+  ]
 }
    @@ -85540,9 +93537,9 @@

    @@ -85553,129 +93550,351 @@

    +
    + + PEP8ConvertRequest + + Model - Request to convert Python code to PEP8 style. +
    + files + + + File Array
    - example: 1 + required + + +
    + + + example: [{"name": "file.py", "content": "fileContent"}]
    - Minimal possible movement for current parameter, should be positive. Used by - - Strategies.EulerSearchOptimizationStrategy - - to determine when this parameter can no longer be optimized. + Files of the project.
    - OptimizationBacktest + File - Model + Model - File for a AI.
    string +
    + + + example: main.py + +
    - The backtest name. + Name of a file.
    - id + content string
    - Id of the backtest. + Contents of the file.
    - progress + Example - - number - -
    - Progress of the backtest as a percentage from 0-1 based on the days lapsed from start-finish. +
    + 
    +{
+  "name": "main.py",
+  "content": "string"
+}
    +
    + + + +

    Responses

    + + + +

    + The + + /ai/tools/pep8-convert + + API provides a response in the following format: +

    +

    + 200 Success +

    + + + + + + +
    + + PEP8ConvertResponse + + Model - Response to a PEP8 conversion request. +
    - exitCode + state - integer + string +
    + + + example: End + +
    - The exit code of this backtest. + State of PEP8 conversion.
    - statistics + version - number Array + number +
    + + + example: 2.0 + +
    - The backtest statistics results. [alpha, annual standard deviation, annual variance, average loss (%), average win (%), beta, compounding annual return (%), drawdown (%), estimated strategy capacity, expectancy, information ratio, loss rate (%), net profit (%), probabilistic sharpe ratio, profit-loss ratio, sharpe ratio, total fees, total orders, tracking error, treynor ratio, win rate (%)]. + Version of the response.
    - parameterSet + payload - ParameterSet object + object +
    + + + example: {'utils.py': 'def add(a,b):\n return a+b\n'} + +
    - Parameters used in the backtest. + A dictionary where the key is the file name and the value is the PEP8 converted code of that file.
    - equity + payloadType - array Array + string +
    + + + example: StringDict + +
    - The backtest equity chart series. + Type of the payload.
    - startDate + Example - - string($date-time) - -
    - The backtest start date. +
    + 
    +{
+  "state": "End",
+  "version": 2.0,
+  "payload": {
+    "utils.py": "def add(a,b):\n    return a+b\n"
+  },
+  "payloadType": "StringDict"
+}
    +
    +

    + 401 Authentication Error +

    + + + + + + +
    + + UnauthorizedError + + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. +
    - endDate + www_authenticate - string($date-time) + string
    - The backtest end date. + Header
    + + + +

    Examples

    + + +

    + The following example demonstates PEP8 conversion of python codes through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+### PEP8 Conversion
+# Send a POST request to the /ai/tools/pep8-convert endpoint to convert code to PEP8 syntax
+response = post(f'{BASE_URL}/ai/tools/pep8-convert', headers=get_headers(), json={
+    "files": [  # List of files to convert
+        {
+            "name": "utils.py",  # Name of the file
+            "content": '''
+# region imports
+from AlgorithmImports import *
+# endregion
+
+class Project(QCAlgorithm):
+
+    def Initialize(self):
+        self.AddEquity("SPY", Resolution.Minute)
+'''  # Content of the file (Python code)
+        }
+    ]
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the PEP8 conversion results
+if result['success']:
+    print("PEP8 Conversion Results:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > AI Assistance > Tools > Syntax Check

    +
    +
    +

    Tools

    +

    Syntax Check

    +
    +
    +

    Introduction

    + + + +

    + Check the syntax of a code. +

    + + + +

    Request

    + + + +

    + Check the syntax of a code. The + + /ai/tools/syntax-check + + API accepts requests in the following format: +

    + + + + + + @@ -85686,21 +93905,9 @@ 

     {
-  "name": "string",
-  "id": "string",
-  "progress": 0,
-  "exitCode": 0,
-  "statistics": [
-    "number"
-  ],
-  "parameterSet": ,
-  "equity": [
-    "array"
-  ],
-  "startDate": "2021-11-26T15:18:27.693Z",
-  "endDate": "2021-11-26T15:18:27.693Z",
-  "outOfSampleDays": 0,
-  "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z"
+  "language": "Py",
+  "files": "[{"name": "file.py", "content": "fileContent"}]"
+  ]
 }
    @@ -85711,22 +93918,40 @@

    + + + + @@ -85737,135 +93962,99 @@ 

     {
-  "value": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy"
+  "name": "main.py",
+  "content": "string"
 }

    + + BasicFilesRequest + + Model - Request to process files. +
    - outOfSampleDays + language - integer + string Enum +
    + + + required + + +
    + + + example: Py + +
    - The backtest out-of-sample day count. + Programming language. Options : ['C#', 'Py']
    - outOfSampleMaxEndDate + files - string($date-time) + File Array +
    + + + required + + +
    + + + example: [{"name": "file.py", "content": "fileContent"}] + +
    - End date of out-of-sample data. + Files to process.
    - OptimizationStrategy + File - Model + Model - File for a AI.
    - value + name - string Enum + string +
    + + + example: main.py + +
    - /. Options : ['QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy'] + Name of a file. +
    + content + + + string + +
    + Contents of the file.
    + + + +

    Responses

    + + + +

    + The + + /ai/tools/syntax-check + + API provides a response in the following format: +

    +

    + 200 Success +

    - - - - - - - - - - - - @@ -85876,14 +94065,12 @@ 

     {
-  "chartName": "string",
-  "width": 0,
-  "height": 0,
-  "row": 0,
-  "column": 0,
-  "sort": 0,
-  "definition": [
+  "state": "End",
+  "version": 2.0,
+  "payload": [
     "string"
+  ],
+  "payloadType": "StringArray"
   ]
 }
    @@ -85924,7 +94111,7 @@

    Examples

    - The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. + The following example demonstates syntax checking of codes through the cloud API. 

    from base64 import b64encode
@@ -85963,154 +94150,42 @@ 
    Examples
    
 # --------------------
 
 
-# The project ID of the project to manage an optimization job
-project_id = 12345678
-
-### Estimate Optimization Cost
-# Send a POST request to the /optimizations/estimate endpoint to estimate cost
-response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
-    "targetTo": "max",  # Direction to optimize (maximize)
-    "targetValue": None,  # Specific target value (None for max/min)
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
-    "compileId": compile_id,  # Compilation ID for the optimization
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value for first parameter
-    "parameters[0][max]": 200,  # Maximum value for first parameter
-    "parameters[0][step]": 50,  # Step size for first parameter
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value for second parameter
-    "parameters[1][max]": 300,  # Maximum value for second parameter
-    "parameters[1][step]": 50,  # Step size for second parameter
-    "constraints": [{  # Constraints for the optimization
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }]
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the estimated cost
-if result['success']:
-    print("Optimization Cost Estimated Successfully:")
-    print(result)
+### Syntax Check
+# Send a POST request to the /ai/tools/syntax-check endpoint to check code syntax if appropriate
+response = post(f'{BASE_URL}/ai/tools/syntax-check', headers=get_headers(), json={
+    "language": "Python",  # Programming language of the code
+    "files": [  # List of files to check
+        {
+            "name": "utils.py",  # Name of the file
+            "content": '''
+# region imports
+from AlgorithmImports import *
+# endregion
 
-### Create Optimization
-# Send a POST request to the /optimizations/create endpoint to create an optimization
-response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
-    "targetTo": "max",  # Direction to optimize
-    "targetValue": None,  # Specific target value
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
-    "compileId": compile_id,  # Compilation ID
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value
-    "parameters[0][max]": 200,  # Maximum value
-    "parameters[0][step]": 50,  # Step size
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value
-    "parameters[1][max]": 300,  # Maximum value
-    "parameters[1][step]": 50,  # Step size
-    "constraints": [{  # Constraints
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }],
-    "estimatedCost": 10,  # Estimated cost of optimization
-    "nodeType": "O2-8",  # Node type for optimization
-    "parallelNodes": 4  # Number of parallel nodes
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Extract the optimization ID from the response
-optimization_id = result['optimizations'][0]['optimizationId']
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Created Successfully:")
-    print(result)
+class Project(QCAlgorithm):
 
-### Update Optimization
-# Send a POST request to the /optimizations/update endpoint to update the optimization
-response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
-    "optimizationId": optimization_id,  # ID of the optimization to update
-    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
+    def Initialize(self):
+        self.AddEquity("SPY", Resolution.Minute)
+'''  # Content of the file (Python code)
+        }
+    ]
 })
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Updated Successfully:")
-    print(result)
-
-### Read Optimization
-# Prepare data payload to read optimization details
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to read
-}
-# Send a POST request to the /optimizations/read endpoint to get details
-response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the details
-if result['success']:
-    print("Optimization Details:")
-    print(result)
-
-### Abort Optimization
-# Prepare data payload to abort the optimization
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to abort
-}
-# Send a POST request to the /optimizations/abort endpoint to abort
-response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Aborted Successfully:")
-    print(result)
-
-### Delete Optimization
-# Prepare data payload to delete the optimization
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to delete
-}
-# Send a POST request to the /optimizations/delete endpoint to delete
-response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Deleted Successfully:")
-    print(result)
-
-### List Optimizations
-# Prepare data payload to list optimizations
-payload = {
-    "projectId": project_id  # ID of the project
-}
-# Send a POST request to the /optimizations/list endpoint to list optimizations
-response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the list
+# Check if the request was successful and print the syntax check results
 if result['success']:
-    print("List of Optimizations:")
+    print("Syntax Check Results:")
     print(result)

     

    -

    API Reference > Optimization Management > Update Optimization

    +

    API Reference > AI Assistance > Tools > Search

    -
    -

    Optimization Management

    -

    Update Optimization

    +
    +

    Tools

    +

    Search

    Introduction

    @@ -86118,7 +94193,7 @@

    Introduction

    - Update the name of an optimization. + Search for content in QuantConnect.

    @@ -86128,9 +94203,9 @@

    Request

    - The + Search for content in QuantConnect. The - /optimizations/update + /ai/tools/search API accepts requests in the following format:

    @@ -86139,15 +94214,89 @@

    Request

    + + + + + + + + + + + + + + +
    - GridChart + SyntaxCheckResponse - Model - The chart display properties. + Model - Response to a syntax check request.
    - chartName - - - string -
    - - - required - - -     -
    - The chart name. -
    - width + state - integer -
    - - - required - - + string Enum
    - Width of the chart. + State of the syntax check. Options : ['End', 'Error']
    - height + version - integer + number
    - required + example: 2.0
    - Height of the chart. + Version of the response.
    - row + payload - integer -
    - - - required - - + string Array
    - Number of rows of the chart. + Code completion suggestions.
    - column + payloadType - integer + string
    - required + example: StringArray
    - Number of columns of the chart. -
    - sort - - - integer - -
    - Sort of the chart. -
    - definition - - - string Array - -
    - The chart definition labels. + Type of the payload.
    - UpdateOptimizationRequest + SearchRequest + + Model - Request to search content in QuantConnect. +
    + language + + + string Enum +
    + + + required + + +
    + + + example: Py + + +     +
    + Programming language of the content to search. Options : ['C#', 'Py'] +
    + criteria + + + SearchCriteria Array +
    + + + required + + +     +
    + Criteria for the search. +
    + Example + +
    + 
    +{
+  "language": "Py",
+  "criteria": [
+    {
+      "input": "option",
+      "type": "Stubs",
+      "count": 1
+    }
+  ]
+}
    +
    +
    + + + + + + + + @@ -86201,8 +94368,9 @@

    Request

     {
-  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
-  "name": "New Optimization Name"
+  "input": "option",
+  "type": "Stubs",
+  "count": 1
 }
    @@ -86218,7 +94386,7 @@

    Responses

    The - /optimizations/update + /ai/tools/search API provides a response in the following format:

    @@ -86230,311 +94398,110 @@

    -
    + + SearchCriteria - Model - Update the name of an optimization. + Model - Search criteria.
    - optimizationId + input @@ -86161,36 +94310,54 @@

    Request


    - example: O-401d3d40b5a0e9f8c46c954a303f3ddd + example: option
    - Id of the optimization to update. + Input for the search.
    - name + type - string + string Enum
    required +     +
    + Type of the search criteria. Options : ['Stubs', 'Forum', 'Docs', 'Examples'] +
    + count + + + integer
    - example: New Optimization Name + required + + +
    + + + example: 1
    - Name to assign to the optimization. + Number of results to return.
    - RestResponse + SearchResponse - Model - Base API response class for the QuantConnect API. + Model - Response to a search request.
    - success + state - boolean + string Enum
    - Indicate if the API request was successful. + State of the search. Options : ['End', 'Error']
    - errors + version - string Array + number +
    + + + example: 2.0 + +
    - List of errors with the API call. + Version of the response.
    - Example + retrivals -
    - 
    -{
-  "success": true,
-  "errors": [
-    "string"
-  ]
-}
    -
    + + SearchRetrieval Array + +
    + List of search results.
    -

    - 401 Authentication Error -

    - - - - - - -
    - - UnauthorizedError - - Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. -
    - www_authenticate + messageId - string + integer +
    + + + example: 0 + +
    - Header + Id of the message.
    - - - -

    Examples

    - - -

    - The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. -

    -
    - 
    from base64 import b64encode
-from hashlib import sha256
-from time import time
-from requests import get, post
-BASE_URL = 'https://www.quantconnect.com/api/v2/'
-
-# You need to replace these with your actual credentials.
-# You can request your credentials at https://www.quantconnect.com/settings/
-# You can find our organization ID at https://www.quantconnect.com/organization/ 
-USER_ID = 0
-API_TOKEN = '____'
-ORGANIZATION_ID = '____'
-
-def get_headers():
-    # Get timestamp
-    timestamp = f'{int(time())}'
-    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
-
-    # Get hased API token
-    hashed_token = sha256(time_stamped_token).hexdigest()
-    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
-    authentication = b64encode(authentication).decode('ascii')
-
-    # Create headers dictionary.
-    return {
-        'Authorization': f'Basic {authentication}',
-        'Timestamp': timestamp
+   
+    
+     Example
+    
+    
+     
    
+      
    +{
+  "state": "End",
+  "version": 2.0,
+  "retrivals": [
+    {
+      "url": "[Index Options - QuantConnect.com](https://www.quantconnect.com/docs/v2/writing-algorithms/universes/index-options)",
+      "score": 0.320344448,
+      "content": "string",
+      "type": 2
     }
-
-# Authenticate to verify credentials
-response = post(f'{BASE_URL}/authenticate', headers = get_headers())
-print(response.json())
-
-# --------------------
-
-
-# The project ID of the project to manage an optimization job
-project_id = 12345678
-
-### Estimate Optimization Cost
-# Send a POST request to the /optimizations/estimate endpoint to estimate cost
-response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
-    "targetTo": "max",  # Direction to optimize (maximize)
-    "targetValue": None,  # Specific target value (None for max/min)
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
-    "compileId": compile_id,  # Compilation ID for the optimization
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value for first parameter
-    "parameters[0][max]": 200,  # Maximum value for first parameter
-    "parameters[0][step]": 50,  # Step size for first parameter
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value for second parameter
-    "parameters[1][max]": 300,  # Maximum value for second parameter
-    "parameters[1][step]": 50,  # Step size for second parameter
-    "constraints": [{  # Constraints for the optimization
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }]
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the estimated cost
-if result['success']:
-    print("Optimization Cost Estimated Successfully:")
-    print(result)
-
-### Create Optimization
-# Send a POST request to the /optimizations/create endpoint to create an optimization
-response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
-    "targetTo": "max",  # Direction to optimize
-    "targetValue": None,  # Specific target value
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
-    "compileId": compile_id,  # Compilation ID
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value
-    "parameters[0][max]": 200,  # Maximum value
-    "parameters[0][step]": 50,  # Step size
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value
-    "parameters[1][max]": 300,  # Maximum value
-    "parameters[1][step]": 50,  # Step size
-    "constraints": [{  # Constraints
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }],
-    "estimatedCost": 10,  # Estimated cost of optimization
-    "nodeType": "O2-8",  # Node type for optimization
-    "parallelNodes": 4  # Number of parallel nodes
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Extract the optimization ID from the response
-optimization_id = result['optimizations'][0]['optimizationId']
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Created Successfully:")
-    print(result)
-
-### Update Optimization
-# Send a POST request to the /optimizations/update endpoint to update the optimization
-response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
-    "optimizationId": optimization_id,  # ID of the optimization to update
-    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Updated Successfully:")
-    print(result)
-
-### Read Optimization
-# Prepare data payload to read optimization details
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to read
-}
-# Send a POST request to the /optimizations/read endpoint to get details
-response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the details
-if result['success']:
-    print("Optimization Details:")
-    print(result)
-
-### Abort Optimization
-# Prepare data payload to abort the optimization
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to abort
-}
-# Send a POST request to the /optimizations/abort endpoint to abort
-response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Aborted Successfully:")
-    print(result)
-
-### Delete Optimization
-# Prepare data payload to delete the optimization
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to delete
-}
-# Send a POST request to the /optimizations/delete endpoint to delete
-response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Deleted Successfully:")
-    print(result)
-
-### List Optimizations
-# Prepare data payload to list optimizations
-payload = {
-    "projectId": project_id  # ID of the project
-}
-# Send a POST request to the /optimizations/list endpoint to list optimizations
-response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the list
-if result['success']:
-    print("List of Optimizations:")
-    print(result)
    
-  
    
- 
-
-
-
     
    
-
    API Reference > Optimization Management > Delete Optimization
    
-
    
-    
    
-        
    Optimization Management
    
-        
    Delete Optimization
    
-    
    
-
    
-
    Introduction
    
-
-
- 
-  
    
-   Delete an optimization.
-  
    
- 
-
-
-
    Request
    
-
-
- 
-  
    
-   The
-   
-    /optimizations/delete
-   
-   API accepts requests in the following format:
-  
    
+  ],
+  "messageId": 0
+}
    +
    + + + -
    - DeleteOptimizationRequest + SearchRetrieval - Model - Delete an optimization. + Model - Search criteria.
    - optimizationId + url @@ -86542,84 +94509,60 @@

    Request


    - required - - -
    - - - example: O-401d3d40b5a0e9f8c46c954a303f3ddd + example: [Index Options - QuantConnect.com](https://www.quantconnect.com/docs/v2/writing-algorithms/universes/index-options)
    - Id of the optimization to delete. + Input for the search.
    - Example + score -
    - 
    -{
-  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd"
-}
    -
    + + number +
    + + + example: 0.320344448 + + +     +
    + Relevance score of the search result.
    - - - -

    Responses

    - - - -

    - The - - /optimizations/delete - - API provides a response in the following format: -

    -

    - 200 Success -

    - - - - - - @@ -86630,10 +94573,10 @@ 

     {
-  "success": true,
-  "errors": [
-    "string"
-  ]
+  "url": "[Index Options - QuantConnect.com](https://www.quantconnect.com/docs/v2/writing-algorithms/universes/index-options)",
+  "score": 0.320344448,
+  "content": "string",
+  "type": 2
 }
    @@ -86673,7 +94616,7 @@

    Examples

    - The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. + The following example demonstates searching content in QuantConnect through the cloud API. 

    from base64 import b64encode
@@ -86691,175 +94634,88 @@ 
    Examples
    
 
 def get_headers():
     # Get timestamp
-    timestamp = f'{int(time())}'
-    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
-
-    # Get hased API token
-    hashed_token = sha256(time_stamped_token).hexdigest()
-    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
-    authentication = b64encode(authentication).decode('ascii')
-
-    # Create headers dictionary.
-    return {
-        'Authorization': f'Basic {authentication}',
-        'Timestamp': timestamp
-    }
-
-# Authenticate to verify credentials
-response = post(f'{BASE_URL}/authenticate', headers = get_headers())
-print(response.json())
-
-# --------------------
-
-
-# The project ID of the project to manage an optimization job
-project_id = 12345678
-
-### Estimate Optimization Cost
-# Send a POST request to the /optimizations/estimate endpoint to estimate cost
-response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
-    "targetTo": "max",  # Direction to optimize (maximize)
-    "targetValue": None,  # Specific target value (None for max/min)
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
-    "compileId": compile_id,  # Compilation ID for the optimization
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value for first parameter
-    "parameters[0][max]": 200,  # Maximum value for first parameter
-    "parameters[0][step]": 50,  # Step size for first parameter
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value for second parameter
-    "parameters[1][max]": 300,  # Maximum value for second parameter
-    "parameters[1][step]": 50,  # Step size for second parameter
-    "constraints": [{  # Constraints for the optimization
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }]
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the estimated cost
-if result['success']:
-    print("Optimization Cost Estimated Successfully:")
-    print(result)
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
 
-### Create Optimization
-# Send a POST request to the /optimizations/create endpoint to create an optimization
-response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
-    "targetTo": "max",  # Direction to optimize
-    "targetValue": None,  # Specific target value
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
-    "compileId": compile_id,  # Compilation ID
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value
-    "parameters[0][max]": 200,  # Maximum value
-    "parameters[0][step]": 50,  # Step size
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value
-    "parameters[1][max]": 300,  # Maximum value
-    "parameters[1][step]": 50,  # Step size
-    "constraints": [{  # Constraints
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }],
-    "estimatedCost": 10,  # Estimated cost of optimization
-    "nodeType": "O2-8",  # Node type for optimization
-    "parallelNodes": 4  # Number of parallel nodes
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Extract the optimization ID from the response
-optimization_id = result['optimizations'][0]['optimizationId']
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Created Successfully:")
-    print(result)
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
 
-### Update Optimization
-# Send a POST request to the /optimizations/update endpoint to update the optimization
-response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
-    "optimizationId": optimization_id,  # ID of the optimization to update
-    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Updated Successfully:")
-    print(result)
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
 
-### Read Optimization
-# Prepare data payload to read optimization details
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to read
-}
-# Send a POST request to the /optimizations/read endpoint to get details
-response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the details
-if result['success']:
-    print("Optimization Details:")
-    print(result)
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
 
-### Abort Optimization
-# Prepare data payload to abort the optimization
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to abort
-}
-# Send a POST request to the /optimizations/abort endpoint to abort
-response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Aborted Successfully:")
-    print(result)
+# --------------------
 
-### Delete Optimization
-# Prepare data payload to delete the optimization
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to delete
-}
-# Send a POST request to the /optimizations/delete endpoint to delete
-response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Deleted Successfully:")
-    print(result)
 
-### List Optimizations
-# Prepare data payload to list optimizations
-payload = {
-    "projectId": project_id  # ID of the project
-}
-# Send a POST request to the /optimizations/list endpoint to list optimizations
-response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
+### Search Content
+# Send a POST request to the /ai/tools/search endpoint to search content
+response = post(f'{BASE_URL}/ai/tools/search', headers=get_headers(), json={
+    "language": "Python",  # Programming language to filter search results
+    "criteria": [  # Search criteria
+        {
+            "input": "option",  # Search term (e.g., "option")
+            "type": "Docs",  # Type of content to search (e.g., documentation)
+            "count": 1  # Number of results to return
+        }
+    ]
+})
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the list
+# Check if the request was successful and print the search results
 if result['success']:
-    print("List of Optimizations:")
+    print("Search Results:")
     print(result)

     

    -

    API Reference > Optimization Management > Abort Optimization

    +

    API Reference > Reports

    -
    -

    Optimization Management

    -

    Abort Optimization

    +
    +

    API Reference

    +

    Reports

    +
    +
    + + +
    +
    +

    + The QuantConnect REST API lets you access your backtest reports from our cloud servers through URL endpoints. +

    +
    +
    +
    + +
    +
    +

    + Backtest Report +

    +
    +
    +     +
    +
    +
    + + + +

     

    +

    API Reference > Reports > Backtest Report

    +
    +
    +

    Reports

    +

    Backtest Report

    Introduction

    @@ -86867,7 +94723,7 @@

    Introduction

    - Abort an optimization. + Read out the report of a backtest.

    @@ -86877,9 +94733,9 @@

    Request

    - The + A JSON object containing info about the backtest to use for the report. The - /optimizations/abort + /backtests/read/report API accepts requests in the following format:

    @@ -86888,15 +94744,39 @@

    Request

    + + + + @@ -86926,7 +94806,8 @@

    Request

     {
-  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd"
+  "projectId": 23456789,
+  "backtestId": "26c7bb06b8487cff1c7b3c44652b30f1"
 }
    @@ -86942,7 +94823,7 @@

    Responses

    The - /optimizations/abort + /backtests/read/report API provides a response in the following format:

    @@ -86954,12 +94835,89 @@

    + + + + + + + + + + + + + + + + + + +
    - - RestResponse - - Model - Base API response class for the QuantConnect API. -
    - success + content - boolean + string
    - Indicate if the API request was successful. + Content of the search result.
    - errors + type - string Array + number +
    + + + example: 2 + +
    - List of errors with the API call. + Type of the search result. 0=Stubs, 1=Forum, 2=Docs, 3=Examples.
    - AbortOptimizationRequest + BacktestReportRequest - Model - Abort an optimization. + Model - Request to read out the report of a backtest.
    - optimizationId + projectId + + + integer +
    + + + required + + +
    + + + example: 23456789 + + +     +
    + Id of the project to read. +
    + backtestId @@ -86910,12 +94790,12 @@

    Request


    - example: O-401d3d40b5a0e9f8c46c954a303f3ddd + example: 26c7bb06b8487cff1c7b3c44652b30f1
    - Id of the optimization to abort. + Id of the backtest to read.
    - RestResponse + BacktestReportGeneratingResponse - Model - Base API response class for the QuantConnect API. + Model - Backtest Report Response wrapper. +
    + generating + + + boolean + +
    + Indicate if the report is generating. +
    + success + + + boolean + +
    + Indicate if the API request was successful. +
    + errors + + + string Array + +
    + List of errors with the API call. +
    + Example + +
    + 
    +{
+  "generating": true,
+  "success": true,
+  "errors": [
+    "string"
+  ]
+}
    +
    +
    + + + + + + + + @@ -87366,41 +95249,13 @@ 

     {
-  "optimizations": [
-    {
-      "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
-      "projectId": 23456789,
-      "name": "string",
-      "status": ,
-      "nodeType": "O2-8",
-      "extremum": "min",
-      "criterion": {
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "extremum": "min",
-        "targetValue": 1
-      },
-      "created": "2021-11-26T15:18:27.693Z",
-      "psr": 0,
-      "sharpeRatio": 0,
-      "trades": 0,
-      "cloneId": 0,
-      "outOfSampleDays": 0,
-      "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z",
-      "parameters": [
-        {
-          "name": "rsi_period",
-          "min": 10,
-          "max": 20,
-          "step": 1,
-          "minStep": 1
-        }
-      ]
-    }
-  ],
-  "success": true,
-  "errors": [
-    "string"
-  ]
+  "organizationId": "5cad178b20a1d52567b534553413b691",
+  "creditBalance": 0,
+  "card": {
+    "brand": "string",
+    "expiration": "12/27",
+    "last4": "string"
+  }
 }
    @@ -87411,202 +95266,52 @@

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - @@ -87617,163 +95322,209 @@ 

     {
-  "optimizationId": "O-401d3d40b5a0e9f8c46c954a303f3ddd",
-  "projectId": 23456789,
-  "name": "string",
-  "status": ,
-  "nodeType": "O2-8",
-  "extremum": "min",
-  "criterion": {
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-    "extremum": "min",
-    "targetValue": 1
-  },
-  "created": "2021-11-26T15:18:27.693Z",
-  "psr": 0,
-  "sharpeRatio": 0,
-  "trades": 0,
-  "cloneId": 0,
-  "outOfSampleDays": 0,
-  "outOfSampleMaxEndDate": "2021-11-26T15:18:27.693Z",
-  "parameters": [
-    {
-      "name": "rsi_period",
-      "min": 10,
-      "max": 20,
-      "step": 1,
-      "minStep": 1
-    }
-  ]
+  "brand": "string",
+  "expiration": "12/27",
+  "last4": "string"
 }

    + + BacktestReport + + Model - Backtest Report Response wrapper.
    + report + + + string + +
    + HTML data of the report with embedded base64 images. +
    success @@ -86992,6 +94950,7 @@ 

     {
+  "report": "string",
   "success": true,
   "errors": [
     "string"
@@ -87035,7 +94994,7 @@ 
    Examples
    
 
  
   
    
-   The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API.
+   The following example demonstates creating, reading, updating, deleting, and listing backtests of a project through the cloud API.
   
    
   
    
    
    from base64 import b64encode
@@ -87074,154 +95033,93 @@ 
    Examples
    
 # --------------------
 
 
-# The project ID of the project to manage an optimization job
-project_id = 12345678
-
-### Estimate Optimization Cost
-# Send a POST request to the /optimizations/estimate endpoint to estimate cost
-response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
-    "targetTo": "max",  # Direction to optimize (maximize)
-    "targetValue": None,  # Specific target value (None for max/min)
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
-    "compileId": compile_id,  # Compilation ID for the optimization
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value for first parameter
-    "parameters[0][max]": 200,  # Maximum value for first parameter
-    "parameters[0][step]": 50,  # Step size for first parameter
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value for second parameter
-    "parameters[1][max]": 300,  # Maximum value for second parameter
-    "parameters[1][step]": 50,  # Step size for second parameter
-    "constraints": [{  # Constraints for the optimization
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }]
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the estimated cost
-if result['success']:
-    print("Optimization Cost Estimated Successfully:")
-    print(result)
-
-### Create Optimization
-# Send a POST request to the /optimizations/create endpoint to create an optimization
-response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
-    "targetTo": "max",  # Direction to optimize
-    "targetValue": None,  # Specific target value
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
-    "compileId": compile_id,  # Compilation ID
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value
-    "parameters[0][max]": 200,  # Maximum value
-    "parameters[0][step]": 50,  # Step size
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value
-    "parameters[1][max]": 300,  # Maximum value
-    "parameters[1][step]": 50,  # Step size
-    "constraints": [{  # Constraints
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }],
-    "estimatedCost": 10,  # Estimated cost of optimization
-    "nodeType": "O2-8",  # Node type for optimization
-    "parallelNodes": 4  # Number of parallel nodes
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Extract the optimization ID from the response
-optimization_id = result['optimizations'][0]['optimizationId']
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Created Successfully:")
-    print(result)
-
-### Update Optimization
-# Send a POST request to the /optimizations/update endpoint to update the optimization
-response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
-    "optimizationId": optimization_id,  # ID of the optimization to update
-    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
+### Create Backtest
+# Define placeholder compilation ID (replace with actual value)
+compile_id = "compile_id..."
+# Send a POST request to the /backtests/create endpoint to create a backtest
+response = post(f'{BASE_URL}/backtests/create', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project to backtest
+    "compileId": compile_id,  # Compilation ID for the backtest
+    "backtestName": f"Backtest {int(time())}"  # Unique name for the backtest using current timestamp
 })
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the backtest ID from the response
+backtest_id = result['backtest']['backtestId']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Optimization Updated Successfully:")
+    print("Backtest Created Successfully:")
     print(result)
 
-### Read Optimization
-# Prepare data payload to read optimization details
+### Read Backtest Statistics
+# Prepare data payload to read backtest statistics
 payload = {
-    "optimizationId": optimization_id  # ID of the optimization to read
+    "projectId": project_id,  # ID of the project
+    "backtestId": backtest_id  # ID of the backtest to read
 }
-# Send a POST request to the /optimizations/read endpoint to get details
-response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
+# Send a POST request to the /backtests/read endpoint to get statistics
+response = post(f'{BASE_URL}/backtests/read', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the details
+# Check if the request was successful and print the statistics
 if result['success']:
-    print("Optimization Details:")
+    print("Backtest Statistics:")
     print(result)
 
-### Abort Optimization
-# Prepare data payload to abort the optimization
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to abort
-}
-# Send a POST request to the /optimizations/abort endpoint to abort
-response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
+### Update Backtest
+# Send a POST request to the /backtests/update endpoint to update backtest details
+response = post(f'{BASE_URL}/backtests/update', headers=get_headers(), json={
+    "projectId": project_id,  # ID of the project
+    "backtestId": backtest_id,  # ID of the backtest to update
+    "name": f"Backtest_{backtest_id}",  # New name for the backtest
+    "note": "The new backtest name is awesome!"  # Additional note
+})
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the result
 if result['success']:
-    print("Optimization Aborted Successfully:")
+    print("Backtest Updated Successfully:")
     print(result)
 
-### Delete Optimization
-# Prepare data payload to delete the optimization
+### Delete Backtest
+# Prepare data payload to delete the backtest
 payload = {
-    "optimizationId": optimization_id  # ID of the optimization to delete
+    "projectId": project_id,  # ID of the project
+    "backtestId": backtest_id  # ID of the backtest to delete
 }
-# Send a POST request to the /optimizations/delete endpoint to delete
-response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
+# Send a POST request to the /backtests/delete endpoint to delete the backtest
+response = post(f'{BASE_URL}/backtests/delete', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the result
 if result['success']:
-    print("Optimization Deleted Successfully:")
+    print("Backtest Deleted Successfully:")
     print(result)
 
-### List Optimizations
-# Prepare data payload to list optimizations
+### List Backtests
+# Prepare data payload to list backtests with statistics
 payload = {
-    "projectId": project_id  # ID of the project
+    "projectId": project_id,  # ID of the project
+    "includeStatistics": True  # Include statistics in the response
 }
-# Send a POST request to the /optimizations/list endpoint to list optimizations
-response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
+# Send a POST request to the /backtests/list endpoint to list backtests
+response = post(f'{BASE_URL}/backtests/list', headers=get_headers(), json=payload)
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the list
 if result['success']:
-    print("List of Optimizations:")
+    print("List of Backtests:")
     print(result)
    
   
    
  
 
 
 
     
    
-
    API Reference > Optimization Management > List Optimization
    
+
    API Reference > Account
    
 
    
-    
    
-        
    Optimization Management
    
-        
    List Optimization
    
+    
    
+        
    API Reference
    
+        
    Account
    
     
    
 
    
 
    Introduction
    
@@ -87229,7 +95127,17 @@ 
    Introduction
    
 
  
   
    
-   List all the optimizations for a project.
+   Read the organization account status.
+  
    
+ 
+
+
+
    Description
    
+
+
+ 
+  
    
+   The organization account status includes the organization Id, QCC balance, and credit card information.
   
    
  
 
@@ -87239,60 +95147,29 @@ 
    Request
    
 
  
   
    
-   If of the Project to get a list of optimizations for. The
+   The
    
-    /optimizations/list
+    /account/read
    
    API accepts requests in the following format:
   
    
   
-     
-   
-    
-    
-   
-   
-    
-    
-   
+   
    
    
     
    
+     
       
-       ListOptimizationRequest
+       /account/read
       
-      Model - Id of the Project to get a list of optimizations for.
+      Method
      
     
    
    
    
-     projectId
-    
-     
-      integer
-      
    
-      
-       
-        required
-       
-      
-      
    
-      
-       
-        example: 23456789
-       
-      
-         
-     
    
-     Id of the Project to get a list of optimizations for.
-    
    
-     Example
-    
-     
    
-      
    -{
-  "projectId": 23456789
-}
    
-     
    
-    
    
+    
+     /account/read
+    
+    method takes no parameters.
+   
   
    
  
 
@@ -87304,7 +95181,7 @@ 
    Responses
    
   
    
    The
    
-    /optimizations/list
+    /account/read
    
    API provides a response in the following format:
   
    
@@ -87316,46 +95193,52 @@

    - ListOptimizationResponse + AccountResponse - Model - Response received when creating an optimization or listing optimizations of a project. + Model - Account information for an organization.
    - optimizations + organizationId - CreateOptimizationResponse Array + string +
    + + + example: 5cad178b20a1d52567b534553413b691 + +
    - Collection of summarized optimization objects. + The organization Id.
    - success + creditBalance - boolean + number
    - Indicate if the API request was successful. + The current account balance.
    - errors + card - string Array + Card object
    - List of errors with the API call. + Credit card information.
    - CreateOptimizationResponse + Card - Model - Response received when launching an optimization job or listing all the optimization jobs of a project. + Model - Credit card information.
    - optimizationId + brand string -
    - - - example: O-401d3d40b5a0e9f8c46c954a303f3ddd - -
    - Id of the optimization job. + Credit card brand.
    - projectId + expiration - integer + string
    - example: 23456789 + example: 12/27
    - Id of the project the optimization belongs to. + The credit card expiration (mm/yy).
    - name + last4 string
    - Name of the optimization. -
    - status - - - OptimizationStatus object - -
    - Status of the optimization. -
    - nodeType - - - string Enum - -
    - Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] -
    - extremum - - - string Enum - -
    - Defines the direction of optimization. Options : ['min', 'max'] -
    - criterion - - - OptimizationTarget object - -
    - Optimization statistical target. -
    - created - - - string($date-time) - -
    - Date and time of when this optimization was created. -
    - psr - - - number - -
    - Probabilistic Sharpe ratio statistic. -
    - sharpeRatio - - - number - -
    - Sharpe ratio statistic. -
    - trades - - - integer - -
    - Number of trades in the best backtest (based on the criterion) of the optimziation. -
    - cloneId - - - integer - -
    - Id of project where this current project was originally cloned. -
    - outOfSampleDays - - - integer - -
    - Number out-of-sample days. -
    - outOfSampleMaxEndDate - - - string($date-time) - -
    - End date of out-of-sample data. -
    - parameters - - - OptimizationParameter Array - -
    - Parameters used in this optimization. + The last 4 digits of the card.
    +

    + 401 Authentication Error +

    - - - -
    - OptimizationNodeType + UnauthorizedError - Model - Optimization node types available in QuantConnect Cloud. + Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash.
    - OptimizationParameter + www_authenticate - string Enum + string
    - Optimization node types available in QuantConnect Cloud. Options : ['O2-8', 'O4-12', 'O8-16'] -
    - Example - -
    - 
    -{
-  "OptimizationParameter": "O2-8"
-}
    -
    + Header
    + + + +

    Examples

    + + +

    + The following example demonstates reading the organization account status through the cloud API. +

    +
    + 
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+### Read Account Status
+# Send a POST request to the /account/read endpoint to read account status
+response = post(f'{BASE_URL}/account/read', headers=get_headers())
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the account status
+if result['success']:
+    print("Account Status:")
+    print(result)
    +
    + + + +

     

    +

    API Reference > Lean Version

    +
    +
    +

    API Reference

    +

    Lean Version

    +
    +
    +

    Introduction

    + + + +

    + Returns a list of lean versions with basic information for each version. +

    + + + +

    Request

    + + + +

    + The + + /lean/versions/read + + API accepts requests in the following format: +

    - - - - - - - - - +
    + - OptimizationTargetTo + /lean/versions/read - Model + Method
    - OptimizationParameter - - - string Enum - -
    - /. Options : ['min', 'max'] -
    - Example - -
    - 
    -{
-  "OptimizationParameter": "min"
-}
    -
    -
    + + /lean/versions/read + + method takes no parameters. +
    + + + +

    Responses

    + + + +

    + The + + /lean/versions/read + + API provides a response in the following format: +

    +

    + 200 Success +

    @@ -87784,9 +95535,22 @@ 

     {
-  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-  "extremum": "min",
-  "targetValue": 1
+  "versions": [
+    {
+      "id": 0,
+      "created": "2021-11-26T15:18:27.693Z",
+      "description": "string",
+      "leanHash": "string",
+      "leanCloudHash": "string",
+      "name": "string",
+      "ref": "string",
+      "public": true
+    }
+  ],
+  "success": true,
+  "errors": [
+    "string"
+  ]
 }
    @@ -87797,7 +95561,7 @@

    @@ -87805,157 +95569,98 @@

    - -
    - OptimizationTarget + LeanVersionsResponse - Model + Model - Contains the LEAN versions with their basic descriptions.
    - target + versions - string Enum + LeanVersion Array
    - The target statistic to track. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] + List of LEAN versions with their basic descriptions.
    - extremum + success - string Enum + boolean
    - Defines the direction of optimization. Options : ['min', 'max'] + Indicate if the API request was successful.
    - targetValue + errors - number -
    - - - example: 1 - - + string Array
    - Desired value for the optimization target statistic. + List of errors with the API call.
    - OptimizationTargetStatistic + LeanVersion Model
    - OptimizationParameter + id - string Enum + integer
    - /. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] + Id of the LEAN version.
    - Example + created -
    - 
    -{
-  "OptimizationParameter": "TotalPerformance.PortfolioStatistics.SharpeRatio"
-}
    -
    -
    - - - - - - + + string($date-time) + +
    + Date when this version was created. + + + + + + @@ -87966,43 +95671,19 @@ 

     {
-  "name": "rsi_period",
-  "min": 10,
-  "max": 20,
-  "step": 1,
-  "minStep": 1
+  "id": 0,
+  "created": "2021-11-26T15:18:27.693Z",
+  "description": "string",
+  "leanHash": "string",
+  "leanCloudHash": "string",
+  "name": "string",
+  "ref": "string",
+  "public": true
 }

    - - OptimizationParameter - - Model -
    - name + description string -
    - - - required - - -
    - - - example: rsi_period - -
    - Name of optimization parameter. + Description of the LEAN version.
    - min + leanHash - number -
    - - - required - - -
    - - - example: 10 - - + string
    - Minimum value of optimization parameter, applicable for boundary conditions. + Commit Hash in the LEAN repository.
    - max + leanCloudHash - number -
    - - - required - - -
    - - - example: 20 - - + string
    - Maximum value of optimization parameter, applicable for boundary conditions. + Commit Hash in the LEAN Cloud repository.
    - step + name - number -
    - - - required - - -
    - - - example: 1 - - + string
    - Movement, should be positive. + Name of the branch where the commit is.
    - minStep + ref - number -
    - - - example: 1 - - + string
    - Minimal possible movement for current parameter, should be positive. Used by + Reference to the branch where the commit is. +
    + public + - Strategies.EulerSearchOptimizationStrategy + boolean - to determine when this parameter can no longer be optimized. +
    + Indicates if the version is available for the public.
    -

    - 401 Authentication Error -

    - - - - - - - - - - -
    - - UnauthorizedError - - Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. -
    - www_authenticate - - - string - -
    - Header -
    @@ -88010,7 +95691,7 @@

    Examples

    - The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. + The following example demonstates getting a list of Lean versions through the cloud API. 

    from base64 import b64encode
@@ -88049,154 +95730,39 @@ 
    Examples
    
 # --------------------
 
 
-# The project ID of the project to manage an optimization job
-project_id = 12345678
-
-### Estimate Optimization Cost
-# Send a POST request to the /optimizations/estimate endpoint to estimate cost
-response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
-    "targetTo": "max",  # Direction to optimize (maximize)
-    "targetValue": None,  # Specific target value (None for max/min)
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
-    "compileId": compile_id,  # Compilation ID for the optimization
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value for first parameter
-    "parameters[0][max]": 200,  # Maximum value for first parameter
-    "parameters[0][step]": 50,  # Step size for first parameter
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value for second parameter
-    "parameters[1][max]": 300,  # Maximum value for second parameter
-    "parameters[1][step]": 50,  # Step size for second parameter
-    "constraints": [{  # Constraints for the optimization
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }]
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the estimated cost
-if result['success']:
-    print("Optimization Cost Estimated Successfully:")
-    print(result)
-
-### Create Optimization
-# Send a POST request to the /optimizations/create endpoint to create an optimization
-response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
-    "targetTo": "max",  # Direction to optimize
-    "targetValue": None,  # Specific target value
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
-    "compileId": compile_id,  # Compilation ID
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value
-    "parameters[0][max]": 200,  # Maximum value
-    "parameters[0][step]": 50,  # Step size
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value
-    "parameters[1][max]": 300,  # Maximum value
-    "parameters[1][step]": 50,  # Step size
-    "constraints": [{  # Constraints
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }],
-    "estimatedCost": 10,  # Estimated cost of optimization
-    "nodeType": "O2-8",  # Node type for optimization
-    "parallelNodes": 4  # Number of parallel nodes
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Extract the optimization ID from the response
-optimization_id = result['optimizations'][0]['optimizationId']
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Created Successfully:")
-    print(result)
-
-### Update Optimization
-# Send a POST request to the /optimizations/update endpoint to update the optimization
-response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
-    "optimizationId": optimization_id,  # ID of the optimization to update
-    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
-})
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Updated Successfully:")
-    print(result)
-
-### Read Optimization
-# Prepare data payload to read optimization details
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to read
-}
-# Send a POST request to the /optimizations/read endpoint to get details
-response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the details
-if result['success']:
-    print("Optimization Details:")
-    print(result)
-
-### Abort Optimization
-# Prepare data payload to abort the optimization
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to abort
-}
-# Send a POST request to the /optimizations/abort endpoint to abort
-response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Aborted Successfully:")
-    print(result)
-
-### Delete Optimization
-# Prepare data payload to delete the optimization
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to delete
-}
-# Send a POST request to the /optimizations/delete endpoint to delete
-response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Optimization Deleted Successfully:")
-    print(result)
-
-### List Optimizations
-# Prepare data payload to list optimizations
-payload = {
-    "projectId": project_id  # ID of the project
-}
-# Send a POST request to the /optimizations/list endpoint to list optimizations
-response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
+### Get Lean Versions
+# Send a POST request to the /lean/versions/read endpoint to get Lean versions
+response = post(f'{BASE_URL}/lean/versions/read', headers=get_headers())
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the list
+# Check if the request was successful and print the versions
 if result['success']:
-    print("List of Optimizations:")
+    print("Lean Versions:")
     print(result)

     

    -

    API Reference > Optimization Management > Estimate Optimization Cost

    +

    API Reference > Agent Management

    -
    -

    Optimization Management

    -

    Estimate Optimization Cost

    +
    +

    API Reference

    +

    Agent Management

    +
    +
    +

    API Reference > Agent Management > Agents

    +
    +
    +

    Agent Management

    +

    Agents

    +
    +
    +

    API Reference > Agent Management > Agents > Create Agent

    +
    +
    +

    Agents

    +

    Create Agent

    Introduction

    @@ -88204,7 +95770,7 @@

    Introduction

    - Estimate the execution time of an optimization with the specified parameters. + Create a new agent.

    @@ -88214,9 +95780,9 @@

    Request

    - Project, compile, and optimization parameters for estimating the execution time of an optimization job. The + Definition of the agent to create. The - /optimizations/estimate + /agents/create API accepts requests in the following format:

    @@ -88225,19 +95791,19 @@

    Request

    - EstimateOptimizationRequest + CreateAgentRequest - Model - Request to estimate the cost of an optimization job. + Model - Request body for creating an agent. - projectId + organizationId - integer + string
    @@ -88247,12 +95813,12 @@

    Request


    - example: 23456789 + example: 3b137fed57f2326cc68e746f4eb99639
    - Id of the project to optimize. + Identifier of the organization that will own the agent. @@ -88271,71 +95837,77 @@

    Request


    - example: Mia First Optimization Job + example: Creative Fluorescent Orange Parrot
    - Name of the optimization. + Name of the agent. - target + description - string Enum + string
    - Target statistic of the optimization to minimize or maximize. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] + Short description of the agent. - targetTo + profileImage - string Enum + string
    - Target extremum of the optimization. Options : ['min', 'max'] + Filename of the agent's profile image. - targetValue + systemPrompt - number -
    - - - example: 1 - - + string
    - Desired value for the optimization target statistic. + System prompt that defines the agent's behavior. - strategy + reasoningEffort string Enum
    - Optimization strategy. Options : ['QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy'] + Reasoning effort applied by the underlying model. Options : ['low', 'medium', 'high'] - compileId + maxTurns + + + + integer + +
    + Maximum number of agent turns allowed in a single deployment. + + + + + toolChoice @@ -88343,101 +95915,150 @@

    Request


    - example: 5d1f2cba3a0ec7407c566614300502b5-173e0419674daf4144ce7c9931155ca8 + example: auto
    - Optimization compile Id. + Tool selection policy for the agent. - parameters + tools - OptimizationParameter Array + string Array + +
    + Identifiers of the tools available to the agent. + + + + + public + + + + boolean + +
    + Whether the agent is shared publicly within the organization. + + + + + model + + + + string
    - required + example: claude-opus-4-7
    - Optimization parameters. + Model identifier the agent runs on. - constraints + chainedAgents - OptimizationConstraint Array + AgentChainedEntry Array
    - Optimization constraints. + Agents to chain after this one in a multi-agent pipeline. - Example + subAgents -
    - 
    -{
-  "projectId": 23456789,
-  "name": "Mia First Optimization Job",
-  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-  "targetTo": "min",
-  "targetValue": 1,
-  "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",
-  "compileId": "5d1f2cba3a0ec7407c566614300502b5-173e0419674daf4144ce7c9931155ca8",
-  "parameters": [
-    {
-      "name": "rsi_period",
-      "min": 10,
-      "max": 20,
-      "step": 1,
-      "minStep": 1
-    }
-  ],
-  "constraints": [
-    {
-      "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-      "operator": "LessOrEqual",
-      "targetValue": 1
-    }
-  ]
-}
    -
    + + AgentSubAgentEntry Array + +
    + Sub-agents the new agent can invoke. - - - - - - - + + + + + + + + + + + + + + + + + + + + @@ -88448,7 +96069,52 @@

    Request

     {
-  "OptimizationConstraint": "TotalPerformance.PortfolioStatistics.SharpeRatio"
+  "organizationId": "3b137fed57f2326cc68e746f4eb99639",
+  "name": "Creative Fluorescent Orange Parrot",
+  "description": "string",
+  "profileImage": "string",
+  "systemPrompt": "string",
+  "reasoningEffort": "low",
+  "maxTurns": 0,
+  "toolChoice": "auto",
+  "tools": [
+    "string"
+  ],
+  "public": true,
+  "model": "claude-opus-4-7",
+  "chainedAgents": [
+    {
+      "agentId": 105,
+      "step": 1,
+      "agent": 
+    }
+  ],
+  "subAgents": [
+    {
+      "agentId": 105,
+      "callableCount": 1
+    }
+  ],
+  "token": {
+    "llmApiTokenUser": "sk-ant-api03-u78VgglQR-EkS-ymkG0QPoSJMl7G0z6mwC5Z3LrpAZ88TCdGpAVa1TQ4SzKIhh3aPhmrmI_A-tznXzcKcRQTBTkXaiVv4AA",
+    "llmProviderApi": "anthropic"
+  },
+  "useQCC": true,
+  "pipeline": "string",
+  "author": {
+    "publicId": "alexandre_catarino",
+    "name": "Alexandre Catarino",
+    "image": "e76004d3a79bb421536616863.jpeg"
+  },
+  "organization": {
+    "id": "3b137fed57f2326cc68e746f4eb99639",
+    "name": "QuantConnect",
+    "profile": "167df5c7cd6b3171771270587.png"
+  },
+  "node": {
+    "id": 0,
+    "sku": "A-8"
+  }
 }
    @@ -88459,59 +96125,70 @@

    Request

    -
    - - OptimizationTargetStatistic - - Model -
    - OptimizationConstraint + token - string Enum + AgentToken object
    - /. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] + User-supplied LLM provider credentials attached to an agent. +
    + useQCC + + + boolean + +
    + Whether to run the agent on the QuantConnect Cloud LLM provider. When false, supply a token. +
    + pipeline + + + string + +
    + Pipeline to assign to the agent. +
    + author + + + AgentTaskOwner object + +
    + Owner of an agent task. +
    + organization + + + AgentOrganization object + +
    + Organization summary embedded on an agent. +
    + node + + + AgentNode object + +
    + Compute node bound to an agent.
    - OptimizationTargetTo + AgentChainedEntry - Model + Model - One entry in an agent's chain of follow-up agents.
    - OptimizationConstraint + agentId - string Enum + integer +
    + + + required + + +
    + + + example: 105 + +
    - /. Options : ['min', 'max'] + Identifier of the chained agent.
    - Example + step -
    - 
    -{
-  "OptimizationConstraint": "min"
-}
    -
    + + integer +
    + + + required + + +
    + + + example: 1 + + +     +
    + Ordinal position of the agent in the chain.
    - - - - - - @@ -88522,7 +96199,9 @@

    Request

     {
-  "OptimizationConstraint": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy"
+  "agentId": 105,
+  "step": 1,
+  "agent": 
 }
    @@ -88533,19 +96212,19 @@

    Request

    +
    - - OptimizationStrategy - - Model -
    - OptimizationConstraint + agent - string Enum + object
    - /. Options : ['QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy'] + Embedded agent definition, populated when the chain entry is expanded.
    - OptimizationParameter + AgentSubAgentEntry - Model + Model - One sub-agent reference attached to an agent.
    - name + agentId - string + integer
    @@ -88555,106 +96234,92 @@

    Request


    - example: rsi_period + example: 105
    - Name of optimization parameter. + Identifier of the sub-agent.
    - min + callableCount - number -
    - - - required - - + integer
    - example: 10 + example: 1
    - Minimum value of optimization parameter, applicable for boundary conditions. + Maximum number of times this sub-agent can be invoked per deployment.
    - max + Example - - number -
    - - - required - - -
    - - - example: 20 - - -     -
    - Maximum value of optimization parameter, applicable for boundary conditions. +
    + 
    +{
+  "agentId": 105,
+  "callableCount": 1
+}
    +
    + + + + + + @@ -88665,11 +96330,8 @@

    Request

     {
-  "name": "rsi_period",
-  "min": 10,
-  "max": 20,
-  "step": 1,
-  "minStep": 1
+  "llmApiTokenUser": "sk-ant-api03-u78VgglQR-EkS-ymkG0QPoSJMl7G0z6mwC5Z3LrpAZ88TCdGpAVa1TQ4SzKIhh3aPhmrmI_A-tznXzcKcRQTBTkXaiVv4AA",
+  "llmProviderApi": "anthropic"
 }
    @@ -88680,64 +96342,64 @@

    Request

    @@ -88748,76 +96410,77 @@

    Request

     {
-  "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-  "operator": "LessOrEqual",
-  "targetValue": 1
+  "publicId": "alexandre_catarino",
+  "name": "Alexandre Catarino",
+  "image": "e76004d3a79bb421536616863.jpeg"
 }
    + + AgentToken + + Model - User-supplied LLM provider credentials attached to an agent. +
    - step + llmApiTokenUser - number -
    - - - required - - + string
    - example: 1 + example: sk-ant-api03-u78VgglQR-EkS-ymkG0QPoSJMl7G0z6mwC5Z3LrpAZ88TCdGpAVa1TQ4SzKIhh3aPhmrmI_A-tznXzcKcRQTBTkXaiVv4AA
    - Movement, should be positive. + API token supplied by the user for the chosen LLM provider.
    - minStep + llmProviderApi - number + string
    - example: 1 + example: anthropic
    - Minimal possible movement for current parameter, should be positive. Used by - - Strategies.EulerSearchOptimizationStrategy - - to determine when this parameter can no longer be optimized. + Identifier of the LLM provider the token is for.
    - OptimizationConstraint + AgentTaskOwner - Model - Backtests in the optimization job that don't respect this constraint are excluded from the optimization result. + Model - Owner of an agent task.
    - target + publicId - string Enum + string +
    + + + example: alexandre_catarino + +
    - The target statistic to track. Options : ['TotalPerformance.PortfolioStatistics.SharpeRatio', 'TotalPerformance.PortfolioStatistics.CompoundingAnnualReturn', 'TotalPerformance.PortfolioStatistics.ProbabilisticSharpeRatio', 'TotalPerformance.PortfolioStatistics.Drawdown'] + Public profile slug of the user.
    - operator + name - string Enum + string
    - required + example: Alexandre Catarino
    - The target comparison operation. Options : ['LessOrEqual', 'Less', 'GreaterOrEqual', 'Greater', 'NotEqual', 'Equals'] + Display name of the user.
    - targetValue + image - number -
    - - - required - - + string
    - example: 1 + example: e76004d3a79bb421536616863.jpeg
    - The threshold value for the target constraint. + Filename of the user's profile image.
    - - - -

    Responses

    - - - -

    - The - - /optimizations/estimate - - API provides a response in the following format: -

    -

    - 200 Success -

    @@ -88828,15 +96491,9 @@ 

     {
-  "estimate": {
-    "estimateId": "string",
-    "time": 60,
-    "balance": 10
-  },
-  "success": true,
-  "errors": [
-    "string"
-  ]
+  "id": "3b137fed57f2326cc68e746f4eb99639",
+  "name": "QuantConnect",
+  "profile": "167df5c7cd6b3171771270587.png"
 }
    @@ -88847,27 +96504,15 @@

    - - - - @@ -88909,14 +96554,30 @@ 

     {
-  "estimateId": "string",
-  "time": 60,
-  "balance": 10
+  "id": 0,
+  "sku": "A-8"
 }

    - EstimateOptimizationResponse + AgentOrganization - Model - Response received when estimating the cost of an optimization. + Model - Organization summary embedded on an agent.
    - estimate + id - Estimate object + string +
    + + + example: 3b137fed57f2326cc68e746f4eb99639 + +
    - Response received when estimating the time to run an optimization job. + Identifier of the organization.
    - success + name - boolean + string +
    + + + example: QuantConnect + +
    - Indicate if the API request was successful. + Display name of the organization.
    - errors + profile - string Array + string +
    + + + example: 167df5c7cd6b3171771270587.png + +
    - List of errors with the API call. + Filename of the organization's profile image.
    - Estimate + AgentNode - Model - Response received when estimating the time to run an optimization job. + Model - Compute node bound to an agent.
    - estimateId - - - string - -
    - Estimate Id. -
    - time + id @@ -88875,30 +96520,30 @@


    - example: 60 + example: 0
    - Estimated time in seconds to run the optimization job. + Identifier of the compute node assigned to the agent, or 0 when not pinned.

    - balance + sku - integer + string
    - example: 10 + example: A-8
    - The organization's QCC balance. + SKU of the compute node assigned to the agent.
    + + + +

    Responses

    + + + +

    + The + + /agents/create + + API provides a response in the following format: +

    +

    + 200 Success +

    401 Authentication Error

    @@ -88951,7 +96612,7 @@

    Examples

    - The following example demonstates creating, reading, updating, deleting, aborting and listing backtests of a project through the cloud API. + The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API. 

    from base64 import b64encode
@@ -88990,161 +96651,254 @@ 
    Examples
    
 # --------------------
 
 
-# The project ID of the project to manage an optimization job
-project_id = 12345678
-
-### Estimate Optimization Cost
-# Send a POST request to the /optimizations/estimate endpoint to estimate cost
-response = post(f'{BASE_URL}/optimizations/estimate', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization (using compile ID prefix)
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target metric
-    "targetTo": "max",  # Direction to optimize (maximize)
-    "targetValue": None,  # Specific target value (None for max/min)
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Optimization strategy
-    "compileId": compile_id,  # Compilation ID for the optimization
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value for first parameter
-    "parameters[0][max]": 200,  # Maximum value for first parameter
-    "parameters[0][step]": 50,  # Step size for first parameter
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value for second parameter
-    "parameters[1][max]": 300,  # Maximum value for second parameter
-    "parameters[1][step]": 50,  # Step size for second parameter
-    "constraints": [{  # Constraints for the optimization
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }]
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
 })
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the estimated cost
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
 if result['success']:
-    print("Optimization Cost Estimated Successfully:")
+    print("List of Agents:")
     print(result)
 
-### Create Optimization
-# Send a POST request to the /optimizations/create endpoint to create an optimization
-response = post(f'{BASE_URL}/optimizations/create', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "name": f"Optimization_{compileId[:5]}",  # Name of the optimization
-    "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",  # Optimization target
-    "targetTo": "max",  # Direction to optimize
-    "targetValue": None,  # Specific target value
-    "strategy": "QuantConnect.Optimizer.Strategies.GridSearchOptimizationStrategy",  # Strategy
-    "compileId": compile_id,  # Compilation ID
-    "parameters[0][key]": "ema_fast",  # First parameter key
-    "parameters[0][min]": 100,  # Minimum value
-    "parameters[0][max]": 200,  # Maximum value
-    "parameters[0][step]": 50,  # Step size
-    "parameters[1][key]": "ema_slow",  # Second parameter key
-    "parameters[1][min]": 200,  # Minimum value
-    "parameters[1][max]": 300,  # Maximum value
-    "parameters[1][step]": 50,  # Step size
-    "constraints": [{  # Constraints
-        "target": "TotalPerformance.PortfolioStatistics.SharpeRatio",
-        "operator": "greater",
-        "target-value": 1
-    }],
-    "estimatedCost": 10,  # Estimated cost of optimization
-    "nodeType": "O2-8",  # Node type for optimization
-    "parallelNodes": 4  # Number of parallel nodes
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
 })
 # Parse the JSON response into python managable dict
 result = response.json()
-# Extract the optimization ID from the response
-optimization_id = result['optimizations'][0]['optimizationId']
-# Check if the request was successful and print the result
+# Check if the request was successful and print the agent's model
 if result['success']:
-    print("Optimization Created Successfully:")
+    print(f"Agent model: {result['agent']['model']}")
     print(result)
 
-### Update Optimization
-# Send a POST request to the /optimizations/update endpoint to update the optimization
-response = post(f'{BASE_URL}/optimizations/update', headers=get_headers(), json={
-    "optimizationId": optimization_id,  # ID of the optimization to update
-    "name": f"Optimization_{optimizationId[:5]}"  # New name for the optimization
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
 })
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Optimization Updated Successfully:")
+    print("Task Created Successfully:")
     print(result)
 
-### Read Optimization
-# Prepare data payload to read optimization details
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to read
-}
-# Send a POST request to the /optimizations/read endpoint to get details
-response = post(f'{BASE_URL}/optimizations/read', headers=get_headers(), json=payload)
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the task name from the response
+task_name = result['task']['name']
 # Check if the request was successful and print the details
 if result['success']:
-    print("Optimization Details:")
+    print("Task Details:")
     print(result)
 
-### Abort Optimization
-# Prepare data payload to abort the optimization
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to abort
-}
-# Send a POST request to the /optimizations/abort endpoint to abort
-response = post(f'{BASE_URL}/optimizations/abort', headers=get_headers(), json=payload)
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the result
 if result['success']:
-    print("Optimization Aborted Successfully:")
+    print("Task Updated Successfully:")
     print(result)
 
-### Delete Optimization
-# Prepare data payload to delete the optimization
-payload = {
-    "optimizationId": optimization_id  # ID of the optimization to delete
-}
-# Send a POST request to the /optimizations/delete endpoint to delete
-response = post(f'{BASE_URL}/optimizations/delete', headers=get_headers(), json=payload)
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Optimization Deleted Successfully:")
+    print("Deployment Created Successfully:")
     print(result)
 
-### List Optimizations
-# Prepare data payload to list optimizations
-payload = {
-    "projectId": project_id  # ID of the project
-}
-# Send a POST request to the /optimizations/list endpoint to list optimizations
-response = post(f'{BASE_URL}/optimizations/list', headers=get_headers(), json=payload)
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the list
 if result['success']:
-    print("List of Optimizations:")
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)

     

    -

    API Reference > Object Store Management

    -
    -
    -

    API Reference

    -

    Object Store Management

    -
    -
    -

    API Reference > Object Store Management > Upload Object Store Files

    +

    API Reference > Agent Management > Agents > Read Agent

    -
    -

    Object Store Management

    -

    Upload Object Store Files

    +
    +

    Agents

    +

    Read Agent

    Introduction

    @@ -89152,7 +96906,7 @@

    Introduction

    - Upload files to the Object Store. + Read an agent by Id.

    @@ -89162,162 +96916,39 @@

    Request

    - Upload files to the Object Store. The - - /object/set - - API accepts requests in the following format: -

    -

    - The - - /object/set - - API requires a file request in the following format: -

    - - - - - - - - - - - - - - - - - - - - - - -
    - - ObjectStoreBinaryFile - - Model - Represents a binary file we we'd like to upload the file to upload to the Object Store. -
    - organizationId - - - string -
    - - - required - - -
    - - - example: 5cad178b20a1d52567b534553413b691 - - -     -
    - Orgainization ID. -
    - key - - - string -
    - - - required - - -
    - - - example: key1 - - -     -
    - Unique key to access the object in Object Store. -
    - objectData - - - binary -
    - - - required - - -     -
    - Object data to be stored. -
    - Example - -
    - 
    -{
-  "organizationId": "5cad178b20a1d52567b534553413b691",
-  "key": "key1",
-  "objectData": b"Hello, world!"
-}
    -
    -
    - - - -

    Responses

    - - - -

    - The + Id of the agent to read. The - /object/set + /agents/read - API provides a response in the following format: + API accepts requests in the following format:

    -

    - 200 Success -

    - - - - @@ -89328,15 +96959,29 @@ 

     {
-  "success": true,
-  "errors": [
-    "string"
-  ]
+  "agentId": 0
 }

    - RestResponse + ReadAgentRequest - Model - Base API response class for the QuantConnect API. + Model - Request body for reading an agent.
    - success - - - boolean - -
    - Indicate if the API request was successful. -
    - errors + agentId - string Array + integer +
    + + + required + +
    - List of errors with the API call. + Identifier of the agent to read.
    + + + +

    Responses

    + + + +

    + The + + /agents/read + + API provides a response in the following format: +

    +

    + 200 Success +

    401 Authentication Error

    @@ -89371,7 +97016,7 @@

    Examples

    - The following example demonstates uploading, getting, deleting, and listing Object Store objects through the cloud API. + The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API. 

    from base64 import b64encode
@@ -89410,92 +97055,254 @@ 
    Examples
    
 # --------------------
 
 
-# The key of the object wishes to manipulate
-key = "..."
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Agents:")
+    print(result)
 
-### Upload Object Store File
-# Send a POST request to the /object/set endpoint to upload a file
-response = post(f'{BASE_URL}/object/set', headers=get_headers(), 
-                data={"organizationId": ORGANIZATION_ID, "key": key},  # Organization ID and key for the object
-                files={"objectData": b"Hello, world!"})  # File content as bytes
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Object Store File Uploaded Successfully:")
+    print("Task Created Successfully:")
     print(result)
 
-### Get Object Store Metadata
-# Prepare data payload to get object metadata
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "key": key  # Key of the object to get metadata for
-}
-# Send a POST request to the /object/properties endpoint to get metadata
-response = post(f'{BASE_URL}/object/properties', headers=get_headers(), json=payload)
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the metadata
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
 if result['success']:
-    print("Object Store Metadata:")
+    print("Task Details:")
     print(result)
 
-### Get Object Store File
-# Prepare data payload to retrieve the object
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "keys": [key]  # List of keys to retrieve (single key in this case)
-}
-# Send a POST request to the /object/get endpoint to get the object
-response = post(f'{BASE_URL}/object/get', headers=get_headers(), json=payload)
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the file content
+# Check if the request was successful and print the result
 if result['success']:
-    print("Object Store File Content:")
+    print("Task Updated Successfully:")
     print(result)
 
-### Delete Object Store File
-# Prepare data payload to delete the object
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "key": key  # Key of the object to delete
-}
-# Send a POST request to the /object/delete endpoint to delete the object
-response = post(f'{BASE_URL}/object/delete', headers=get_headers(), json=payload)
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Object Store File Deleted Successfully:")
+    print("Deployment Created Successfully:")
     print(result)
 
-### List Object Store Files
-# Define an empty path to list all objects (replace with specific path if needed)
-path = ""
-# Prepare data payload to list objects
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "path": path  # Path to list objects from
-}
-# Send a POST request to the /object/list endpoint to list objects
-response = post(f'{BASE_URL}/object/list', headers=get_headers(), json=payload)
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the list
 if result['success']:
-    print("List of Object Store Files:")
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)

     

    -

    API Reference > Object Store Management > Get Object Store Metadata

    +

    API Reference > Agent Management > Agents > Update Agent

    -
    -

    Object Store Management

    -

    Get Object Store Metadata

    +
    +

    Agents

    +

    Update Agent

    Introduction

    @@ -89503,7 +97310,7 @@

    Introduction

    - Get Object Store properties of a specific organization and key. It doesn't work if the key is a directory in the Object Store. + Update an existing agent.

    @@ -89513,9 +97320,9 @@

    Request

    - Get Object Store properties of a specific organization and key. The + Id of the agent to update and the fields to change. The - /object/properties + /agents/update API accepts requests in the following format:

    @@ -89524,43 +97331,329 @@

    Request

    - GetObjectStorePropertiesRequest + UpdateAgentRequest - Model - Request to get Object Store properties of a specific organization and key. + Model - Request body for updating an agent. - organizationId + agentId - string + integer
    required +     +
    + Identifier of the agent to update. + + + + + name + + + + string + +
    + New name for the agent. + + + + + description + + + + string + +
    + New description for the agent. + + + + + profileImage + + + + string + +
    + New profile image filename for the agent. + + + + + systemPrompt + + + + string + +
    + New system prompt for the agent. + + + + + reasoningEffort + + + + string Enum + +
    + New reasoning effort for the agent. Options : ['low', 'medium', 'high'] + + + + + maxTurns + + + + integer + +
    + New maximum number of agent turns per deployment. + + + + + toolChoice + + + + string + +
    + New tool selection policy for the agent. + + + + + tools + + + + string Array + +
    + New list of tools available to the agent. + + + + + public + + + + boolean + +
    + Whether the agent should be shared publicly within the organization. + + + + + model + + + + string
    - example: 5cad178b20a1d52567b534553413b691 + example: claude-opus-4-7
    - Id of the organization that owns the Object Store. + New model identifier for the agent. - key + chainedAgents + + + + AgentChainedEntry Array + +
    + New chain of agents to invoke after this one. + + + + + subAgents + + + + AgentSubAgentEntry Array + +
    + New list of sub-agents the agent can invoke. + + + + + token + + + + AgentToken object + +
    + User-supplied LLM provider credentials attached to an agent. + + + + + useQCC + + + + boolean + +
    + Whether to run the agent on the QuantConnect Cloud LLM provider. When false, supply a token. + + + + + pipeline string + +
    + New pipeline to assign to the agent. + + + + + author + + + + AgentTaskOwner object + +
    + Owner of an agent task. + + + + + organization + + + + AgentOrganization object + +
    + Organization summary embedded on an agent. + + + + + node + + + + AgentNode object + +
    + Compute node bound to an agent. + + + + + Example + + +
    + 
    +{
+  "agentId": 0,
+  "name": "string",
+  "description": "string",
+  "profileImage": "string",
+  "systemPrompt": "string",
+  "reasoningEffort": "low",
+  "maxTurns": 0,
+  "toolChoice": "string",
+  "tools": [
+    "string"
+  ],
+  "public": true,
+  "model": "claude-opus-4-7",
+  "chainedAgents": [
+    {
+      "agentId": 105,
+      "step": 1,
+      "agent": 
+    }
+  ],
+  "subAgents": [
+    {
+      "agentId": 105,
+      "callableCount": 1
+    }
+  ],
+  "token": {
+    "llmApiTokenUser": "sk-ant-api03-u78VgglQR-EkS-ymkG0QPoSJMl7G0z6mwC5Z3LrpAZ88TCdGpAVa1TQ4SzKIhh3aPhmrmI_A-tznXzcKcRQTBTkXaiVv4AA",
+    "llmProviderApi": "anthropic"
+  },
+  "useQCC": true,
+  "pipeline": "string",
+  "author": {
+    "publicId": "alexandre_catarino",
+    "name": "Alexandre Catarino",
+    "image": "e76004d3a79bb421536616863.jpeg"
+  },
+  "organization": {
+    "id": "3b137fed57f2326cc68e746f4eb99639",
+    "name": "QuantConnect",
+    "profile": "167df5c7cd6b3171771270587.png"
+  },
+  "node": {
+    "id": 0,
+    "sku": "A-8"
+  }
+}
    +
    + + + + + + + + + + + + + + + + + + + + @@ -89586,75 +97715,127 @@

    Request

     {
-  "organizationId": "5cad178b20a1d52567b534553413b691",
-  "key": "key1"
+  "agentId": 105,
+  "step": 1,
+  "agent": 
 }
    + + AgentChainedEntry + + Model - One entry in an agent's chain of follow-up agents. +
    + agentId + + + integer
    @@ -89570,12 +97663,48 @@

    Request


    - example: key1 + example: 105
    - Key in the Object Store. + Identifier of the chained agent. +
    + step + + + integer +
    + + + required + + +
    + + + example: 1 + + +     +
    + Ordinal position of the agent in the chain. +
    + agent + + + object + +
    + Embedded agent definition, populated when the chain entry is expanded.
    - - - -

    Responses

    - - - -

    - The - - /object/properties - - API provides a response in the following format: -

    -

    - 200 Success -

    + + + + + +
    - GetObjectStorePropertiesResponse + AgentSubAgentEntry - Model - Response received when fetching Object Store file properties. + Model - One sub-agent reference attached to an agent.
    - metadata + agentId - ObjectStoreProperties object + integer +
    + + + required + + +
    + + + example: 105 + +
    - Object Store file properties. + Identifier of the sub-agent.
    - success + callableCount - boolean + integer +
    + + + example: 1 + + +     +
    + Maximum number of times this sub-agent can be invoked per deployment. +
    + Example + +
    + 
    +{
+  "agentId": 105,
+  "callableCount": 1
+}
    +
    +
    + + + + + + + + + @@ -89665,19 +97846,8 @@ 

     {
-  "metadata": {
-    "key": "string",
-    "modified": "2021-11-26T15:18:27.693Z",
-    "created": "2021-11-26T15:18:27.693Z",
-    "size": 24,
-    "md5": "string",
-    "mime": "string",
-    "preview": "string"
-  },
-  "success": true,
-  "errors": [
-    "string"
-  ]
+  "llmApiTokenUser": "sk-ant-api03-u78VgglQR-EkS-ymkG0QPoSJMl7G0z6mwC5Z3LrpAZ88TCdGpAVa1TQ4SzKIhh3aPhmrmI_A-tznXzcKcRQTBTkXaiVv4AA",
+  "llmProviderApi": "anthropic"
 }
    @@ -89688,100 +97858,145 @@

    +
    + + AgentToken + + Model - User-supplied LLM provider credentials attached to an agent. +
    + llmApiTokenUser + + + string +
    + + + example: sk-ant-api03-u78VgglQR-EkS-ymkG0QPoSJMl7G0z6mwC5Z3LrpAZ88TCdGpAVa1TQ4SzKIhh3aPhmrmI_A-tznXzcKcRQTBTkXaiVv4AA + +
    - Indicate if the API request was successful. + API token supplied by the user for the chosen LLM provider.
    - errors + llmProviderApi - string Array + string +
    + + + example: anthropic + +
    - List of errors with the API call. + Identifier of the LLM provider the token is for.
    - ObjectStoreProperties + AgentTaskOwner - Model - Object Store file properties. + Model - Owner of an agent task.
    - key + publicId string +
    + + + example: alexandre_catarino + +
    - Object Store key. + Public profile slug of the user.
    - modified + name - string($date-time) + string +
    + + + example: Alexandre Catarino + +
    - Last time it was modified. + Display name of the user.
    - created + image - string($date-time) + string +
    + + + example: e76004d3a79bb421536616863.jpeg + +
    - Date this project was created. + Filename of the user's profile image.
    - size + Example - - number -
    - - - example: 24 - - -     -
    - Object Store file size. +
    + 
    +{
+  "publicId": "alexandre_catarino",
+  "name": "Alexandre Catarino",
+  "image": "e76004d3a79bb421536616863.jpeg"
+}
    +
    + + + + + + @@ -89792,208 +98007,75 @@ 

     {
-  "key": "string",
-  "modified": "2021-11-26T15:18:27.693Z",
-  "created": "2021-11-26T15:18:27.693Z",
-  "size": 24,
-  "md5": "string",
-  "mime": "string",
-  "preview": "string"
+  "id": "3b137fed57f2326cc68e746f4eb99639",
+  "name": "QuantConnect",
+  "profile": "167df5c7cd6b3171771270587.png"
 }

    + + AgentOrganization + + Model - Organization summary embedded on an agent. +
    - md5 + id string +
    + + + example: 3b137fed57f2326cc68e746f4eb99639 + +
    - MD5 (hashing algorithm) hash authentication code. + Identifier of the organization.
    - mime + name string +
    + + + example: QuantConnect + +
    - MIME type. + Display name of the organization.
    - preview + profile string +
    + + + example: 167df5c7cd6b3171771270587.png + +
    - Preview of the Object Store file content. + Filename of the organization's profile image.
    -

    - 401 Authentication Error -

    + + + + + + + + -
    - UnauthorizedError + AgentNode - Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash. + Model - Compute node bound to an agent.
    - www_authenticate + id + + + integer +
    + + + example: 0 + + +     +
    + Identifier of the compute node assigned to the agent, or 0 when not pinned. +
    + sku string +
    + + + example: A-8 + +
    - Header + SKU of the compute node assigned to the agent. +
    + Example + +
    + 
    +{
+  "id": 0,
+  "sku": "A-8"
+}
    +
    - - - -

    Examples

    - - -

    - The following example demonstates uploading, getting, deleting, and listing Object Store objects through the cloud API. -

    -
    - 
    from base64 import b64encode
-from hashlib import sha256
-from time import time
-from requests import get, post
-BASE_URL = 'https://www.quantconnect.com/api/v2/'
-
-# You need to replace these with your actual credentials.
-# You can request your credentials at https://www.quantconnect.com/settings/
-# You can find our organization ID at https://www.quantconnect.com/organization/ 
-USER_ID = 0
-API_TOKEN = '____'
-ORGANIZATION_ID = '____'
-
-def get_headers():
-    # Get timestamp
-    timestamp = f'{int(time())}'
-    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
-
-    # Get hased API token
-    hashed_token = sha256(time_stamped_token).hexdigest()
-    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
-    authentication = b64encode(authentication).decode('ascii')
-
-    # Create headers dictionary.
-    return {
-        'Authorization': f'Basic {authentication}',
-        'Timestamp': timestamp
-    }
-
-# Authenticate to verify credentials
-response = post(f'{BASE_URL}/authenticate', headers = get_headers())
-print(response.json())
-
-# --------------------
-
-
-# The key of the object wishes to manipulate
-key = "..."
-
-### Upload Object Store File
-# Send a POST request to the /object/set endpoint to upload a file
-response = post(f'{BASE_URL}/object/set', headers=get_headers(), 
-                data={"organizationId": ORGANIZATION_ID, "key": key},  # Organization ID and key for the object
-                files={"objectData": b"Hello, world!"})  # File content as bytes
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Object Store File Uploaded Successfully:")
-    print(result)
-
-### Get Object Store Metadata
-# Prepare data payload to get object metadata
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "key": key  # Key of the object to get metadata for
-}
-# Send a POST request to the /object/properties endpoint to get metadata
-response = post(f'{BASE_URL}/object/properties', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the metadata
-if result['success']:
-    print("Object Store Metadata:")
-    print(result)
-
-### Get Object Store File
-# Prepare data payload to retrieve the object
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "keys": [key]  # List of keys to retrieve (single key in this case)
-}
-# Send a POST request to the /object/get endpoint to get the object
-response = post(f'{BASE_URL}/object/get', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the file content
-if result['success']:
-    print("Object Store File Content:")
-    print(result)
-
-### Delete Object Store File
-# Prepare data payload to delete the object
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "key": key  # Key of the object to delete
-}
-# Send a POST request to the /object/delete endpoint to delete the object
-response = post(f'{BASE_URL}/object/delete', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the result
-if result['success']:
-    print("Object Store File Deleted Successfully:")
-    print(result)
-
-### List Object Store Files
-# Define an empty path to list all objects (replace with specific path if needed)
-path = ""
-# Prepare data payload to list objects
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "path": path  # Path to list objects from
-}
-# Send a POST request to the /object/list endpoint to list objects
-response = post(f'{BASE_URL}/object/list', headers=get_headers(), json=payload)
-# Parse the JSON response into python managable dict
-result = response.json()
-# Check if the request was successful and print the list
-if result['success']:
-    print("List of Object Store Files:")
-    print(result)
    -
    - - - -

     

    -

    API Reference > Object Store Management > Get Object Store File

    -
    -
    -

    Object Store Management

    -

    Get Object Store File

    -
    -
    -

    Introduction

    - - - -

    - Get Object Store file of a specific organization and key. -

    - - - -

    Request

    - - - -

    - Get Object Store files of a specific organization and key. The - - /object/get - - API accepts requests in the following format: -

    - - - - - -
    - Get Object Store files of a specific organization and key. -
    @@ -90005,7 +98087,7 @@

    Responses

    The - /object/get + /agents/update API provides a response in the following format:

    @@ -90017,36 +98099,12 @@

    - GetObjectStoreResponse + RestResponse - Model - Response received when fetching an Object Store file. + Model - Base API response class for the QuantConnect API. - - - jobId - - - - string - -
    - Id of the job, which you can use to request a download URL. - - - - - url - - - - string - -
    - The URL to download the object. This can also be null. To download the object, paste the full URL (including the URL parameters) into a browser. - - success @@ -90079,8 +98137,6 @@ 

     {
-  "jobId": "string",
-  "url": "string",
   "success": true,
   "errors": [
     "string"
@@ -90124,7 +98180,7 @@ 
    Examples
    
 
  
   
    
-   The following example demonstates uploading, getting, deleting, and listing Object Store objects through the cloud API.
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
   
    
   
    
    
    from base64 import b64encode
@@ -90163,92 +98219,254 @@ 
    Examples
    
 # --------------------
 
 
-# The key of the object wishes to manipulate
-key = "..."
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Agents:")
+    print(result)
 
-### Upload Object Store File
-# Send a POST request to the /object/set endpoint to upload a file
-response = post(f'{BASE_URL}/object/set', headers=get_headers(), 
-                data={"organizationId": ORGANIZATION_ID, "key": key},  # Organization ID and key for the object
-                files={"objectData": b"Hello, world!"})  # File content as bytes
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Object Store File Uploaded Successfully:")
+    print("Task Created Successfully:")
     print(result)
 
-### Get Object Store Metadata
-# Prepare data payload to get object metadata
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "key": key  # Key of the object to get metadata for
-}
-# Send a POST request to the /object/properties endpoint to get metadata
-response = post(f'{BASE_URL}/object/properties', headers=get_headers(), json=payload)
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the metadata
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
 if result['success']:
-    print("Object Store Metadata:")
+    print("Task Details:")
     print(result)
 
-### Get Object Store File
-# Prepare data payload to retrieve the object
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "keys": [key]  # List of keys to retrieve (single key in this case)
-}
-# Send a POST request to the /object/get endpoint to get the object
-response = post(f'{BASE_URL}/object/get', headers=get_headers(), json=payload)
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the file content
+# Check if the request was successful and print the result
 if result['success']:
-    print("Object Store File Content:")
+    print("Task Updated Successfully:")
     print(result)
 
-### Delete Object Store File
-# Prepare data payload to delete the object
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "key": key  # Key of the object to delete
-}
-# Send a POST request to the /object/delete endpoint to delete the object
-response = post(f'{BASE_URL}/object/delete', headers=get_headers(), json=payload)
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Object Store File Deleted Successfully:")
+    print("Deployment Created Successfully:")
     print(result)
 
-### List Object Store Files
-# Define an empty path to list all objects (replace with specific path if needed)
-path = ""
-# Prepare data payload to list objects
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "path": path  # Path to list objects from
-}
-# Send a POST request to the /object/list endpoint to list objects
-response = post(f'{BASE_URL}/object/list', headers=get_headers(), json=payload)
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the list
 if result['success']:
-    print("List of Object Store Files:")
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)
    
   
    
  
 
 
 
     
    
-
    API Reference > Object Store Management > Delete Object Store File
    
+
    API Reference > Agent Management > Agents > Delete Agent
    
 
    
-    
    
-        
    Object Store Management
    
-        
    Delete Object Store File
    
+    
    
+        
    Agents
    
+        
    Delete Agent
    
     
    
 
    
 
    Introduction
    
@@ -90256,79 +98474,49 @@ 
    Introduction
    
 
  
   
    
-   Delete the Object Store file of a specific organization and key.
+   Delete an agent.
   
    
  
-
-
-
    Request
    
-
-
- 
-  
    
-   Delete the Object Store file of a specific organization and key. The
-   
-    /object/delete
-   
-   API accepts requests in the following format:
-  
    
-  
-   
-    
-     
-    
-   
-   
-    
-    
-   
+
+
+
    Request
    
+
+
+ 
+  
    
+   Id of the agent to delete. The
+   
+    /agents/delete
+   
+   API accepts requests in the following format:
+  
    
+  
    
-      
-       DeleteObjectStoreRequest
-      
-      Model - Request to delete an object in the Object Store for a specific organization and key.
-     
    
-     organizationId
-    
-     
-      string
-      
    
-      
-       
-        required
-       
-      
-      
    
-      
-       
-        example: 5cad178b20a1d52567b534553413b691
-       
-      
-         
-     
    
-     Id of the organization that owns the Object Store.
-    
    
+   
+    
+     
+    
+   
@@ -90339,8 +98527,7 @@ 
    Request
    
       
     {
-  "organizationId": "5cad178b20a1d52567b534553413b691",
-  "key": "key1"
+  "agentId": 0
 }
    
      
    
@@ -90356,7 +98543,7 @@ 
    Responses
    
    The
    
-    /object/delete
+    /agents/delete
    
    API provides a response in the following format:
   
    
@@ -90449,7 +98636,7 @@ 
    Examples
    
-   The following example demonstates uploading, getting, deleting, and listing Object Store objects through the cloud API.
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
   
    
    
    from base64 import b64encode
@@ -90488,92 +98675,254 @@ 
    Examples
    
 # --------------------
 
 
-# The key of the object wishes to manipulate
-key = "..."
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Agents:")
+    print(result)
 
-### Upload Object Store File
-# Send a POST request to the /object/set endpoint to upload a file
-response = post(f'{BASE_URL}/object/set', headers=get_headers(), 
-                data={"organizationId": ORGANIZATION_ID, "key": key},  # Organization ID and key for the object
-                files={"objectData": b"Hello, world!"})  # File content as bytes
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Object Store File Uploaded Successfully:")
+    print("Task Created Successfully:")
     print(result)
 
-### Get Object Store Metadata
-# Prepare data payload to get object metadata
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "key": key  # Key of the object to get metadata for
-}
-# Send a POST request to the /object/properties endpoint to get metadata
-response = post(f'{BASE_URL}/object/properties', headers=get_headers(), json=payload)
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the metadata
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
 if result['success']:
-    print("Object Store Metadata:")
+    print("Task Details:")
     print(result)
 
-### Get Object Store File
-# Prepare data payload to retrieve the object
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "keys": [key]  # List of keys to retrieve (single key in this case)
-}
-# Send a POST request to the /object/get endpoint to get the object
-response = post(f'{BASE_URL}/object/get', headers=get_headers(), json=payload)
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the file content
+# Check if the request was successful and print the result
 if result['success']:
-    print("Object Store File Content:")
+    print("Task Updated Successfully:")
     print(result)
 
-### Delete Object Store File
-# Prepare data payload to delete the object
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "key": key  # Key of the object to delete
-}
-# Send a POST request to the /object/delete endpoint to delete the object
-response = post(f'{BASE_URL}/object/delete', headers=get_headers(), json=payload)
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Object Store File Deleted Successfully:")
+    print("Deployment Created Successfully:")
     print(result)
 
-### List Object Store Files
-# Define an empty path to list all objects (replace with specific path if needed)
-path = ""
-# Prepare data payload to list objects
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "path": path  # Path to list objects from
-}
-# Send a POST request to the /object/list endpoint to list objects
-response = post(f'{BASE_URL}/object/list', headers=get_headers(), json=payload)
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the list
 if result['success']:
-    print("List of Object Store Files:")
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)
    
   
     
    
-
    API Reference > Object Store Management > List Object Store Files
    
+
    API Reference > Agent Management > Agents > List Agents
    
-    
    
-        
    Object Store Management
    
-        
    List Object Store Files
    
+    
    
+        
    Agents
    
+        
    List Agents
    
     
    
 
    Introduction
    
@@ -90581,7 +98930,7 @@ 
    Introduction
    
-   List the Object Store files under a specific directory in an organization.
+   List the agents of an organization.
   
    
@@ -90591,9 +98940,9 @@ 
    Request
    
-   List the Object Store files under a specific directory in an organization. The
+   Id of the organization whose agents to list. The
    
-    /object/list
+    /agents/list
    
    API accepts requests in the following format:
   
    
@@ -90602,9 +98951,9 @@ 
    Request
    
@@ -90621,33 +98970,9 @@ 
    Request
    
         required
        
-      
    
-      
-       
-        example: 5cad178b20a1d52567b534553413b691
-       
-      
    
-     Id of the organization to list the Object Store files from.
-    
-   
-   
-    
-    
@@ -90658,8 +98983,7 @@ 
    Request
    
       
     {
-  "organizationId": "5cad178b20a1d52567b534553413b691",
-  "path": "/"
+  "organizationId": "string"
 }
    
      
    
@@ -90675,178 +98999,378 @@ 
    Responses
    
    The
    
-    /object/list
+    /agents/list
    
    API provides a response in the following format:
   
    
    200 Success
   
    
+  
    
+   401 Authentication Error
+  
    
+      
+       DeleteAgentRequest
+      
+      Model - Request body for deleting an agent.
+     
    
    
    
     
-     key
+     agentId
     
     
      
-      string
+      integer
       
    
       
        
         required
        
       
-      
    
-      
-       
-        example: key1
-       
-      
          
      
    
-     Key of the Object Store file to delete.
+     Identifier of the agent to delete.
         		
    
    
    
    
      
     
   
 
  
   
   
  
 
 
 
 
 
 
  
   
  
 
 
  
   
     
    
      
       
-       ListObjectStoreRequest
+       ListAgentsRequest
       
-      Model - Request to list the Object Store files under a specific directory in an organization.
+      Model - Request body for listing the agents of an organization.
      
     
    
    
       
      
      
    
-     path
-    
-     
-      string
-      
    
-      
-       
-        example: /
-       
-      
-         
-     
    
-     Path to a directory in the Object Store.
+     Identifier of the organization whose agents to list.
         		
    
    
    
    
      
     
   
   
   
    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-    
    
    
     
    
      
       
-       ListObjectStoreResponse
+       UnauthorizedError
       
-      Model - Response received containing a list of stored objects metadata, as well as the total size of all of them.
+      Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash.
      
     
    
        
    
    
     
-     path
-    
-     
-      string
-      
    
-      
-       
-        example: Mia
-       
-      
-         
-     
    
-     Path to the directory in the Object Store.
-    
    
-     objects
-    
-     
-      ObjectStoreSummary Array
-     
-     
    
-     List of directories and files stored in the directory at the given path. If the path contains directories, this list of objects doesn't contain the children of those directories.
-    
    
-     page
-    
-     
-      integer
-     
-     
    
-     The current page number in the paginated response.
-    
    
-     totalPages
-    
-     
-      integer
-     
-     
    
-     The total number of pages in the paginated response.
-    
    
-     objectStorageUsed
-    
-     
-      integer
-     
-     
    
-     Size of all objects stored in bytes.
-    
    
-     objectStorageUsedHuman
+     www_authenticate
     
     
      
       string
-      
    
-      
-       
-        example: 2.27 GB
-       
-      
-         
-     
    
-     Size of all the objects stored in human-readable format.
-    
    
-     success
-    
-     
-      boolean
-     
-     
    
-     Indicate if the API request was successful.
-    
    
-     errors
-    
-     
-      string Array
      
      
    
-     List of errors with the API call.
-    
    
-     Example
-    
-     
    
-      
    -{
-  "path": "Mia",
-  "objects": [
-    {
-      "key": "Mia/Test",
-      "name": "string",
-      "modified": "2021-11-26T15:18:27.693Z",
-      "mime": "application/json",
-      "folder": true,
-      "size": 13
-    }
-  ],
-  "page": 0,
-  "totalPages": 0,
-  "objectStorageUsed": 0,
-  "objectStorageUsedHuman": "2.27 GB",
-  "success": true,
-  "errors": [
-    "string"
-  ]
-}
    
-     
    
+     Header
         		
    
    
   
    
+ 
+
+
+
    Examples
    
+
+ 
+  
    
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
+  
    
+  
    
+   
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Agents:")
+    print(result)
+
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Created Successfully:")
+    print(result)
+
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
+if result['success']:
+    print("Task Details:")
+    print(result)
+
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Updated Successfully:")
+    print(result)
+
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Created Successfully:")
+    print(result)
+
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
+    print(result)
    
+  
    
+ 
+
+
+
     
    
+
    API Reference > Agent Management > Tasks
    
+
    
+    
    
+        
    Agent Management
    
+        
    Tasks
    
+    
    
+
    
+
    API Reference > Agent Management > Tasks > Create Task
    
+
    
+    
    
+        
    Tasks
    
+        
    Create Task
    
+    
    
+
    
+
    Introduction
    
+
+
+ 
+  
    
+   Create a new agent task.
+  
    
+ 
+
+
+
    Request
    
+
+
+ 
+  
    
+   Organization, agent, name, prompt, and project preference for the new task. The
+   
+    /agents/tasks/create
+   
+   API accepts requests in the following format:
+  
    
   
-   
-   
-    
-    
@@ -90942,17 +99472,33 @@ 
    
      
    
       
     {
-  "key": "Mia/Test",
+  "organizationId": "string",
+  "agentId": 47,
   "name": "string",
-  "modified": "2021-11-26T15:18:27.693Z",
-  "mime": "application/json",
-  "folder": true,
-  "size": 13
+  "prompt": "string",
+  "projectPreference": "project-each-task-deployment"
 }
    
      
    
     
    
    
    
     
    
      
       
-       ObjectStoreSummary
+       CreateAgentTaskRequest
       
-      Model - Summary information of the Object Store.
+      Model - Request body for creating an agent task.
      
     
    
        
    
    
     
-     key
+     organizationId
     
     
      
@@ -90854,41 +99378,41 @@ 
    
       
    
       
        
-        example: Mia/Test
+        required
        
       
          
      
    
-     Object Store key.
-    
    
-     name
-    
-     
-      string
-     
-     
    
-     File or folder name.
+     Identifier of the organization that will own the task.
         		
    
    
    
    
     
-     modified
+     agentId
     
     
      
-      string($date-time)
+      integer
+      
    
+      
+       
+        required
+       
+      
+      
    
+      
+       
+        example: 47
+       
+      
          
      
    
-     Last time it was modified.
+     Identifier of the agent the task should use.
         		
    
    
    
    
     
-     mime
+     name
     
     
      
@@ -90896,42 +99420,48 @@ 
    
       
    
       
        
-        example: application/json
+        required
        
       
          
      
    
-     MIME type.
+     Name of the task. The platform may rewrite this to a normalized form on create.
     
    		
    
    
    
    
     
-     folder
+     prompt
     
     
      
-      boolean
+      string
+      
    
+      
+       
+        required
+       
+      
          
      
    
-     True if it is a folder, false otherwise.
+     Prompt the agent will execute when the task is deployed.
         		
    
    
    
    
     
-     size
+     projectPreference
     
     
      
-      number
+      string Enum
       
    
       
        
-        example: 13
+        required
        
       
          
      
    
-     Object Store file size.
+     How projects should be mapped to deployments of this task. Options : ['project-each-task-deployment', 'project-for-task']
         		
    
    
    
    
   
    
+ 
+
+
+
    Responses
    
+
+
+ 
+  
    
+   The
+   
+    /agents/tasks/create
+   
+   API provides a response in the following format:
+  
    
+  
    
+   200 Success
+  
    
   
    
    401 Authentication Error
   
    
@@ -90987,7 +99533,7 @@ 
    Examples
    
 
  
   
    
-   The following example demonstates uploading, getting, deleting, and listing Object Store objects through the cloud API.
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
   
    
   
    
    
    from base64 import b64encode
@@ -91026,106 +99572,254 @@ 
    Examples
    
 # --------------------
 
 
-# The key of the object wishes to manipulate
-key = "..."
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Agents:")
+    print(result)
 
-### Upload Object Store File
-# Send a POST request to the /object/set endpoint to upload a file
-response = post(f'{BASE_URL}/object/set', headers=get_headers(), 
-                data={"organizationId": ORGANIZATION_ID, "key": key},  # Organization ID and key for the object
-                files={"objectData": b"Hello, world!"})  # File content as bytes
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Object Store File Uploaded Successfully:")
+    print("Task Created Successfully:")
     print(result)
 
-### Get Object Store Metadata
-# Prepare data payload to get object metadata
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "key": key  # Key of the object to get metadata for
-}
-# Send a POST request to the /object/properties endpoint to get metadata
-response = post(f'{BASE_URL}/object/properties', headers=get_headers(), json=payload)
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the metadata
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
 if result['success']:
-    print("Object Store Metadata:")
+    print("Task Details:")
     print(result)
 
-### Get Object Store File
-# Prepare data payload to retrieve the object
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "keys": [key]  # List of keys to retrieve (single key in this case)
-}
-# Send a POST request to the /object/get endpoint to get the object
-response = post(f'{BASE_URL}/object/get', headers=get_headers(), json=payload)
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the file content
+# Check if the request was successful and print the result
 if result['success']:
-    print("Object Store File Content:")
+    print("Task Updated Successfully:")
     print(result)
 
-### Delete Object Store File
-# Prepare data payload to delete the object
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "key": key  # Key of the object to delete
-}
-# Send a POST request to the /object/delete endpoint to delete the object
-response = post(f'{BASE_URL}/object/delete', headers=get_headers(), json=payload)
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Object Store File Deleted Successfully:")
+    print("Deployment Created Successfully:")
     print(result)
 
-### List Object Store Files
-# Define an empty path to list all objects (replace with specific path if needed)
-path = ""
-# Prepare data payload to list objects
-payload = {
-    "organizationId": ORGANIZATION_ID,  # ID of the organization
-    "path": path  # Path to list objects from
-}
-# Send a POST request to the /object/list endpoint to list objects
-response = post(f'{BASE_URL}/object/list', headers=get_headers(), json=payload)
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the list
 if result['success']:
-    print("List of Object Store Files:")
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)
    
   
    
  
 
 
 
     
    
-
    API Reference > AI Assistance
    
-
    
-    
    
-        
    API Reference
    
-        
    AI Assistance
    
-    
    
-
    
-
    API Reference > AI Assistance > Tools
    
-
    
-    
    
-        
    AI Assistance
    
-        
    Tools
    
-    
    
-
    
-
    API Reference > AI Assistance > Tools > Backtest Initialization
    
+
    API Reference > Agent Management > Tasks > Read Task
    
 
    
-    
    
-        
    Tools
    
-        
    Backtest Initialization
    
+    
    
+        
    Tasks
    
+        
    Read Task
    
     
    
 
    
 
    Introduction
    
@@ -91133,7 +99827,7 @@ 
    Introduction
    
 
  
   
    
-   Run a backtest for a few seconds to initialize the algorithm and get initialization errors if any.
+   Read an agent task by Id.
   
    
  
 
@@ -91143,9 +99837,9 @@ 
    Request
    
 
  
   
    
-   Run a backtest for a few seconds to initialize the algorithm and get initialization errors if any. The
+   Id of the task to read. The
    
-    /ai/tools/backtest-init
+    /agents/tasks/read
    
    API accepts requests in the following format:
   
    
@@ -91154,115 +99848,28 @@ 
    Request
    
     
      
       
-       BasicFilesRequest
+       ReadAgentTaskRequest
       
-      Model - Request to process files.
+      Model - Request body for reading an agent task.
      
     
    
    
     
-     language
-    
-    
-     
-      string Enum
-      
    
-      
-       
-        required
-       
-      
-      
    
-      
-       
-        example: Py
-       
-      
-         
-     
    
-     Programming language. Options : ['C#', 'Py']
-    
-   
-   
-    
-     files
+     taskId
     
     
      
-      File Array
+      integer
       
    
       
        
         required
        
       
-      
    
-      
-       
-        example: [{"name": "file.py", "content": "fileContent"}]
-       
-      
-         
-     
    
-     Files to process.
-    
-   
-   
-    
-     Example
-    
-    
-     
    
-      
    -{
-  "language": "Py",
-  "files": "[{"name": "file.py", "content": "fileContent"}]"
-  ]
-}
    
-     
    
-    
-   
-  
-  
-   
-    
-     
-    
-   
-   
-    
-    
-   
-   
-    
-    
@@ -91273,8 +99880,7 @@ 
    Request
    
       
     {
-  "name": "main.py",
-  "content": "string"
+  "taskId": 0
 }
    
      
    
@@ -91290,101 +99896,13 @@ 
    Responses
    
    The
    
-    /ai/tools/backtest-init
+    /agents/tasks/read
    
    API provides a response in the following format:
   
    
    200 Success
   
    
-  
    
-      
-       File
-      
-      Model - File for a AI.
-     
    
-     name
-    
-     
-      string
-      
    
-      
-       
-        example: main.py
-       
-      
-         
-     
    
-     Name of a file.
-    
    
-     content
-    
-     
-      string
      
      
    
-     Contents of the file.
+     Identifier of the task to read.
         		
    
    
    
    
      
     
   
   
    
-   
-    
-     
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-  
    
-      
-       BacktestInitResponse
-      
-      Model - Response to a backtest initialization request.
-     
    
-     state
-    
-     
-      string Enum
-     
-     
    
-     State of the backtest. Options : ['End', 'Error']
-    
    
-     version
-    
-     
-      number
-      
    
-      
-       
-        example: 2.0
-       
-      
-         
-     
    
-     Version of the response.
-    
    
-     payload
-    
-     
-      string
-     
-     
    
-     Information about the backtest initialization.
-    
    
-     payloadType
-    
-     
-      string
-      
    
-      
-       
-        example: String
-       
-      
-         
-     
    
-     Type of the payload.
-    
    
-     Example
-    
-     
    
-      
    -{
-  "state": "End",
-  "version": 2.0,
-  "payload": "string",
-  "payloadType": "String"
-}
    
-     
    
-    
    
   
    
    401 Authentication Error
   
    
@@ -91419,7 +99937,7 @@ 
    Examples
    
 
  
   
    
-   The following example demonstates initializing a backtest for a specific algorithm through the cloud API.
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
   
    
   
    
    
    from base64 import b64encode
@@ -91458,42 +99976,254 @@ 
    Examples
    
 # --------------------
 
 
-### Initialize Backtest
-# Send a POST request to the /ai/tools/backtest-init endpoint to initialize a backtest
-response = post(f'{BASE_URL}/ai/tools/backtest-init', headers=get_headers(), json={
-    "language": "Python",  # Programming language of the algorithm
-    "files": [  # List of files for the backtest
-        {
-            "name": "utils.py",  # Name of the file
-            "content": '''
-# region imports
-from AlgorithmImports import *
-# endregion
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Agents:")
+    print(result)
 
-class Project(QCAlgorithm):
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
 
-    def Initialize(self):
-        self.AddEquity("SPY", Resolution.Minute)
-'''  # Content of the file (Python code)
-        }
-    ]
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
 })
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the backtest initialization result
+# Extract the task ID from the response
+task_id = result['task']['id']
+# Check if the request was successful and print the result
 if result['success']:
-    print("Backtest Initialized Successfully:")
+    print("Task Created Successfully:")
+    print(result)
+
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
+if result['success']:
+    print("Task Details:")
+    print(result)
+
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Updated Successfully:")
+    print(result)
+
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Created Successfully:")
+    print(result)
+
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)
    
   
    
  
 
 
 
     
    
-
    API Reference > AI Assistance > Tools > Code Completion
    
+
    API Reference > Agent Management > Tasks > Update Task
    
 
    
-    
    
-        
    Tools
    
-        
    Code Completion
    
+    
    
+        
    Tasks
    
+        
    Update Task
    
     
    
 
    
 
    Introduction
    
@@ -91501,7 +100231,7 @@ 
    Introduction
    
 
  
   
    
-   Show the code completion for a specific text input.
+   Update an agent task.
   
    
  
 
@@ -91511,9 +100241,9 @@ 
    Request
    
 
  
   
    
-   Show the code completion for a specific text input. The
+   Id and new name of the task to update. The
    
-    /ai/tools/complete
+    /agents/tasks/update
    
    API accepts requests in the following format:
   
    
@@ -91522,76 +100252,40 @@ 
    Request
    
     
      
       
-       CodeCompletionRequest
+       UpdateAgentTaskRequest
       
-      Model - Request to show code completion for a specific text input.
+      Model - Request body for updating an agent task.
      
     
    
    
     
-     language
-    
-    
-     
-      string Enum
-      
    
-      
-       
-        required
-       
-      
-      
    
-      
-       
-        example: Py
-       
-      
-         
-     
    
-     Programming language for the code completion. Options : ['C#', 'Py']
-    
-   
-   
-    
-     sentence
+     taskId
     
     
      
-      string
+      integer
       
    
       
        
         required
        
       
-      
    
-      
-       
-        example: self.add_eq
-       
-      
          
      
    
-     Sentence to complete.
+     Identifier of the task to update.
     
    
    
     
-     responseSizeLimit
+     name
     
     
      
-      integer
-      
    
-      
-       
-        example: 10
-       
-      
+      string
          
      
    
-     Maximum size of the responses.
+     New name for the task.
     
    
    
@@ -91602,9 +100296,8 @@ 
    Request
    
      
    
       
     {
-  "language": "Py",
-  "sentence": "self.add_eq",
-  "responseSizeLimit": 10
+  "taskId": 0,
+  "name": "string"
 }
    
      
    
     
@@ -91620,7 +100313,7 @@ 
    Responses
    
   
    
    The
    
-    /ai/tools/complete
+    /agents/tasks/update
    
    API provides a response in the following format:
   
    
@@ -91632,70 +100325,34 @@ 
    
     
      
       
-       CodeCompletionResponse
+       RestResponse
       
-      Model - Response to a code completion request.
+      Model - Base API response class for the QuantConnect API.
      
     
    
    
     
-     state
-    
-    
-     
-      string Enum
-     
-     
    
-     State of the code completion. Options : ['End']
-    
-   
-   
-    
-     version
+     success
     
     
      
-      number
-      
    
-      
-       
-        example: 2.0
-       
-      
+      boolean
          
      
    
-     Version of the response.
+     Indicate if the API request was successful.
     
    
    
     
-     payload
+     errors
     
     
      
       string Array
      
      
    
-     Code completion suggestions.
-    
-   
-   
-    
-     payloadType
-    
-    
-     
-      string
-      
    
-      
-       
-        example: StringArray
-       
-      
-         
-     
    
-     Type of the payload.
+     List of errors with the API call.
     
    
    
@@ -91706,12 +100363,9 @@ 
    
      
    
       
     {
-  "state": "End",
-  "version": 2.0,
-  "payload": [
+  "success": true,
+  "errors": [
     "string"
-  ],
-  "payloadType": "StringArray"
   ]
 }
    
      
    
@@ -91752,7 +100406,7 @@ 
    Examples
    
 
  
   
    
-   The following example demonstates code completion for a specific algorithm attribute through the cloud API.
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
   
    
   
    
    
    from base64 import b64encode
@@ -91791,29 +100445,254 @@ 
    Examples
    
 # --------------------
 
 
-### Code Completion
-# Send a POST request to the /ai/tools/complete endpoint to get code completion
-response = post(f'{BASE_URL}/ai/tools/complete', headers=get_headers(), json={
-    "language": "Python",  # Programming language for code completion
-    "sentence": "self.add_equity('AAPL",  # Partial code to complete
-    "responseSizeLimit": 30  # Maximum size of the completion response
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
 })
-# Parse the JSON response
+# Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the code completion results
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
 if result['success']:
-    print("Code Completion Results:")
+    print("List of Agents:")
+    print(result)
+
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Created Successfully:")
+    print(result)
+
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
+if result['success']:
+    print("Task Details:")
+    print(result)
+
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Updated Successfully:")
+    print(result)
+
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Created Successfully:")
+    print(result)
+
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)
    
   
    
  
 
 
 
     
    
-
    API Reference > AI Assistance > Tools > Error Enhancement
    
+
    API Reference > Agent Management > Tasks > Delete Task
    
 
    
-    
    
-        
    Tools
    
-        
    Error Enhancement
    
+    
    
+        
    Tasks
    
+        
    Delete Task
    
     
    
 
    
 
    Introduction
    
@@ -91821,7 +100700,7 @@ 
    Introduction
    
 
  
   
    
-   Show additional context and suggestions for error messages.
+   Delete an agent task.
   
    
  
 
@@ -91831,9 +100710,9 @@ 
    Request
    
 
  
   
    
-   Show additional context and suggestions for error messages. The
+   Id of the task to delete. The
    
-    /ai/tools/error-enhance
+    /agents/tasks/delete
    
    API accepts requests in the following format:
   
    
@@ -91842,90 +100721,19 @@ 
    Request
    
     
      
       
-       ErrorEnhanceRequest
-      
-      Model - Request to show additional context and suggestions for error messages.
-     
-    
-   
-   
-    
-     language
-    
-    
-     
-      string Enum
-      
    
-      
-       
-        required
-       
-      
-      
    
-      
-       
-        example: Py
-       
-      
-         
-     
    
-     Programming language for the code completion. Options : ['C#', 'Py']
-    
-   
-   
-    
-     error
-    
-    
-     
-      Error object
-      
    
-      
-       
-        required
-       
-      
-         
-     
    
-     Error information.
-    
-   
-   
-    
-     Example
-    
-    
-     
    
-      
    -{
-  "language": "Py",
-  "error": {
-    "message": "string",
-    "stacktrace": "string"
-  }
-}
    
-     
    
-    
-   
-  
-  
-   
-    
-     
-   
-   
-    
-    
@@ -91957,8 +100753,7 @@ 
    Request
    
       
     {
-  "message": "string",
-  "stacktrace": "string"
+  "taskId": 0
 }
    
      
    
@@ -91974,7 +100769,7 @@ 
    Responses
    
    The
    
-    /ai/tools/error-enhance
+    /agents/tasks/delete
    
    API provides a response in the following format:
   
    
@@ -91986,76 +100781,34 @@ 
    
     
    
-    
-   
-   
-    
-    
-   
-   
-    
@@ -92066,10 +100819,10 @@ 
    
      
    
       
     {
-  "state": "End",
-  "version": 2.0,
-  "payload": "string",
-  "payloadType": "String"
+  "success": true,
+  "errors": [
+    "string"
+  ]
 }
    
      
    
     
@@ -92109,7 +100862,7 @@ 
    Examples
    
-   The following example demonstates how to get additional context and suggestions for error messages through the cloud API.
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
   
    
    
    from base64 import b64encode
@@ -92148,122 +100901,291 @@ 
    Examples
    
 # --------------------
 
 
-### Error Enhancement
-# Send a POST request to the /ai/tools/error-enhance endpoint to enhance error message
-response = post(f'{BASE_URL}/ai/tools/error-enhance/', headers=get_headers(), json={
-    "language": "Python",  # Programming language of the code
-    "error": {  # Error details
-        "message": '''  at initialize
-        self._option = self.add_index_option("SPX", Resolution.MINUTE, "SPXW").symbol
-                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-    in c1afe80a-e056-4841-b0c1-d9c562cf2bd8.py: line 15
-    The specified market wasn't found in the markets lookup. Requested: spxw. You can add markets by calling QuantConnect.Market.Add(string,int) (Parameter 'market')''',  # Error message
-        "stacktrace": ""  # Stacktrace (empty in this case)
-    }
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
 })
-# Parse the JSON response
+# Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the enhanced error details
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
 if result['success']:
-    print("Enhanced Error Details:")
+    print("List of Agents:")
+    print(result)
+
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Created Successfully:")
+    print(result)
+
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
+if result['success']:
+    print("Task Details:")
+    print(result)
+
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Updated Successfully:")
+    print(result)
+
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Created Successfully:")
+    print(result)
+
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)
    
   
     
    
-
    API Reference > AI Assistance > Tools > PEP8 Conversion
    
+
    API Reference > Agent Management > Tasks > List Tasks
    
-    
    
-        
    Tools
    
-        
    PEP8 Conversion
    
+    
    
+        
    Tasks
    
+        
    List Tasks
    
     
    
 
    Introduction
    
-
- 
-  
    
-   Update Python code to follow PEP8 style.
-  
    
- 
-
-
-
    Request
    
-
-
- 
-  
    
-   Update Python code to follow PEP8 style. The
-   
-    /ai/tools/pep8-convert
-   
-   API accepts requests in the following format:
-  
    
-  
    
-      
-       Error
+       DeleteAgentTaskRequest
       
-      Model - Error information.
+      Model - Request body for deleting an agent task.
      
     
    
        
    
    
     
-     message
+     taskId
     
     
      
-      string
+      integer
       
    
       
        
@@ -91934,19 +100742,7 @@ 
    Request
    
           
          
      
    
-     Error message.
-    
    
-     stacktrace
-    
-     
-      string
-     
-     
    
-     Stack trace of the error.
+     Identifier of the task to delete.
         		
    
    
    
    
      
     
   
    
      
       
-       ErrorEnhanceResponse
+       RestResponse
       
-      Model - Response to error enhancement request.
+      Model - Base API response class for the QuantConnect API.
      
     
    
    
    
    
     
-     state
-    
-     
-      string
-      
    
-      
-       
-        example: End
-       
-      
-         
-     
    
-     State of the code completion.
-    
    
-     version
-    
-     
-      number
-      
    
-      
-       
-        example: 2.0
-       
-      
-         
-     
    
-     Version of the response.
-    
    
-     payload
+     success
     
     
      
-      string
+      boolean
      
      
    
-     Error message suggestions.
+     Indicate if the API request was successful.
         		
    
    
    
    
     
-     payloadType
+     errors
     
     
      
-      string
-      
    
-      
-       
-        example: String
-       
-      
+      string Array
          
      
    
-     Type of the payload.
+     List of errors with the API call.
         		
    
    
    
    
 
  
   
   
  
 
 
 
 
 
 
    
-   
-    
-     
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-  
    
-      
-       PEP8ConvertRequest
-      
-      Model - Request to convert Python code to PEP8 style.
-     
    
-     files
-    
-     
-      File Array
-      
    
-      
-       
-        required
-       
-      
-      
    
-      
-       
-        example: [{"name": "file.py", "content": "fileContent"}]
-       
-      
-         
-     
    
-     Files of the project.
-    
    
-     Example
-    
-     
    
-      
    -{
-  "files": "[{"name": "file.py", "content": "fileContent"}]"
-  ]
-}
    
-     
    
-    
    
+
+ 
+  
    
+   List the agent tasks of an organization.
+  
    
+ 
+
+
+
    Request
    
+
+
+ 
+  
    
+   Id of the organization whose tasks to list. The
+   
+    /agents/tasks/list
+   
+   API accepts requests in the following format:
+  
    
   
-   
-   
-    
-    
@@ -92299,8 +101209,7 @@ 
    Request
    
       
     {
-  "name": "main.py",
-  "content": "string"
+  "organizationId": "string"
 }
    
      
    
@@ -92316,115 +101225,13 @@ 
    Responses
    
    The
    
-    /ai/tools/pep8-convert
+    /agents/tasks/list
    
    API provides a response in the following format:
   
    
    200 Success
   
    
-  
    
    
     
    
      
       
-       File
+       ListAgentTasksRequest
       
-      Model - File for a AI.
+      Model - Request body for listing the agent tasks of an organization.
      
     
    
        
    
    
     
-     name
+     organizationId
     
     
      
@@ -92271,24 +101193,12 @@ 
    Request
    
       
    
       
        
-        example: main.py
+        required
        
       
          
      
    
-     Name of a file.
-    
    
-     content
-    
-     
-      string
-     
-     
    
-     Contents of the file.
+     Identifier of the organization whose tasks to list.
         		
    
    
    
    
      
     
   
   
    
-   
-    
-     
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-  
    
-      
-       PEP8ConvertResponse
-      
-      Model - Response to a PEP8 conversion request.
-     
    
-     state
-    
-     
-      string
-      
    
-      
-       
-        example: End
-       
-      
-         
-     
    
-     State of PEP8 conversion.
-    
    
-     version
-    
-     
-      number
-      
    
-      
-       
-        example: 2.0
-       
-      
-         
-     
    
-     Version of the response.
-    
    
-     payload
-    
-     
-      object
-      
    
-      
-       
-        example: {'utils.py': 'def add(a,b):\n    return a+b\n'}
-       
-      
-         
-     
    
-     A dictionary where the key is the file name and the value is the PEP8 converted code of that file.
-    
    
-     payloadType
-    
-     
-      string
-      
    
-      
-       
-        example: StringDict
-       
-      
-         
-     
    
-     Type of the payload.
-    
    
-     Example
-    
-     
    
-      
    -{
-  "state": "End",
-  "version": 2.0,
-  "payload": {
-    "utils.py": "def add(a,b):\n    return a+b\n"
-},
-  "payloadType": "StringDict"
-}
    
-     
    
-    
    
   
    
    401 Authentication Error
   
    
@@ -92459,7 +101266,7 @@ 
    Examples
    
 
  
   
    
-   The following example demonstates PEP8 conversion of python codes through the cloud API.
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
   
    
   
    
    
    from base64 import b64encode
@@ -92498,41 +101305,261 @@ 
    Examples
    
 # --------------------
 
 
-### PEP8 Conversion
-# Send a POST request to the /ai/tools/pep8-convert endpoint to convert code to PEP8 syntax
-response = post(f'{BASE_URL}/ai/tools/pep8-convert', headers=get_headers(), json={
-    "files": [  # List of files to convert
-        {
-            "name": "utils.py",  # Name of the file
-            "content": '''
-# region imports
-from AlgorithmImports import *
-# endregion
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Agents:")
+    print(result)
 
-class Project(QCAlgorithm):
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
 
-    def Initialize(self):
-        self.AddEquity("SPY", Resolution.Minute)
-'''  # Content of the file (Python code)
-        }
-    ]
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
 })
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the PEP8 conversion results
+# Extract the task ID from the response
+task_id = result['task']['id']
+# Check if the request was successful and print the result
 if result['success']:
-    print("PEP8 Conversion Results:")
+    print("Task Created Successfully:")
+    print(result)
+
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
+if result['success']:
+    print("Task Details:")
+    print(result)
+
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Updated Successfully:")
+    print(result)
+
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Created Successfully:")
+    print(result)
+
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)
    
   
    
  
 
 
 
     
    
-
    API Reference > AI Assistance > Tools > Syntax Check
    
+
    API Reference > Agent Management > Deployments
    
 
    
-    
    
-        
    Tools
    
-        
    Syntax Check
    
+    
    
+        
    Agent Management
    
+        
    Deployments
    
+    
    
+
    
+
    API Reference > Agent Management > Deployments > Create Deployment
    
+
    
+    
    
+        
    Deployments
    
+        
    Create Deployment
    
     
    
 
    
 
    Introduction
    
@@ -92540,7 +101567,7 @@ 
    Introduction
    
 
  
   
    
-   Check the syntax of a code.
+   Deploy an agent task.
   
    
  
 
@@ -92550,9 +101577,9 @@ 
    Request
    
 
  
   
    
-   Check the syntax of a code. The
+   Id of the task to deploy. The
    
-    /ai/tools/syntax-check
+    /agents/deployments/create
    
    API accepts requests in the following format:
   
    
@@ -92561,58 +101588,28 @@ 
    Request
    
     
      
       
-       BasicFilesRequest
+       CreateAgentDeploymentRequest
       
-      Model - Request to process files.
+      Model - Request body for creating an agent deployment.
      
     
    
    
     
-     language
-    
-    
-     
-      string Enum
-      
    
-      
-       
-        required
-       
-      
-      
    
-      
-       
-        example: Py
-       
-      
-         
-     
    
-     Programming language. Options : ['C#', 'Py']
-    
-   
-   
-    
-     files
+     taskId
     
     
      
-      File Array
+      integer
       
    
       
        
         required
        
       
-      
    
-      
-       
-        example: [{"name": "file.py", "content": "fileContent"}]
-       
-      
          
      
    
-     Files to process.
+     Identifier of the task to deploy.
     
    
    
@@ -92623,143 +101620,387 @@ 
    Request
    
      
    
       
     {
-  "language": "Py",
-  "files": "[{"name": "file.py", "content": "fileContent"}]"
-  ]
+  "taskId": 0
 }
    
      
    
     
    
   
+ 
+
+
+
    Responses
    
+
+
+ 
+  
    
+   The
+   
+    /agents/deployments/create
+   
+   API provides a response in the following format:
+  
    
+  
    
+   200 Success
+  
    
+  
    
+   401 Authentication Error
+  
    
   
-    
-   
-   
-    
-   
-   
-    
-    
    
    
     
    
      
       
-       File
+       UnauthorizedError
       
-      Model - File for a AI.
+      Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash.
      
     
    
        
    
    
     
-     name
-    
-     
-      string
-      
    
-      
-       
-        example: main.py
-       
-      
-         
-     
    
-     Name of a file.
-    
    
-     content
+     www_authenticate
     
     
      
       string
      
      
    
-     Contents of the file.
-    
    
-     Example
-    
-     
    
-      
    -{
-  "name": "main.py",
-  "content": "string"
-}
    
-     
    
+     Header
         		
    
    
   
    
  
 
 
-
    Responses
    
+
    Examples
    
+
+ 
+  
    
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
+  
    
+  
    
+   
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Agents:")
+    print(result)
+
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Created Successfully:")
+    print(result)
+
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
+if result['success']:
+    print("Task Details:")
+    print(result)
+
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Updated Successfully:")
+    print(result)
+
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Created Successfully:")
+    print(result)
+
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
+    print(result)
    
+  
    
+ 
+
+
+
     
    
+
    API Reference > Agent Management > Deployments > Read Deployment
    
+
    
+    
    
+        
    Deployments
    
+        
    Read Deployment
    
+    
    
+
    
+
    Introduction
    
 
 
  
   
    
-   The
+   Read an agent deployment by Id.
+  
    
+ 
+
+
+
    Request
    
+
+
+ 
+  
    
+   Id of the deployment to read. The
    
-    /ai/tools/syntax-check
+    /agents/deployments/read
    
-   API provides a response in the following format:
+   API accepts requests in the following format:
   
    
-  
    
-   200 Success
-  
    
   
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
@@ -92783,18 +102024,29 @@ 
    
      
    
       
     {
-  "state": "End",
-  "version": 2.0,
-  "payload": [
-    "string"
-  ],
-  "payloadType": "StringArray"
-  ]
+  "deploymentId": "string"
 }
    
      
    
     
    
    
    
     
    
      
       
-       SyntaxCheckResponse
+       ReadAgentDeploymentRequest
       
-      Model - Response to a syntax check request.
+      Model - Request body for reading an agent deployment.
      
     
    
        
    
    
     
-     state
-    
-     
-      string Enum
-     
-     
    
-     State of the syntax check. Options : ['End', 'Error']
-    
    
-     version
-    
-     
-      number
-      
    
-      
-       
-        example: 2.0
-       
-      
-         
-     
    
-     Version of the response.
-    
    
-     payload
-    
-     
-      string Array
-     
-     
    
-     Code completion suggestions.
-    
    
-     payloadType
+     deploymentId
     
     
      
@@ -92767,12 +102008,12 @@ 
    
       
    
       
        
-        example: StringArray
+        required
        
       
          
      
    
-     Type of the payload.
+     Identifier of the deployment to read.
     
    		
    
    
    
    
   
    
+ 
+
+
+
    Responses
    
+
+
+ 
+  
    
+   The
+   
+    /agents/deployments/read
+   
+   API provides a response in the following format:
+  
    
+  
    
+   200 Success
+  
    
   
    
    401 Authentication Error
   
    
@@ -92829,7 +102081,7 @@ 
    Examples
    
 
  
   
    
-   The following example demonstates syntax checking of codes through the cloud API.
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
   
    
   
    
    
    from base64 import b64encode
@@ -92868,42 +102120,254 @@ 
    Examples
    
 # --------------------
 
 
-### Syntax Check
-# Send a POST request to the /ai/tools/syntax-check endpoint to check code syntax if appropriate
-response = post(f'{BASE_URL}/ai/tools/syntax-check', headers=get_headers(), json={
-    "language": "Python",  # Programming language of the code
-    "files": [  # List of files to check
-        {
-            "name": "utils.py",  # Name of the file
-            "content": '''
-# region imports
-from AlgorithmImports import *
-# endregion
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Agents:")
+    print(result)
 
-class Project(QCAlgorithm):
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
 
-    def Initialize(self):
-        self.AddEquity("SPY", Resolution.Minute)
-'''  # Content of the file (Python code)
-        }
-    ]
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
 })
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the syntax check results
+# Extract the task ID from the response
+task_id = result['task']['id']
+# Check if the request was successful and print the result
 if result['success']:
-    print("Syntax Check Results:")
+    print("Task Created Successfully:")
+    print(result)
+
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
+if result['success']:
+    print("Task Details:")
+    print(result)
+
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Updated Successfully:")
+    print(result)
+
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Created Successfully:")
+    print(result)
+
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)
    
   
    
  
 
 
 
     
    
-
    API Reference > AI Assistance > Tools > Search
    
+
    API Reference > Agent Management > Deployments > Update Deployment
    
 
    
-    
    
-        
    Tools
    
-        
    Search
    
+    
    
+        
    Deployments
    
+        
    Update Deployment
    
     
    
 
    
 
    Introduction
    
@@ -92911,7 +102375,7 @@ 
    Introduction
    
 
  
   
    
-   Search for content in QuantConnect.
+   Send a follow-up prompt to a running agent deployment.
   
    
  
 
@@ -92921,9 +102385,9 @@ 
    Request
    
 
  
   
    
-   Search for content in QuantConnect. The
+   Id of the deployment and the new prompt to run. The
    
-    /ai/tools/search
+    /agents/deployments/prompt
    
    API accepts requests in the following format:
   
    
@@ -92932,89 +102396,15 @@ 
    Request
    
     
      
       
-       SearchRequest
-      
-      Model - Request to search content in QuantConnect.
-     
-    
-   
-   
-    
-     language
-    
-    
-     
-      string Enum
-      
    
-      
-       
-        required
-       
-      
-      
    
-      
-       
-        example: Py
-       
-      
-         
-     
    
-     Programming language of the content to search. Options : ['C#', 'Py']
-    
-   
-   
-    
-     criteria
-    
-    
-     
-      SearchCriteria Array
-      
    
-      
-       
-        required
-       
-      
-         
-     
    
-     Criteria for the search.
-    
-   
-   
-    
-     Example
-    
-    
-     
    
-      
    -{
-  "language": "Py",
-  "criteria": [
-    {
-      "input": "option",
-      "type": "Stubs",
-      "count": 1
-    }
-  ]
-}
    
-     
    
-    
-   
-  
-  
-   
-    
-     
-   
-   
-    
-    
@@ -93086,9 +102458,8 @@ 
    Request
    
       
     {
-  "input": "option",
-  "type": "Stubs",
-  "count": 1
+  "deploymentId": "A-99bd84b60376ecda56c1145b2074ca32",
+  "prompt": "Test with ROC(30)"
 }
    
      
    
@@ -93104,183 +102475,384 @@ 
    Responses
    
    The
    
-    /ai/tools/search
+    /agents/deployments/prompt
    
    API provides a response in the following format:
   
    
    200 Success
   
    
+  
    
+   401 Authentication Error
+  
    
-      
-       SearchCriteria
+       UpdateAgentDeploymentRequest
       
-      Model - Search criteria.
+      Model - Request body for sending a follow-up prompt to an agent deployment.
      
     
    
        
    
    
     
-     input
+     deploymentId
     
     
      
@@ -93028,39 +102418,21 @@ 
    Request
    
       
    
       
        
-        example: option
-       
-      
-         
-     
    
-     Input for the search.
-    
    
-     type
-    
-     
-      string Enum
-      
    
-      
-       
-        required
+        example: A-99bd84b60376ecda56c1145b2074ca32
        
       
          
      
    
-     Type of the search criteria. Options : ['Stubs', 'Forum', 'Docs', 'Examples']
+     Identifier of the running deployment to send the follow-up prompt to.
         		
    
    
    
    
     
-     count
+     prompt
     
     
      
-      integer
+      string
       
    
       
        
@@ -93070,12 +102442,12 @@ 
    Request
    
       
    
       
        
-        example: 1
+        example: Test with ROC(30)
        
       
          
      
    
-     Number of results to return.
+     New prompt for the deployment to run.
         		
    
    
    
    
      
     
   
   
   
    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-   
-   
-    
-    
    
    
     
    
      
       
-       SearchResponse
+       UnauthorizedError
       
-      Model - Response to a search request.
+      Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash.
      
     
    
        
    
    
     
-     state
-    
-     
-      string Enum
-     
-     
    
-     State of the search. Options : ['End', 'Error']
-    
    
-     version
-    
-     
-      number
-      
    
-      
-       
-        example: 2.0
-       
-      
-         
-     
    
-     Version of the response.
-    
    
-     retrivals
-    
-     
-      SearchRetrieval Array
-     
-     
    
-     List of search results.
-    
    
-     messageId
+     www_authenticate
     
     
      
-      integer
-      
    
-      
-       
-        example: 0
-       
-      
+      string
          
      
    
-     Id of the message.
-    
    
-     Example
-    
-     
    
-      
    -{
-  "state": "End",
-  "version": 2.0,
-  "retrivals": [
-    {
-      "url": "[Index Options - QuantConnect.com](https://www.quantconnect.com/docs/v2/writing-algorithms/universes/index-options)",
-      "score": 0.320344448,
-      "content": "string",
-      "type": 2
-    }
-  ],
-  "messageId": 0
-}
    
-     
    
+     Header
         		
    
    
   
    
+ 
+
+
+
    Examples
    
+
+ 
+  
    
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
+  
    
+  
    
+   
    from base64 import b64encode
+from hashlib import sha256
+from time import time
+from requests import get, post
+BASE_URL = 'https://www.quantconnect.com/api/v2/'
+
+# You need to replace these with your actual credentials.
+# You can request your credentials at https://www.quantconnect.com/settings/
+# You can find our organization ID at https://www.quantconnect.com/organization/ 
+USER_ID = 0
+API_TOKEN = '____'
+ORGANIZATION_ID = '____'
+
+def get_headers():
+    # Get timestamp
+    timestamp = f'{int(time())}'
+    time_stamped_token = f'{API_TOKEN}:{timestamp}'.encode('utf-8')
+
+    # Get hased API token
+    hashed_token = sha256(time_stamped_token).hexdigest()
+    authentication = f'{USER_ID}:{hashed_token}'.encode('utf-8')
+    authentication = b64encode(authentication).decode('ascii')
+
+    # Create headers dictionary.
+    return {
+        'Authorization': f'Basic {authentication}',
+        'Timestamp': timestamp
+    }
+
+# Authenticate to verify credentials
+response = post(f'{BASE_URL}/authenticate', headers = get_headers())
+print(response.json())
+
+# --------------------
+
+
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Agents:")
+    print(result)
+
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Created Successfully:")
+    print(result)
+
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
+if result['success']:
+    print("Task Details:")
+    print(result)
+
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Updated Successfully:")
+    print(result)
+
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Created Successfully:")
+    print(result)
+
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
+    print(result)
    
+  
    
+ 
+
+
+
     
    
+
    API Reference > Agent Management > Deployments > List Deployments
    
+
    
+    
    
+        
    Deployments
    
+        
    List Deployments
    
+    
    
+
    
+
    Introduction
    
+
+
+ 
+  
    
+   List the deployments of an agent task.
+  
    
+ 
+
+
+
    Request
    
+
+
+ 
+  
    
+   Id of the task whose deployments to list. The
+   
+    /agents/deployments/list
+   
+   API accepts requests in the following format:
+  
    
   
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
@@ -93291,15 +102863,29 @@ 
    
      
    
       
     {
-  "url": "[Index Options - QuantConnect.com](https://www.quantconnect.com/docs/v2/writing-algorithms/universes/index-options)",
-  "score": 0.320344448,
-  "content": "string",
-  "type": 2
+  "taskId": 0
 }
    
      
    
     
    
    
    
     
    
      
       
-       SearchRetrieval
+       ListAgentDeploymentsRequest
       
-      Model - Search criteria.
+      Model - Request body for listing deployments of an agent task.
      
     
    
        
    
    
     
-     url
-    
-     
-      string
-      
    
-      
-       
-        example: [Index Options - QuantConnect.com](https://www.quantconnect.com/docs/v2/writing-algorithms/universes/index-options)
-       
-      
-         
-     
    
-     Input for the search.
-    
    
-     score
-    
-     
-      number
-      
    
-      
-       
-        example: 0.320344448
-       
-      
-         
-     
    
-     Relevance score of the search result.
-    
    
-     content
-    
-     
-      string
-     
-     
    
-     Content of the search result.
-    
    
-     type
+     taskId
     
     
      
-      number
+      integer
       
    
       
        
-        example: 2
+        required
        
       
          
      
    
-     Type of the search result. 0=Stubs, 1=Forum, 2=Docs, 3=Examples.
+     Identifier of the task whose deployments to list.
         		
    
    
    
    
   
    
+ 
+
+
+
    Responses
    
+
+
+ 
+  
    
+   The
+   
+    /agents/deployments/list
+   
+   API provides a response in the following format:
+  
    
+  
    
+   200 Success
+  
    
   
    
    401 Authentication Error
   
    
@@ -93334,7 +102920,7 @@ 
    Examples
    
 
  
   
    
-   The following example demonstates searching content in QuantConnect through the cloud API.
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
   
    
   
    
    
    from base64 import b64encode
@@ -93373,67 +102959,254 @@ 
    Examples
    
 # --------------------
 
 
-### Search Content
-# Send a POST request to the /ai/tools/search endpoint to search content
-response = post(f'{BASE_URL}/ai/tools/search', headers=get_headers(), json={
-    "language": "Python",  # Programming language to filter search results
-    "criteria": [  # Search criteria
-        {
-            "input": "option",  # Search term (e.g., "option")
-            "type": "Docs",  # Type of content to search (e.g., documentation)
-            "count": 1  # Number of results to return
-        }
-    ]
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
 })
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the search results
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
 if result['success']:
-    print("Search Results:")
-    print(result)
    
-  
    
- 
-
+    print("List of Agents:")
+    print(result)
 
-
     
    
-
    API Reference > Reports
    
-
    
-    
    
-        
    API Reference
    
-        
    Reports
    
-    
    
-
    
-
- 
-  
    
-   
    
-    
    
-     The QuantConnect REST API lets you access your backtest reports from our cloud servers through URL endpoints.
-    
    
-   
    
-   
    
-    
    
-     
-      
    
-       
    
-        
    
-         Backtest Report
-        
    
-       
    
-      
    
-         
-    
    
-   
    
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Created Successfully:")
+    print(result)
+
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
+if result['success']:
+    print("Task Details:")
+    print(result)
+
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Updated Successfully:")
+    print(result)
+
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Created Successfully:")
+    print(result)
+
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
+    print(result)

     

    -

    API Reference > Reports > Backtest Report

    +

    API Reference > Agent Management > Deployments > Stop Deployment

    -
    -

    Reports

    -

    Backtest Report

    +
    +

    Deployments

    +

    Stop Deployment

    Introduction

    @@ -93441,7 +103214,7 @@

    Introduction

    - Read out the report of a backtest. + Stop a running agent deployment.

    @@ -93451,9 +103224,9 @@

    Request

    - A JSON object containing info about the backtest to use for the report. The + Id of the deployment to stop. The - /backtests/read/report + /agents/deployments/stop API accepts requests in the following format:

    @@ -93462,39 +103235,15 @@

    Request

    - BacktestReportRequest + StopAgentDeploymentRequest - Model - Request to read out the report of a backtest. + Model - Request body for stopping an agent deployment. - projectId - - - - integer -
    - - - required - - -
    - - - example: 23456789 - - -     -
    - Id of the project to read. - - - - - backtestId + deploymentId @@ -93505,15 +103254,9 @@

    Request

    required     -
    - - - example: 26c7bb06b8487cff1c7b3c44652b30f1 - -
    - Id of the backtest to read. + Identifier of the deployment to stop. @@ -93524,8 +103267,7 @@

    Request

     {
-  "projectId": 23456789,
-  "backtestId": "26c7bb06b8487cff1c7b3c44652b30f1"
+  "deploymentId": "string"
 }
    @@ -93541,7 +103283,7 @@

    Responses

    The - /backtests/read/report + /agents/deployments/stop API provides a response in the following format:

    @@ -93553,89 +103295,12 @@

    - BacktestReportGeneratingResponse - - Model - Backtest Report Response wrapper. - - - - - - generating - - - - boolean - -
    - Indicate if the report is generating. - - - - - success - - - - boolean - -
    - Indicate if the API request was successful. - - - - - errors - - - - string Array - -
    - List of errors with the API call. - - - - - Example - - -
    - 
    -{
-  "generating": true,
-  "success": true,
-  "errors": [
-    "string"
-  ]
-}
    -
    - - - - - - - - - - - @@ -4761,7 +4361,7 @@

    Futures

    One file per ticker per trading day per data format. Trade, quote, and open interest data are separate files. @@ -4772,7 +4372,7 @@

    Futures

    One file per ticker per trading day per data format. Trade, quote, and open interest data are separate files. @@ -4783,7 +4383,7 @@

    Futures

    One file per ticker per data format. Trade, quote, and open interest data are separate files. @@ -4794,7 +4394,7 @@

    Futures

    One file per ticker per data format. Trade, quote, and open interest data are separate files. @@ -4914,13 +4514,13 @@

    Futures

    = 756 files

    - 756 files @ 5 QCC/file + 756 files @ 50 QCC/file
    - => 756 * 5 QCC + => 756 * 50 QCC
    - = 3,780 QCC + = 37,800 QCC
    - = $37.80 USD + = $378 USD @@ -37422,7 +37022,7 @@

    Options

    @@ -22235,10 +22319,16 @@ 

    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
+#load "../Initialize.csx"
    +
    +
    + 
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-using System;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    +
    +
    + 
    using System;
 using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
@@ -22248,9 +22338,8 @@ 
    
 using QuantConnect.Research;
 using QuantConnect.Securities;
 
-// Import Plotly for plotting.
-#r "../Plotly.NET.dll"
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Create a QuantBook.
@@ -22306,7 +22395,7 @@ 
    
 // Assign the Layout to the chart.
 chart.WithLayout(layout);
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    +display(chart) 
    # Import plotly library for plotting.
 import plotly.graph_objects as go
 
@@ -22394,13 +22483,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
  • - Import the data types. + Load the necessary assembly files. 
    • #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    +
    +
  • + Import the data types. +
    • +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -22981,18 +23074,21 @@ 
    
    
    #load "../Initialize.csx"
    - 
    #r "../Plotly.NET.dll"
-using Plotly.NET;
-using Plotly.NET.LayoutObjects;
    + 
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    - 
    #load "../QuantConnect.csx"
-using QuantConnect;
+   
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Research;
 using QuantConnect.Securities;
 using QuantConnect.Data.Market;
-    
+using Plotly.NET;
+using Plotly.NET.Interactive;
+using Plotly.NET.LayoutObjects;
+
 var qb = new QuantBook();
 // Get the front-month ES contract as of December 31, 2021.
 var future = qb.AddFuture(Futures.Indices.SP500EMini);
@@ -23021,7 +23117,7 @@ 
    
 layout.SetValue("title", title);
 
 chart.WithLayout(layout);
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    Candlestick plot of ES18H22 OHLC Candlestick plot of ES18H22 OHLC @@ -23063,18 +23159,21 @@ 

    #load "../Initialize.csx"
    - 
    #r "../Plotly.NET.dll"
-using Plotly.NET;
-using Plotly.NET.LayoutObjects;
    + 
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    - 
    #load "../QuantConnect.csx"
-using QuantConnect;
+   
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Research;
 using QuantConnect.Securities;
 using QuantConnect.Data.Market;
-    
+using Plotly.NET;
+using Plotly.NET.Interactive;
+using Plotly.NET.LayoutObjects;
+
 var qb = new QuantBook();
 // Get the front-month ES contract as of December 31, 2021.
 var future = qb.AddFuture(Futures.Indices.SP500EMini);
@@ -23100,7 +23199,7 @@ 
    
 layout.SetValue("title", title);
 
 chart.WithLayout(layout);
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    Line chart of close price of Future contracts Line chart of open interest of ES18H22 @@ -23345,13 +23444,17 @@

    Create Subscriptions

    #load "../Initialize.csx"
  • - Import the data types. + Load the necessary assembly files. 
    • #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    +
    +
  • + Import the data types. +
    • +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -24029,10 +24132,16 @@ 
    
   
    
   
    
    
    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-using System;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    using System;
 using System.Collections.Generic;
 using QuantConnect;
 using QuantConnect.Data;
@@ -24041,9 +24150,8 @@ 
    
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
 
-// Import Plotly for plotting.
-#r "../Plotly.NET.dll"
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Create a QuantBook.
@@ -24098,7 +24206,7 @@ 
    
 chart.WithLayout(layout);
 
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    # Instantiate a QuantBook instance.
 qb = QuantBook()
 # Set the date being studied.
@@ -24115,7 +24223,7 @@ 
    
     fill_forward=False, extended_market_hours=False
 )
 
-chain = list(option_history)[-1].OptionChains.values()[0]
+chain = list(option_history)[-1].option_chains.values()[0]
 # Study the closest expiring contracts.
 expiry = min(x.expiry for x in chain)
 # Filter for the closest expiring calls to study only.
@@ -24164,13 +24272,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
  • - Import the data types. + Load the necessary assembly files. 
    • #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    +
    +
  • + Import the data types. +
    • +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -24771,10 +24883,13 @@ 
    
         xaxis_rangeslider_visible=False
     )
 ).show()
    - 
    #load "../QuantConnect.csx"
-#r "../Plotly.NET.dll"
-
-using QuantConnect;
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+
    +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -24782,6 +24897,7 @@ 
    
 using QuantConnect.Data.Market;
 using QuantConnect.Securities;
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Add the underlying Future contract 
@@ -24829,7 +24945,7 @@ 
    
 layout.SetValue("yaxis", yAxis);
 layout.SetValue("title", title);
 chart.WithLayout(layout);
-HTML(GenericChart.toChartHTML(chart))
    +display(chart)
    Candlestick plot of the prices for an ES Future Option contractCandlestick plot of the prices for an ES Future Option contract @@ -24867,13 +24983,17 @@

    Create Subscriptions

    #load "../Initialize.csx"
  • - Import the data types. + Load the necessary assembly files. 
    • #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    +
    +
  • + Import the data types. +
    • +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -27761,8 +27881,12 @@ 
    
    
    
    
     
    import plotly.graph_objects as go
    
-    
    #r "../Plotly.NET.dll"
-using Plotly.NET;
+    
    #r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+   
    
+   
    
+    
    using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
    
    
    
    
  • 
@@ -27843,7 +27967,7 @@ 
    
    
    • 
    
    
     
    fig.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Candlestick charts display the open, high, low, and close prices of the security.
@@ -27951,7 +28075,7 @@ 
    
    
    
    
     
    plt.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Line charts display the value of the property you selected in a time series.
@@ -27976,10 +28100,16 @@ 
    
   
    
   
    
    
    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-using System;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    using System;
 using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
@@ -27989,9 +28119,8 @@ 
    
 using QuantConnect.Research;
 using QuantConnect.Securities;
 
-// Import Plotly for plotting.
-#r "../Plotly.NET.dll"
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Create a QuantBook.
@@ -28042,7 +28171,7 @@ 
    
 // Assign the Layout to the chart.
 chart.WithLayout(layout);
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    # Import plotly library for plotting.
 import plotly.graph_objects as go
 
@@ -28117,13 +28246,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
  • - Import the data types. + Load the necessary assembly files. 
    • #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    +
    +
  • + Import the data types. +
    • +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -31011,8 +31144,12 @@ 
    
    
    
    
     
    import plotly.graph_objects as go
    
-    
    #r "../Plotly.NET.dll"
-using Plotly.NET;
+    
    #r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+   
    
+   
    
+    
    using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
    
    
    
    
  • 
@@ -31093,7 +31230,7 @@ 
    
    
    • 
    
    
     
    fig.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Candlestick charts display the open, high, low, and close prices of the security.
@@ -31201,7 +31338,7 @@ 
    
    
    
    
     
    plt.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Line charts display the value of the property you selected in a time series.
@@ -31226,10 +31363,16 @@ 
    
   
    
   
    
    
    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-using System;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    using System;
 using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
@@ -31239,9 +31382,8 @@ 
    
 using QuantConnect.Research;
 using QuantConnect.Securities;
 
-// Import Plotly for plotting.
-#r "../Plotly.NET.dll"
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Create a QuantBook.
@@ -31292,7 +31434,7 @@ 
    
 // Assign the Layout to the chart.
 chart.WithLayout(layout);
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    # Import plotly library for plotting.
 import plotly.graph_objects as go
 
@@ -31367,13 +31509,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
  • - Import the data types. + Load the necessary assembly files. 
    • #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    +
    +
  • + Import the data types. +
    • +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -33633,8 +33779,12 @@ 
    
    
    
    
     
    import plotly.graph_objects as go
    
-    
    #r "../Plotly.NET.dll"
-using Plotly.NET;
+    
    #r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+   
    
+   
    
+    
    using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
    
    
    
    
  • 
@@ -33715,7 +33865,7 @@ 
    
    
    • 
    
    
     
    fig.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Candlestick charts display the open, high, low, and close prices of the security.
@@ -33823,7 +33973,7 @@ 
    
    
    
    
     
    plt.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Line charts display the value of the property you selected in a time series.
@@ -33848,10 +33998,16 @@ 
    
   
    
   
    
    
    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-using System;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    using System;
 using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
@@ -33861,9 +34017,8 @@ 
    
 using QuantConnect.Research;
 using QuantConnect.Securities;
 
-// Import Plotly for plotting.
-#r "../Plotly.NET.dll"
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Create a QuantBook.
@@ -33914,7 +34069,7 @@ 
    
 // Assign the Layout to the chart.
 chart.WithLayout(layout);
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    # Import plotly library for plotting.
 import plotly.graph_objects as go
 
@@ -34198,13 +34353,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
  • - Import the data types. + Load the necessary assembly files. 
    • #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    +
    +
  • + Import the data types. +
    • +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -34483,10 +34642,16 @@ 
    
   
    
   
    
    
    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-using System;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    using System;
 using System.Collections.Generic;
 using QuantConnect;
 using QuantConnect.Data;
@@ -34495,9 +34660,8 @@ 
    
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
 
-// Import Plotly for plotting.
-#r "../Plotly.NET.dll"
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Create a QuantBook.
@@ -34549,7 +34713,7 @@ 
    
 chart.WithLayout(layout);
 
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    # Instantiate a QuantBook instance.
 qb = QuantBook()
 # Set the date being studied.
@@ -34744,13 +34908,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
  • - Import the data types. + Load the necessary assembly files. 
    • #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    +
    +
  • + Import the data types. +
    • +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -35383,10 +35551,13 @@ 
    
         xaxis_rangeslider_visible=False
     )
 ).show()
    - 
    #load "../QuantConnect.csx"
-#r "../Plotly.NET.dll"
-
-using QuantConnect;
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+
    +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -35394,6 +35565,7 @@ 
    
 using QuantConnect.Data.Market;
 using QuantConnect.Securities.Option;
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Get the SPX Option chain for January 1, 2024.
@@ -35441,7 +35613,7 @@ 
    
 layout.SetValue("yaxis", yAxis);
 layout.SetValue("title", title);
 chart.WithLayout(layout);
-HTML(GenericChart.toChartHTML(chart))
    +display(chart)
    Candlestick plot of the prices for an SPX Index Option contractCandlestick plot of the prices for an SPX Index Option contract @@ -35487,10 +35659,13 @@

    # Plot the open interest history. history.plot(title=f'{contract_symbol.value} Open Interest') plt.show() - 
    #load "../QuantConnect.csx"
-#r "../Plotly.NET.dll"
-
-using QuantConnect;
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
+  
    
+   
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -35498,6 +35673,7 @@ 
    
 using QuantConnect.Data.Market;
 using QuantConnect.Securities.Option;
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Get the VIX weekly Option chain for January 1, 2024.
@@ -35539,7 +35715,7 @@ 
    
 layout.SetValue("yaxis", yAxis);
 layout.SetValue("title", title);
 chart.WithLayout(layout);
-HTML(GenericChart.toChartHTML(chart))
+display(chart)
 
    
   
    
   Line plot of the open interest for a VIWX Index Option contract
@@ -35583,11 +35759,14 @@ 
    Create Subscriptions
    
     Load the required assembly files and data types.
    
    
    
-    
    #load "../Initialize.csx"
-#load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+    
    #load "../Initialize.csx"
    
+   
    
+   
    
+    
    #load "../QuantConnect.csx"
+#r "../Microsoft.Data.Analysis.dll"
    
+   
    
+   
    
+    
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -35704,16 +35883,16 @@ 
    
 var subsetHistoryDataObjects = qb.History<CBOE>(new[] {vix, v3m}, 10);
 var allHistoryDataObjects = qb.History<CBOE>(qb.Securities.Keys, 10);
    
    
    # DataFrame
-single_history_df = qb.History(vix, 10)
-subset_history_df = qb.History([vix, v3m], 10)
-all_history_df = qb.History(qb.Securities.Keys, 10)
+single_history_df = qb.history(vix, 10)
+subset_history_df = qb.history([vix, v3m], 10)
+all_history_df = qb.history(qb.securities.keys(), 10)
 
 # Slice objects
-all_history_slice = qb.History(10)
+all_history_slice = qb.history(10)
 
 # CBOE objects
-single_history_data_objects = qb.History[CBOE](vix, 10)
-subset_history_data_objects = qb.History[CBOE]([vix, v3m], 10)
    all_history_data_objects = qb.History[CBOE](qb.Securities.Keys, 10)
    
+single_history_data_objects = qb.history[CBOE](vix, 10)
+subset_history_data_objects = qb.history[CBOE]([vix, v3m], 10)
    all_history_data_objects = qb.history[CBOE](qb.securities.keys(), 10)

    The preceding calls return the most recent bars, excluding periods of time when the exchange was closed. @@ -35752,15 +35931,15 @@

    // CBOE objects var singleHistoryDataObjects = qb.History<CBOE>(vix, TimeSpan.FromDays(3));
    var subsetHistoryDataObjects = qb.History<CBOE>(new[] {vix, v3m}, TimeSpan.FromDays(3));
    var allHistoryDataObjects = qb.History<CBOE>(TimeSpan.FromDays(3));
    # DataFrame
-single_history_df = qb.History(vix, timedelta(days=3))
-subset_history_df = qb.History([vix, v3m], timedelta(days=3))
-all_history_df = qb.History(qb.Securities.Keys, timedelta(days=3))
+single_history_df = qb.history(vix, timedelta(days=3))
+subset_history_df = qb.history([vix, v3m], timedelta(days=3))
+all_history_df = qb.history(qb.securities.keys(), timedelta(days=3))
 
 # Slice objects
-all_history_slice = qb.History(timedelta(days=3))
+all_history_slice = qb.history(timedelta(days=3))
 
 # CBOE objects
-single_history_data_objects = qb.History[CBOE](vix, timedelta(days=3))
    subset_history_data_objects = qb.History[CBOE]([vix, v3m], timedelta(days=3))
    all_history_data_objects = qb.History[CBOE](qb.Securities.Keys, timedelta(days=3))
    +single_history_data_objects = qb.history[CBOE](vix, timedelta(days=3))
    subset_history_data_objects = qb.history[CBOE]([vix, v3m], timedelta(days=3))
    all_history_data_objects = qb.history[CBOE](qb.securities.keys(), timedelta(days=3))

    The preceding calls return the most recent bars or ticks, excluding periods of time when the exchange was closed. @@ -35816,15 +35995,15 @@

    end_time = datetime(2021, 3, 1) # DataFrame -single_history_df = qb.History(vix, start_time, end_time) -subset_history_df = qb.History([vix, v3m], start_time, end_time) -all_history_df = qb.History(qb.Securities.Keys, start_time, end_time) +single_history_df = qb.history(vix, start_time, end_time) +subset_history_df = qb.history([vix, v3m], start_time, end_time) +all_history_df = qb.history(qb.securities.keys(), start_time, end_time) # Slice objects -all_history_slice = qb.History(start_time, end_time) +all_history_slice = qb.history(start_time, end_time) # CBOE objects -single_history_data_objects = qb.History[CBOE](vix, start_time, end_time)
    subset_history_data_objects = qb.History[CBOE]([vix, v3m], start_time, end_time)
    all_history_data_objects = qb.History[CBOE](qb.Securities.Keys, start_time, end_time)
    +single_history_data_objects = qb.history[CBOE](vix, start_time, end_time)
    subset_history_data_objects = qb.history[CBOE]([vix, v3m], start_time, end_time)
    all_history_data_objects = qb.history[CBOE](qb.securities.keys(), start_time, end_time)

    The preceding calls return the bars or ticks that have a timestamp within the defined period of time. @@ -37293,8 +37472,12 @@ 

    import plotly.graph_objects as go
    - 
    #r "../Plotly.NET.dll"
-using Plotly.NET;
+    
    #r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+
    +
    + 
    using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
  • @@ -37375,7 +37558,7 @@ 

    • fig.show()
    - 
    HTML(GenericChart.toChartHTML(chart))
    + 
    display(chart)

    Candlestick charts display the open, high, low, and close prices of the alternative data. @@ -37483,7 +37666,7 @@ 

    plt.show()
    - 
    HTML(GenericChart.toChartHTML(chart))
    + 
    display(chart)

    Line charts display the value of the property you selected in a time series. @@ -37508,18 +37691,23 @@ 

    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
+#load "../Initialize.csx"
    +
    +
    + 
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-using QuantConnect;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    +
    +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.DataSource;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
 
-// Import Plotly for plotting.
-#r "../Plotly.NET.dll"
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Create a QuantBook.
@@ -37558,7 +37746,7 @@ 
    
 chart.WithLayout(layout);
 
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    +display(chart) 
    # Create a QuantBook
 qb = QuantBook()
 
@@ -37623,11 +37811,14 @@ 
    Define Custom Data
    
    methods.
   
    
   
    
-   
    #load "../Initialize.csx"
-#load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+   
    #load "../Initialize.csx"
    
+  
    
+  
    
+   
    #load "../QuantConnect.csx"
+#r "../Microsoft.Data.Analysis.dll"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -38168,8 +38359,12 @@ 
    
    
    
    
     
    import plotly.graph_objects as go
    
-    
    #r "../Plotly.NET.dll"
-using Plotly.NET;
+    
    #r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+   
    
+   
    
+    
    using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
    
    
    
    
  • 
@@ -38250,7 +38445,7 @@ 
    
    
    • 
    
    
     
    fig.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Candlestick charts display the open, high, low, and close prices of the security.
@@ -38344,7 +38539,7 @@ 
    
    
    
    
     
    plt.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Line charts display the value of the property you selected in a time series.
@@ -39968,21 +40163,23 @@ 
    
   
    
   
    
    
    // Load the assembly files and data types in their own cell.
-#load "../Initialize.csx"
-
-// Load the necessary assembly files.
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-#r "../Plotly.NET.dll"
-#r "../Plotly.NET.Interactive.dll"
-
-// Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    // Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
 using QuantConnect;
 using QuantConnect.Research;
-            
+
 using Plotly.NET;
 using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
-            
+
 using Accord.Math;
 using Accord.Statistics;
    
   
    
@@ -40054,7 +40251,7 @@ 
    Create Candlestick Chart
    
 chart.WithLayout(layout);
 
 // Show the plot.
-HTML(GenericChart.toChartHTML(chart));
    
+display(chart);

    The Jupyter Notebook displays the candlestick chart. @@ -40103,7 +40300,7 @@

    Create Line Chart

    chart.WithLayout(layout); // Show the plot. -HTML(GenericChart.toChartHTML(chart)); +display(chart);

    The Jupyter Notebook displays the line chart. @@ -40152,7 +40349,7 @@

    Create Scatter Plot

    chart.WithLayout(layout); // Show the plot. -HTML(GenericChart.toChartHTML(chart)); +display(chart);

    The Jupyter Notebook displays the scatter plot. @@ -40213,7 +40410,7 @@

    Create Heat Map

    heatmap.WithLayout(layout); // Show the plot. -HTML(GenericChart.toChartHTML(heatmap)) +display(heatmap)

    The Jupyter Notebook displays the heat map. @@ -40268,7 +40465,7 @@

    Create 3D Chart

    chart.WithLayout(layout); // Show the plot. -HTML(GenericChart.toChartHTML(heatmap)) +display(chart)

    The Jupyter Notebook displays the scatter plot. @@ -40308,8 +40505,11 @@

    Get Universe Data

    method. The object that returns contains a universe data collection for each day. With this object, you can iterate through each day and then iterate through the universe data objects of each day to analyze the universe constituents.

    +

    + Fundamental Universes +

    - For example, follow these steps to get the US Equity Fundamental data for a specific universe: + Follow these steps to get the US Equity Fundamental data for a specific universe:

    1. @@ -40471,6 +40671,388 @@

      Get Universe Data

      var peRatio = fundamental.ValuationRatios.PERatio; } } +} + +
    +

    + ETF Constituents Universes +

    +

    + Follow these steps to get the data of a specific ETF Constituents universe: +

    +
      +
    1. + Load the assembly files and data types in their own cell. +
      2. +
      + 
      #load "../Initialize.csx"
      +
      +
    2. + Load the necessary assembly files. +
      3. +
      + 
      #load "../QuantConnect.csx"
      +
      +
    3. + Import the + + QuantConnect + + package and the universe data type. +
      4. +
      + 
      using QuantConnect;
+using QuantConnect.Research;
+using QuantConnect.Data.UniverseSelection;
      +
      +
    4. + Create a + + QuantBook + + . +
      5. +
      + 
      var qb = new QuantBook();
      + 
      qb = QuantBook()
      +
      +
    5. + Define a universe. +
      6. +

      + The following example defines a dynamic universe that contains the 3 Equities with the largest weight in the SPY ETF. + To see all the + + ETFConstituentUniverse + + attributes you can use to define a filter function for an ETF Constituents universe, see + + Data Point Attributes + + . + To create the universe, call the + + Universe.ETF + + + universe.etf + + method with the ETF ticker and the selection function, then pass the result to the + + AddUniverse + + + add_universe + + method. +

      +
      + 
      var universe = qb.AddUniverse(
+    qb.Universe.ETF("SPY", universeFilterFunc: constituents => constituents
+        .Where(c => c.Weight != 0m)
+        .OrderBy(c => c.Weight)
+        .TakeLast(3)
+        .Select(c => c.Symbol)
+    )
+);
      + 
      def select_assets(constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
+    sorted_by_weight = sorted([c for c in constituents if c.weight], key=lambda c: c.weight)
+    return [c.symbol for c in sorted_by_weight[-3:]]
+
+universe = qb.add_universe(qb.universe.etf('SPY', universe_filter_func=select_assets))
      +
      +
    6. + Call the + + UniverseHistory + + + universe_history + + method with the universe, a start date, and an end date. +
      7. +
      + 
      var universeHistory = qb.UniverseHistory(universe, new DateTime(2025, 11, 6), new DateTime(2025, 11, 11));
      + 
      universe_history = qb.universe_history(universe, datetime(2025, 11, 6), datetime(2025, 11, 11))
      +
      +

      + The end date arguments is optional. If you omit it, the method returns + + ETFConstituentUniverse + + data between the start date and the current day. +

      +

      + The + + universe_history + + method returns a Series where the multi-index is the universe + + Symbol + + and the time when universe selection would occur in a backtest. + Each row in the data column contains a list of + + ETFConstituentUniverse + + objects. + Here is an example of a Series: +

      +
      + 
      time
+2025-11-07    [AAPL R735QTJ8XC9X: ¤0.00, MSFT R735QTJ8XC9X: ...
+2025-11-08    [AAPL R735QTJ8XC9X: ¤0.00, MSFT R735QTJ8XC9X: ...
+2025-11-11    [AAPL R735QTJ8XC9X: ¤0.00, MSFT R735QTJ8XC9X: ...
+Name: data, dtype: object
      +
      +

      + To get a flat DataFrame instead of a Series, set the + + flatten + + argument to + + True + + . +

      +
      + 
      qb.universe_history(universe, datetime(2025, 11, 6), datetime(2025, 11, 11), flatten=True)
      +
      +
      +
    - - BacktestReport + RestResponse - Model - Backtest Report Response wrapper. + Model - Base API response class for the QuantConnect API.
    - report - - - string - -
    - HTML data of the report with embedded base64 images. -
    success @@ -93668,7 +103333,6 @@ 

     {
-  "report": "string",
   "success": true,
   "errors": [
     "string"
@@ -93712,7 +103376,7 @@ 
    Examples
    
 
  
   
    
-   The following example demonstates creating, reading, updating, deleting, and listing backtests of a project through the cloud API.
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
   
    
   
    
    
    from base64 import b64encode
@@ -93751,93 +103415,254 @@ 
    Examples
    
 # --------------------
 
 
-### Create Backtest
-# Define placeholder compilation ID (replace with actual value)
-compile_id = "compile_id..."
-# Send a POST request to the /backtests/create endpoint to create a backtest
-response = post(f'{BASE_URL}/backtests/create', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project to backtest
-    "compileId": compile_id,  # Compilation ID for the backtest
-    "backtestName": f"Backtest {int(time())}"  # Unique name for the backtest using current timestamp
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
 })
 # Parse the JSON response into python managable dict
 result = response.json()
-# Extract the backtest ID from the response
-backtest_id = result['backtest']['backtestId']
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Agents:")
+    print(result)
+
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Backtest Created Successfully:")
+    print("Task Created Successfully:")
     print(result)
 
-### Read Backtest Statistics
-# Prepare data payload to read backtest statistics
-payload = {
-    "projectId": project_id,  # ID of the project
-    "backtestId": backtest_id  # ID of the backtest to read
-}
-# Send a POST request to the /backtests/read endpoint to get statistics
-response = post(f'{BASE_URL}/backtests/read', headers=get_headers(), json=payload)
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the statistics
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
 if result['success']:
-    print("Backtest Statistics:")
+    print("Task Details:")
     print(result)
 
-### Update Backtest
-# Send a POST request to the /backtests/update endpoint to update backtest details
-response = post(f'{BASE_URL}/backtests/update', headers=get_headers(), json={
-    "projectId": project_id,  # ID of the project
-    "backtestId": backtest_id,  # ID of the backtest to update
-    "name": f"Backtest_{backtest_id}",  # New name for the backtest
-    "note": "The new backtest name is awesome!"  # Additional note
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
 })
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the result
 if result['success']:
-    print("Backtest Updated Successfully:")
+    print("Task Updated Successfully:")
     print(result)
 
-### Delete Backtest
-# Prepare data payload to delete the backtest
-payload = {
-    "projectId": project_id,  # ID of the project
-    "backtestId": backtest_id  # ID of the backtest to delete
-}
-# Send a POST request to the /backtests/delete endpoint to delete the backtest
-response = post(f'{BASE_URL}/backtests/delete', headers=get_headers(), json=payload)
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
 # Parse the JSON response into python managable dict
 result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
 # Check if the request was successful and print the result
 if result['success']:
-    print("Backtest Deleted Successfully:")
+    print("Deployment Created Successfully:")
     print(result)
 
-### List Backtests
-# Prepare data payload to list backtests with statistics
-payload = {
-    "projectId": project_id,  # ID of the project
-    "includeStatistics": True  # Include statistics in the response
-}
-# Send a POST request to the /backtests/list endpoint to list backtests
-response = post(f'{BASE_URL}/backtests/list', headers=get_headers(), json=payload)
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
 # Parse the JSON response into python managable dict
 result = response.json()
 # Check if the request was successful and print the list
 if result['success']:
-    print("List of Backtests:")
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)
    
   
    
  
 
 
 
     
    
-
    API Reference > Account
    
+
    API Reference > Agent Management > Deployments > Delete Deployment
    
 
    
-    
    
-        
    API Reference
    
-        
    Account
    
+    
    
+        
    Deployments
    
+        
    Delete Deployment
    
     
    
 
    
 
    Introduction
    
@@ -93845,17 +103670,7 @@ 
    Introduction
    
 
  
   
    
-   Read the organization account status.
-  
    
- 
-
-
-
    Description
    
-
-
- 
-  
    
-   The organization account status includes the organization Id, QCC balance, and credit card information.
+   Delete an agent deployment.
   
    
  
 
@@ -93865,61 +103680,26 @@ 
    Request
    
 
  
   
    
-   The
-   
-    /account/read
-   
-   API accepts requests in the following format:
-  
    
-  
-   
-    
-     
-    
-   
-   
-  
    
-      
-       /account/read
-      
-      Method
-     
    
-    
-     /account/read
-    
-    method takes no parameters.
-   
    
- 
-
-
-
    Responses
    
-
-
- 
-  
    
-   The
+   Id of the deployment to delete. The
    
-    /account/read
+    /agents/deployments/delete
    
-   API provides a response in the following format:
+   API accepts requests in the following format:
   
    
-  
    
-   200 Success
-  
    
   
-   
-   
-    
-    
-   
-   
-    
-    
@@ -93967,69 +103723,62 @@ 
    
      
    
       
     {
-  "organizationId": "5cad178b20a1d52567b534553413b691",
-  "creditBalance": 0,
-  "card": {
-    "brand": "string",
-    "expiration": "12/27",
-    "last4": "string"
-  }
+  "deploymentId": "string"
 }
    
      
    
     
    
    
    
     
    
      
       
-       AccountResponse
+       DeleteAgentDeploymentRequest
       
-      Model - Account information for an organization.
+      Model - Request body for deleting an agent deployment.
      
     
    
        
    
    
     
-     organizationId
+     deploymentId
     
     
      
@@ -93927,36 +103707,12 @@ 
    
       
    
       
        
-        example: 5cad178b20a1d52567b534553413b691
+        required
        
       
          
      
    
-     The organization Id.
-    
    
-     creditBalance
-    
-     
-      number
-     
-     
    
-     The current account balance.
-    
    
-     card
-    
-     
-      Card object
-     
-     
    
-     Credit card information.
+     Identifier of the deployment to delete.
         		
    
    
    
    
   
    
+ 
+
+
+
    Responses
    
+
+
+ 
+  
    
+   The
+   
+    /agents/deployments/delete
+   
+   API provides a response in the following format:
+  
    
+  
    
+   200 Success
+  
    
   
-    
-   
-   
-    
@@ -94040,9 +103789,10 @@ 
    
      
    
       
     {
-  "brand": "string",
-  "expiration": "12/27",
-  "last4": "string"
+  "success": true,
+  "errors": [
+    "string"
+  ]
 }
    
      
    
     
@@ -94082,7 +103832,7 @@ 
    Examples
    
-   The following example demonstates reading the organization account status through the cloud API.
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
   
    
    
    from base64 import b64encode
@@ -94121,25 +103871,254 @@ 
    Examples
    
 # --------------------
 
 
-### Read Account Status
-# Send a POST request to the /account/read endpoint to read account status
-response = post(f'{BASE_URL}/account/read', headers=get_headers())
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
+})
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the account status
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
 if result['success']:
-    print("Account Status:")
+    print("List of Agents:")
+    print(result)
+
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Created Successfully:")
+    print(result)
+
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
+if result['success']:
+    print("Task Details:")
+    print(result)
+
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Updated Successfully:")
+    print(result)
+
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Created Successfully:")
+    print(result)
+
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)
    
   
     
    
-
    API Reference > Lean Version
    
+
    API Reference > Agent Management > Deployments > Read Conversation
    
-    
    
-        
    API Reference
    
-        
    Lean Version
    
+    
    
+        
    Deployments
    
+        
    Read Conversation
    
     
    
 
    Introduction
    
@@ -94147,7 +104126,7 @@ 
    Introduction
    
-   Returns a list of lean versions with basic information for each version.
+   Read the conversation transcript of an agent deployment.
   
    
@@ -94157,92 +104136,39 @@ 
    Request
    
-   The
+   Id of the deployment whose conversation to read. The
    
-    /lean/versions/read
+    /agents/deployments/conversation/read
    
    API accepts requests in the following format:
   
    
-  
    
    
     
    
      
       
-       Card
+       RestResponse
       
-      Model - Credit card information.
+      Model - Base API response class for the QuantConnect API.
      
     
    
        
    
    
     
-     brand
-    
-     
-      string
-     
-     
    
-     Credit card brand.
-    
    
-     expiration
+     success
     
     
      
-      string
-      
    
-      
-       
-        example: 12/27
-       
-      
+      boolean
          
      
    
-     The credit card expiration (mm/yy).
+     Indicate if the API request was successful.
         		
    
    
    
    
     
-     last4
+     errors
     
     
      
-      string
+      string Array
      
      
    
-     The last 4 digits of the card.
+     List of errors with the API call.
         		
    
    
    
    
 
  
   
   
  
 
 
 
 
 
 
  
   
  
 
 
  
   
    
-   
-    
-     
-    
-   
-   
-  
    
-      
-       /lean/versions/read
-      
-      Method
-     
    
-    
-     /lean/versions/read
-    
-    method takes no parameters.
-   
    
- 
-
-
-
    Responses
    
-
-
- 
-  
    
-   The
-   
-    /lean/versions/read
-   
-   API provides a response in the following format:
-  
    
-  
    
-   200 Success
-  
    
   
-    
-   
-   
-    
-    
-   
-   
-    
@@ -94253,152 +104179,53 @@ 
    
      
    
       
     {
-  "versions": [
-    {
-      "id": 0,
-      "created": "2021-11-26T15:18:27.693Z",
-      "description": "string",
-      "leanHash": "string",
-      "leanCloudHash": "string",
-      "name": "string",
-      "ref": "string",
-      "public": true
-    }
-  ],
-  "success": true,
-  "errors": [
-    "string"
-  ]
+  "deploymentId": "string"
 }
    
      
    
     
    
    
    
     
    
      
       
-       LeanVersionsResponse
+       ReadAgentConversationRequest
       
-      Model - Contains the LEAN versions with their basic descriptions.
+      Model - Request body for reading an agent deployment's conversation.
      
     
    
        
    
    
     
-     versions
-    
-     
-      LeanVersion Array
-     
-     
    
-     List of LEAN versions with their basic descriptions.
-    
    
-     success
-    
-     
-      boolean
-     
-     
    
-     Indicate if the API request was successful.
-    
    
-     errors
+     deploymentId
     
     
      
-      string Array
+      string
+      
    
+      
+       
+        required
+       
+      
          
      
    
-     List of errors with the API call.
+     Identifier of the deployment whose conversation to read.
         		
    
    
    
    
   
    
+ 
+
+
+
    Responses
    
+
+
+ 
+  
    
+   The
+   
+    /agents/deployments/conversation/read
+   
+   API provides a response in the following format:
+  
    
+  
    
+   200 Success
+  
    
+  
    
+   401 Authentication Error
+  
    
   
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-    
-   
-   
-    
-   
-   
-    
-    
-   
-   
-    
-    
    
    
     
    
      
       
-       LeanVersion
+       UnauthorizedError
       
-      Model
+      Model - Unauthorized response from the API. Key is missing, invalid, or timestamp is too old for hash.
      
     
    
        
    
    
     
-     id
-    
-     
-      integer
-     
-     
    
-     Id of the LEAN version.
-    
    
-     created
-    
-     
-      string($date-time)
-     
-     
    
-     Date when this version was created.
-    
    
-     description
-    
-     
-      string
-     
-     
    
-     Description of the LEAN version.
-    
    
-     leanHash
-    
-     
-      string
-     
-     
    
-     Commit Hash in the LEAN repository.
-    
    
-     leanCloudHash
-    
-     
-      string
-     
-     
    
-     Commit Hash in the LEAN Cloud repository.
-    
    
-     name
-    
-     
-      string
-     
-     
    
-     Name of the branch where the commit is.
-    
    
-     ref
+     www_authenticate
     
     
      
       string
      
      
    
-     Reference to the branch where the commit is.
-    
    
-     public
-    
-     
-      boolean
-     
-     
    
-     Indicates if the version is available for the public.
-    
    
-     Example
-    
-     
    
-      
    -{
-  "id": 0,
-  "created": "2021-11-26T15:18:27.693Z",
-  "description": "string",
-  "leanHash": "string",
-  "leanCloudHash": "string",
-  "name": "string",
-  "ref": "string",
-  "public": true
-}
    
-     
    
+     Header
         		
    
    
   
    
@@ -94409,7 +104236,7 @@ 
    Examples
    
 
  
   
    
-   The following example demonstates getting a list of Lean versions through the cloud API.
+   The following example demonstrates listing, creating, reading, and updating an agent; creating, reading, updating, deleting, and listing agent tasks; deploying a task; and reading, prompting, stopping, and deleting a deployment through the cloud API.
   
    
   
    
    
    from base64 import b64encode
@@ -94448,14 +104275,243 @@ 
    Examples
    
 # --------------------
 
 
-### Get Lean Versions
-# Send a POST request to the /lean/versions/read endpoint to get Lean versions
-response = post(f'{BASE_URL}/lean/versions/read', headers=get_headers())
+### List Agents
+# Send a POST request to the /agents/list endpoint to check for an existing "My Claude" agent
+response = post(f'{BASE_URL}/agents/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose agents to list
+})
 # Parse the JSON response into python managable dict
 result = response.json()
-# Check if the request was successful and print the versions
+# Search the returned agents for one already named "My Claude"
+my_claude = next((a for a in result.get('agents', []) if a['name'] == 'My Claude'), None)
+# Check if the request was successful and print the list
 if result['success']:
-    print("Lean Versions:")
+    print("List of Agents:")
+    print(result)
+
+### Create Agent (only when "My Claude" does not exist yet)
+if not my_claude:
+    # Send a POST request to the /agents/create endpoint to create a new agent
+    response = post(f'{BASE_URL}/agents/create', headers=get_headers(), json={
+        "organizationId": ORGANIZATION_ID,                        # Organization that owns the agent
+        "name": "Agent Placeholder",                              # Placeholder name; renamed below
+        "description": "Customize your assistant's capabilities.",
+        "systemPrompt": "I am a bot",                             # System prompt for the agent
+        "model": "claude-opus-4-7",                               # Model identifier the agent runs on
+        "reasoningEffort": "high",                                # Reasoning effort: low | medium | high
+        "maxTurns": 100,                                          # Max agent turns per deployment
+        "toolChoice": "auto",                                     # Tool selection policy
+        "public": False,                                          # Keep the agent private
+        "useQCC": True                                            # Use the QuantConnect Cloud LLM provider
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Capture the created agent
+    my_claude = result['agent']
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Created Successfully:")
+        print(result)
+
+    ### Update Agent (rename the new agent to "My Claude")
+    # Send a POST request to the /agents/update endpoint to rename the agent
+    response = post(f'{BASE_URL}/agents/update', headers=get_headers(), json={
+        "agentId": my_claude['id'],  # ID of the agent to update
+        "name": "My Claude"          # New name for the agent
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    # Check if the request was successful and print the result
+    if result['success']:
+        print("Agent Updated Successfully:")
+        print(result)
+
+# Capture the agent ID for downstream calls
+agent_id = my_claude['id']
+
+### Read Agent
+# Send a POST request to the /agents/read endpoint and check the model the agent runs on
+response = post(f'{BASE_URL}/agents/read', headers=get_headers(), json={
+    "agentId": agent_id  # ID of the agent to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the agent's model
+if result['success']:
+    print(f"Agent model: {result['agent']['model']}")
+    print(result)
+
+### Delete Agent (left here for reference; uncomment to delete the agent)
+# response = post(f'{BASE_URL}/agents/delete', headers=get_headers(), json={
+#     "agentId": agent_id  # ID of the agent to delete
+# })
+# result = response.json()
+# if result['success']:
+#     print("Agent Deleted Successfully:")
+#     print(result)
+
+### Create Task
+# Send a POST request to the /agents/tasks/create endpoint to create a task
+response = post(f'{BASE_URL}/agents/tasks/create', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID,                  # Organization that owns the task
+    "agentId": 47,                                      # ID of the agent the task uses
+    "name": f"Task_{int(time())}",                      # Unique task name
+    "prompt": "Build a buy and hold strategy for SPY",  # Prompt the agent will run
+    "projectPreference": "project-each-task-deployment" # New project per deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task ID from the response
+task_id = result['task']['id']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Created Successfully:")
+    print(result)
+
+### Read Task
+# Send a POST request to the /agents/tasks/read endpoint to read task details
+response = post(f'{BASE_URL}/agents/tasks/read', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the task name from the response
+task_name = result['task']['name']
+# Check if the request was successful and print the details
+if result['success']:
+    print("Task Details:")
+    print(result)
+
+### Update Task
+# Send a POST request to the /agents/tasks/update endpoint to rename the task
+response = post(f'{BASE_URL}/agents/tasks/update', headers=get_headers(), json={
+    "taskId": task_id,           # ID of the task to update
+    "name": f"Test - {task_name}" # Prepend "Test - " to the existing name
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Updated Successfully:")
+    print(result)
+
+### List Tasks
+# Send a POST request to the /agents/tasks/list endpoint to list tasks in the org
+response = post(f'{BASE_URL}/agents/tasks/list', headers=get_headers(), json={
+    "organizationId": ORGANIZATION_ID  # Organization whose tasks to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Tasks:")
+    print(result)
+
+### Create Deployment
+# Send a POST request to the /agents/deployments/create endpoint to deploy the task
+response = post(f'{BASE_URL}/agents/deployments/create', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to deploy
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Extract the deployment ID from the response
+deployment_id = result['deployment']['deploymentId']
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Created Successfully:")
+    print(result)
+
+### Read Deployment
+# Poll the /agents/deployments/read endpoint until the deployment reaches a terminal status
+from time import sleep
+while True:
+    response = post(f'{BASE_URL}/agents/deployments/read', headers=get_headers(), json={
+        "deploymentId": deployment_id  # ID of the deployment to read
+    })
+    # Parse the JSON response into python managable dict
+    result = response.json()
+    status = result['deployment']['status']
+    print(f"Deployment status: {status}")
+    # Stop polling once the deployment is no longer running
+    if status in ('Completed', 'Stopped', 'Failed'):
+        break
+    # Wait before polling again
+    sleep(10)
+# Print the final deployment details
+print("Deployment Details:")
+print(result)
+
+### List Deployments
+# Send a POST request to the /agents/deployments/list endpoint to list deployments of the task
+response = post(f'{BASE_URL}/agents/deployments/list', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task whose deployments to list
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the list
+if result['success']:
+    print("List of Deployments:")
+    print(result)
+
+### Update Deployment (send a follow-up prompt)
+# Send a POST request to the /agents/deployments/prompt endpoint with a new prompt
+response = post(f'{BASE_URL}/agents/deployments/prompt', headers=get_headers(), json={
+    "deploymentId": deployment_id,      # ID of the running deployment
+    "prompt": "Replace SPY for QQQ"     # Follow-up prompt for the deployment
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Prompt Sent Successfully:")
+    print(result)
+
+### Read Conversation
+# Send a POST request to the /agents/deployments/conversation/read endpoint to read the transcript
+response = post(f'{BASE_URL}/agents/deployments/conversation/read', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment whose conversation to read
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the conversation
+if result['success']:
+    print("Conversation Transcript:")
+    print(result)
+
+### Stop Deployment
+# Send a POST request to the /agents/deployments/stop endpoint to stop the running deployment
+response = post(f'{BASE_URL}/agents/deployments/stop', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to stop
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Stopped Successfully:")
+    print(result)
+
+### Delete Deployment
+# Send a POST request to the /agents/deployments/delete endpoint to delete the deployment
+response = post(f'{BASE_URL}/agents/deployments/delete', headers=get_headers(), json={
+    "deploymentId": deployment_id  # ID of the deployment to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Deployment Deleted Successfully:")
+    print(result)
+
+### Delete Task
+# Send a POST request to the /agents/tasks/delete endpoint to delete the task
+response = post(f'{BASE_URL}/agents/tasks/delete', headers=get_headers(), json={
+    "taskId": task_id  # ID of the task to delete
+})
+# Parse the JSON response into python managable dict
+result = response.json()
+# Check if the request was successful and print the result
+if result['success']:
+    print("Task Deleted Successfully:")
     print(result)
    
   
    
  
@@ -94464,7 +104520,7 @@ 
    Examples
    
 
     
    
 
    API Reference > Examples
    
 
    
-    
    
+    
    
         
    API Reference
    
         
    Examples
    
     
    
diff --git a/single-page/Quantconnect-Lean-Cli.html b/single-page/Quantconnect-Lean-Cli.html
index 4c08b1117a..647163e46d 100644
--- a/single-page/Quantconnect-Lean-Cli.html
+++ b/single-page/Quantconnect-Lean-Cli.html
@@ -3006,17 +3006,17 @@ 
    
   
    
   
    
    
    Data that will be purchased and downloaded:
-┌───────────────────────────┬─────────┬────────────────────────┬────────────┬─────────┐
-│ Dataset                   │ Vendor  │ Details                │ File count │ Price   │
-├───────────────────────────┼─────────┼────────────────────────┼────────────┼─────────┤
-│ Binance Crypto Price Data │ CoinAPI │ Data type: Trade       │ 32         │ 800 QCC │
-│                           │         │ Ticker: BTCBUSD        │            │         │
-│                           │         │ Resolution: Second     │            │         │
-│                           │         │ Start date: 2022-05-05 │            │         │
-│                           │         │ End date: 2022-06-05   │            │         │
-└───────────────────────────┴─────────┴────────────────────────┴────────────┴─────────┘
-Total price: 800 QCC
-Organization balance: 1,000 QCC
+┌─────────────┬──────────┬────────────────────────┬────────────┬────────┐
+│ Dataset     │ Vendor   │ Details                │ File count │ Price  │
+├─────────────┼──────────┼────────────────────────┼────────────┼────────┤
+│ US Equities │ AlgoSeek │ Data type: Trade       │ 3          │ 15 QCC │
+│             │          │ Ticker: SPY            │            │        │
+│             │          │ Resolution: Minute     │            │        │
+│             │          │ Start date: 2023-01-01 │            │        │
+│             │          │ End date: 2023-01-05   │            │        │
+└─────────────┴──────────┴────────────────────────┴────────────┴────────┘
+Total price: 15 QCC
+Organization balance: 10,000 QCC
 
 Data Terms of Use has been signed previously.
 Find full agreement at: https://www.quantconnect.com/terms/data/?organization=<organizationId>
@@ -3124,7 +3124,46 @@ 
    
     
      lean data download
     
-    to start an interactive downloading wizard and then enter the dataset category number.
+    to start an interactive downloading wizard and then select
+    
+     QuantConnect
+    
+    to list the categories in the
+    
+     Dataset Market
+    
+    .
+    
    
+     
    $ lean data download
+Select a historical data provider:
+1) Interactive Brokers
+2) Oanda
+3) Bitfinex
+4) Coinbase Advanced Trade
+5) Binance
+6) Kraken
+7) CharlesSchwab
+8) IQFeed
+9) Polygon
+10) FactSet
+11) AlphaVantage
+12) CoinApi
+13) ThetaData
+14) QuantConnect
+15) Local
+16) Terminal Link
+17) Bybit
+18) TradeStation
+19) Alpaca
+20) Tastytrade
+21) Eze
+22) dYdX
+23) Databento
+Enter an option: 14
    
+    
    
+   
+   
  • 
+    Enter the dataset category number.
     
    
      
    $ lean data download
 Select a category:
@@ -3147,38 +3186,37 @@ 
    
     
    
      
    $ lean data download
 Select a dataset:
-1) Binance Crypto Future Margin Rate Data
-2) Binance Crypto Future Price Data
-3) Binance Crypto Price Data
-4) Binance US Crypto Price Data
-5) Bitcoin Metadata
-6) Bitfinex Crypto Price Data
-7) Brain Language Metrics on Company Filings
-8) Brain ML Stock Ranking
-9) CFD Data
-10) Coinbase Crypto Price Data
-11) Composite Factor Bundle
-12) Cross Asset Model
-13) FOREX Data
-14) Insider Trading
-15) Kraken Crypto Price Data
-16) NFT Sales
-17) Tactical
-18) US Congress Trading
-19) US ETF Constituents
-20) US Equities
-21) US Equity Coarse Universe
-22) US Equity Options
-23) US Equity Security Master
-24) US Federal Reserve (FRED)
-25) US Futures
-26) US Futures Security Master
-27) US Index Options
-28) US SEC Filings
-29) US Treasury Yield Curve
-30) VIX Central Contango
-31) VIX Daily Price
-Enter an option: 20
    
+1) Bitcoin Metadata
+2) Brain Language Metrics on Company Filings
+3) Brain ML Stock Ranking
+4) CFD Data
+5) Composite Factor Bundle
+6) Cross Asset Model
+7) FOREX Data
+8) Insider Trading
+9) International Future Universe
+10) NFT Sales
+11) Tactical
+12) US Congress Trading
+13) US ETF Constituents
+14) US Equities
+15) US Equity Coarse Universe
+16) US Equity Option Universe
+17) US Equity Options
+18) US Equity Security Master
+19) US Future Option Universe
+20) US Future Options
+21) US Future Universe
+22) US Futures
+23) US Futures Security Master
+24) US Index Option Universe
+25) US Index Options
+26) US Interest Rate
+27) US SEC Filings
+28) US Treasury Yield Curve
+29) VIX Central Contango
+30) VIX Daily Price
+Enter an option: 13
    
     
    
     
    
      If you don't have an active subscription to the US Equity Security Master, you'll get the following error message:
@@ -4035,444 +4073,6 @@ 
    Equity Options
    
  
 
 
-
    Crypto
    
-
- 
-  
    
-   Crypto algorithms require at least one of the
-   
-    CoinAPI datasets
-   
-   . The CoinAPI datasets are available in several resolutions. The resolution you need depends on the Crypto subscriptions you create in your algorithm and the resolution of data you get in
-   
-    history requests
-   
-   . The following table describes the file format and costs of each resolution:
-  
    
-  
-   
-    
-     
-     
-     
-    
-   
-   
-    
-     
-     
-     
-    
-    
-     
-     
-     
-    
-    
-     
-     
-     
-    
-    
-     
-     
-     
-    
-    
-     
-     
-     
-    
-   
-  
    
-      Resolution
-     
-      File Format
-     
-      Cost per file
-     
    
-      Tick
-     
-      One file per security per trading day per brokerage per data format. Quote and trade data are separate files.
-      
    
-         		
-      100 QCC = $1 USD
-     
    
-      Second
-     
-      One file per security per trading day per brokerage per data format. Quote and trade data are separate files.
-      
    
-         		
-      25 QCC = $0.25 USD
-     
    
-      Minute
-     
-      One file per security per trading day per brokerage per data format. Quote and trade data are separate files.
-      
    
-         		
-      5 QCC = $0.05 USD
-     
    
-      Hour
-     
-      One file per security per brokerage.
-      
    
-         		
-      400 QCC = $4 USD
-     
    
-      Daily
-     
-      One file per security per brokerage.
-      
    
-         		
-      100 QCC = $1 USD
-     
    
-  
    
-   If you add universes to your algorithm, you also need
-   
-    CryptoUniverse
-   
-   data. The file format of
-   
-    CryptoUniverse
-   
-   data is one file per day per brokerage and each file costs 100 QCC = $1 USD.
-  
    
-  
    
-   For example, the following algorithm creates a universe of 100 Cryptocurrencies and then subscribes to minute resolution data for each one in the universe:
-  
    
-  
    
-   
    public class CryptoDataAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2020, 1, 1);
-        SetEndDate(2021, 1, 1);
-        UniverseSettings.Asynchronous = true;            
-        AddUniverse(CryptoUniverse.Coinbase(universeDay => universeDay.OrderByDescending(cf => cf.VolumeInUsd).Take(100).Select(x => x.Symbol))
-        );
-    }
-}
    
-   
    class CryptoDataAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2020, 1, 1)
-        self.set_end_date(2021, 1, 1)
-        self.universe_settings.asynchronous = True
-        self.add_universe(CryptoUniverse.coinbase(self.universe_filter))
-
-    def universe_filter(self, universe_day: List[CryptoUniverse]) -> List[Symbol]:
-        sorted_by_dollar_volume = sorted(universe_day, key=lambda cf: (cf.volume_in_usd or 0), reverse=True)
-        return [cf.symbol for cf in sorted_by_dollar_volume[:100]]
    
-  
    
-  
    
-   The following table shows the data cost of the preceding algorithm:
-  
    
-  
-   
-    
-     
-     
-     
-     
-    
-   
-   
-    
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-    
-   
-  
    
-      Dataset
-     
-      Package
-     
-      Initial Cost
-     
-      Ongoing Cost
-     
    
-      Coinbase Crypto Price Data
-     
-      Universe Download
-     
-      365 days
-      
    
-      => 365 files
-      
    
-      
    
-      365 files @ 100 QCC/file
-      
    
-      => 365 * 100 QCC
-      
    
-      = 36,500 QCC
-      
    
-      = $365 USD
-         		
-      1 file per day @ 100 QCC/file
-      
    
-      => 100 QCC/day
-      
    
-      = $1 USD/day
-     
    
-      Coinbase Crypto Price Data
-     
-      Minute Download
-     
-      100 securities over 365 trading days with 2 data formats
-      
    
-      => 1 * 100 * 365 * 2 files
-      
    
-      = 73,000 files
-      
    
-      
    
-      73,000 files @ 5 QCC/file
-      
    
-      => 73,000 * 5 QCC
-      
    
-      = 365,000 QCC
-      
    
-      = $3,650 USD
-         		
-      100 securities with 2 data formats
-      
    
-      => 100 * 2 files/day
-      
    
-      = 200 files/day
-      
    
-      
    
-      200 files/day @ 5 QCC/file
-      
    
-      => 200 * 5 QCC/day
-      
    
-      = 1,000 QCC/day
-      
    
-      = $10 USD/day
-     
    
-  
    
-   The preceding table assumes you download trade and quote data, but you can run backtests with only trade data.
-  
    
- 
-
-
-
    Crypto Future
    
-
- 
-  
    
-   Crypto Future algorithms require data from one of the price datasets and one of the margin rate datasets.
-    The price datasets are available in several resolutions. 
-    The resolution you need depends on the Crypto Future subscriptions you create in your algorithm and the resolution of data you get in
-   
-    history requests
-   
-   . 
-    The following table describes the file format and costs of each resolution:
-  
    
-  
-   
-    
-     
-     
-     
-    
-   
-   
-    
-     
-     
-     
-    
-    
-     
-     
-     
-    
-    
-     
-     
-     
-    
-    
-     
-     
-     
-    
-    
-     
-     
-     
-    
-   
-  
    
-      Resolution
-     
-      File Format
-     
-      Cost per file
-     
    
-      Tick
-     
-      One file per security per trading day per data format. Quote and trade data are separate files.
-      
    
-         		
-      100 QCC = $1 USD
-     
    
-      Second
-     
-      One file per security per trading day per data format. Quote and trade data are separate files.
-      
    
-         		
-      25 QCC = $0.25 USD
-     
    
-      Minute
-     
-      One file per security per trading day per data format. Quote and trade data are separate files.
-      
    
-         		
-      5 QCC = $0.05 USD
-     
    
-      Hour
-     
-      One file per security. Quote and trade data are separate files.
-      
    
-         		
-      400 QCC = $4 USD
-     
    
-      Daily
-     
-      One file per security. Quote and trade data are separate files.
-      
    
-         		
-      100 QCC = $1 USD
-     
    
-  
    
-   The file format of the margin rate data is one file per Crypto Future and each file costs 100 QCC = $1 USD.
-  
    
-  
    
-   For example, the following algorithm subscribes to minute resolution data for one Crypto Future:
-  
    
-  
    
-   
    public class CryptoFutureDataAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2020, 1, 1);
-        SetEndDate(2021, 1, 1);
-        SetBrokerageModel(BrokerageName.BinanceFutures, AccountType.Margin);
-        AddCryptoFuture("BTCUSD");
-    }
-}
    
-   
    class CryptoFutureDataAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2020, 1, 1)
-        self.set_end_date(2021, 1, 1)
-        self.set_brokerage_model(BrokerageName.BINANCE_FUTURES, AccountType.MARGIN)
-        self.add_crypto_future('BTCUSD')
    
-  
    
-  
    
-   The following table shows the data cost of the preceding algorithm:
-  
    
-  
-   
-    
-     
-     
-     
-     
-    
-   
-   
-    
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-    
-   
-  
    
-      Dataset
-     
-      Package
-     
-      Initial Cost
-     
-      Ongoing Cost
-     
    
-      Binance Crypto Future Price Data
-     
-      Minute Download
-     
-      1 security over 365 trading days with 2 data formats
-      
    
-      => 1 * 365 * 2 files
-      
    
-      = 730 files
-      
    
-      
    
-      730 files @ 5 QCC/file
-      
    
-      => 730 * 5 QCC
-      
    
-      = 3,650 QCC
-      
    
-      = $36.50 USD
-         		
-      2 files per day @ 5 QCC/file
-      
    
-      => 2 * 5 QCC/day
-      
    
-      = 10 QCC/day
-      
    
-      = $0.10 USD/day
-     
    
-      Binance Crypto Future Margin Rate Data
-     
-      Download On Premise
-     
-      1 security
-      
    
-      => 1 file
-      
    
-      
    
-      1 file @ 100 QCC/file
-      
    
-      => 1 * 100 QCC
-      
    
-      = 100 QCC
-      
    
-      = $1 USD
-         		
-      1 security
-      
    
-      => 1 file/day
-      
    
-      
    
-      1 file/day @ 100 QCC/file
-      
    
-      => 1 * 100 QCC/day
-      
    
-      = 100 QCC/day
-      
    
-      = $1 USD/day
-     
    
-  
    
-   The preceding table assumes you download trade and quote data, but you can run backtests with only trade data.
-  
    
- 
-
-
 
    Forex
    
 
  
@@ -4750,7 +4350,7 @@ 
    Futures
    
       One file per ticker per trading day per data format. Trade, quote, and open interest data are separate files.

    		• - 6 QCC = $0.06 USD + 150 QCC = $1.50 USD
    - 5 QCC = $0.05 USD + 100 QCC = $1.00 USD
    - 5 QCC = $0.05 USD + 50 QCC = $0.50 USD
    - 300 QCC = $3 USD + 1500 QCC = $15 USD
    - 100 QCC = $1 USD + 1500 QCC = $15 USD
    1 ticker with 3 data formats @@ -4928,11 +4528,11 @@

    Futures

    => 3 files/day

    - 3 file/day @ 5 QCC/file + 3 file/day @ 50 QCC/file
    - => 15 QCC/day + => 150 QCC/day
    - = $0.15 USD/day + = $1.50 USD/day
    - --bybit-use-testnet <enum: live|paper> + --bybit-use-testnet <enum: live|paper|demo> @@ -46024,7 +45624,7 @@

    Options

    - --bybit-use-testnet <enum: live|paper> + --bybit-use-testnet <enum: live|paper|demo> diff --git a/single-page/Quantconnect-Local-Platform.html b/single-page/Quantconnect-Local-Platform.html index a8f95acd8f..1bb60f6e89 100644 --- a/single-page/Quantconnect-Local-Platform.html +++ b/single-page/Quantconnect-Local-Platform.html @@ -396,7 +396,7 @@

    Custom LEAN Images

    -

    On-Premises Compute

    +

    On-Premise Compute

    @@ -4035,9 +4035,12 @@

    Delete a parameter
  • Remove the - + GetParameter + + get_parameter + calls that were associated with the parameter from your code files.
    • @@ -5141,7 +5144,11 @@

    the data providers we support in the cloud - . Your live algorithms run on our co-located servers that have 10 GB transfer speeds and low latency. + . Your live algorithms run on our co-located servers racked in + + Equinix + + that have 10 GB transfer speeds and low latency.

    @@ -8530,8 +8537,10 @@

    Example

    bbdf[['price', 'lowerband', 'middleband', 'upperband']].plot();
    - 
    #load "../QuantConnect.csx"
-using QuantConnect;
+   
    #load "../QuantConnect.csx"
    
+
    +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
diff --git a/single-page/Quantconnect-Research-Environment.html b/single-page/Quantconnect-Research-Environment.html
index 8d40b9665c..cfaf38cb11 100644
--- a/single-page/Quantconnect-Research-Environment.html
+++ b/single-page/Quantconnect-Research-Environment.html
@@ -105,7 +105,8 @@ 
    Table of Content
    
 
  • 10.2 Backtest Analysis
    • 
 
  • 10.3 Optimization Analysis
    • 
 
  • 10.4 Live Analysis
    • 
-
  • 10.5 Live Deployment Automation
    • 
+
  • 10.5 Live Reconciliation
    • 
+
  • 10.6 Live Deployment Automation
    • 
 
  • 11 Applying Research
    • 
 
  • 11.1 Key Concepts
    • 
 
  • 11.2 Mean Reversion
    • 
@@ -206,8 +207,10 @@ 
    Example
    
 bbdf[['price', 'lowerband', 'middleband', 'upperband']].plot();
    - 
    #load "../QuantConnect.csx"
-using QuantConnect;
+   
    #load "../QuantConnect.csx"
    
+
    +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -915,8 +918,8 @@ 
    Import C# Libraries
    
    
    
    
     
    #load "../QuantConnect.csx"
-#r "../Plotly.NET.dll"
-#r "../Plotly.NET.Interactive.dll"
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
 #r "../Deedle.dll"
    
    
    
    
  • 
@@ -6037,10 +6040,14 @@ 
    
   
    
   
    
    
    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
-#load "../QuantConnect.csx"
-using QuantConnect;
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
 using QuantConnect.Data.Consolidators;
@@ -6151,13 +6158,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
    
    
    
    
  • 
-    Import the data types.
+    Load the necessary assembly files.
    
    • 
    
    
     
    #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    
+   
    
+   
  • 
+    Import the data types.
+   
    • 
+   
    
+    
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -10244,8 +10255,12 @@ 
    
    
    • 
    
    
     
    import plotly.graph_objects as go
    
-    
    #r "../Plotly.NET.dll"
-using Plotly.NET;
+    
    #r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+   
    
+   
    
+    
    using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
    
    
    
    
  • 
@@ -10320,7 +10335,7 @@ 
    
    
    • 
    
    
     
    fig.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Candlestick charts display the open, high, low, and close prices of the security.
@@ -10420,7 +10435,7 @@ 
    
    
    
    
     
    plt.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Line charts display the value of the property you selected in a time series.
@@ -10463,10 +10478,16 @@ 
    
   
    
   
    
    
    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-using System;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    using System;
 using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
@@ -10476,9 +10497,8 @@ 
    
 using QuantConnect.Research;
 using QuantConnect.Securities;
 
-// Import Plotly for plotting.
-#r "../Plotly.NET.dll"
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Create a QuantBook.
@@ -10529,7 +10549,7 @@ 
    
 // Assign the Layout to the chart.
 chart.WithLayout(layout);
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    # Import plotly library for plotting.
 import plotly.graph_objects as go
 
@@ -10608,13 +10628,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
  • - Import the data types. + Load the necessary assembly files. 
    • #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    +
    +
  • + Import the data types. +
    • +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -11163,10 +11187,11 @@ 
    Plot Data
    
     package.
    
    
    
-    
    #r "../Plotly.NET.dll"
-#r "../Plotly.NET.Interactive.dll"
-
-using Plotly.NET;
+    
    #r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+   
    
+   
    
+    
    using Plotly.NET;
 using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
    
    
    
@@ -11256,7 +11281,7 @@ 
    Plot Data
    
    
    
    
     
    plt.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Line charts display the value of the property you selected in a time series.
@@ -11281,18 +11306,23 @@ 
    
   
    
   
    
    
    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-using QuantConnect;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Fundamental;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
 
-// Import Plotly for plotting.
-#r "../Plotly.NET.dll"
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Create a QuantBook.
@@ -11331,7 +11361,7 @@ 
    
 chart.WithLayout(layout);
 
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    # Create a QuantBook
 qb = QuantBook()
 
@@ -11612,13 +11642,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
  • - Import the data types. + Load the necessary assembly files. 
    • #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    +
    +
  • + Import the data types. +
    • +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -11881,10 +11915,16 @@ 
    
   
    
   
    
    
    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-using System;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    using System;
 using System.Collections.Generic;
 using QuantConnect;
 using QuantConnect.Data;
@@ -11893,9 +11933,8 @@ 
    
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
 
-// Import Plotly for plotting.
-#r "../Plotly.NET.dll"
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Create a QuantBook.
@@ -11946,7 +11985,7 @@ 
    
 chart.WithLayout(layout);
 
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    # Instantiate a QuantBook instance.
 qb = QuantBook()
 # Set the date being studied.
@@ -12139,13 +12178,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
  • - Import the data types. + Load the necessary assembly files. 
    • #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    +
    +
  • + Import the data types. +
    • +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -12759,10 +12802,13 @@ 
    
         xaxis_rangeslider_visible=False
     )
 ).show()
    - 
    #load "../QuantConnect.csx"
-#r "../Plotly.NET.dll"
-
-using QuantConnect;
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+
    +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -12770,6 +12816,7 @@ 
    
 using QuantConnect.Data.Market;
 using QuantConnect.Securities.Option;
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Get the SPY Option chain for January 1, 2024.
@@ -12781,10 +12828,10 @@ 
    
 // Select a contract from the chain.
 var expiry = chain.Select(contract => contract.Expiry).Min();
 var contractSymbol = chain
-    .Where(contract => 
-        contract.Expiry == expiry && 
+    .Where(contract =>
+        contract.Expiry == expiry &&
         contract.Right == OptionRight.Call &&
-        contract.Greeks.Delta > 0.3m && 
+        contract.Greeks.Delta > 0.3m &&
         contract.Greeks.Delta < 0.7m
     )
     .OrderByDescending(contract => contract.OpenInterest)
@@ -12815,7 +12862,7 @@ 
    
 layout.SetValue("yaxis", yAxis);
 layout.SetValue("title", title);
 chart.WithLayout(layout);
-HTML(GenericChart.toChartHTML(chart))
    +display(chart)
    Candlestick plot of the prices for a SPY Option contract Candlestick plot of the prices for a SPY Option contract @@ -12859,10 +12906,13 @@

    # Plot the open interest history. history.plot(title=f'{contract_symbol.value} Open Interest') plt.show() - 
    #load "../QuantConnect.csx"
-#r "../Plotly.NET.dll"
-
-using QuantConnect;
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
+  
    
+   
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -12870,6 +12920,7 @@ 
    
 using QuantConnect.Data.Market;
 using QuantConnect.Securities.Option;
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Get the TSLA Option chain for January 1, 2024.
@@ -12909,7 +12960,7 @@ 
    
 layout.SetValue("yaxis", yAxis);
 layout.SetValue("title", title);
 chart.WithLayout(layout);
-HTML(GenericChart.toChartHTML(chart))
+display(chart)
 
    
   
    
   Line plot of the open interest for a TSLA Option contract
@@ -12948,13 +12999,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
    
    
    
  • 
-    Import the data types.
+    Load the necessary assembly files.
    
    • 
    
    
     
    #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    
+   
    
+   
  • 
+    Import the data types.
+   
    • 
+   
    
+    
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -13017,8 +13072,8 @@ 
    Create Subscriptions
    
     Supported Assets
    
    section of the
-   
-    CoinAPI dataset listings
+   
+    QuantConnect dataset listings
    
    .
   
    
@@ -16236,8 +16291,12 @@ 
    
    
    
    
     
    import plotly.graph_objects as go
    
-    
    #r "../Plotly.NET.dll"
-using Plotly.NET;
+    
    #r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+   
    
+   
    
+    
    using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
    
    
    
    
  • 
@@ -16318,7 +16377,7 @@ 
    
    
    • 
    
    
     
    fig.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Candlestick charts display the open, high, low, and close prices of the security.
@@ -16426,7 +16485,7 @@ 
    
    
    
    
     
    plt.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Line charts display the value of the property you selected in a time series.
@@ -16451,10 +16510,16 @@ 
    
   
    
   
    
    
    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-using System;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    using System;
 using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
@@ -16464,9 +16529,8 @@ 
    
 using QuantConnect.Research;
 using QuantConnect.Securities;
 
-// Import Plotly for plotting.
-#r "../Plotly.NET.dll"
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Create a QuantBook.
@@ -16517,7 +16581,7 @@ 
    
 // Assign the Layout to the chart.
 chart.WithLayout(layout);
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    
    
    # Import plotly library for plotting.
 import plotly.graph_objects as go
 
@@ -16592,13 +16656,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
    
    
    
    
  • 
-    Import the data types.
+    Load the necessary assembly files.
    
    • 
    
    
     
    #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    
+   
    
+   
  • 
+    Import the data types.
+   
    • 
+   
    
+    
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -19877,8 +19945,12 @@ 
    
    
    
    
     
    import plotly.graph_objects as go
    
-    
    #r "../Plotly.NET.dll"
-using Plotly.NET;
+    
    #r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+   
    
+   
    
+    
    using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
    
    
    
    
  • 
@@ -19959,7 +20031,7 @@ 
    
    
    • 
    
    
     
    fig.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Candlestick charts display the open, high, low, and close prices of the security.
@@ -20067,7 +20139,7 @@ 
    
    
    
    
     
    plt.show()
    
-    
    HTML(GenericChart.toChartHTML(chart))
    
+    
    display(chart)
    
    
    
    
    
     Line charts display the value of the property you selected in a time series.
@@ -20092,10 +20164,16 @@ 
    
   
    
   
    
    
    // Load the required assembly files and data types in a separate cell.
-#load "../Initialize.csx"
-
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-using System;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    using System;
 using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
@@ -20105,9 +20183,8 @@ 
    
 using QuantConnect.Research;
 using QuantConnect.Securities;
 
-// Import Plotly for plotting.
-#r "../Plotly.NET.dll"
 using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
 
 // Create a QuantBook.
@@ -20158,7 +20235,7 @@ 
    
 // Assign the Layout to the chart.
 chart.WithLayout(layout);
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    
    
    # Import plotly library for plotting.
 import plotly.graph_objects as go
 
@@ -20520,13 +20597,17 @@ 
    Create Subscriptions
    
     
    #load "../Initialize.csx"
    
    
    
    
  • 
-    Import the data types.
+    Load the necessary assembly files.
    
    • 
    
    
     
    #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    
+   
    
+   
  • 
+    Import the data types.
+   
    • 
+   
    
+    
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -20643,9 +20724,12 @@ 
    Create Subscriptions

    		- + DataMappingMode.OpenInterest + + DataMappingMode.OPEN_INTEREST +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + + + lastupdate + + period + + sharesheld + + weight +
    + time + + symbol + + + + +
    + 2025-11-07 + + AAPL R735QTJ8XC9X + + 2025-11-05 + + 1 days + + 178991437.0 + + 0.069116 +
    + MSFT R735QTJ8XC9X + + 2025-11-05 + + 1 days + + 89652295.0 + + 0.064993 +
    + NVDA RHM8UTD8DT2D + + 2025-11-05 + + 1 days + + 294289223.0 + + 0.082117 +
    + 2025-11-08 + + AAPL R735QTJ8XC9X + + 2025-11-06 + + 1 days + + 179242780.0 + + 0.069801 +
    + MSFT R735QTJ8XC9X + + 2025-11-06 + + 1 days + + 89778184.0 + + 0.064423 +
    + NVDA RHM8UTD8DT2D + + 2025-11-06 + + 1 days + + 294702444.0 + + 0.080012 +
    + 2025-11-11 + + AAPL R735QTJ8XC9X + + 2025-11-07 + + 1 days + + 178922101.0 + + 0.069303 +
    + MSFT R735QTJ8XC9X + + 2025-11-07 + + 1 days + + 89617567.0 + + 0.064299 +
    + NVDA RHM8UTD8DT2D + + 2025-11-07 + + 1 days + + 294175231.0 + + 0.079933 +
    +

    +

    + The + + UniverseHistory + + method returns an + + IEnumerable<IEnumerable<ETFConstituentUniverse>> + + object. +

    +
  • + Iterate through the Series to access the universe data. +
    • +
  • + Iterate through the result to access the universe data. +
    • +
    + 
    for date, constituents in universe_history.droplevel('symbol', axis=0).items():
+    for constituent in constituents:
+        symbol = constituent.symbol
+        weight = constituent.weight
    + 
    foreach (var constituents in universeHistory)
+{
+    foreach (ETFConstituentUniverse constituent in constituents)
+    {
+        var symbol = constituent.Symbol;
+        var weight = constituent.Weight;
+    }
 }
    @@ -40482,12 +41064,7 @@

    Available Universes

    - To get universe data for other types of universes, you usually just need to replace - - Fundamental - - in the preceding code snippets with the universe data type. - The following table shows the datasets that support universe selection and their respective data type. + The following table shows the datasets that support universe selection and their respective data type. For more information, about universe selection with these datasets and the data points you can use in the filter function, see the dataset's documentation.

    @@ -40507,142 +41084,106 @@

    Available Universes

    - + @@ -40865,21 +41442,6 @@

    Available Universes

    - - - - - @@ -8911,7 +9229,7 @@

    DataNormalizationMode - + data_normalization_mode @@ -17784,17 +18102,17 @@

    # Create a list of opening auction tick data. trades = [ tick for tick in self._asset.cache.get_all[Tick]() - if tick.TickType == TickType.TRADE and - tick.Price > 0 and - tick.SaleCondition and + if tick.tick_type == TickType.TRADE and + tick.price > 0 and + tick.sale_condition and ( - tick.ParsedSaleCondition == self.officialOpen or - tick.ParsedSaleCondition == self.openingPrints + tick.parsed_sale_condition == self.officialOpen or + tick.parsed_sale_condition == self.openingPrints ) ] # Log the opening auction tick price for tick in trades: - self.log(f"{self.Time},{tick.Price},{tick.Quantity},{tick.ParsedSaleCondition},{tick.ExchangeCode},{tick.Exchange}") + self.log(f"{self.time},{tick.price},{tick.quantity},{tick.parsed_sale_condition},{tick.exchange_code},{tick.exchange}") @@ -22903,6 +23221,422 @@

    # The arbitary delta criterion might be set due to hedging need or risk adjustment. selected = sorted(chain, key=lambda x: abs(x.greeks.delta - 0.4))[0] +

    + Example 4: Delta-Hedged Short Straddle +

    +

    + This example demonstrates a delta-hedged + + short straddle + + strategy that sells an at-the-money (ATM) straddle with 7-30 days to expiration and continuously neutralizes the portfolio delta using the underlying shares. + The + + StraddleSelector + + helper class picks the farthest expiry within the DTE range and the strike closest to the spot price. + On each + + slice + + , the combined Option delta is accumulated from the + + OptionChain + + and the net portfolio delta is computed by adding the underlying position. + The + + DeltaHedger + + helper enforces a minimum share threshold and a rehedge band to avoid over-trading, and applies a time delay between successive hedges. + A + + Scheduled Event + + closes the straddle on expiration day and assignment events are handled to keep the underlying position clean. +

    +
    + 
    public class DeltaHedgedStraddleAlgorithm : QCAlgorithm
+{
+    private Equity _spy;
+    private Symbol _canonicalOption;
+    private OptionStrategy _shortStraddle;
+    private StraddleSelector _straddleSelector;
+    private DeltaHedger _hedger;
+    private decimal _straddleWeight = 0.5m, _delta = 0m;
+    private DateTime? _exitDay;
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(1_000_000);
+        Settings.SeedInitialPrices = true;
+        _spy = AddEquity("SPY", dataNormalizationMode: DataNormalizationMode.Raw);
+        // Initialize the straddle selector with 7-30 day DTE range.
+        _straddleSelector = new StraddleSelector(minDte: 7, maxDte: 30);
+        // Initialize the delta hedger with 30 share minimum, 20 share rehedge band, and 4 hour delay.
+        _hedger = new DeltaHedger(30, 20, TimeSpan.FromHours(4));
+        _canonicalOption = QuantConnect.Symbol.CreateCanonicalOption(_spy.Symbol);
+        // Schedule the rebalance method to run 30 minutes after market open.
+        Schedule.On(
+            DateRules.EveryDay(_spy.Symbol),
+            TimeRules.AfterMarketOpen(_spy.Symbol, 30),
+            Rebalance
+        );
+        // Schedule the close expiring method to run 60 minutes before market close.
+        Schedule.On(
+            DateRules.EveryDay(_spy.Symbol),
+            TimeRules.BeforeMarketClose(_spy.Symbol, 60),
+            CloseExpiring
+        );
+        SetWarmUp(TimeSpan.FromDays(45));
+    }
+
+    public override void OnData(Slice slice)
+    {
+        if (IsWarmingUp || !slice.Bars.Any()) return;
+        if (!slice.OptionChains.TryGetValue(_canonicalOption, out var chain) || _shortStraddle == null) return;
+
+        // Accumulate the delta from all option legs in the straddle.
+        foreach (var leg in _shortStraddle.OptionLegs)
+        {
+            if (chain.Contracts.TryGetValue(leg.Symbol, out var contract))
+            {
+                _delta += contract.Greeks.Delta;
+            }
+        }
+        // Calculate the net portfolio delta including underlying and options.
+        var netDelta = _hedger.ComputeNetDelta(this, _shortStraddle.OptionLegs, _spy.Holdings.Quantity, _delta);
+        // Reset the delta accumulator for the next iteration.
+        _delta = 0m;
+        // Exit if it's not time to hedge or net delta is zero.
+        if (!(_hedger.ShouldHedge(Time) && netDelta != 0)) return;
+        // Calculate the hedge quantity needed to neutralize delta.
+        var hedgeQty = _hedger.GetQuantity(netDelta);
+        if (hedgeQty.HasValue)
+        {
+            MarketOrder(_spy.Symbol, hedgeQty.Value, tag: $"Daily delta hedge ({netDelta:F2} share delta)");
+            // Record the hedge operation with timestamp and delta.
+            _hedger.RecordHedge(Time, netDelta);
+            // Update the net delta after the hedge.
+            netDelta += hedgeQty.Value;
+        }
+        Plot("Portfolio Delta", "Delta", netDelta);
+    }
+
+    private void Rebalance()
+    {
+        if (IsWarmingUp || Portfolio.Invested) return;
+        var chain = OptionChain(_spy.Symbol);
+        if (!chain.Any()) return;
+        // Use the straddle selector to find the best ATM straddle.
+        var result = _straddleSelector.Select(chain, _spy.Price, Time.Date);
+        if (result == null) return;
+        _delta = result.Value.Delta;
+        _shortStraddle = OptionStrategies.ShortStraddle(_canonicalOption, result.Value.Strike, result.Value.Expiry);
+        // Calculate the number of contracts based on portfolio value and weight.
+        var qty = (int)(Portfolio.TotalPortfolioValue * _straddleWeight / (_spy.Price * 100m));
+        if (qty > 0)
+            Buy(_shortStraddle, qty, tag: $"Sell ATM straddle exp {result.Value.Expiry:d}");
+        _hedger.Reset();
+        _exitDay = result.Value.Expiry.Date;
+    }
+
+    private void CloseExpiring()
+    {
+        if (_shortStraddle == null || !_exitDay.HasValue || Time.Date < _exitDay.Value) return;
+        // Liquidate all positions before expiration.
+        Liquidate(tag: "Expiry hedge liquidation");
+    }
+
+    public override void OnOrderEvent(OrderEvent orderEvent)
+    {
+        // Handle option assignment events.
+        if (orderEvent.Status == OrderStatus.Filled && orderEvent.IsAssignment)
+        {
+            // Liquidate all option legs of the straddle.
+            foreach (var leg in _shortStraddle.OptionLegs)
+            {
+                Liquidate(leg.Symbol, tag: "Assignment liquidation");
+            }
+            // Determine the direction based on whether it's a call or put assignment.
+            var direction = Securities[orderEvent.Symbol].Symbol.ID.OptionRight == OptionRight.Call ? 1 : -1;
+            MarketOrder(_spy.Symbol, -_spy.Holdings.Quantity + orderEvent.FillQuantity * direction * 100);
+            _shortStraddle = null;
+        }
+    }
+}
+
+public class StraddleSelector
+{
+    private readonly int _minDte, _maxDte;
+
+    public StraddleSelector(int minDte, int maxDte)
+    {
+        // Store the minimum days to expiration for contract selection.
+        _minDte = minDte;
+        // Store the maximum days to expiration for contract selection.
+        _maxDte = maxDte;
+    }
+
+    public (decimal Delta, DateTime Expiry, decimal Strike)? Select(IEnumerable<OptionContract> chain, decimal spotPrice, DateTime currentDate)
+    {
+        // Filter contracts to those within the specified DTE range.
+        var contracts = chain.Where(c =>
+        {
+            var dte = (c.Expiry.Date - currentDate).Days;
+            return dte >= _minDte && dte <= _maxDte;
+        }).ToList();
+        if (!contracts.Any()) return null;
+        // Select the farthest expiration date from the filtered contracts.
+        var expiry = contracts.Max(c => c.Expiry);
+        // Filter to only contracts with the selected expiration.
+        contracts = contracts.Where(c => c.Expiry == expiry).ToList();
+        // Find the strike price closest to the current spot price.
+        var strike = contracts.OrderBy(c => Math.Abs(c.Strike - spotPrice)).First().Strike;
+        // Filter to only contracts with the selected strike.
+        contracts = contracts.Where(c => c.Strike == strike).ToList();
+        // Ensure we have both call and put for a straddle.
+        if (contracts.Count < 2) return null;
+        // Return the combined delta, expiry, and strike for the straddle.
+        return (contracts.Sum(c => c.Greeks.Delta), expiry, strike);
+    }
+}
+
+public class DeltaHedger
+{
+    private readonly int _minShares, _rehedgeBand;
+    private readonly TimeSpan _hedgeDelay;
+    private DateTime _nextHedgeTime;
+    private decimal? _lastHedgedDelta;
+
+    public DeltaHedger(int minShares, int rehedgeBand, TimeSpan hedgeDelay)
+    {
+        // Store the minimum number of shares required to execute a hedge.
+        _minShares = minShares;
+        // Store the rehedge band threshold to prevent excessive hedging.
+        _rehedgeBand = rehedgeBand;
+        // Store the time delay between hedging operations.
+        _hedgeDelay = hedgeDelay;
+        Reset();
+    }
+
+    public void Reset()
+    {
+        _nextHedgeTime = DateTime.MinValue;
+        _lastHedgedDelta = null;
+    }
+
+    public bool ShouldHedge(DateTime currentTime)
+    {
+        // Check if enough time has passed since the last hedge.
+        return currentTime >= _nextHedgeTime;
+    }
+
+    public decimal ComputeNetDelta(QCAlgorithm algorithm, IEnumerable<Leg> legs, decimal underlyingQuantity, decimal contractDelta)
+    {
+        // Calculate the net portfolio delta by combining underlying and option positions.
+        return underlyingQuantity + legs.Sum(leg =>
+            algorithm.Securities[leg.Symbol].Holdings.Quantity * contractDelta * 100);
+    }
+
+    public int? GetQuantity(decimal netDelta)
+    {
+        if (_lastHedgedDelta.HasValue
+            && Math.Abs(netDelta - _lastHedgedDelta.Value) < _rehedgeBand
+            && Math.Abs(netDelta) < _minShares + _rehedgeBand)
+        {
+            return null;
+        }
+        // Calculate the required hedge quantity to neutralize the net delta.
+        var hedgeQty = (int)Math.Round(-netDelta);
+        if (Math.Abs(hedgeQty) < _minShares) return null;
+        // Return the calculated hedge quantity.
+        return hedgeQty;
+    }
+
+    public void RecordHedge(DateTime currentTime, decimal netDelta)
+    {
+        // Record the delta value after this hedge operation.
+        _lastHedgedDelta = netDelta;
+        // Set the next allowed hedge time based on the configured delay.
+        _nextHedgeTime = currentTime + _hedgeDelay;
+    }
+}
    + 
    class DeltaHedgedStraddleAlgo(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(1_000_000)
+        self.settings.seed_initial_prices = True
+        self._spy = self.add_equity("SPY", data_normalization_mode=DataNormalizationMode.RAW)
+        # Initialize the straddle selector with 7-30 day DTE range.
+        self._straddle_selector = StraddleSelector(min_dte=7, max_dte=30)
+        # Initialize the delta hedger with 30 share minimum, 20 share rehedge band, and 4 hour delay.
+        self._hedger = DeltaHedger(30, 20, timedelta(hours=4))
+        self._canonical_option = Symbol.create_canonical_option(self._spy)
+        # Set the straddle position size to 50% of portfolio value.
+        self._straddle_weight = 0.5
+        self._short_straddle = None
+        self._exit_day = None
+        # Schedule the rebalance method to run 30 minutes after market open.
+        self.schedule.on(
+            self.date_rules.every_day(self._spy),
+            self.time_rules.after_market_open(self._spy, 30),
+            self._rebalance
+        )
+        # Schedule the close expiring method to run 60 minutes before market close.
+        self.schedule.on(
+            self.date_rules.every_day(self._spy),
+            self.time_rules.before_market_close(self._spy, 60),
+            self._close_expiring
+        )
+        self.set_warm_up(timedelta(45))
+
+    def on_data(self, data):
+        if self.is_warming_up or not data.bars:
+            return
+        chain = data.option_chains.get(self._canonical_option)
+        if not chain or not self._short_straddle:
+            return
+        # Accumulate the delta from all option legs in the straddle.
+        for leg in self._short_straddle.option_legs:
+            contract = chain.contracts.get(leg.symbol)
+            if contract:
+                self._delta += contract.greeks.delta
+        # Calculate the net portfolio delta including underlying and options.
+        net_delta = self._hedger.compute_net_delta(self, self._short_straddle.option_legs, self._spy.holdings.quantity, self._delta)
+        # Reset the delta accumulator for the next iteration.
+        self._delta = 0
+        # Exit if it's not time to hedge or net delta is zero.
+        if not (self._hedger.should_hedge(self.time) and net_delta):
+            return
+        # Calculate the hedge quantity needed to neutralize delta.
+        hedge_qty = self._hedger.get_quantity(net_delta)
+        if hedge_qty:
+            self.market_order(self._spy, hedge_qty, tag=f"Daily delta hedge ({net_delta:.2f} share delta)")
+            # Record the hedge operation with timestamp and delta.
+            self._hedger.record_hedge(self.time, net_delta)
+            # Update the net delta after the hedge.
+            net_delta += hedge_qty
+        self.plot("Portfolio Delta", "Delta", net_delta)
+
+    def _rebalance(self):
+        if self.is_warming_up or self.portfolio.invested:
+            return
+        chain = self.option_chain(self._spy)
+        if not chain:
+            return
+        # Use the straddle selector to find the best ATM straddle.
+        result = self._straddle_selector.select(chain, self._spy.price, self.time.date())
+        if not result:
+            return
+        self._delta, expiry, strike = result
+        self._short_straddle = OptionStrategies.short_straddle(self._canonical_option, strike, expiry)
+        # Calculate the number of contracts based on portfolio value and weight.
+        qty = int(self.portfolio.total_portfolio_value * self._straddle_weight / (self._spy.price * 100.0))
+        if qty:
+            self.buy(self._short_straddle, qty, tag=f"Sell ATM straddle exp {expiry.date()}")
+        self._hedger.reset()
+        self._exit_day = expiry.date()
+
+    def _close_expiring(self):
+        if not self._short_straddle or not self._exit_day or self.time.date() < self._exit_day:
+            return
+        # Liquidate all positions before expiration.
+        self.liquidate(tag="Expiry hedge liquidation")
+
+    def on_order_event(self, order_event):
+        # Handle option assignment events.
+        if order_event.status == OrderStatus.FILLED and order_event.is_assignment:
+            # Liquidate all option legs of the straddle.
+            for leg in self._short_straddle.option_legs:
+                self.liquidate(leg.symbol, tag="Assignment liquidation")
+            # Determine the direction based on whether it's a call or put assignment.
+            direction = 1 if self.securities[order_event.symbol].right == OptionRight.CALL else -1
+            self.market_order(self._spy, -self._spy.holdings.quantity + order_event.quantity * direction * 100)
+            self._short_straddle = None
+
+
+class StraddleSelector:
+
+    def __init__(self, min_dte, max_dte):
+        # Store the minimum days to expiration for contract selection.
+        self._min_dte = min_dte
+        # Store the maximum days to expiration for contract selection.
+        self._max_dte = max_dte
+
+    def select(self, chain, spot_price, current_date):
+        # Filter contracts to those within the specified DTE range.
+        contracts = [
+            contract for contract in chain
+            if self._min_dte <= (contract.expiry.date() - current_date).days <= self._max_dte
+        ]
+        if not contracts:
+            return
+        # Select the farthest expiration date from the filtered contracts.
+        expiry = max(contract.expiry for contract in contracts)
+        # Filter to only contracts with the selected expiration.
+        contracts = [contract for contract in contracts if contract.expiry == expiry]
+        # Find the strike price closest to the current spot price.
+        strike = min(contracts, key=lambda c: abs(c.strike - spot_price)).strike
+        # Filter to only contracts with the selected strike.
+        contracts = [contract for contract in contracts if contract.strike == strike]
+        # Ensure we have both call and put for a straddle.
+        if len(contracts) < 2:
+            return
+        # Return the combined delta, expiry, and strike for the straddle.
+        return sum(contract.greeks.delta for contract in contracts), expiry, strike
+
+
+class DeltaHedger:
+
+    def __init__(self, min_shares, rehedge_band, hedge_delay):
+        # Store the minimum number of shares required to execute a hedge.
+        self._min_shares = min_shares
+        # Store the rehedge band threshold to prevent excessive hedging.
+        self._rehedge_band = rehedge_band
+        # Store the time delay between hedging operations.
+        self._hedge_delay = hedge_delay
+        self.reset()
+
+    def reset(self):
+        self._next_hedge_time = datetime.min
+        self._last_hedged_delta = None
+
+    def should_hedge(self, current_time):
+        # Check if enough time has passed since the last hedge.
+        return current_time >= self._next_hedge_time
+
+    def compute_net_delta(self, algorithm, legs, underlying_quantity, contract_delta):
+        # Calculate the net portfolio delta by combining underlying and option positions.
+        return underlying_quantity + sum(
+            algorithm.securities[leg.symbol].holdings.quantity * contract_delta * 100
+            for leg in legs
+        )
+
+    def get_quantity(self, net_delta):
+        if (
+            self._last_hedged_delta is not None
+            and abs(net_delta - self._last_hedged_delta) < self._rehedge_band
+            and abs(net_delta) < self._min_shares + self._rehedge_band
+        ):
+            return
+        # Calculate the required hedge quantity to neutralize the net delta.
+        hedge_qty = int(round(-net_delta))
+        if abs(hedge_qty) < self._min_shares:
+            return
+        # Return the calculated hedge quantity.
+        return hedge_qty
+
+    def record_hedge(self, current_time, net_delta):
+        # Record the delta value after this hedge operation.
+        self._last_hedged_delta = net_delta
+        # Set the next allowed hedge time based on the configured delay.
+        self._next_hedge_time = current_time + self._hedge_delay
    +
    @@ -27905,8 +28639,8 @@

    Introduction

    on_data     method. For more information about the specific datasets we use for backtests, see the - - CoinAPI datasets + + QuantConnect datasets . To trade Crypto live, you can use the @@ -27997,8 +28731,8 @@

    Create Subscriptions

    To view the supported assets, see the - - CoinAPI datasets + + QuantConnect datasets .

    @@ -29689,7 +30423,7 @@

    Create Subscriptions

    To view the supported assets in the Crypto Futures dataset, see - + Supported Assets . @@ -31331,7 +32065,7 @@

    Create Subscriptions

    To view the supported Forex pairs, see - + Supported Assets . @@ -32423,7 +33157,7 @@

    # Create a consolidator that produces 5-minute quote bars to reduce reduce noise. consolidator = QuoteBarConsolidator(timedelta(minutes=5)) # Define the consolidator handler so it updates the EMA with the consoildated bars. - consolidator.DataConsolidated += lambda _, bar: self._forex.ema.update(bar.end_time, bar.close) + consolidator.data_consolidated += lambda _, bar: self._forex.ema.update(bar.end_time, bar.close) # Warm up the indicator with historical data. for bar in self.history[QuoteBar](self._forex.symbol, 1000): consolidator.update(bar) @@ -92374,6 +93108,395 @@

    Time Zone

    ], "earlyCloses": {} }, + "Index-cboe-[*]": { + "exchangeTimeZone": "America/Chicago", + "monday": [ + { + "start": "08:30:00", + "end": "15:15:00", + "state": "market" + } + ], + "tuesday": [ + { + "start": "08:30:00", + "end": "15:15:00", + "state": "market" + } + ], + "wednesday": [ + { + "start": "08:30:00", + "end": "15:15:00", + "state": "market" + } + ], + "thursday": [ + { + "start": "08:30:00", + "end": "15:15:00", + "state": "market" + } + ], + "friday": [ + { + "start": "08:30:00", + "end": "15:15:00", + "state": "market" + } + ], + "holidays": [ + "1/1/1998", + "1/1/1999", + "1/1/2001", + "1/1/2002", + "1/1/2003", + "1/1/2004", + "1/2/2006", + "1/1/2007", + "1/1/2008", + "1/1/2009", + "1/1/2010", + "1/1/2011", + "1/2/2012", + "1/1/2013", + "1/1/2014", + "1/1/2015", + "1/1/2016", + "1/2/2017", + "1/1/2018", + "1/1/2019", + "1/1/2020", + "1/1/2021", + "1/1/2022", + "1/1/2024", + "1/1/2025", + "1/1/2026", + "1/1/2027", + "1/2/2023", + "1/2/2007", + "1/9/2025", + "1/19/1998", + "1/18/1999", + "1/17/2000", + "1/15/2001", + "1/21/2002", + "1/20/2003", + "1/19/2004", + "1/17/2005", + "1/16/2006", + "1/15/2007", + "1/21/2008", + "1/19/2009", + "1/18/2010", + "1/17/2011", + "1/16/2012", + "1/21/2013", + "1/20/2014", + "1/19/2015", + "1/18/2016", + "1/16/2017", + "1/15/2018", + "1/21/2019", + "1/20/2020", + "1/18/2021", + "1/17/2022", + "1/16/2023", + "1/15/2024", + "1/20/2025", + "1/19/2026", + "1/18/2027", + "2/16/1998", + "2/15/1999", + "2/21/2000", + "2/19/2001", + "2/18/2002", + "2/17/2003", + "2/16/2004", + "2/21/2005", + "2/20/2006", + "2/19/2007", + "2/18/2008", + "2/16/2009", + "2/15/2010", + "2/21/2011", + "2/20/2012", + "2/18/2013", + "2/17/2014", + "2/16/2015", + "2/15/2016", + "2/20/2017", + "2/19/2018", + "2/18/2019", + "2/17/2020", + "2/15/2021", + "2/21/2022", + "2/20/2023", + "2/19/2024", + "2/17/2025", + "2/16/2026", + "2/15/2027", + "4/10/1998", + "4/2/1999", + "4/21/2000", + "4/13/2001", + "3/29/2002", + "4/18/2003", + "4/9/2004", + "3/25/2005", + "4/14/2006", + "4/6/2007", + "3/21/2008", + "4/10/2009", + "4/2/2010", + "4/22/2011", + "4/6/2012", + "3/29/2013", + "4/18/2014", + "4/3/2015", + "3/25/2016", + "4/14/2017", + "3/30/2018", + "4/19/2019", + "4/10/2020", + "4/2/2021", + "4/15/2022", + "4/7/2023", + "3/29/2024", + "4/18/2025", + "4/3/2026", + "3/26/2027", + "5/25/1998", + "5/31/1999", + "5/29/2000", + "5/28/2001", + "5/27/2002", + "5/26/2003", + "5/31/2004", + "5/30/2005", + "5/29/2006", + "5/28/2007", + "5/26/2008", + "5/25/2009", + "5/31/2010", + "5/30/2011", + "5/28/2012", + "5/27/2013", + "5/26/2014", + "5/25/2015", + "5/30/2016", + "5/29/2017", + "5/28/2018", + "5/27/2019", + "5/25/2020", + "5/31/2021", + "5/30/2022", + "5/29/2023", + "5/27/2024", + "5/26/2025", + "5/25/2026", + "5/31/2027", + "6/11/2004", + "6/20/2022", + "6/19/2023", + "6/19/2024", + "6/19/2025", + "6/19/2026", + "6/18/2027", + "7/3/1998", + "7/5/1999", + "7/4/2000", + "7/4/2001", + "7/4/2002", + "7/4/2003", + "7/5/2004", + "7/4/2005", + "7/4/2006", + "7/4/2007", + "7/4/2008", + "7/3/2009", + "7/5/2010", + "7/4/2011", + "7/4/2012", + "7/4/2013", + "7/4/2014", + "7/3/2015", + "7/4/2016", + "7/4/2017", + "7/4/2018", + "7/4/2019", + "7/3/2020", + "7/5/2021", + "7/4/2022", + "7/4/2023", + "7/4/2024", + "7/4/2025", + "7/3/2026", + "7/5/2027", + "9/7/1998", + "9/6/1999", + "9/4/2000", + "9/3/2001", + "9/2/2002", + "9/1/2003", + "9/6/2004", + "9/5/2005", + "9/4/2006", + "9/3/2007", + "9/1/2008", + "9/7/2009", + "9/6/2010", + "9/5/2011", + "9/3/2012", + "9/2/2013", + "9/1/2014", + "9/7/2015", + "9/5/2016", + "9/4/2017", + "9/3/2018", + "9/2/2019", + "9/7/2020", + "9/6/2021", + "9/5/2022", + "9/4/2023", + "9/2/2024", + "9/1/2025", + "9/7/2026", + "9/6/2027", + "9/11/2001", + "9/12/2001", + "9/13/2001", + "9/14/2001", + "10/29/2012", + "10/30/2012", + "11/26/1998", + "11/25/1999", + "11/23/2000", + "11/22/2001", + "11/28/2002", + "11/27/2003", + "11/25/2004", + "11/24/2005", + "11/23/2006", + "11/22/2007", + "11/27/2008", + "11/26/2009", + "11/25/2010", + "11/24/2011", + "11/22/2012", + "11/28/2013", + "11/27/2014", + "11/26/2015", + "11/24/2016", + "11/23/2017", + "11/22/2018", + "11/28/2019", + "11/26/2020", + "11/25/2021", + "11/24/2022", + "11/23/2023", + "11/28/2024", + "11/27/2025", + "11/26/2026", + "11/25/2027", + "12/05/2018", + "12/25/1998", + "12/24/1999", + "12/25/2000", + "12/25/2001", + "12/25/2002", + "12/25/2003", + "12/24/2004", + "12/26/2005", + "12/25/2006", + "12/25/2007", + "12/25/2008", + "12/25/2009", + "12/24/2010", + "12/26/2011", + "12/25/2012", + "12/25/2013", + "12/25/2014", + "12/25/2015", + "12/26/2016", + "12/25/2017", + "12/25/2018", + "12/25/2019", + "12/25/2020", + "12/24/2021", + "12/26/2022", + "12/25/2023", + "12/25/2024", + "12/25/2025", + "12/25/2026", + "12/24/2027" + ], + "earlyCloses": { + "7/3/2000": "12:00:00", + "7/3/2001": "12:00:00", + "7/5/2002": "12:00:00", + "7/3/2003": "12:00:00", + "7/3/2006": "12:00:00", + "7/3/2007": "12:00:00", + "7/3/2008": "12:00:00", + "7/3/2012": "12:00:00", + "7/3/2013": "12:00:00", + "7/3/2014": "12:00:00", + "7/3/2017": "12:00:00", + "7/3/2018": "12:00:00", + "7/3/2019": "12:00:00", + "7/3/2023": "12:00:00", + "7/3/2024": "12:00:00", + "7/3/2025": "12:00:00", + "11/26/1999": "12:00:00", + "11/24/2000": "12:00:00", + "11/23/2001": "12:00:00", + "11/29/2002": "12:00:00", + "11/28/2003": "12:00:00", + "11/26/2004": "12:00:00", + "11/25/2005": "12:00:00", + "11/24/2006": "12:00:00", + "11/23/2007": "12:00:00", + "11/28/2008": "12:00:00", + "11/27/2009": "12:00:00", + "11/26/2010": "12:00:00", + "11/25/2011": "12:00:00", + "11/23/2012": "12:00:00", + "11/29/2013": "12:00:00", + "11/28/2014": "12:00:00", + "11/27/2015": "12:00:00", + "11/25/2016": "12:00:00", + "11/24/2017": "12:00:00", + "11/23/2018": "12:00:00", + "11/29/2019": "12:00:00", + "11/27/2020": "12:00:00", + "11/26/2021": "12:00:00", + "11/25/2022": "12:00:00", + "11/24/2023": "12:00:00", + "11/29/2024": "12:00:00", + "11/28/2025": "12:00:00", + "11/27/2026": "12:00:00", + "11/26/2027": "12:00:00", + "12/24/2001": "12:00:00", + "12/24/2002": "12:00:00", + "12/24/2003": "12:00:00", + "12/26/2003": "12:00:00", + "12/24/2007": "12:00:00", + "12/24/2008": "12:00:00", + "12/24/2009": "12:00:00", + "12/24/2012": "12:00:00", + "12/24/2013": "12:00:00", + "12/24/2014": "12:00:00", + "12/24/2015": "12:00:00", + "12/24/2017": "12:00:00", + "12/24/2018": "12:00:00", + "12/24/2019": "12:00:00", + "12/24/2020": "12:00:00", + "12/24/2024": "12:00:00", + "12/24/2025": "12:00:00", + "12/24/2026": "12:00:00" + } + }, "Future-cme-MES": { "exchangeTimeZone": "America/New_York", "sunday": [ @@ -113685,7 +114808,7 @@

    Create Subscriptions

    To view the supported assets in the US Cash Indices dataset, see the - + Supported Indices . @@ -129078,7 +130201,7 @@

    OANDA Subscriptions

    To view the supported CFD contracts, see - + Supported Assets . For more information about the specific dataset we use for backtests, see the @@ -147130,7 +148253,7 @@

    Custom Security Properties

    for security in changes.added_securities: # Create an SMA indicator with 10 periods for the asset. # Use duck typing to store it on the security object. - security.indicator = self.sma(security.Symbol, 10) + security.indicator = self.sma(security.symbol, 10) # Warm up the indicator with historical data. self.warm_up_indicator(security.symbol, security.indicator) @@ -148814,6 +149937,58 @@

    Schedule

    Trigger an event on the last tradable date of each week for a specific symbol minus an offset. + + + + + + + + + + + + + + + + @@ -159345,7 +161052,7 @@

    Crypto Universes

    @@ -159374,7 +161081,7 @@

    Crypto Universes

    @@ -159403,7 +161110,7 @@

    Crypto Universes

    @@ -159432,7 +161139,7 @@

    Crypto Universes

    @@ -159461,7 +161168,7 @@

    Crypto Universes

    @@ -159920,6 +161627,134 @@

    # Place orders to rebalance the portfolio. self.set_holdings(targets) +

    + Example 4: Indicator Universe +

    +

    + The following example shows how to select a universe of Crypto pairs based on indicators. +

    +
    + 
    class CryptoIndicatorUniverseSelectionAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        # Add a universe of Crypto pairs based on indicators.
+        self._selection_data_by_symbol = {}
+        self._universe = self.add_universe(CryptoUniverse.coinbase(self._select_assets))
+        # Add a warm-up period to warm up the indicators in the universe selection.
+        self.set_warm_up(timedelta(60))
+
+    def _select_assets(self, data):
+        # Update the indicator of all Crypto pairs in the universe dataset and
+        # get the subset of them that have their indicator ready.
+        ready_pairs = [
+            c for c in data
+            if self._selection_data_by_symbol.setdefault(c.symbol, SelectionData()).update(c)
+        ]
+        # As Crypto pairs leave the universe dataset, delete their SelectionData objects.
+        for symbol in self._selection_data_by_symbol.keys() - {c.symbol for c in data}:
+            del self._selection_data_by_symbol[symbol]
+        # During warm-up, keep the universe empty.
+        if self.is_warming_up:
+            return []
+        # Select a subset of the Crypto pairs based on their indicators.
+        # Example: Pairs trading above their bollinger band with high volume.
+        filtered = []
+        for c in ready_pairs:
+            selection_data = self._selection_data_by_symbol[c.symbol]
+            if c.close > selection_data.bb.upper_band.current.value:
+                filtered.append(c)
+        filtered = sorted(filtered, key=lambda c: self._selection_data_by_symbol[c.symbol].mean_daily_usd_volume)[-10:]
+        return [c.symbol for c in filtered]
+
+
+class SelectionData:
+
+    def __init__(self):
+        self.mean_daily_usd_volume = SimpleMovingAverage(30)
+        self.bb = BollingerBands(30, 2)
+    
+    def update(self, c):
+        # Update all the indicators and return true if they are all ready.
+        return (
+            self.mean_daily_usd_volume.update(c.end_time, c.volume_in_usd) &
+            self.bb.update(TradeBar(c.end_time, c.symbol, c.open, c.high, c.low, c.close, c.volume_in_usd))
+        )
    + 
    public class CryptoIndicatorUniverseSelectionAlgorithm : QCAlgorithm
+{
+    private Dictionary<Symbol, SelectionData> _selectionDataBySymbol = new();
+    private Universe _universe;
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        // Add a universe of Crypto pairs based on indicators.
+        _universe = AddUniverse(CryptoUniverse.Coinbase(_SelectAssets));
+        // Add a warm-up period to warm up the indicators in the universe selection.
+        SetWarmUp(TimeSpan.FromDays(60));
+    }
+
+    private IEnumerable<Symbol> _SelectAssets(IEnumerable<CryptoUniverse> data)
+    {
+        // Update the indicator of all Crypto pairs in the universe dataset and
+        // get the subset of them that have their indicator ready.
+        var readyPairs = new List<CryptoUniverse>();
+        foreach (var c in data)
+        {
+            if (!_selectionDataBySymbol.TryGetValue(c.Symbol, out var sd))
+            {
+                sd = new SelectionData();
+                _selectionDataBySymbol[c.Symbol] = sd;
+            }
+            if (sd.Update(c))
+            {
+                readyPairs.Add(c);
+            }
+        }
+        // As Crypto pairs leave the universe dataset, delete their SelectionData objects.
+        var activePairs = data.Select(c => c.Symbol).ToHashSet();
+        foreach (var symbol in _selectionDataBySymbol.Keys.Where(s => !activePairs.Contains(s)).ToList())
+        {
+            _selectionDataBySymbol.Remove(symbol);
+        }
+        // During warm-up, keep the universe empty.
+        if (IsWarmingUp)
+        {
+            return Enumerable.Empty<Symbol>();
+        }
+        // Select a subset of the Crypto pairs based on their indicators.
+        // Example: Pairs trading above their bollinger band with high volume.
+        return readyPairs
+            .Where(c => c.Close > _selectionDataBySymbol[c.Symbol].BB.UpperBand.Current.Value)
+            .OrderBy(c => _selectionDataBySymbol[c.Symbol].MeanDailyUsdVolume.Current.Value)
+            .TakeLast(10)
+            .Select(c => c.Symbol);
+    }
+}
+
+public class SelectionData
+{
+    public SimpleMovingAverage MeanDailyUsdVolume { get; }
+    public BollingerBands BB { get; }
+
+    public SelectionData()
+    {
+        MeanDailyUsdVolume = new SimpleMovingAverage(30);
+        BB = new BollingerBands(30, 2);
+    }
+
+    public bool Update(CryptoUniverse c)
+    {
+        // Update all the indicators and return true if they are all ready.
+        return (
+            MeanDailyUsdVolume.Update(c.EndTime, (decimal)c.VolumeInUsd) &
+            BB.Update(new TradeBar(c.EndTime, c.Symbol, c.Open, c.High, c.Low, c.Close, (decimal)c.VolumeInUsd))
+        );
+    }
+}
    +
    @@ -161046,6 +162881,58 @@

    Date Rules

    Trigger an event on the last tradable date of each week for a specific symbol minus an offset. + + + + + + + + + + + + + + + + @@ -165252,7 +167226,7 @@

    Data Summary

    Data Density @@ -165260,7 +167234,7 @@

    Data Summary

    Resolution @@ -165290,7 +167264,7 @@

    Requesting Data

    - To add Bybit Crypto Future Margin Rate data to your algorithm, call the + To add Binance Crypto Future Price data to your algorithm, call the AddCryptoFuture @@ -165304,23 +167278,22 @@

    Requesting Data

    so you can access the data later in your algorithm.

    - 
    from AlgorithmImports import *
-from QuantConnect.DataSource import *
-
-class CoinAPIDataAlgorithm(QCAlgorithm):
+   
    class CoinAPIDataAlgorithm(QCAlgorithm):
 
     def initialize(self) -> None:
         self.set_start_date(2020, 6, 1)
         self.set_end_date(2021, 6, 1)
 
-        # Set Account Currency to Tether
-        self.set_account_currency("USDT", 100000)
+        # Set Account Currency to Binance Stable Coin for USD
+        self.set_account_currency("BUSD")
+        self.set_cash(100000)
 
-        self.set_brokerage_model(BrokerageName.BYBIT, AccountType.MARGIN)
+        self.set_brokerage_model(BrokerageName.BINANCE_FUTURES, AccountType.MARGIN)
+        self.set_brokerage_model(BrokerageName.BINANCE_COIN_FUTURES, AccountType.MARGIN)
         
-        crypto_future = self.add_crypto_future("BTCUSDT", Resolution.MINUTE)
+        crypto_future = self.add_crypto_future("BTCBUSD", Resolution.MINUTE)
         # perpetual futures does not have a filter function
-        self.btcusdt = crypto_future.symbol
    
+        self.btcbusd = crypto_future.symbol
    public class CoinAPIDataAlgorithm : QCAlgorithm
 {
     private Symbol _symbol ;
@@ -165330,12 +167303,14 @@ 
    Requesting Data
    
         SetStartDate(2020, 6, 1);
         SetEndDate(2021, 6, 1);
 
-        // Set Account Currency to Tether
-        SetAccountCurrency("USDT", 100000);
+        // Set Account Currency to Binance Stable Coin for USD
+        SetAccountCurrency("BUSD");
+        SetCash(100000);
 
-        SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin);
+        SetBrokerageModel(BrokerageName.BinanceFutures, AccountType.Margin);
+        SetBrokerageModel(BrokerageName.BinanceCoinFutures, AccountType.Margin);
         
-        var cryptoFuture = AddCryptoFuture("BTCUSDT", Resolution.Minute);
+        var cryptoFuture = AddCryptoFuture("BTCBUSD", Resolution.Minute);
         // perpetual futures does not have a filter function
         _symbol = cryptoFuture.Symbol;
     }
@@ -165355,14 +167330,28 @@ 
    Accessing Data
    
 
  
   
    
-   To get the current Bybit Crypto Margin Rate data, index the
+   To get the current Binance Crypto Future Price data, index the
    
-    MarginInterestRates
+    Bars
    
    
-    margin_interest_rates
+    bars
    
-   property of the current
+   ,
+   
+    QuoteBars
+   
+   
+    quote_bars
+   
+   , or
+   
+    Ticks
+   
+   
+    ticks
+   
+   properties of the current
    
     
      Slice
@@ -165388,17 +167377,42 @@ 
    Accessing Data
    
   
    
   
    
    
    def on_data(self, slice: Slice) -> None:
-        if self.btcusdt in slice.margin_interest_rates:
-            interest_rate = slice.margin_interest_rates[self.btcusdt].interest_rate
-            self.log(f"{self.btcusdt} close at {slice.time}: {interest_rate}")
    
+    if self.btcbusd in slice.bars:
+        trade_bar = slice.bars[self.btcbusd]
+        self.log(f"{self.btcbusd} close at {slice.time}: {trade_bar.close}")
+
+    if self.btcbusd in slice.quote_bars:
+        quote_bar = slice.quote_bars[self.btcbusd]
+        self.log(f"{self.btcbusd} bid at {slice.time}: {quote_bar.bid.close}")
+
+    if self.btcbusd in slice.ticks:
+        ticks = slice.ticks[self.btcbusd]
+        for tick in ticks:
+            self.log(f"{self.btcbusd} price at {slice.time}: {tick.price}")
    public override void OnData(Slice slice)
 {
-    if (slice.MarginInterestRates.ContainsKey(_symbol))
+    if (slice.Bars.ContainsKey(_symbol))
     {
-        var interestRate = slice.MarginInterestRates[_symbol].InterestRate;
-        Log($"{_symbol} price at {slice.Time}: {interestRate}");
+        var tradeBar = slice.Bars[_symbol];
+        Log($"{_symbol} price at {slice.Time}: {tradeBar.Close}");
     }
-}
    + + if (slice.QuoteBars.ContainsKey(_symbol)) + { + var quoteBar = slice.QuoteBars[_symbol]; + Log($"{_symbol} bid at {slice.Time}: {quoteBar.Bid.Close}"); + } + + if (slice.Ticks.ContainsKey(_symbol)) + { + var ticks = slice.Ticks[_symbol]; + foreach (var tick in ticks) + { + Log($"{_symbol} price at {slice.Time}: {tick.Price}"); + } + } +} +

    You can also iterate through all of the data objects in the current @@ -165409,19 +167423,42 @@

    Accessing Data

    def on_data(self, slice: Slice) -> None:
-    for symbol, margin_interest_rate in slice.margin_interest_rates.items():
-        interest_rate = margin_interest_rate.interest_rate
-        self.log(f"{symbol} close at {slice.time}: {interest_rate}")
    + for symbol, trade_bar in slice.bars.items(): + self.log(f"{symbol} close at {slice.time}: {trade_bar.close}") + + for symbol, quote_bar in slice.quote_bars.items(): + self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}") + + for symbol, ticks in slice.ticks.items(): + for tick in ticks: + self.log(f"{symbol} price at {slice.time}: {tick.price}") 
    public override void OnData(Slice slice)
 {
-    foreach (var kvp in slice.MarginInterestRates)
+    foreach (var kvp in slice.Bars)
     {
         var symbol = kvp.Key;
-        var marginInterestRate = kvp.Value;
-        var interestRate = marginInterestRate.InterestRate;
-        Log($"{symbol} price at {slice.Time}: {interestRate}");
+        var tradeBar = kvp.Value;
+        Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
     }
-}
    + + foreach (var kvp in slice.QuoteBars) + { + var symbol = kvp.Key; + var quoteBar = kvp.Value; + Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}"); + } + + foreach (var kvp in slice.Ticks) + { + var symbol = kvp.Key; + var ticks = kvp.Value; + foreach (var tick in ticks) + { + Log($"{symbol} price at {slice.Time}: {tick.Price}"); + } + } +} +

    For more information about accessing Crypto Future data, see @@ -165437,21 +167474,14 @@

    Historical Data

    - To get historical Bybit Crypto Future Margin Rate data, call the + To get historical Binance Crypto Future Price data, call the - History<MarginInterestRate> + History history - method with - - - MarginInterestRate - - data type and - - the Crypto Future + method with the Crypto Future Symbol @@ -165459,11 +167489,24 @@

    Historical Data

    # DataFrame
-history_df = self.history(MarginInterestRate, self.btcusdt, 100, Resolution.DAILY)
+history_df = self.history(self.btcbusd, 100, Resolution.DAILY)
 
-# MarginInterestRate objects
-history = self.history[MarginInterestRate](self.btcusdt, 100, Resolution.DAILY)
    - 
    var history = History<MarginInterestRate>(_symbol, 100, Resolution.Daily);
    +# TradeBar objects +history_trade_bars = self.history[TradeBar](self.btcbusd, 100, Resolution.MINUTE) + +# QuoteBar objects +history_quote_bars = self.history[QuoteBar](self.btcbusd, 100, Resolution.MINUTE) + +# Tick objects +history_ticks = self.history[Tick](self.btcbusd, timedelta(seconds=10), Resolution.TICK) + 
    // TradeBar objects 
+var historyTradeBars = History(_symbol, 100, Resolution.Daily);
+
+// QuoteBar objects 
+var historyQuoteBars = History<QuoteBar>(_symbol, 100, Resolution.Minute);
+
+// Tick objects 
+var historyTicks = History<Tick>(_symbol, TimeSpan.FromSeconds(10), Resolution.Tick);

    For more information about historical data, see @@ -165479,12 +167522,12 @@

    Remove Subscriptions

    - To unsubscribe from a Crypto pair that you added with the + To unsubscribe from a Crypto Future contract that you added with the - AddCrypto + AddCryptoFuture - add_crypto + add_crypto_future method, call the @@ -165496,7 +167539,7 @@

    Remove Subscriptions

    method.

    - 
    self.remove_security(self.btcusdt)
    + 
    self.remove_security(self.btcbusd)
    RemoveSecurity(_symbol);

    @@ -165507,11 +167550,7 @@

    Remove Subscriptions

    remove_security - method cancels your open orders for the security and liquidates your holdings in the -     - virtual pair - - . + method cancels your open orders for the security and liquidates your Crypto Future holdings.

    @@ -165527,52 +167566,135 @@

    Supported Assets

     + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + + + @@ -165580,7 +167702,7 @@

    Supported Assets

    AEVOUSDT     + + + - - + + + + @@ -165606,62 +167739,79 @@

    Supported Assets

    AIXBTUSDT     - - + + + + + + + - - + + + + @@ -165669,16 +167819,19 @@

    Supported Assets

    APTUSDT     - - + + + @@ -165691,6 +167844,12 @@

    Supported Assets

     + + + + + + - - + + + + + + + + @@ -165785,45 +167970,53 @@

    Supported Assets

     + + - - + + + - - + + + + + + + + - - + + - - + + + + + + + @@ -165906,195 +168125,201 @@

    Supported Assets

    CATIUSDT     + + + + - - + + + - - + + + + - - + + @@ -166146,22 +168382,42 @@

    Supported Assets

    EPTUSDT     + + + + + + + + @@ -166169,18 +168425,21 @@

    Supported Assets

    ETHWUSDT     + - @@ -166331,11 +168587,11 @@

    Supported Assets

     - - + + @@ -166346,13 +168602,22 @@

    Supported Assets

    HAEDALUSDT     + + + @@ -166365,25 +168630,36 @@

    Supported Assets

     + + - - + + + + + + + + + + + @@ -166411,14 +168701,17 @@

    Supported Assets

     - - + + + @@ -166431,14 +168724,14 @@

    Supported Assets

     - - + + @@ -166451,19 +168744,19 @@

    Supported Assets

     - - + + - - + + + + + + + + + + + + @@ -166537,7 +168856,7 @@

    Supported Assets

    LITUSDT     - - - - + + @@ -166651,16 +168964,16 @@

    Supported Assets

     - - + + - - - + + - - - @@ -166703,19 +169008,10 @@

    Supported Assets

    MORPHOUSDT     - - - @@ -166726,47 +169022,50 @@

    Supported Assets

    MUBARAKUSDT     + - @@ -166811,14 +169107,11 @@

    Supported Assets

     - - - + + @@ -166831,17 +169124,20 @@

    Supported Assets

     - - + + + @@ -166851,88 +169147,77 @@

    Supported Assets

     - - + + - - - - - + + - - - - + + + + - - @@ -166945,25 +169230,16 @@

    Supported Assets

     - - - + - - @@ -167006,24 +169279,21 @@

    Supported Assets

    QTUMUSDT     - - + + - - - + + + + @@ -167046,67 +169322,70 @@

    Supported Assets

    RENUSDT     + + + + + - - @@ -167245,19 +169530,19 @@

    Supported Assets

     + + - - + + - - - @@ -167288,25 +169570,42 @@

    Supported Assets

     + + - - + + + + + + + + + + + + + + + + + + - - + + + + - - + + @@ -167411,8 +169741,6 @@

    Supported Assets

     - - @@ -167422,43 +169750,37 @@

    Supported Assets

     + + + - - + + - - - - - @@ -167468,68 +169790,65 @@

    Supported Assets

     - @@ -167540,61 +169859,67 @@

    Supported Assets

    XLMUSDT     + + + + - - + + - - +
    - International Future Universe + Binance Crypto Price Data -
      -
    • - - FutureFilterUniverse - -
      • -
    • - - FutureUniverse - -
      • -
    + + CryptoUniverse +     		- + Learn more
    - US ETF Constituents + Binance US Crypto Price Data - ETFConstituentUniverse + CryptoUniverse - + Learn more
    - US Equity Option Universe - -
      -
    • - - OptionFilterUniverse - -
      • -
    • - - OptionUniverse - -
      • -
    + Bitfinex Crypto Price Data     		- + + CryptoUniverse + + + Learn more
    - US Future Option Universe + Bybit Crypto Price Data -
      -
    • - - OptionFilterUniverse - -
      • -
    • - - OptionUniverse - -
      • -
    + + CryptoUniverse +     		- + Learn more
    - US Future Universe + Coinbase Crypto Price Data -
      -
    • - - FutureFilterUniverse - -
      • -
    • - - FutureUniverse - -
      • -
    + + CryptoUniverse +     		- + Learn more
    - US Index Option Universe + International Future Universe
    • - OptionFilterUniverse + FutureFilterUniverse
    • - OptionUniverse + FutureUniverse
    		- + Learn more
    - Binance Crypto Price Data + Kraken Crypto Price Data @@ -40650,82 +41191,118 @@

    Available Universes

         		- + Learn more
    - Binance US Crypto Price Data + US ETF Constituents - CryptoUniverse + ETFConstituentUniverse - + Learn more
    - Bitfinex Crypto Price Data + US Equity Option Universe - - CryptoUniverse - +
      +
    • + + OptionFilterUniverse + +
      • +
    • + + OptionUniverse + +
      • +
    		- + Learn more
    - Bybit Crypto Price Data + US Future Option Universe - - CryptoUniverse - +
      +
    • + + OptionFilterUniverse + +
      • +
    • + + OptionUniverse + +
      • +
    		- + Learn more
    - Coinbase Crypto Price Data + US Future Universe - - CryptoUniverse - +
      +
    • + + FutureFilterUniverse + +
      • +
    • + + FutureUniverse + +
      • +
    		- + Learn more
    - Kraken Crypto Price Data + US Index Option Universe - - CryptoUniverse - +
      +
    • + + OptionFilterUniverse + +
      • +
    • + + OptionUniverse + +
      • +
    		- + Learn more
    - WallStreetBets - - - QuiverWallStreetBetsUniverse - - - - Learn more - -
    Corporate Buybacks @@ -40938,12 +41500,18 @@

    The below example studies the top-minus-bottom PE Ratio universe, in which the top 10 PE Ratio stocks are brought, and the bottom 10 are sold in equal weighting daily. We carry out a mini-backtest to analyze its performance.

    -
    + 
    // Load the required assembly files and data types.
-#load "../Initialize.csx"
+#load "../Initialize.csx"
    +
    +
    + 
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-
-using QuantConnect;
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    +
    +
    + 
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
 using QuantConnect.Algorithm;
@@ -41034,7 +41602,7 @@ 
    
 chart.WithLayout(layout);
 
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    +display(chart) 
    # Instantiate the QuantBook instance for researching.
 qb = QuantBook()
 
@@ -41468,14 +42036,16 @@ 
    
   
    
   
    
    
    // Load the assembly files and data types in their own cell. 
-#load "../Initialize.csx"
-
-// Load the necessary assembly files. 
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files. 
 #load "../QuantConnect.csx"
 #r "nuget: Plotly.NET"
-#r "nuget: Plotly.NET.Interactive"
-
-// Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    // Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
 using QuantConnect;
 using QuantConnect.Data.Market;
 using QuantConnect.Indicators;
@@ -41776,14 +42346,16 @@ 
    
   
    
   
    
    
    // Load the assembly files and data types in their own cell. 
-#load "../Initialize.csx"
-
-// Load the necessary assembly files. 
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files. 
 #load "../QuantConnect.csx"
 #r "nuget: Plotly.NET"
-#r "nuget: Plotly.NET.Interactive"
-
-// Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    // Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
 using QuantConnect;
 using QuantConnect.Data.Market;
 using QuantConnect.Indicators;
@@ -42081,14 +42653,16 @@ 
    
   
    
   
    
    
    // Load the assembly files and data types in their own cell. 
-#load "../Initialize.csx"
-
-// Load the necessary assembly files. 
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files. 
 #load "../QuantConnect.csx"
 #r "nuget: Plotly.NET"
-#r "nuget: Plotly.NET.Interactive"
-
-// Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    // Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
 using QuantConnect;
 using QuantConnect.Data.Market;
 using QuantConnect.Indicators;
@@ -42429,18 +43003,20 @@ 
    
   
    
   
    
    
    // Load the assembly files and data types in their own cell.
-#load "../Initialize.csx"
-
-// Load the necessary assembly files.
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-#r "../Plotly.NET.dll"
-#r "../Plotly.NET.Interactive.dll"
-
-// Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    // Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
 using QuantConnect;
 using QuantConnect.Indicators;
 using QuantConnect.Research;
-            
+
 using Plotly.NET;
 using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
@@ -42491,7 +43067,7 @@ 
    
 chart.WithLayout(layout);
 
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    
    
    # Instantiate the QuantBook instance for researching.
 qb = QuantBook()
 # Request SPY data to work with the indicator.
@@ -42693,9 +43269,9 @@ 
    Create Indicator Timeseries
    
     
     # Method to update the indicator values. Note that it only receives 1 IBaseData object (Tick, TradeBar, QuoteBar) argument.
     def update(self, input: BaseData) -> bool:
-        count = self._window.Count
+        count = self._window.count
         
-        self._window.Add(input.Close)
+        self._window.add(input.close)
         
         # Update the Value and other attributes as the indicator current value.
         if count >= 2:
@@ -42863,18 +43439,20 @@ 
    
   
    
   
    
    
    // Load the assembly files and data types in their own cell.
-#load "../Initialize.csx"
-
-// Load the necessary assembly files.
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-#r "../Plotly.NET.dll"
-#r "../Plotly.NET.Interactive.dll"
-
-// Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    // Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
 using QuantConnect;
 using QuantConnect.Indicators;
 using QuantConnect.Research;
-            
+
 using Plotly.NET;
 using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
@@ -42976,7 +43554,7 @@ 
    
 chart.WithLayout(layout);
 
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    
    
    # Instantiate the QuantBook instance for researching.
 qb = QuantBook()
 # Request SPY data to work with the indicator.
@@ -43003,9 +43581,9 @@ 
    
     
     # Method to update the indicator values. Note that it only receives 1 IBaseData object (Tick, TradeBar, QuoteBar) argument.
     def update(self, input: BaseData) -> bool:
-        count = self._window.Count
+        count = self._window.count
         
-        self._window.Add(input.Close)
+        self._window.add(input.close)
         
         # Update the Value and other attributes as the indicator current value.
         if count >= 2:
@@ -43338,18 +43916,20 @@ 
    
   
    
   
    
    
    // Load the assembly files and data types in their own cell.
-#load "../Initialize.csx"
-
-// Load the necessary assembly files.
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
 #load "../QuantConnect.csx"
-#r "../Plotly.NET.dll"
-#r "../Plotly.NET.Interactive.dll"
-
-// Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+  
    
+  
    
+   
    // Import the QuantConnect, Plotly.NET, and Accord packages for calculation and plotting.
 using QuantConnect;
 using QuantConnect.Indicators;
 using QuantConnect.Research;
-            
+
 using Plotly.NET;
 using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
@@ -43442,7 +44022,7 @@ 
    
 chart.WithLayout(layout);
 
 // Display the plot.
-HTML(GenericChart.toChartHTML(chart))
    
+display(chart)
    
    
    # Instantiate the QuantBook instance for researching.
 qb = QuantBook()
 # Request SPY data to work with the indicator.
@@ -44267,11 +44847,14 @@ 
    Example for Plotting
    
    
    
    
     
    // Execute the following command in first
-#load "../Initialize.csx"
-
-// Create a QuantBook object
-#load "../QuantConnect.csx"
-using QuantConnect;
+#load "../Initialize.csx"
    
+   
    
+   
    
+    
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    
+   
    
+   
    
+    
    using QuantConnect;
 using QuantConnect.Research;
 
 var qb = new QuantBook();
    
@@ -44312,8 +44895,12 @@ 
    Example for Plotting
    
     packages.
    
    
    
-    
    #r "../Plotly.NET.dll"
-using Plotly.NET;
+    
    #r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+   
    
+   
    
+    
    using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
    
    
    
    
  • 
@@ -44383,15 +44970,11 @@ 
    Example for Plotting
    
     
      Line
     
-    object and create the
-    
-     HTML
-    
-    object.
+    object and display the chart.
    
    • 
    
    
     
    chart.WithLayout(layout);
-var result = HTML(GenericChart.toChartHTML(chart));
    
+display(chart);
    
    
    
   
   
    
@@ -44508,11 +45091,11 @@ 
    Example for Logging
    
 
     if (_emaShort > _emaLong && !Portfolio[_symbol].IsLong)
     {
-        MarketOrder(_symbol, 100, tag: $"BUY: ema-short: {_emaShort:F4}  >  ema-long: {_emaLong:F4}");
+        MarketOrder(_symbol, 100, tag: $"BUY: ema-short: {_emaShort:F4} > ema-long: {_emaLong:F4}");
     }
     else if (_emaShort < _emaLong && !Portfolio[_symbol].IsShort)
     {
-        MarketOrder(_symbol, -100, tag: $"SELL: ema-short: {_emaShort:F4}  <  ema-long: {_emaLong:F4}");
+        MarketOrder(_symbol, -100, tag: $"SELL: ema-short: {_emaShort:F4} < ema-long: {_emaLong:F4}");
     }
 }
    
     
    def on_data(self, data: Slice):
@@ -44522,15 +45105,18 @@ 
    Example for Logging
    
     ema_short = self._ema_short.current.value
     ema_long = self._ema_long.current.value
     if ema_short > ema_long and not self.portfolio[self._symbol].is_long:
-        self.market_order(self._symbol, 100, tag=f'BUY: ema-short: {ema_short:.4f}  >  ema-long: {ema_long:.4f}')
+        self.market_order(self._symbol, 100, tag=f'BUY: ema-short: {ema_short:.4f} > ema-long: {ema_long:.4f}')
     elif ema_short < ema_long and not self.portfolio[self._symbol].is_short:
-        self.market_order(self._symbol, -100, tag=f'SELL: ema-short: {ema_short:.4f}  <  ema-long: {ema_long:.4f}')
    
+        self.market_order(self._symbol, -100, tag=f'SELL: ema-short: {ema_short:.4f} < ema-long: {ema_long:.4f}')
    
    
    
    
  • 
     In the
-    
+    
      OnOrderEvent
     
+    
+     on_order_event
+    
     method, log each fill to the content string.
    
    • 
    
    
@@ -44582,13 +45168,16 @@ 
    Example for Logging
    
         
     .
    
-   
    
+   
    
     
    // Execute the following command in first
-#load "../Initialize.csx"
-
-// Create a QuantBook object
-#load "../QuantConnect.csx"
-using QuantConnect;
+#load "../Initialize.csx"
    
+   
    
+   
    
+    
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    
+   
    
+   
    
+    
    using QuantConnect;
 using QuantConnect.Research;
 
 var qb = new QuantBook();
    
@@ -44712,11 +45301,11 @@ 
    Example for Logging
    
         // Place a market order when the EMAs cross.
         if (_emaShort > _emaLong && !Portfolio[_symbol].IsLong)
         {
-            MarketOrder(_symbol, 100, tag: $"BUY: ema-short: {_emaShort:F4}  >  ema-long: {_emaLong:F4}");
+            MarketOrder(_symbol, 100, tag: $"BUY: ema-short: {_emaShort:F4} > ema-long: {_emaLong:F4}");
         }
         else if (_emaShort < _emaLong && !Portfolio[_symbol].IsShort)
         {
-            MarketOrder(_symbol, -100, tag: $"SELL: ema-short: {_emaShort:F4}  <  ema-long: {_emaLong:F4}");
+            MarketOrder(_symbol, -100, tag: $"SELL: ema-short: {_emaShort:F4} < ema-long: {_emaLong:F4}");
         }
     }
 
@@ -44758,9 +45347,9 @@ 
    Example for Logging
    
         ema_short = self._ema_short.current.value
         ema_long = self._ema_long.current.value
         if ema_short > ema_long and not self.portfolio[self._symbol].is_long:
-            self.market_order(self._symbol, 100, tag=f'BUY: ema-short: {ema_short:.4f}  >  ema-long: {ema_long:.4f}')
+            self.market_order(self._symbol, 100, tag=f'BUY: ema-short: {ema_short:.4f} > ema-long: {ema_long:.4f}')
         elif ema_short < ema_long and not self.portfolio[self._symbol].is_short:
-            self.market_order(self._symbol, -100, tag=f'SELL: ema-short: {ema_short:.4f}  <  ema-long: {ema_long:.4f}')
+            self.market_order(self._symbol, -100, tag=f'SELL: ema-short: {ema_short:.4f} < ema-long: {ema_long:.4f}')
 
     def on_order_event(self, order_event: OrderEvent) -> None:
         if order_event.status != OrderStatus.FILLED:
@@ -50530,10 +51119,13 @@ 
    Read Backtest Results
    
    method with the project Id and backtest ID.
   
    
   
    
-   
    #load "../Initialize.csx"
-#load "../QuantConnect.csx"
-
-using QuantConnect;
+   
    #load "../Initialize.csx"
    
+  
    
+  
    
+   
    #load "../QuantConnect.csx"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Api;
 
 var backtest = api.ReadBacktest(projectId, backtestId);
    
@@ -51324,10 +51916,14 @@ 
    
   
    
   
    
    
    // Load the necessary assemblies.
-#load "../Initialize.csx"
-#load "../QuantConnect.csx"
-
-using QuantConnect;
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Api;
 using QuantConnect.Research;
 
@@ -51473,10 +52069,14 @@ 
    
   
    
   
    
    
    // Load the necessary assemblies.
-#load "../Initialize.csx"
-#load "../QuantConnect.csx"
-
-using QuantConnect;
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Api;
 using QuantConnect.Research;
 
@@ -51550,10 +52150,13 @@ 
    Read Live Results
    
    method with the project Id and deployment ID.
   
    
   
    
-   
    #load "../Initialize.csx"
-#load "../QuantConnect.csx"
-
-using QuantConnect;
+   
    #load "../Initialize.csx"
    
+  
    
+  
    
+   
    #load "../QuantConnect.csx"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Api;
 
 var liveAlgorithm = api.ReadLiveAlgorithm(projectId, deployId);
    
@@ -51640,74 +52243,6 @@ 
    Read Live Results
    
  
 
 
-
    Reconciliation
    
-
- 
-  
    
-   Reconciliation is a way to
-   
-    quantify
-   
-   the difference between an algorithm's live performance and its out-of-sample (OOS) performance (a backtest run over the live deployment period).
-  
    
-  
    
-   Seeing the difference between live performance and OOS performance gives you a way to determine if the algorithm is making unrealistic assumptions, exploiting data differences, or merely exhibiting behavior that is impractical or impossible in live trading.
-  
    
-  
    
-   A perfectly reconciled algorithm has an exact overlap between its live equity and OOS backtest curves. Any deviation means that the performance of the algorithm has differed for some reason. Several factors can contribute to this, often stemming from the algorithm design.
-  
    
-  
    
-   Live Deployment Reconciliation
-   
-   
-  
    
-  
    
-   Reconciliation is scored using two metrics: returns correlation and dynamic time warping (DTW) distance.
-  
    
-  
    
-   What is DTW Distance?
-  
    
-  
    
-   Dynamic Time Warp (DTW) Distance quantifies the difference between two time-series. It is an algorithm that measures the shortest path between the points of two time-series. It uses Euclidean distance as a measurement of
-   
-    point-to-point distance
-   
-   and returns an overall measurement of the distance on the scale of the initial time-series values. We apply DTW to the returns curve of the live and OOS performance, so the DTW distance measurement is on the scale of percent returns.
-  
    
-  $$\begin{equation}
-DTW(X,Y) = min\bigg\{\sum_{l=1}^{L}\left(x_{m_l} - y_{n_l}\right)^{2}\in P^{N\times M}\bigg\}
-\end{equation}$$
-  
    
-   For the reasons outlined in our research notebook on the topic (linked below), QuantConnect annualizes the daily DTW. An annualized distance provides a user with a measurement of the annual difference in the magnitude of returns between the two curves. A perfect score is 0, meaning the returns for each day were precisely the same. A DTW score of 0 is nearly impossible to achieve, and we consider anything below 0.2 to be a decent score. A distance of 0.2 means the returns between an algorithm's live and OOS performance deviated by 20% over a year.
-  
    
-  
    
-   What is Returns Correlation?
-  
    
-  
    
-   Returns correlation is the simple Pearson correlation between the live and OOS returns. Correlation gives us a rudimentary understanding of how the returns move together. Do they trend up and down at the same time? Do they deviate in direction or timing?
-  
    
-  $$\begin{equation}
-\rho_{XY} = \frac{cov(X, Y)}{\sigma_X\sigma_Y}
-\end{equation}$$
-  
    
-   An algorithm's returns correlation should be as close to 1 as possible. We consider a good score to be 0.8 or above, meaning that there is a strong positive correlation. This indicates that the returns move together most of the time and that for any given return you see from one of the curves, the other curve usually has a similar direction return (positive or negative).
-  
    
-  
    
-   Why Do We Need Both DTW and Returns Correlation?
-  
    
-  
    
-   Each measurement provides insight into distinct elements of time-series similarity, but neither measurement alone gives us the whole picture. Returns correlation tells us whether or not the live and OOS returns move together, but it doesn't account for the possible differences in the magnitude of the returns. DTW distance measures the difference in magnitude of returns but provides no insight into whether or not the returns move in the same direction. It is possible for there to be two cases of equity curve similarity where both pairs have the same DTW distance, but one has perfectly negatively correlated returns, and the other has a perfectly positive correlation. Similarly, it is possible for two pairs of equity curves to each have perfect correlation but substantially different DTW distance. Having both measurements provides us with a more comprehensive understanding of the actual similarity between live and OOS performance. We outline several interesting cases and go into more depth on the topic of reconciliation in
-   
-    research
-   
-   we have published.
-  
    
- 
-
-
 
    Plot Order Fills
    
 
  
@@ -52218,10 +52753,14 @@ 
    
   
    
   
    
    
    // Load the necessary assemblies.
-#load "../Initialize.csx"
-#load "../QuantConnect.csx"
-
-using QuantConnect;
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Api;
 using QuantConnect.Research;
 
@@ -52254,9 +52793,1079 @@ 
    
 
 
 
     
    
-
    Meta Analysis > Live Deployment Automation
    
+
    Meta Analysis > Live Reconciliation
    
 
    
     
    
+        
    Meta Analysis
    
+        
    Live Reconciliation
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   This page shows you how to generate the out-of-sample (OOS) backtest reconciliation curve from a live deployment in the Research Environment so you can quantitatively and visually compare live versus backtest performance. You read the live deployment's launch datetime and starting equity, run a backtest with matching parameters, and overlay the two "Strategy Equity" curves along with every order fill on a single chart per security.
+  
    
+  
    
+   Reconciliation is a way to
+   
+    quantify
+   
+   the difference between an algorithm's live performance and its out-of-sample (OOS) performance (a backtest run over the live deployment period).
+  
    
+  
    
+   Seeing the difference between live performance and OOS performance gives you a way to determine if the algorithm is making unrealistic assumptions, exploiting data differences, or merely exhibiting behavior that is impractical or impossible in live trading.
+  
    
+  
    
+   A perfectly reconciled algorithm has an exact overlap between its live equity and OOS backtest curves. Any deviation means that the performance of the algorithm has differed for some reason. Several factors can contribute to this, often stemming from the algorithm design. For a catalogue of common deviation causes (data, modeling, brokerage, third-party indicators, and real-time scheduled events), see
+   
+    Reconciliation
+   
+   in the Writing Algorithms documentation.
+  
    
+  Live Deployment Reconciliation
+  
+  
+  
    
+   Reconciliation is scored using two metrics: returns correlation and dynamic time warping (DTW) distance.
+  
    
+  
    
+   What is DTW Distance?
+  
    
+  
    
+   Dynamic Time Warp (DTW) Distance quantifies the difference between two time-series. It is an algorithm that measures the shortest path between the points of two time-series. It uses Euclidean distance as a measurement of
+   
+    point-to-point distance
+   
+   and returns an overall measurement of the distance on the scale of the initial time-series values. We apply DTW to the returns curve of the live and OOS performance, so the DTW distance measurement is on the scale of percent returns.
+  
    
+  $$\begin{equation}
+DTW(X,Y) = min\bigg\{\sum_{l=1}^{L}\left(x_{m_l} - y_{n_l}\right)^{2}\in P^{N\times M}\bigg\}
+\end{equation}$$
+  
    
+   For the reasons outlined in our research notebook on the topic (linked below), QuantConnect annualizes the daily DTW. An annualized distance provides a user with a measurement of the annual difference in the magnitude of returns between the two curves. A perfect score is 0, meaning the returns for each day were precisely the same. A DTW score of 0 is nearly impossible to achieve, and we consider anything below 0.2 to be a decent score. A distance of 0.2 means the returns between an algorithm's live and OOS performance deviated by 20% over a year.
+  
    
+  
    
+   What is Returns Correlation?
+  
    
+  
    
+   Returns correlation is the simple Pearson correlation between the live and OOS returns. Correlation gives us a rudimentary understanding of how the returns move together. Do they trend up and down at the same time? Do they deviate in direction or timing?
+  
    
+  $$\begin{equation}
+\rho_{XY} = \frac{cov(X, Y)}{\sigma_X\sigma_Y}
+\end{equation}$$
+  
    
+   An algorithm's returns correlation should be as close to 1 as possible. We consider a good score to be 0.8 or above, meaning that there is a strong positive correlation. This indicates that the returns move together most of the time and that for any given return you see from one of the curves, the other curve usually has a similar direction return (positive or negative).
+  
    
+  
    
+   Why Do We Need Both DTW and Returns Correlation?
+  
    
+  
    
+   Each measurement provides insight into distinct elements of time-series similarity, but neither measurement alone gives us the whole picture. Returns correlation tells us whether or not the live and OOS returns move together, but it doesn't account for the possible differences in the magnitude of the returns. DTW distance measures the difference in magnitude of returns but provides no insight into whether or not the returns move in the same direction. It is possible for there to be two cases of equity curve similarity where both pairs have the same DTW distance, but one has perfectly negatively correlated returns, and the other has a perfectly positive correlation. Similarly, it is possible for two pairs of equity curves to each have perfect correlation but substantially different DTW distance. Having both measurements provides us with a more comprehensive understanding of the actual similarity between live and OOS performance. We outline several interesting cases and go into more depth on the topic of reconciliation in
+   
+    research
+   
+   we have published.
+  
    
+ 
+
+
+
    Get Live Deployment Parameters
    
+
+ 
+  
    
+   Follow these steps to read the live deployment's start datetime, starting equity, and end datetime — the three parameters the OOS backtest must match. All three values come from the live "Strategy Equity" chart, so you only need the project Id.
+  
    
+  
      
+   
    1. 
+    Define the project Id.
+   
      2. 
+   
      
+    
      var projectId = 23034953;
      
+    
      project_id = 23034953
      
+   
      
+   
      
+    The following table provides links to documentation that explains how to get the project Id, depending on the platform you use:
+   
      
+   
+    
+     
+      
+      
+     
+    
+    
+     
+      
+      
+     
+     
+      
+      
+     
+     
+      
+      
+     
+    
+   
      
+       Platform
+      
+       Project Id
+      
      
+       Cloud Platform
+      
+       
+        Get Project Id
+       
+      
      
+       Local Platform
+      
+       
+        Get Project Id
+       
+      
      
+       CLI
+      
+       
+        Get Project Id
+       
+      
      
+   
    2. 
+    Read the live "Strategy Equity" chart with the
+    
+     ReadLiveChart
+    
+    
+     read_live_chart
+    
+    method. The first and last
+    
+     Equity
+    
+    points give you the start datetime, starting equity, and end datetime.
+   
      3. 
+   
      
+    
      var nowSec = (int)DateTimeOffset.UtcNow.ToUnixTimeSeconds();
+
+Chart ReadLiveChartWithRetry(int projectId, string chartName)
+{
+    for (var attempt = 0; attempt < 10; attempt++)
+    {
+        var result = api.ReadLiveChart(projectId, chartName, 0, nowSec, 500);
+        if (result.Success) return result.Chart;
+        Console.WriteLine($"Chart data is loading... (attempt {attempt + 1}/10)");
+        Thread.Sleep(10000);
+    }
+    throw new Exception($"Failed to read {chartName} chart after 10 attempts");
+}
+
+var strategyEquity = ReadLiveChartWithRetry(projectId, "Strategy Equity");
+// The first few points in the series can have a null close, so keep only
+// the points with a valid close value before extracting start/end.
+var validValues = strategyEquity.Series["Equity"].Values
+    .OfType<Candlestick>()
+    .Where(v => v.Close.HasValue)
+    .ToList();
+
+// Start datetime and starting equity: first valid point.
+var startDatetime = validValues.First().Time;
+var startingCash = validValues.First().Close.Value;
+// End datetime: last valid timestamp of the live Strategy Equity series.
+// Uncomment the next line instead to reconcile up to "now" and see what
+// would have happened had you not stopped the live algorithm:
+// var endDatetime = DateTime.UtcNow;
+var endDatetime = validValues.Last().Time;
+
+Console.WriteLine($"Start (UTC): {startDatetime}");
+Console.WriteLine($"Starting equity: ${startingCash:N2}");
+Console.WriteLine($"End (UTC): {endDatetime}");
      
+    
      from datetime import datetime
+from time import sleep, time
+
+def read_chart(project_id, chart_name, start=0, end=int(time()), count=500):
+    # Retry up to 10 times until the chart data finishes loading.
+    for attempt in range(10):
+        result = api.read_live_chart(project_id, chart_name, start, end, count)
+        if result.success:
+            return result.chart
+        print(f"Chart data is loading... (attempt {attempt + 1}/10)")
+        sleep(10)
+    raise RuntimeError(f"Failed to read {chart_name} chart after 10 attempts")
+
+strategy_equity = read_chart(project_id, 'Strategy Equity')
+# The first few points in the series can have a None close, so keep only
+# the points with a valid close value before extracting start/end.
+valid_values = [v for v in strategy_equity.series['Equity'].values if v.close is not None]
+
+# Start datetime and starting equity: first valid point.
+start_datetime = valid_values[0].time
+starting_cash = valid_values[0].close
+# End datetime: last valid timestamp of the live Strategy Equity series.
+# Uncomment the next line instead to reconcile up to "now" and see what
+# would have happened had you not stopped the live algorithm:
+# end_datetime = datetime.utcnow()
+end_datetime = valid_values[-1].time
+
+print(f"Start (UTC): {start_datetime}")
+print(f"Starting equity: ${starting_cash:,.2f}")
+print(f"End (UTC): {end_datetime}")
      
+   
      
+  
    
+ 
+
+
+
    Run OOS Backtest
    
+
+ 
+  
    
+   Follow these steps to run an out-of-sample backtest that mirrors the live deployment.
+  
    
+  
      
+   
    1. 
+    In the project's main algorithm file, set the start date and starting cash to match the values you read in the previous step. Use
+    
+     SetStartDate
+    
+    
+     set_start_date
+    
+    and
+    
+     SetCash
+    
+    
+     set_cash
+    
+    so the backtest begins at the same moment and with the same equity as the live deployment. You can either hard-code the values or expose them as
+    
+     parameters
+    
+    .
+   
      2. 
+   
    2. 
+    Compile the project by calling the
+    
+     CreateCompile
+    
+    
+     create_compile
+    
+    method, then poll
+    
+     ReadCompile
+    
+    
+     read_compile
+    
+    until the compile state is
+    
+     BuildSuccess
+    
+    .
+   
      3. 
+   
      
+    
      var compilation = api.CreateCompile(projectId);
+var compileId = compilation.CompileId;
+
+// Poll until the build succeeds.
+for (var attempt = 0; attempt < 10; attempt++)
+{
+    var result = api.ReadCompile(projectId, compileId);
+    if (result.State == CompileState.BuildSuccess) break;
+    if (result.State == CompileState.BuildError)
+    {
+        throw new Exception($"Compilation failed: {string.Join(Environment.NewLine, result.Logs)}");
+    }
+    Console.WriteLine($"Compile in queue... (attempt {attempt + 1}/10)");
+    Thread.Sleep(5000);
+}
      
+    
      from time import sleep
+
+compilation = api.create_compile(project_id)
+compile_id = compilation.compile_id
+
+# Poll until the build succeeds.
+for attempt in range(10):
+    result = api.read_compile(project_id, compile_id)
+    if result.state == 'BuildSuccess':
+        break
+    if result.state == 'BuildError':
+        raise Exception(f"Compilation failed: {result.logs}")
+    print(f"Compile in queue... (attempt {attempt + 1}/10)")
+    sleep(5)
      
+   
      
+   
    3. 
+    Create the OOS backtest with the
+    
+     CreateBacktest
+    
+    
+     create_backtest
+    
+    method.
+   
      4. 
+   
      
+    
      var backtest = api.CreateBacktest(projectId, compileId, "OOS Reconciliation");
+var backtestId = backtest.BacktestId;
+Console.WriteLine($"Backtest Id: {backtestId}");
      
+    
      backtest = api.create_backtest(project_id, compile_id, 'OOS Reconciliation')
+backtest_id = backtest.backtest_id
+print(f"Backtest Id: {backtest_id}")
      
+   
      
+   
    4. 
+    Poll the
+    
+     ReadBacktest
+    
+    
+     read_backtest
+    
+    method until the
+    
+     completed
+    
+    flag is
+    
+     True
+    
+    . Log the
+    
+     progress
+    
+    attribute on each poll so you can watch the backtest advance.
+   
      5. 
+   
      
+    
      var completed = false;
+while (!completed)
+{
+    var result = api.ReadBacktest(projectId, backtestId);
+    completed = result.Completed;
+    Console.WriteLine($"Backtest running... {result.Progress:P2}");
+    Thread.Sleep(10000);
+}
+Console.WriteLine("Backtest completed.");
      
+    
      completed = False
+while not completed:
+    result = api.read_backtest(project_id, backtest_id)
+    completed = result.completed
+    print(f"Backtest running... {result.progress:.2%}")
+    sleep(10)
+print("Backtest completed.")
      
+   
      
+  
    
+ 
+
+
+
    Plot Equity Curves
    
+
+ 
+  
    
+   Follow these steps to plot the live and OOS backtest equity curves on the same axes.
+  
    
+  
      
+   
    1. 
+    Read the live "Strategy Equity" chart using the retry helper from the previous step.
+   
      2. 
+   
      
+    
      var liveEquityChart = ReadLiveChartWithRetry(projectId, "Strategy Equity");
      
+    
      live_equity_chart = read_chart(project_id, 'Strategy Equity')
      
+   
      
+   
    2. 
+    Read the backtest "Strategy Equity" chart by calling the
+    
+     ReadBacktestChart
+    
+    
+     read_backtest_chart
+    
+    method with the same retry pattern.
+   
      3. 
+   
      
+    
      Chart ReadBacktestChartWithRetry(int projectId, string backtestId, string chartName)
+{
+    for (var attempt = 0; attempt < 10; attempt++)
+    {
+        var result = api.ReadBacktestChart(projectId, chartName, 0, nowSec, 500, backtestId);
+        if (result.Success) return result.Chart;
+        Console.WriteLine($"Chart data is loading... (attempt {attempt + 1}/10)");
+        Thread.Sleep(10000);
+    }
+    throw new Exception($"Failed to read backtest {chartName} chart after 10 attempts");
+}
+
+var backtestEquityChart = ReadBacktestChartWithRetry(projectId, backtestId, "Strategy Equity");
      
+    
      def read_backtest_chart(project_id, backtest_id, chart_name, start=0, end=int(time()), count=500):
+    for attempt in range(10):
+        result = api.read_backtest_chart(project_id, chart_name, start, end, count, backtest_id)
+        if result.success:
+            return result.chart
+        print(f"Chart data is loading... (attempt {attempt + 1}/10)")
+        sleep(10)
+    raise RuntimeError(f"Failed to read backtest {chart_name} chart after 10 attempts")
+
+backtest_equity_chart = read_backtest_chart(project_id, backtest_id, 'Strategy Equity')
      
+   
      
+   
    3. 
+    Extract the
+    
+     Equity
+    
+    series from each chart, filtering out points with a null close. Python uses a
+    
+     pandas.Series
+    
+    indexed by timestamp so the two curves can be aligned on the union of their timestamps; C# keeps two lists of
+    
+     Candlestick
+    
+    points and lets Plotly.NET align them on the same x-axis.
+   
      4. 
+   
      
+    
      var liveValues = liveEquityChart.Series["Equity"].Values
+    .OfType<Candlestick>()
+    .Where(v => v.Close.HasValue)
+    .ToList();
+var backtestValues = backtestEquityChart.Series["Equity"].Values
+    .OfType<Candlestick>()
+    .Where(v => v.Close.HasValue)
+    .ToList();
      
+    
      import pandas as pd
+
+def to_naive(t):
+    ts = pd.Timestamp(t)
+    return ts.tz_convert('UTC').tz_localize(None) if ts.tzinfo else ts
+
+def to_series(chart, series_name='Equity'):
+    values = [v for v in chart.series[series_name].values if v.close is not None]
+    return pd.Series(
+        [v.close for v in values],
+        index=pd.DatetimeIndex([to_naive(v.time) for v in values])
+    )
+
+live_series = to_series(live_equity_chart)
+backtest_series = to_series(backtest_equity_chart)
+
+# Keep every timestamp from both sources; align and forward-fill on the union.
+df = pd.concat([live_series.rename('Live'), backtest_series.rename('OOS Backtest')], axis=1).sort_index().ffill()
      
+   
      
+   
    4. 
+    Plot both curves on the same axis. Python uses
+    
+     matplotlib
+    
+    ; C# uses
+    
+     Plotly.NET
+    
+    — load
+    
+     Plotly.NET
+    
+    and
+    
+     Plotly.NET.Interactive
+    
+    from NuGet and alias
+    
+     Plotly.NET.Chart
+    
+    to avoid ambiguity with
+    
+     QuantConnect.Chart
+    
+    .
+   
      5. 
+   
      
+    
      #r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
+using PlotlyChart = Plotly.NET.Chart;
+using Plotly.NET;
+using Plotly.NET.Interactive;
+using Plotly.NET.LayoutObjects;
+
+var equityChart = PlotlyChart.Combine(new[]
+{
+    Chart2D.Chart.Line<DateTime, decimal, string>(
+        liveValues.Select(v => v.Time),
+        liveValues.Select(v => v.Close.Value),
+        Name: "Live"),
+    Chart2D.Chart.Line<DateTime, decimal, string>(
+        backtestValues.Select(v => v.Time),
+        backtestValues.Select(v => v.Close.Value),
+        Name: "OOS Backtest")
+}).WithTitle("Live vs OOS Backtest Equity");
+
+display(equityChart);
      
+    
      import matplotlib.pyplot as plt
+
+fig, ax = plt.subplots(figsize=(12, 6))
+ax.plot(df.index, df['Live'], label='Live')
+ax.plot(df.index, df['OOS Backtest'], label='OOS Backtest')
+ax.set_title('Live vs OOS Backtest Equity')
+ax.set_xlabel('Time')
+ax.set_ylabel('Portfolio Value ($)')
+ax.legend()
+plt.show()
      
+   
      
+   Live vs OOS backtest equity curves
+   
    5. 
+    Score the reconciliation with the annualized returns DTW distance and the Pearson correlation of daily returns. Use
+    
+     tslearn
+    
+    's
+    
+     dtw
+    
+    with a Sakoe-Chiba band so the algorithm runs in linear time. The
+    
+     tslearn
+    
+    library is Python-only; run this step in a Python research notebook.
+   
      6. 
+   
      
+    
      from tslearn.metrics import dtw as DynamicTimeWarping
+
+returns = df.pct_change().dropna()
+
+# Pearson correlation between live and OOS backtest daily returns (closer to 1 is better).
+returns_correlation = returns.corr().iloc[0, 1]
+
+# Raw DTW distance on the returns curves.
+raw_dtw = DynamicTimeWarping(
+    returns['Live'], returns['OOS Backtest'],
+    global_constraint='sakoe_chiba', sakoe_chiba_radius=3
+)
+# Annualize so the distance is on the scale of yearly percent returns (closer to 0 is better).
+annualized_dtw = abs(((1 + (raw_dtw / returns.shape[0])) ** 252) - 1)
+
+print(f"Returns correlation: {returns_correlation:.3f}")
+print(f"Annualized returns DTW: {annualized_dtw:.3f}")
      
+   
      
+  
    
+ 
+
+
+
    Plot Order Fills
    
+
+ 
+  
    
+   Follow these steps to overlay live and OOS backtest order fills on a single marker-only chart per symbol. The chart deliberately omits candlesticks and any price history so the comparison between live and backtest executions is not drowned out by other series.
+  
    
+  
      
+   
    1. 
+    Read the live and backtest orders. Each call to
+    
+     ReadLiveOrders
+    
+    
+     read_live_orders
+    
+    and
+    
+     ReadBacktestOrders
+    
+    
+     read_backtest_orders
+    
+    returns at most 100 orders, so paginate in 100-Id windows until the endpoint returns an empty window. The first window can take a few seconds to load, so retry while it is empty.
+   
      2. 
+   
      
+    
      List<ApiOrderResponse> ReadAllOrders(Func<int, int, List<ApiOrderResponse>> fetchWindow)
+{
+    var all = new List<ApiOrderResponse>();
+    // Retry the first window while the response is empty (may be loading).
+    List<ApiOrderResponse> first = null;
+    for (var attempt = 0; attempt < 10; attempt++)
+    {
+        first = fetchWindow(0, 100);
+        if (first.Any()) break;
+        Console.WriteLine($"Orders loading... (attempt {attempt + 1}/10)");
+        Thread.Sleep(10000);
+    }
+    if (first == null || !first.Any()) return all;
+    all.AddRange(first);
+    // Paginate in 100-Id windows until the endpoint returns an empty window.
+    var start = 100;
+    while (true)
+    {
+        var window = fetchWindow(start, start + 100);
+        if (!window.Any()) break;
+        all.AddRange(window);
+        start += 100;
+    }
+    return all;
+}
+
+var liveOrders = ReadAllOrders((s, e) => api.ReadLiveOrders(projectId, s, e));
+var backtestOrders = ReadAllOrders((s, e) => api.ReadBacktestOrders(projectId, backtestId, s, e));
+Console.WriteLine($"Live orders: {liveOrders.Count}, OOS orders: {backtestOrders.Count}");
      
+    
      from time import sleep
+
+def read_all_orders(fetch_window):
+    orders = []
+    # Retry the first window while the response is empty (may be loading).
+    first = []
+    for attempt in range(10):
+        first = fetch_window(0, 100)
+        if first:
+            break
+        print(f"Orders loading... (attempt {attempt + 1}/10)")
+        sleep(10)
+    if not first:
+        return orders
+    orders.extend(first)
+    # Paginate in 100-Id windows until the endpoint returns an empty window.
+    start = 100
+    while True:
+        window = fetch_window(start, start + 100)
+        if not window:
+            break
+        orders.extend(window)
+        start += 100
+    return orders
+
+live_orders = read_all_orders(lambda s, e: api.read_live_orders(project_id, s, e))
+backtest_orders = read_all_orders(lambda s, e: api.read_backtest_orders(project_id, backtest_id, s, e))
+print(f"Live orders: {len(live_orders)}, OOS orders: {len(backtest_orders)}")
      
+   
      
+   
      
+    For more on the order objects returned, see
+    
+     Plot Order Fills
+    
+    in the Live Analysis documentation.
+   
      
+   
    2. 
+    Organize the trade times and prices for each security into a dictionary for both the live and backtest fills.
+   
      3. 
+   
      
+    
      var liveBySymbol = liveOrders.Select(x => x.Order).GroupBy(o => o.Symbol);
+var backtestBySymbol = backtestOrders.Select(x => x.Order)
+    .GroupBy(o => o.Symbol)
+    .ToDictionary(g => g.Key, g => g.ToList());
      
+    
      import pandas as pd
+
+def to_naive(t):
+    # Strip tzinfo so plotly can serialize the fill times.
+    ts = pd.Timestamp(t)
+    return ts.tz_convert('UTC').tz_localize(None) if ts.tzinfo else ts
+
+class OrderData:
+    def __init__(self):
+        self.buy_fill_times = []
+        self.buy_fill_prices = []
+        self.sell_fill_times = []
+        self.sell_fill_prices = []
+
+def group_by_symbol(orders):
+    data_by_symbol = {}
+    for order in [x.order for x in orders]:
+        if order.symbol not in data_by_symbol:
+            data_by_symbol[order.symbol] = OrderData()
+        data = data_by_symbol[order.symbol]
+        is_buy = order.quantity > 0
+        (data.buy_fill_times if is_buy else data.sell_fill_times).append(to_naive(order.last_fill_time))
+        (data.buy_fill_prices if is_buy else data.sell_fill_prices).append(order.price)
+    return data_by_symbol
+
+live_by_symbol = group_by_symbol(live_orders)
+backtest_by_symbol = group_by_symbol(backtest_orders)
      
+   
      
+   
    3. 
+    Plot one figure per symbol with four marker traces: live buys, live sells, backtest buys, backtest sells. Distinct markers keep live versus backtest executions visually separable.
+   
      4. 
+   
      
+    
      foreach (var liveGroup in liveBySymbol)
+{
+    var symbol = liveGroup.Key;
+    var live = liveGroup.ToList();
+    var bt = backtestBySymbol.TryGetValue(symbol, out var btList) ? btList : new List<Order>();
+
+    var traces = new[]
+    {
+        Chart2D.Chart.Point<DateTime, decimal, string>(
+            live.Where(o => o.Quantity > 0).Select(o => o.LastFillTime ?? o.Time),
+            live.Where(o => o.Quantity > 0).Select(o => o.Price),
+            Name: "Live Buys"),
+        Chart2D.Chart.Point<DateTime, decimal, string>(
+            live.Where(o => o.Quantity < 0).Select(o => o.LastFillTime ?? o.Time),
+            live.Where(o => o.Quantity < 0).Select(o => o.Price),
+            Name: "Live Sells"),
+        Chart2D.Chart.Point<DateTime, decimal, string>(
+            bt.Where(o => o.Quantity > 0).Select(o => o.LastFillTime ?? o.Time),
+            bt.Where(o => o.Quantity > 0).Select(o => o.Price),
+            Name: "OOS Backtest Buys"),
+        Chart2D.Chart.Point<DateTime, decimal, string>(
+            bt.Where(o => o.Quantity < 0).Select(o => o.LastFillTime ?? o.Time),
+            bt.Where(o => o.Quantity < 0).Select(o => o.Price),
+            Name: "OOS Backtest Sells")
+    };
+    var fillsChart = PlotlyChart.Combine(traces).WithTitle($"{symbol} Live vs OOS Backtest Fills");
+    display(fillsChart);
+}
      
+    
      import plotly.graph_objects as go
+
+symbols = set(live_by_symbol.keys()) | set(backtest_by_symbol.keys())
+
+for symbol in symbols:
+    live = live_by_symbol.get(symbol, OrderData())
+    bt = backtest_by_symbol.get(symbol, OrderData())
+
+    fig = go.Figure(layout=go.Layout(
+        title=go.layout.Title(text=f'{symbol.value} Live vs OOS Backtest Fills'),
+        xaxis_title='Fill Time',
+        yaxis_title='Fill Price',
+        height=600
+    ))
+
+    fig.add_trace(go.Scatter(
+        x=live.buy_fill_times, y=live.buy_fill_prices, mode='markers', name='Live Buys',
+        marker=go.scatter.Marker(color='aqua', symbol='triangle-up', size=12)
+    ))
+    fig.add_trace(go.Scatter(
+        x=live.sell_fill_times, y=live.sell_fill_prices, mode='markers', name='Live Sells',
+        marker=go.scatter.Marker(color='indigo', symbol='triangle-down', size=12)
+    ))
+    fig.add_trace(go.Scatter(
+        x=bt.buy_fill_times, y=bt.buy_fill_prices, mode='markers', name='OOS Backtest Buys',
+        marker=go.scatter.Marker(color='aqua', symbol='triangle-up-open', size=12, line=dict(width=2))
+    ))
+    fig.add_trace(go.Scatter(
+        x=bt.sell_fill_times, y=bt.sell_fill_prices, mode='markers', name='OOS Backtest Sells',
+        marker=go.scatter.Marker(color='indigo', symbol='triangle-down-open', size=12, line=dict(width=2))
+    ))
+
+    fig.show()
      
+   
      
+  
    
+  
    
+   Note: the preceding plots only show the last fill of each trade. If your trade has partial fills, the plots only display the last fill.
+  
    
+ 
+
+
+
    Examples
    
+
+ 
+  
    
+   Example 1: Generate an OOS Reconciliation Curve
+  
    
+  
    
+   The following example reads the live deployment's "Strategy Equity" chart, runs an OOS backtest that matches its start datetime and starting cash, and plots both the equity curves and the order fills side by side. Before running it, make sure your project's main algorithm file sets
+   
+    StartDate
+   
+   
+    start_date
+   
+   and
+   
+    Cash
+   
+   
+    cash
+   
+   to match the live deployment (or reads them as parameters).
+  
    
+  
    
+   
    // Load the necessary assemblies.
+#load "../Initialize.csx"
+#load "../QuantConnect.csx"
+#r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
+
+using QuantConnect;
+using QuantConnect.Api;
+using QuantConnect.Research;
+using System;
+using System.Linq;
+using System.Threading;
+using PlotlyChart = Plotly.NET.Chart;
+using Plotly.NET;
+using Plotly.NET.Interactive;
+using Plotly.NET.LayoutObjects;
+
+// Instantiate QuantBook instance for researching.
+var qb = new QuantBook();
+var projectId = qb.ProjectId;   // Replace if the live algorithm is not in this project.
+var nowSec = (int)DateTimeOffset.UtcNow.ToUnixTimeSeconds();
+
+// Read the live Strategy Equity chart. The first and last valid points give
+// you the start datetime, starting cash, and end datetime.
+var liveEquity = api.ReadLiveChart(projectId, "Strategy Equity", 0, nowSec, 500).Chart;
+var equityValues = liveEquity.Series["Equity"].Values
+    .OfType<Candlestick>()
+    .Where(v => v.Close.HasValue)
+    .ToList();
+var startDate = equityValues.First().Time;
+var startingCash = equityValues.First().Close.Value;
+// Uncomment to reconcile up to "now" instead:
+// var endDate = DateTime.UtcNow;
+var endDate = equityValues.Last().Time;
+
+// Compile and create the OOS backtest (start date and cash must be set in the algorithm itself).
+var compilation = api.CreateCompile(projectId);
+var compileId = compilation.CompileId;
+while (api.ReadCompile(projectId, compileId).State != CompileState.BuildSuccess)
+{
+    Thread.Sleep(5000);
+}
+var backtest = api.CreateBacktest(projectId, compileId, "OOS Reconciliation");
+var backtestId = backtest.BacktestId;
+
+var completed = false;
+while (!completed)
+{
+    var result = api.ReadBacktest(projectId, backtestId);
+    completed = result.Completed;
+    Console.WriteLine($"Backtest running... {result.Progress:P2}");
+    Thread.Sleep(10000);
+}
+Console.WriteLine("Backtest completed.");
+
+// Read the backtest Strategy Equity chart.
+var backtestEquity = api.ReadBacktestChart(projectId, "Strategy Equity", 0, nowSec, 500, backtestId).Chart;
+
+// Read live and backtest orders for the fill overlay. Each call returns at
+// most 100 orders, so paginate in 100-Id windows until we get an empty window.
+// The first window can take a few seconds to load, so retry while empty.
+List<ApiOrderResponse> ReadAllOrders(Func<int, int, List<ApiOrderResponse>> fetchWindow)
+{
+    var all = new List<ApiOrderResponse>();
+    List<ApiOrderResponse> first = null;
+    for (var attempt = 0; attempt < 10; attempt++)
+    {
+        first = fetchWindow(0, 100);
+        if (first.Any()) break;
+        Thread.Sleep(10000);
+    }
+    if (first == null || !first.Any()) return all;
+    all.AddRange(first);
+    var start = 100;
+    while (true)
+    {
+        var window = fetchWindow(start, start + 100);
+        if (!window.Any()) break;
+        all.AddRange(window);
+        start += 100;
+    }
+    return all;
+}
+
+var liveOrders = ReadAllOrders((s, e) => api.ReadLiveOrders(projectId, s, e));
+var backtestOrders = ReadAllOrders((s, e) => api.ReadBacktestOrders(projectId, backtestId, s, e));
+
+Console.WriteLine($"Start: {startDate}, Starting cash: {startingCash}, End: {endDate}");
+Console.WriteLine($"Live orders: {liveOrders.Count}, OOS orders: {backtestOrders.Count}");
+
+// Overlay the two equity curves with Plotly.NET.
+var backtestValues = backtestEquity.Series["Equity"].Values
+    .OfType<Candlestick>()
+    .Where(v => v.Close.HasValue)
+    .ToList();
+
+var equityChart = PlotlyChart.Combine(new[]
+{
+    Chart2D.Chart.Line<DateTime, decimal, string>(
+        equityValues.Select(v => v.Time),
+        equityValues.Select(v => v.Close.Value),
+        Name: "Live"),
+    Chart2D.Chart.Line<DateTime, decimal, string>(
+        backtestValues.Select(v => v.Time),
+        backtestValues.Select(v => v.Close.Value),
+        Name: "OOS Backtest")
+}).WithTitle("Live vs OOS Backtest Equity");
+display(equityChart);
+
+// Overlay the live and backtest fills per symbol.
+var liveBySymbol = liveOrders.Select(x => x.Order).GroupBy(o => o.Symbol);
+var backtestBySymbol = backtestOrders.Select(x => x.Order).GroupBy(o => o.Symbol).ToDictionary(g => g.Key, g => g.ToList());
+
+foreach (var liveGroup in liveBySymbol)
+{
+    var symbol = liveGroup.Key;
+    var live = liveGroup.ToList();
+    var bt = backtestBySymbol.TryGetValue(symbol, out var btList) ? btList : new List<Order>();
+
+    var traces = new[]
+    {
+        Chart2D.Chart.Point<DateTime, decimal, string>(
+            live.Where(o => o.Quantity > 0).Select(o => o.LastFillTime ?? o.Time),
+            live.Where(o => o.Quantity > 0).Select(o => o.Price),
+            Name: "Live Buys"),
+        Chart2D.Chart.Point<DateTime, decimal, string>(
+            live.Where(o => o.Quantity < 0).Select(o => o.LastFillTime ?? o.Time),
+            live.Where(o => o.Quantity < 0).Select(o => o.Price),
+            Name: "Live Sells"),
+        Chart2D.Chart.Point<DateTime, decimal, string>(
+            bt.Where(o => o.Quantity > 0).Select(o => o.LastFillTime ?? o.Time),
+            bt.Where(o => o.Quantity > 0).Select(o => o.Price),
+            Name: "OOS Backtest Buys"),
+        Chart2D.Chart.Point<DateTime, decimal, string>(
+            bt.Where(o => o.Quantity < 0).Select(o => o.LastFillTime ?? o.Time),
+            bt.Where(o => o.Quantity < 0).Select(o => o.Price),
+            Name: "OOS Backtest Sells")
+    };
+    var fillsChart = PlotlyChart.Combine(traces).WithTitle($"{symbol} Live vs OOS Backtest Fills");
+    display(fillsChart);
+}
    
+   
    # Instantiate QuantBook instance for researching.
+from datetime import datetime
+from time import sleep, time
+import pandas as pd
+import matplotlib.pyplot as plt
+import plotly.graph_objects as go
+from tslearn.metrics import dtw as DynamicTimeWarping
+
+qb = QuantBook()
+project_id = qb.project_id   # Replace if the live algorithm is not in this project.
+
+# Read the live Strategy Equity chart. The first and last points give you the
+# start datetime, starting cash, and end datetime.
+def read_chart(project_id, chart_name, start=0, end=int(time()), count=500):
+    for attempt in range(10):
+        result = api.read_live_chart(project_id, chart_name, start, end, count)
+        if result.success:
+            return result.chart
+        sleep(10)
+    raise RuntimeError(f"Failed to read {chart_name} chart after 10 attempts")
+
+live_equity_chart = read_chart(project_id, 'Strategy Equity')
+# Skip leading points with a None close (common at the start of a deployment).
+valid_values = [v for v in live_equity_chart.series['Equity'].values if v.close is not None]
+start_datetime = valid_values[0].time
+starting_cash = valid_values[0].close
+# Uncomment to reconcile up to "now" instead:
+# end_datetime = datetime.utcnow()
+end_datetime = valid_values[-1].time
+
+# Compile and create the OOS backtest (start date and cash must be set in the algorithm itself).
+compilation = api.create_compile(project_id)
+compile_id = compilation.compile_id
+while api.read_compile(project_id, compile_id).state != 'BuildSuccess':
+    sleep(5)
+backtest = api.create_backtest(project_id, compile_id, 'OOS Reconciliation')
+backtest_id = backtest.backtest_id
+completed = False
+while not completed:
+    result = api.read_backtest(project_id, backtest_id)
+    completed = result.completed
+    print(f'Backtest running... {result.progress:.2%}')
+    sleep(10)
+
+# Read the backtest Strategy Equity chart.
+def read_backtest_chart(project_id, backtest_id, chart_name, start=0, end=int(time()), count=500):
+    for attempt in range(10):
+        result = api.read_backtest_chart(project_id, chart_name, start, end, count, backtest_id)
+        if result.success:
+            return result.chart
+        sleep(10)
+    raise RuntimeError(f"Failed to read backtest {chart_name} chart after 10 attempts")
+
+backtest_equity_chart = read_backtest_chart(project_id, backtest_id, 'Strategy Equity')
+
+# Overlay the two equity curves.
+def to_naive(t):
+    ts = pd.Timestamp(t)
+    return ts.tz_convert('UTC').tz_localize(None) if ts.tzinfo else ts
+
+def to_series(chart, series_name='Equity'):
+    values = [v for v in chart.series[series_name].values if v.close is not None]
+    return pd.Series(
+        [v.close for v in values],
+        index=pd.DatetimeIndex([to_naive(v.time) for v in values])
+    )
+
+df = pd.concat([
+    to_series(live_equity_chart).rename('Live'),
+    to_series(backtest_equity_chart).rename('OOS Backtest')
+], axis=1).sort_index().ffill()
+
+fig, ax = plt.subplots(figsize=(12, 6))
+ax.plot(df.index, df['Live'], label='Live')
+ax.plot(df.index, df['OOS Backtest'], label='OOS Backtest')
+ax.set_title('Live vs OOS Backtest Equity')
+ax.set_xlabel('Time'); ax.set_ylabel('Portfolio Value ($)')
+ax.legend(); plt.show()
+
+# Score the reconciliation: returns correlation and annualized returns DTW distance.
+returns = df.pct_change().dropna()
+returns_correlation = returns.corr().iloc[0, 1]
+raw_dtw = DynamicTimeWarping(
+    returns['Live'], returns['OOS Backtest'],
+    global_constraint='sakoe_chiba', sakoe_chiba_radius=3
+)
+annualized_dtw = abs(((1 + (raw_dtw / returns.shape[0])) ** 252) - 1)
+print(f"Returns correlation: {returns_correlation:.3f}")
+print(f"Annualized returns DTW: {annualized_dtw:.3f}")
+
+# Overlay the live and backtest fills per symbol.
+class OrderData:
+    def __init__(self):
+        self.buy_fill_times, self.buy_fill_prices = [], []
+        self.sell_fill_times, self.sell_fill_prices = [], []
+
+def group_by_symbol(orders):
+    out = {}
+    for order in [x.order for x in orders]:
+        out.setdefault(order.symbol, OrderData())
+        d = out[order.symbol]
+        is_buy = order.quantity > 0
+        (d.buy_fill_times if is_buy else d.sell_fill_times).append(to_naive(order.last_fill_time))
+        (d.buy_fill_prices if is_buy else d.sell_fill_prices).append(order.price)
+    return out
+
+def read_all_orders(fetch_window):
+    orders = []
+    first = []
+    for attempt in range(10):
+        first = fetch_window(0, 100)
+        if first:
+            break
+        sleep(10)
+    if not first:
+        return orders
+    orders.extend(first)
+    start = 100
+    while True:
+        window = fetch_window(start, start + 100)
+        if not window:
+            break
+        orders.extend(window)
+        start += 100
+    return orders
+
+live_by_symbol = group_by_symbol(read_all_orders(lambda s, e: api.read_live_orders(project_id, s, e)))
+backtest_by_symbol = group_by_symbol(read_all_orders(lambda s, e: api.read_backtest_orders(project_id, backtest_id, s, e)))
+
+for symbol in set(live_by_symbol) | set(backtest_by_symbol):
+    live = live_by_symbol.get(symbol, OrderData())
+    bt = backtest_by_symbol.get(symbol, OrderData())
+    fig = go.Figure(layout=go.Layout(
+        title=go.layout.Title(text=f'{symbol.value} Live vs OOS Backtest Fills'),
+        xaxis_title='Fill Time', yaxis_title='Fill Price', height=600))
+    fig.add_trace(go.Scatter(x=live.buy_fill_times, y=live.buy_fill_prices, mode='markers',
+        name='Live Buys', marker=go.scatter.Marker(color='aqua', symbol='triangle-up', size=12)))
+    fig.add_trace(go.Scatter(x=live.sell_fill_times, y=live.sell_fill_prices, mode='markers',
+        name='Live Sells', marker=go.scatter.Marker(color='indigo', symbol='triangle-down', size=12)))
+    fig.add_trace(go.Scatter(x=bt.buy_fill_times, y=bt.buy_fill_prices, mode='markers',
+        name='OOS Backtest Buys', marker=go.scatter.Marker(color='aqua', symbol='triangle-up-open', size=12, line=dict(width=2))))
+    fig.add_trace(go.Scatter(x=bt.sell_fill_times, y=bt.sell_fill_prices, mode='markers',
+        name='OOS Backtest Sells', marker=go.scatter.Marker(color='indigo', symbol='triangle-down-open', size=12, line=dict(width=2))))
+    fig.show()
    
+  
    
+ 
+
+
+
     
    
+
    Meta Analysis > Live Deployment Automation
    
+
    
+    
    
         
    Meta Analysis
    
         
    Live Deployment Automation
    
     
    
@@ -52309,13 +53918,17 @@ 
    Get Project Ids
    
     
    #load "../Initialize.csx"
    
    
    
    
  • 
-    Import the data types.
+    Load the necessary assembly files.
    
    • 
    
    
     
    #load "../QuantConnect.csx"
-#r "../Microsoft.Data.Analysis.dll"
-
-using QuantConnect;
+#r "../Microsoft.Data.Analysis.dll"
    
+   
    
+   
  • 
+    Import the data types.
+   
    • 
+   
    
+    
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Algorithm;
 using QuantConnect.Research;
@@ -52547,10 +54160,14 @@ 
    
   
    
   
    
    
    // Load the necessary assemblies.
-#load "../Initialize.csx"
-#load "../QuantConnect.csx"
-
-using QuantConnect;
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Api;
 using QuantConnect.Research;
 
@@ -52601,7 +54218,7 @@ 
    
     "ib-password": "password",
     "ib-weekly-restart-utc-time": "00:00:00"
 }
-result = api.CreateLiveAlgorithm(qb.project_id, compile_id, live_node_id, brokerage_settings)
    
+result = api.create_live_algorithm(qb.project_id, compile_id, live_node_id, brokerage_settings)
    
   
    
  
 
@@ -52875,10 +54492,15 @@ 
    Import Libraries
    
    libraries by the following:
   
    
   
    
-   
    #load "../Initialize.csx"
-#load "../QuantConnect.csx"
-
-using QuantConnect;
+   
    // Load the required assembly files and data types in a separate cell.
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
 using QuantConnect.Algorithm;
@@ -53427,10 +55049,14 @@ 
    Examples
    
   
    
   
    
    
    // Load the required assembly files and data types.
-#load "../Initialize.csx"
-#load "../QuantConnect.csx"
-
-using QuantConnect;
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
 using QuantConnect.Algorithm;
@@ -53588,7 +55214,7 @@ 
    Examples
    
     symbols[assets[i]] = qb.add_equity(assets[i], Resolution.MINUTE).symbol
 
 # Call the History method with qb.Securities.Keys for all tickers, time argument(s), and resolution to request historical data for the symbol.
-history = qb.history(qb.securities.Keys, datetime(2021, 1, 1), datetime(2021, 12, 31), Resolution.DAILY)
+history = qb.history(qb.securities.keys(), datetime(2021, 1, 1), datetime(2021, 12, 31), Resolution.DAILY)
 
 # Select the close column and then call the unstack method.
 df = history['close'].unstack(level=0)
@@ -54185,7 +55811,7 @@ 
    Examples
    
     qb.add_equity(assets[i],Resolution.MINUTE).symbol
 
 # Call the History method with qb.securities.keys for all tickers, time argument(s), and resolution to request historical data for the symbol.
-history = qb.history(qb.securities.Keys, datetime(2019, 1, 1), datetime(2021, 12, 31), Resolution.DAILY)
+history = qb.history(qb.securities.keys(), datetime(2019, 1, 1), datetime(2021, 12, 31), Resolution.DAILY)
 
 # Select the close column and then call the unstack method.
 df = history['close'].unstack(level=0)
@@ -54265,7 +55891,7 @@ 
    Examples
    
         self.regressor = RandomForestRegressor(n_estimators=100, min_samples_split=5, random_state = 1990)
         
         # Get historical data
-        history = self.history(self.securities.Keys, 360, Resolution.DAILY)
+        history = self.history(self.securities.keys(), 360, Resolution.DAILY)
         
         # Select the close column and then call the unstack method.
         df = history['close'].unstack(level=0)
@@ -54289,7 +55915,7 @@ 
    Examples
    
         
         qb = self
         # Fetch history on our universe
-        df = qb.history(qb.securities.Keys, 2, Resolution.DAILY)
+        df = qb.history(qb.securities.keys(), 2, Resolution.DAILY)
         if df.empty: return
     
         # Make all of them into a single time index.
@@ -54362,10 +55988,15 @@ 
    Import Libraries
    
    libraries by the following:
   
    
   
    
-   
    #load "../Initialize.csx"
-#load "../QuantConnect.csx"
-
-using QuantConnect;
+   
    // Load the required assembly files and data types in a separate cell.
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
 using QuantConnect.Algorithm;
@@ -54917,10 +56548,14 @@ 
    Examples
    
   
    
   
    
    
    // Load the required assembly files and data types.
-#load "../Initialize.csx"
-#load "../QuantConnect.csx"
-
-using QuantConnect;
+#load "../Initialize.csx"
    
+  
    
+  
    
+   
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    
+  
    
+  
    
+   
    using QuantConnect;
 using QuantConnect.Data;
 using QuantConnect.Data.Market;
 using QuantConnect.Algorithm;
@@ -55252,7 +56887,7 @@ 
    Examples
    
     def every_day_before_market_close(self) -> None:
         qb = self
         # Fetch history on our universe
-        history = qb.history(qb.securities.Keys, 252*2, Resolution.DAILY)
+        history = qb.history(qb.securities.keys(), 252*2, Resolution.DAILY)
         if history.empty: return
     
         # Select the close column and then call the unstack method, then call pct_change to compute the daily return.
@@ -56354,7 +57989,7 @@ 
    Examples
    
         
         # Get the real-time log close price for all assets and store in a Series
         series = pd.Series()
-        for symbol in qb.securities.Keys:
+        for symbol in qb.securities.keys():
             series[symbol] = np.log(qb.securities[symbol].close)
             
         # Get the spread
@@ -56869,7 +58504,7 @@ 
    Examples
    
     qb.add_equity(assets[i],Resolution.MINUTE).symbol
 
 # Call the History method with qb.Securities.Keys for all tickers, time argument(s), and resolution to request historical data for the symbol.
-history = qb.history(qb.Securities.Keys, datetime(2021, 1, 1), datetime(2021, 12, 31), Resolution.DAILY)
+history = qb.history(qb.securities.keys(), datetime(2021, 1, 1), datetime(2021, 12, 31), Resolution.DAILY)
 
 # Select the close column and then call the unstack method.
 close_price = history['close'].unstack(level=0)
@@ -57047,7 +58682,7 @@ 
    Examples
    
         
         # Get the real-time log close price for all assets and store in a Series
         series = pd.Series()
-        for symbol in qb.securities.Keys:
+        for symbol in qb.securities.keys():
             series[symbol] = np.log(qb.securities[symbol].close)
             
         # Get the spread
@@ -58105,7 +59740,7 @@ 
    Set Up Algorithm
    
 
     # ==============================
     
-    if prediction > qb.Securities[self.asset].Price:
+    if prediction > qb.securities[self.asset].price:
         self.set_holdings(self.asset, 1.)
     else:
         self.set_holdings(self.asset, -1.)
    
@@ -58321,7 +59956,7 @@ 
    Examples
    
 
         # ==============================
         
-        if prediction > qb.Securities[self.asset].Price:
+        if prediction > qb.securities[self.asset].price:
             self.set_holdings(self.asset, 1.)
         else:
             self.set_holdings(self.asset, -1.)
    
@@ -58911,7 +60546,7 @@ 
    Examples
    
 history = qb.history(list(symbols.keys()), datetime(2019, 1, 1), datetime(2021, 12, 31), Resolution.DAILY)
 
 # Call SPY history as reference.
-spy = qb.history(qb.add_equity("SPY").Symbol, datetime(2019, 1, 1), datetime(2021, 12, 31), Resolution.DAILY)
+spy = qb.history(qb.add_equity("SPY").symbol, datetime(2019, 1, 1), datetime(2021, 12, 31), Resolution.DAILY)
 
 # Call the History method with list of buyback tickers, time argument(s), and resolution to request buyback data for the symbol.
 history_buybacks = qb.history(list(symbols.values()), datetime(2019, 1, 1), datetime(2021, 12, 31), Resolution.DAILY)
@@ -59708,7 +61343,7 @@ 
    Examples
    
         
         # Subscribe to the universe price data
         for symbol in etf_symbols:
-            security = qb.AddSecurity(symbol, Resolution.Daily)
+            security = qb.add_security(symbol, Resolution.DAILY)
             security_timezone = security.Exchange.TimeZone
             security_symbols.append(symbol)
         
@@ -59737,7 +61372,7 @@ 
    Examples
    
             print(f'Error: The ETF universe file does not exist')
             return
         security_ids = df[df.columns[1]].values
-        symbols = [qb.Symbol(security_id) for security_id in security_ids]
+        symbols = [qb.symbol(security_id) for security_id in security_ids]
         return symbols
     
 # Instantiate a QuantBook.
diff --git a/single-page/Quantconnect-Writing-Algorithms.html b/single-page/Quantconnect-Writing-Algorithms.html
index e52c6213e0..20f3c5db71 100644
--- a/single-page/Quantconnect-Writing-Algorithms.html
+++ b/single-page/Quantconnect-Writing-Algorithms.html
@@ -154,21 +154,29 @@ 
    Table of Content
    
 
  • 6.1 Overview
    • 
 
  • 6.2 QuantConnect
    • 
 
  • 6.2.1 Binance Crypto Future Margin Rate Data
    • 
-
  • 6.2.2 Bybit Crypto Future Margin Rate Data
    • 
-
  • 6.2.3 CFD Data
    • 
-
  • 6.2.4 Cash Indices
    • 
-
  • 6.2.5 FOREX Data
    • 
-
  • 6.2.6 Fear and Greed
    • 
-
  • 6.2.7 International Future Universe
    • 
-
  • 6.2.8 US ETF Constituents
    • 
-
  • 6.2.9 US Equities Short Availability
    • 
-
  • 6.2.10 US Equity Coarse Universe
    • 
-
  • 6.2.11 US Equity Option Universe
    • 
-
  • 6.2.12 US Equity Security Master
    • 
-
  • 6.2.13 US Future Option Universe
    • 
-
  • 6.2.14 US Future Universe
    • 
-
  • 6.2.15 US Futures Security Master
    • 
-
  • 6.2.16 US Index Option Universe
    • 
+
  • 6.2.2 Binance Crypto Future Price Data
    • 
+
  • 6.2.3 Binance Crypto Price Data
    • 
+
  • 6.2.4 Binance US Crypto Price Data
    • 
+
  • 6.2.5 Bitfinex Crypto Price Data
    • 
+
  • 6.2.6 Bybit Crypto Future Margin Rate Data
    • 
+
  • 6.2.7 Bybit Crypto Future Price Data
    • 
+
  • 6.2.8 Bybit Crypto Price Data
    • 
+
  • 6.2.9 CFD Data
    • 
+
  • 6.2.10 Cash Indices
    • 
+
  • 6.2.11 Coinbase Crypto Price Data
    • 
+
  • 6.2.12 FOREX Data
    • 
+
  • 6.2.13 Fear and Greed
    • 
+
  • 6.2.14 International Future Universe
    • 
+
  • 6.2.15 Kraken Crypto Price Data
    • 
+
  • 6.2.16 US ETF Constituents
    • 
+
  • 6.2.17 US Equities Short Availability
    • 
+
  • 6.2.18 US Equity Coarse Universe
    • 
+
  • 6.2.19 US Equity Option Universe
    • 
+
  • 6.2.20 US Equity Security Master
    • 
+
  • 6.2.21 US Future Option Universe
    • 
+
  • 6.2.22 US Future Universe
    • 
+
  • 6.2.23 US Futures Security Master
    • 
+
  • 6.2.24 US Index Option Universe
    • 
 
  • 6.3 AlgoSeek
    • 
 
  • 6.3.1 US Equities
    • 
 
  • 6.3.2 US Equity Options
    • 
@@ -179,24 +187,17 @@ 
    Table of Content
    
 
  • 6.4.1 US Fundamental Data
    • 
 
  • 6.5 TickData
    • 
 
  • 6.5.1 International Futures
    • 
-
  • 6.6 CoinAPI
    • 
-
  • 6.6.1 Binance Crypto Future Price Data
    • 
-
  • 6.6.2 Binance Crypto Price Data
    • 
-
  • 6.6.3 Binance US Crypto Price Data
    • 
-
  • 6.6.4 Bitfinex Crypto Price Data
    • 
-
  • 6.6.5 Bybit Crypto Future Price Data
    • 
-
  • 6.6.6 Bybit Crypto Price Data
    • 
-
  • 6.6.7 Coinbase Crypto Price Data
    • 
-
  • 6.6.8 Kraken Crypto Price Data
    • 
-
  • 6.7 Benzinga
    • 
-
  • 6.7.1 Benzinga News Feed
    • 
-
  • 6.8 Blockchain
    • 
-
  • 6.8.1 Bitcoin Metadata
    • 
-
  • 6.9 Brain
    • 
-
  • 6.9.1 Brain Language Metrics on Company Filings
    • 
-
  • 6.9.2 Brain ML Stock Ranking
    • 
-
  • 6.9.3 Brain Sentiment Indicator
    • 
-
  • 6.9.4 Brain Wikipedia Page Views
    • 
+
  • 6.6 Benzinga
    • 
+
  • 6.6.1 Benzinga News Feed
    • 
+
  • 6.7 Blockchain
    • 
+
  • 6.7.1 Bitcoin Metadata
    • 
+
  • 6.8 Brain
    • 
+
  • 6.8.1 Brain Language Metrics on Company Filings
    • 
+
  • 6.8.2 Brain ML Stock Ranking
    • 
+
  • 6.8.3 Brain Sentiment Indicator
    • 
+
  • 6.8.4 Brain Wikipedia Page Views
    • 
+
  • 6.9 Bureau of Labor Statistics
    • 
+
  • 6.9.1 US Bureau of Labor Statistics (BLS)
    • 
 
  • 6.10 CoinGecko
    • 
 
  • 6.10.1 Crypto Market Cap
    • 
 
  • 6.11 EOD Historical Data
    • 
@@ -227,7 +228,6 @@ 
    Table of Content
    
 
  • 6.18.3 Insider Trading
    • 
 
  • 6.18.4 US Congress Trading
    • 
 
  • 6.18.5 US Government Contracts
    • 
-
  • 6.18.6 WallStreetBets
    • 
 
  • 6.19 RegAlytics
    • 
 
  • 6.19.1 US Regulatory Alerts - Financial Sector
    • 
 
  • 6.20 Securities and Exchange Commission
    • 
@@ -2228,7 +2228,7 @@ 
    
         self.set_end_date(2024, 12, 31)
         
         # Request AAPL data to trade it. We need a resolution denser than 6-hour for the consolidator.
-        self.aapl = self.add_equity("AAPL", Resolution.Hour).symbol
+        self.aapl = self.add_equity("AAPL", Resolution.HOUR).symbol
 
         # Create a 6-hour consolidator for smoothing the noise.
         self.consolidator = TradeBarConsolidator(timedelta(hours=6))
@@ -2507,7 +2507,7 @@ 
    
         self.set_end_date(2024, 12, 31)
         
         # Request ES future data for trading.
-        self._future = self.add_future("ES", Resolution.Minute,
+        self._future = self.add_future("ES", Resolution.MINUTE,
             extended_market_hours=True,
             data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
             data_mapping_mode=DataMappingMode.OPEN_INTEREST,
@@ -2529,7 +2529,7 @@ 
    
 
         # Cancel unfilled orders if they are not filled within 2 minutes to avoid stale fills.
         # Note that the Time property in the order ticket is in UTC timezone.
-        if self.ticket.status != OrderStatus.Filled and self.ticket.time + timedelta(minutes=2) < self.utc_time:
+        if self.ticket.status != OrderStatus.FILLED and self.ticket.time + timedelta(minutes=2) < self.utc_time:
             # You can change to a different time zone and compare the log differences.
             self.ticket.cancel(f"{self.time} :: utc: {self.utc_time} :: Order canceled due to placed over 2 minutes")
 
@@ -3223,6 +3223,8 @@ 
    Markets

    		+ Market.CBOE +
    Market.EUREX
    Market.HKFE @@ -3232,6 +3234,8 @@

    Markets

    Market.USA     + Market.CBOE +
    Market.EUREX
    Market.HKFE @@ -5495,7 +5499,7 @@

    self.set_holdings(self.spy, -1) # Schedule a switch 25 days later. self.last_scheduled_event = self.schedule.on( - self.date_rules.On(self.time + timedelta(25)), + self.date_rules.on(self.time + timedelta(25)), self.time_rules.at(9, 30), self.switch ) @@ -5510,7 +5514,7 @@

    # Schedule the next switch 25 days later. self.schedule.remove(self.last_scheduled_event) self.last_scheduled_event = self.schedule.on( - self.date_rules.On(self.time + timedelta(25)), + self.date_rules.on(self.time + timedelta(25)), self.time_rules.at(9, 30), self.switch ) @@ -7337,6 +7341,224 @@

    Performance Chart

    +

    Memory Metrics

    + + +

    + The static + + OS + + class in the + + QuantConnect + + namespace exposes memory and CPU metrics that you can read from your algorithm to monitor resource usage during backtests and live trading. +

    +

    + The following table lists the static members of the + + OS + + class that report memory and CPU usage: +

    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + Member + + Data Type + + Description +
    + + ApplicationMemoryUsed + + + APPLICATION_MEMORY_USED + + + + long + + + int + + + Private memory allocated to the current process, including managed and unmanaged memory, in megabytes. +
    + + TotalPhysicalMemoryUsed + + + TOTAL_PHYSICAL_MEMORY_USED + + + + long + + + int + + + Managed-runtime RAM usage (sampled with + + GC.GetTotalMemory + + + gc.get_total_memory + + ), in megabytes. LEAN's memory monitor samples this value. +
    + + CpuUsage + + + CPU_USAGE + + + + decimal + + + float + + + Total CPU usage as a percentage, sampled on a background thread. +
    + + GetServerStatistics() + + + get_server_statistics() + + + + Dictionary<string, string> + + + Dictionary[str, str] + + + Snapshot dictionary with + + CPU Usage + + , + + Used RAM + + , + + Total RAM + + , + + Hostname + + , and + + LEAN Version + + entries. +
    +

    + The following examples show how to read these metrics from an algorithm: +

    +
    + 
    // Log the current memory usage of the algorithm process.
+Log($"Total physical memory used: {OS.TotalPhysicalMemoryUsed} MB");
+Log($"Application memory used: {OS.ApplicationMemoryUsed} MB");
+
+// Log the current CPU usage as a percentage.
+Log($"CPU usage: {OS.CpuUsage}%");
+
+// Log a full snapshot of the server statistics.
+foreach (var kvp in OS.GetServerStatistics()) Log($"{kvp.Key}: {kvp.Value}");
    + 
    # Log the current memory usage of the algorithm process.
+self.log(f"Total physical memory used: {OS.TOTAL_PHYSICAL_MEMORY_USED} MB")
+self.log(f"Application memory used: {OS.APPLICATION_MEMORY_USED} MB")
+
+# Log the current CPU usage as a percentage.
+self.log(f"CPU usage: {OS.CPU_USAGE}%")
+
+# Log a full snapshot of the server statistics.
+for kvp in OS.get_server_statistics():
+    self.log(f"{kvp.key}: {kvp.value}")
    +
    +

    + LEAN monitors the process's RAM usage during backtests and live trading so it can stop the algorithm gracefully when it approaches the node's memory limit, instead of letting the runtime crash. + To avoid killing an algorithm over a short-lived spike, LEAN does not act on the raw reading — it smooths the sampled value first and checks the smoothed value against the limit. +

    +

    + LEAN samples + + OS.TotalPhysicalMemoryUsed + + + OS.TOTAL_PHYSICAL_MEMORY_USED + + once per minute and feeds each sample into an exponential moving average. + The engine sets + + emaPeriod = 60 + + , which gives you roughly a 1-hour rolling window. +

    +
    + 
    // Update the smoothed memory reading each minute.
+sample = OS.TotalPhysicalMemoryUsed;
+memoryUsed = Convert.ToInt64((emaPeriod-1)/emaPeriod * memoryUsed + (1/emaPeriod)*sample);
    + 
    # Update the smoothed memory reading each minute.
+sample = OS.TOTAL_PHYSICAL_MEMORY_USED
+memory_used = int((ema_period - 1) / ema_period * memory_used + (1 / ema_period) * sample)
    +
    +

    + If the smoothed value exceeds the node's memory limit, LEAN terminates the algorithm with the runtime message + + Execution Security Error: Memory Usage Maxed Out - {memoryCap}MB max, with last sample of {lastSample}MB. + + , where + + {memoryCap} + + is the node's configured limit and + + {lastSample} + + is the most recent reading. +

    + + +

    JIT Compilation

    @@ -8457,32 +8679,32 @@

    Set Account Currency

    set_account_currency - method. By default, the account currency is USD and your starting cash is $100,000. If you call the + method. By default, the account currency is USD and your starting cash is $100,000. You must call the SetAccountCurrency set_account_currency - method, you must call it before you call the + method before you + + add data + + , and we recommend calling it before the SetCash set_cash - method or - - add data - - . If you call the + method. If you call the SetAccountCurrency set_account_currency - method more than once, only the first call takes effect. + method more than once with different currencies, only the first call takes effect. 

    // Set the account currency and your starting cash.
@@ -8495,6 +8717,99 @@ 
    Set Account Currency
    
 self.set_account_currency("INR") # Set the account currency to Indian Rupees and its quantity to 100,000 INR.
 self.set_account_currency("BTC", 10) # Set the account currency to Bitcoin and its quantity to 10 BTC.
    +

    + If you call + + SetAccountCurrency + + + set_account_currency + + after + + SetCash + + + set_cash + + , one of the following applies: +

    +
      +
    • + If you previously called + + SetCash(amount) + + + set_cash(amount) + + and the new account currency differs, the amount carries over to the new account currency. +
      • +
    • + If you previously called + + SetCash(symbol, amount) + + + set_cash(symbol, amount) + + and the new account currency differs, the previous balance stays in its own + + CashBook + + entry and the new account currency starts at zero (or at + + startingCash + + + starting_cash + + if you provide it). +
      • +
    • + If the new account currency matches the previous one and you provide + + startingCash + + + starting_cash + + , the new amount overrides the previous one. +
      • +
    +
    + 
    // SetCash(amount) then a different account currency: the amount carries over to BTC.
+SetCash(1);
+SetAccountCurrency("BTC"); // CashBook: BTC = 1.
+
+// SetCash(symbol, amount) then a different account currency: USD balance is preserved, BTC starts at 0.
+SetCash("USD", 100000);
+SetAccountCurrency("BTC"); // CashBook: USD = 100000, BTC = 0.
+
+// SetCash(symbol, amount) then a different account currency with startingCash: USD is preserved, EUR starts at 50000.
+SetCash("USD", 100000);
+SetAccountCurrency("EUR", 50000); // CashBook: USD = 100000, EUR = 50000.
+
+// Same account currency with startingCash: the new amount overrides the previous one.
+SetCash(100000);
+SetAccountCurrency("USD", 200000); // CashBook: USD = 200000.
+
    + 
    # set_cash(amount) then a different account currency: the amount carries over to BTC.
+self.set_cash(1)
+self.set_account_currency("BTC") # CashBook: BTC = 1.
+
+# set_cash(symbol, amount) then a different account currency: USD balance is preserved, BTC starts at 0.
+self.set_cash("USD", 100000)
+self.set_account_currency("BTC") # CashBook: USD = 100000, BTC = 0.
+
+# set_cash(symbol, amount) then a different account currency with starting_cash: USD is preserved, EUR starts at 50000.
+self.set_cash("USD", 100000)
+self.set_account_currency("EUR", 50000) # CashBook: USD = 100000, EUR = 50000.
+
+# Same account currency with starting_cash: the new amount overrides the previous one.
+self.set_cash(100000)
+self.set_account_currency("USD", 200000) # CashBook: USD = 200000.
    +
    @@ -8896,9 +9211,12 @@

    Default Value: - + DataMappingMode.OpenInterest + + DataMappingMode.OPEN_INTEREST +

    + + self.date_rules.quarter_start(days_offset: int = 0) + + + DateRules.QuarterStart(int daysOffset = 0) + + + Trigger an event on the first day of each quarter plus an offset. +
    + + self.date_rules.quarter_start(symbol: Symbol, days_offset: int = 0) + + + DateRules.QuarterStart(Symbol symbol, int daysOffset = 0) + + + Trigger an event on the first tradable date of each quarter for a specific symbol plus an offset. +
    + + self.date_rules.quarter_end(days_offset: int = 0) + + + DateRules.QuarterEnd(int daysOffset = 0) + + + Trigger an event on the last day of each quarter minus an offset. +
    + + self.date_rules.quarter_end(symbol: Symbol, days_offset: int = 0) + + + DateRules.QuarterEnd(Symbol symbol, int daysOffset = 0) + + + Trigger an event on the last tradable date of each quarter for a specific symbol minus an offset. +
    @@ -150008,6 +151183,168 @@

    Live Trading Considerations

    +

    Common Questions

    + + + + + +

    + How often does a fundamental universe run? +

    +

    + Fundamental universes run once per day by default. To change the cadence, set + + UniverseSettings.Schedule + + + universe_settings.schedule + + before adding the universe. See + + Schedule + + . +

    +

    + Why do some Fundamental properties return NaN? +

    +

    + Not every company reports every Morningstar field, so unreported values come back as NaN. Filter the + + Fundamental + + objects to exclude NaN values for the property you use before you sort or rank by it. +

    +

    + Does the fundamental universe include delisted companies? +

    +

    + Yes. The + + Morningstar US Fundamentals + + dataset includes delisted tickers so your backtests are free of survivorship bias. The universe excludes ETFs, ADRs, and OTC securities. +

    +

    + Can I call AddEquity inside the universe filter function? +

    +

    + No. Return a list of + + Symbol + + objects from the filter function and LEAN subscribes to them automatically. Calling + + AddEquity + + + add_equity + + inside the filter creates duplicate subscriptions and unexpected behavior. +

    +

    + Can I combine technical indicators with fundamental selection? +

    +

    + Yes. Keep a per-symbol + + SelectionData + + helper that updates a + + manual indicator + + from the daily price and volume on the + + Fundamental + + object, then filter or rank by the indicator value. You cannot attach custom attributes to + + Fundamental + + objects, so use a separate dictionary keyed by + + Symbol + + . For more information, see + + Indicator Universes + + . +

    +

    + Why is my algorithm running out of memory with a fundamental universe? +

    +

    + Each asset in the universe consumes about 5 MB of RAM, so selecting thousands of stocks quickly exhausts the node memory. Tighten the filter so it returns only the assets you trade, and check the RAM capacity of your + + backtesting + + and + + live trading nodes + + . +

    + + +

    Examples

    @@ -150074,102 +151411,194 @@

    Fundamental - object has adjusted price and volume information, so you can do any price-related analysis. + object has daily price and volume information, so you can do any price-related analysis. The following algorithm defines a separate class to contain the indicator of each asset.

    - 
    using System.Collections.Concurrent;
-	
-public class UpTrendLiquidUniverseAlgorithm : QCAlgorithm
+   
    public class UpTrendLiquidUniverseAlgorithm  : QCAlgorithm
 {    
-    // Create a concurrent dictionary to store the EMA data for universe selection.
-    private ConcurrentDictionary<Symbol, SelectionData> _selectionDataBySymbol = new();
+    private Dictionary<Symbol, SelectionData> _selectionDataBySymbol = new();
+    private Universe _universe;
 
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-
+        Settings.SeedInitialPrices = true;
         // Add the custom universe.
-        AddUniverse(SelectAssets);
+        UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
+        _universe = AddUniverse(SelectAssets);
+        // Add a warm-up period to warm up the indicators.
+        SetWarmUp(TimeSpan.FromDays(300));
     }
-    
-    private IEnumerable<Symbol> SelectAssets(IEnumerable<Fundamental> fundamental)
+
+    private IEnumerable<Symbol> SelectAssets(IEnumerable<Fundamental> fundamentals)
     {
-        return (from f in fundamental
-            // Create/Update the EMA indicators of each stock.
-            let avg = _selectionDataBySymbol.GetOrAdd(f.Symbol, sym => new SelectionData(200))
-            where avg.Update(f.EndTime, f.AdjustedPrice)
-            // Select the Equities that are above their EMA and have a daily volume of $1B.
-            // These assets are in an uptrend and are very liquid.
-            where avg.Ema.IsReady && f.Price > avg.Ema.Current.Value && f.DollarVolume > 1000000000
-            // Select the 10 most liquid Equities to avoid extra slippage.   
-            orderby f.DollarVolume descending
-            select f.Symbol).Take(10);
+        // Update the indicator of all stocks in the universe dataset and
+        // get the subset of stocks that have their indicator ready.
+        var readyStocks = new List<Fundamental>();
+        foreach (var f in fundamentals)
+        {
+            if (!_selectionDataBySymbol.TryGetValue(f.Symbol, out var sd))
+            {
+                sd = new SelectionData(this, f, 200);
+                _selectionDataBySymbol[f.Symbol] = sd;
+            }
+            if (sd.Update(f))
+            {
+                readyStocks.Add(f);
+            }
+        }
+        // As assests leave the Fundamental dataset, delete their SelectionData object.
+        var activeStocks = fundamentals.Select(f => f.Symbol).ToHashSet();
+        foreach (var symbol in _selectionDataBySymbol.Keys.Where(s => !activeStocks.Contains(s)).ToList())
+        {
+            _selectionDataBySymbol.Remove(symbol);
+        }
+        // During warm-up, keep the universe empty.
+        if (IsWarmingUp)
+        {
+            return Enumerable.Empty<Symbol>();
+        }
+        // Select the Equities that are above their EMA and have a daily volume of $1B.
+        // These assets are in an uptrend and are very liquid.
+        return readyStocks
+            .Select(f => _selectionDataBySymbol[f.Symbol])
+            .Where(x => x.IsAboveEma && x.Volume > 1000000000)
+            // Select the 10 most liquid Equities to avoid extra slippage.    
+            .OrderBy(x => x.Volume)
+            .TakeLast(10)
+            .Select(x => x.Symbol);
     }
 }
 
 // Create a separate class to contain the EMA information of each asset.
-class SelectionData
+public class SelectionData
 {
-    public readonly ExponentialMovingAverage Ema;
+    private QCAlgorithm _algorithm;
+    private decimal _priceScaleFactor;
+    private ExponentialMovingAverage _ema { get; }
+    public Symbol Symbol;
+    public bool IsAboveEma = false;
+    public decimal Volume = 0;
 
-    // Create an EMA indicator for trend estimation and filtering.
-    public SelectionData(int period)
+    public SelectionData(QCAlgorithm algorithm, Fundamental f, int period)
     {
-        Ema = new ExponentialMovingAverage(period);
+        _algorithm = algorithm;
+        _priceScaleFactor = f.PriceScaleFactor;
+        // Create an EMA indicator for trend estimation and filtering.
+        Symbol = f.Symbol;
+        _ema = new ExponentialMovingAverage(period);
     }
 
     // Update your variables and indicators with the latest data.
-    // You may also want to use the History API here to warm-up the indicator.
-    public bool Update(DateTime time, decimal value)
+    public bool Update(Fundamental f)
     {
-        return Ema.Update(time, value);
+        // If there hasn't been a split or dividend since the last trading
+        // day, just update the indicator like normal.
+        if (f.PriceScaleFactor == _priceScaleFactor)
+        {
+            return _update(f.EndTime, f.Volume, f.Price);
+        }
+        // Otherwise, reset the indicator and warm it up with the new
+        // adjusted history.
+        _priceScaleFactor = f.PriceScaleFactor;
+        _ema.Reset();
+        var history = _algorithm.History<TradeBar>(
+            Symbol,
+            _ema.WarmUpPeriod,
+            Resolution.Daily,
+            dataNormalizationMode: DataNormalizationMode.ScaledRaw
+        );
+        foreach (var bar in history)
+        {
+            _update(bar.EndTime, bar.Volume, bar.Close);
+        }
+        return _ema.IsReady;
+    }
+
+    private bool _update(DateTime endTime, decimal volume, decimal price)
+    {
+        Volume = volume * price;
+        if (_ema.Update(endTime, price))
+        {
+            IsAboveEma = price > _ema.Current.Value;
+        }
+        return _ema.IsReady;
     }
 }
    
    
    class UpTrendLiquidUniverseAlgorithm(QCAlgorithm):
 	
-    # Create a dictionary to store the EMA data for universe selection.
     _selection_data_by_symbol = {}
 
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        
+        self.settings.seed_initial_prices = True
         # Add the custom universe.
-        self.add_universe(self._select_assets)
+        self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
+        self._universe = self.add_universe(self._select_assets)
+        # Add a warm-up period to warm up the indicators.
+        self.set_warm_up(timedelta(300))
     
-    def _select_assets(self, fundamental: List[Fundamental]) -> List[Symbol]:
-        for f in fundamental:
-            # Create/Update the EMA indicators of each stock.
-            if f.symbol not in self._selection_data_by_symbol:
-                self._selection_data_by_symbol[f.symbol] = SelectionData(f.symbol, 200)
-            self._selection_data_by_symbol[f.symbol].update(f.end_time, f.adjusted_price, f.dollar_volume)
-        
+    def _select_assets(self, fundamentals: List[Fundamental]) -> List[Symbol]:
+        # Update the indicator of all stocks in the universe dataset and
+        # get the subset of stocks that have their indicator ready.
+        ready_stocks = [
+            f for f in fundamentals
+            if self._selection_data_by_symbol.setdefault(f.symbol, SelectionData(self, f, 200)).update(f)
+        ]
+        # As assests leave the Fundamental dataset, delete their SelectionData object.
+        for symbol in self._selection_data_by_symbol.keys() - {f.symbol for f in fundamentals}:
+            del self._selection_data_by_symbol[symbol]
+        # During warm-up, keep the universe empty.
+        if self.is_warming_up:
+            return []
         # Select the Equities that are above their EMA and have a daily volume of $1B.
         # These assets are in an uptrend and are very liquid.
-        selected = [x for x in self._selection_data_by_symbol.values() if x.is_above_ema and x.volume > 1_000_000_000]
-            
+        selected = [self._selection_data_by_symbol[f.symbol] for f in ready_stocks]
+        selected = [x for x in selected if x.is_above_ema and x.volume > 1_000_000_000]          
         # Select the 10 most liquid Equities to avoid extra slippage.    
-        return [ x.symbol for x in sorted(selected, key=lambda x: x.volume)[-10:] ]
+        return [x.symbol for x in sorted(selected, key=lambda x: x.volume)[-10:]]
 
 
 # Create a separate class to contain the EMA information of each asset.
-class SelectionData(object):
+class SelectionData:
 
-    def __init__(self, symbol, period):
+    def __init__(self, algorithm, f, period):
+        self._algorithm = algorithm
+        self._price_scale_factor = f.price_scale_factor
         # Create an EMA indicator for trend estimation and filtering.
-        self.symbol = symbol
+        self.symbol = f.symbol
         self._ema = ExponentialMovingAverage(period)
         self.is_above_ema = False
         self.volume = 0
 
     # Update your variables and indicators with the latest data.
-    # You may also want to use the History API here to warm-up the indicator.
-    def update(self, time, price, volume):
-        self.volume = volume
-        if self._ema.update(time, price):
-            self.is_above_ema = price > self._ema.current.value
    
+    def update(self, f):
+        # If there hasn't been a split or dividend since the last trading
+        # day, just update the indicator like normal.
+        if f.price_scale_factor == self._price_scale_factor:
+            return self._update(f.end_time, f.volume, f.price)
+        # Otherwise, reset the indicator and warm it up with the new 
+        # adjusted history.
+        self._price_scale_factor = f.price_scale_factor
+        self._ema.reset()
+        history = self._algorithm.history[TradeBar](
+            self.symbol, 
+            self._ema.warm_up_period, 
+            Resolution.DAILY, 
+            data_normalization_mode=DataNormalizationMode.SCALED_RAW
+        )
+        for bar in history:
+            self._update(bar.end_time, bar.volume, bar.close)
+        return self._ema.is_ready
+    
+    def _update(self, end_time, volume, price):
+        self.volume = volume * price 
+        if self._ema.update(end_time, price):
+            self.is_above_ema = price > self._ema.current.value
+        return self._ema.is_ready

    In this example, the @@ -150244,99 +151673,184 @@

    You can use this ratio to select assets that are above their 10-day SMA and sort the results by the Equities that have had the biggest jump since yesterday.

    - 
    using System.Collections.Concurrent;
-
-public class HighRelativeVolumeUniverseAlgorithm : QCAlgorithm
+   
    public class HighRelativeVolumeUniverseAlgorithm  : QCAlgorithm
 {    
-    // Create a dictionary to store the EMA data for universe selection.
-    private ConcurrentDictionary<Symbol, SelectionData> _selectionDataBySymbol = new();
+    private Dictionary<Symbol, SelectionData> _selectionDataBySymbol = new();
+    private Universe _universe;
 
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-
-        // Add a universe with custom selection rules for filtering.
-        AddUniverse(SelectAssets);
+        Settings.SeedInitialPrices = true;
+        // Add the custom universe.
+        UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
+        _universe = AddUniverse(SelectAssets);
+        // Add a warm-up period to warm up the indicators.
+        SetWarmUp(TimeSpan.FromDays(30));
     }
-    
-    private IEnumerable<Symbol> SelectAssets(IEnumerable<Fundamental> fundamental)
+
+    private IEnumerable<Symbol> SelectAssets(IEnumerable<Fundamental> fundamentals)
     {
-        return (from f in fundamental
-            // Create/Update the volume SMA indicator of each stock.
-            let avg = _selectionDataBySymbol.GetOrAdd(f.Symbol, sym => new SelectionData(f.Symbol, 10))
-            where avg.Update(f.EndTime, f.Volume)
-            // Select the Equities with higher trading volume than their SMA, indicating higher capital flow.
-            where avg.VolumeRatio > 1
+        // Update the indicator of all stocks in the universe dataset and
+        // get the subset of stocks that have their indicator ready.
+        var readyStocks = new List<Fundamental>();
+        foreach (var f in fundamentals)
+        {
+            if (!_selectionDataBySymbol.TryGetValue(f.Symbol, out var sd))
+            {
+                sd = new SelectionData(this, f, 10);
+                _selectionDataBySymbol[f.Symbol] = sd;
+            }
+            if (sd.Update(f))
+            {
+                readyStocks.Add(f);
+            }
+        }
+        // As assests leave the Fundamental dataset, delete their SelectionData object.
+        var activeStocks = fundamentals.Select(f => f.Symbol).ToHashSet();
+        foreach (var symbol in _selectionDataBySymbol.Keys.Where(s => !activeStocks.Contains(s)).ToList())
+        {
+            _selectionDataBySymbol.Remove(symbol);
+        }
+        // During warm-up, keep the universe empty.
+        if (IsWarmingUp)
+        {
+            return Enumerable.Empty<Symbol>();
+        }
+        // Select the Equities with higher trading volume than their SMA, indicating higher capital flow.
+        return readyStocks
+            .Select(f => _selectionDataBySymbol[f.Symbol])
+            .Where(x => x.VolumeRatio > 1)
             // Select the 10 Equities with the highest relative volume, since they have the highest capactity
-            // for scalp-trading or intra-day movement.
-            orderby avg.VolumeRatio descending
-            select f.Symbol).Take(10);
+            // for scalp-trading or intra-day movement. 
+            .OrderBy(x => x.VolumeRatio)
+            .TakeLast(10)
+            .Select(x => x.Symbol);
     }
 }
 
-// Define a separate class to contain and calculate the SMA of each Equity.
-class SelectionData
+// Create a separate class to contain the SMA information of each asset.
+public class SelectionData
 {
-    public readonly Symbol Symbol;
-    public readonly SimpleMovingAverage VolumeSma;
-    public decimal VolumeRatio;
+    private QCAlgorithm _algorithm;
+    private decimal _priceScaleFactor;
+    private SimpleMovingAverage _sma { get; }
+    public Symbol Symbol;
+    public decimal VolumeRatio = 0;
 
-    public SelectionData(Symbol symbol, int period)
+    public SelectionData(QCAlgorithm algorithm, Fundamental f, int period)
     {
-        // Create an SMA of volume to track the popularity of the stock.
-        Symbol = symbol;
-        VolumeSma = new SimpleMovingAverage(period);
+        _algorithm = algorithm;
+        _priceScaleFactor = f.PriceScaleFactor;
+        // Create an EMA indicator for trend estimation and filtering.
+        Symbol = f.Symbol;
+        _sma = new SimpleMovingAverage(period);
     }
-    
-    public bool Update(DateTime time, decimal value)
+
+    // Update your variables and indicators with the latest data.
+    public bool Update(Fundamental f)
+    {
+        // If there hasn't been a split or dividend since the last trading
+        // day, just update the indicator like normal.
+        if (f.PriceScaleFactor == _priceScaleFactor)
+        {
+            return _update(f.EndTime, f.Volume);
+        }
+        // Otherwise, reset the indicator and warm it up with the new
+        // adjusted history.
+        _priceScaleFactor = f.PriceScaleFactor;
+        _sma.Reset();
+        var history = _algorithm.History<TradeBar>(
+            Symbol,
+            _sma.WarmUpPeriod,
+            Resolution.Daily,
+            dataNormalizationMode: DataNormalizationMode.ScaledRaw
+        );
+        foreach (var bar in history)
+        {
+            _update(bar.EndTime, bar.Volume);
+        }
+        return _sma.IsReady;
+    }
+
+    private bool _update(DateTime endTime, decimal volume)
     {
         // Update the SMA with today's data and calculate the relative volume position for filtering.
-        var ready = VolumeSma.Update(time, value);
-        VolumeRatio = VolumeSma.Current.Value != 0m ? value / VolumeSma.Current.Value : -1m;
+        var ready = _sma.Update(endTime, volume);
+        VolumeRatio = _sma.Current.Value != 0m ? volume / _sma.Current.Value : -1m;
         return ready;
     }
 }
    
    
    class HighRelativeVolumeUniverseAlgorithm(QCAlgorithm):
-    
-    # Create a dictionary to store the EMA data for universe selection.
-    _selection_data_by_symbol = {}
 
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        
-        # Add a universe with custom selection rules for filtering.
-        self.add_universe(self._select_assets)
+        self.settings.seed_initial_prices = True
+        # Add the custom universe.
+        self._selection_data_by_symbol = {}
+        self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
+        self._universe = self.add_universe(self._select_assets)
+        # Add a warm-up period to warm up the indicators.
+        self.set_warm_up(timedelta(30))
     
-    def _select_assets(self, fundamental: List[Fundamental]) -> List[Symbol]:
-        # Create/Update the volume SMA indicator of each stock.
-        for f in fundamental:
-            if f.symbol not in self._selection_data_by_symbol:
-                self._selection_data_by_symbol[f.symbol] = SelectionData(f.symbol, 10)
-            self._selection_data_by_symbol[f.symbol].update(f.end_time, f.adjusted_price, f.dollar_volume)
-
+    def _select_assets(self, fundamentals: List[Fundamental]) -> List[Symbol]:
+        # Update the indicator of all stocks in the universe dataset and
+        # get the subset of stocks that have their indicator ready.
+        ready_stocks = [
+            f for f in fundamentals
+            if self._selection_data_by_symbol.setdefault(f.symbol, SelectionData(self, f, 10)).update(f)
+        ]
+        # As assests leave the Fundamental dataset, delete their SelectionData object.
+        for symbol in self._selection_data_by_symbol.keys() - {f.symbol for f in fundamentals}:
+            del self._selection_data_by_symbol[symbol]
+        # During warm-up, keep the universe empty.
+        if self.is_warming_up:
+            return []
         # Select the Equities with higher trading volume than their SMA, indicating higher capital flow.
-        selected = [sd for sd in self._selection_data_by_symbol.values() if sd.volume_ratio > 1]
-            
+        selected = [self._selection_data_by_symbol[f.symbol] for f in ready_stocks]
+        selected = [sd for sd in selected if sd.volume_ratio > 1]        
         # Select the 10 Equities with the highest relative volume, since they have the highest capactity
         # for scalp-trading or intra-day movement.
-        return [ x.symbol for x in sorted(selected, key=lambda x: x.volume_ratio)[-10:] ]
+        return [x.symbol for x in sorted(selected, key=lambda x: x.volume_ratio)[-10:]]
 
 
-# Define a separate class to contain and calculate the SMA of each Equity.
-class SelectionData(object):
-    
-    def __init__(self, symbol, period):
-        self.symbol = symbol
-        self.volume_ratio = 0
+# Create a separate class to contain the SMA information of each asset.
+class SelectionData:
+
+    def __init__(self, algorithm, f, period):
+        self._algorithm = algorithm
+        self._price_scale_factor = f.price_scale_factor
         # Create an SMA of volume to track the popularity of the stock.
+        self.symbol = f.symbol
         self._sma = SimpleMovingAverage(period)
 
-    def update(self, time, price, volume):
+    # Update your variables and indicators with the latest data.
+    def update(self, f):
+        # If there hasn't been a split or dividend since the last trading
+        # day, just update the indicator like normal.
+        if f.price_scale_factor == self._price_scale_factor:
+            return self._update(f.end_time, f.volume)
+        # Otherwise, reset the indicator and warm it up with the new 
+        # adjusted history.
+        self._price_scale_factor = f.price_scale_factor
+        self._sma.reset()
+        history = self._algorithm.history[TradeBar](
+            self.symbol, 
+            self._sma.warm_up_period, 
+            Resolution.DAILY, 
+            data_normalization_mode=DataNormalizationMode.SCALED_RAW
+        )
+        for bar in history:
+            self._update(bar.end_time, bar.volume)
+        return self._sma.is_ready
+    
+    def _update(self, end_time, volume):
         # Update the SMA with today's data and calculate the relative volume position for filtering.
-        if self._sma.update(time, volume):
-            self.volume_ratio = volume / self._sma.current.value if self._sma.current.value != 0 else -1
    
+        if self._sma.update(end_time, volume):
+            self.volume_ratio = volume / self._sma.current.value if self._sma.current.value != 0 else -1
+        return self._sma.is_ready

    Example 4: 10 "Fastest Moving" Stocks With a 50-Day EMA > 200 Day EMA @@ -150370,6 +151884,190 @@

    Research post.

    +

    + Example 6: Stocks Far Above Their SMA +

    +

    + The following example shows how to select a universe of US Equities based on their price and SMA indicator. + The + + SelectionData + + class keeps track of the SMA indicator for each stock in the universe dataset. + When a split or dividend occurs for a stock, the data in its indicator becomes invalid because it doesn't account for the price adjustments that the split or dividend causes. + The + + SelectionData + + class resets and warms up the indicator with the + + ScaledRaw + + + SCALED_RAW + + + data normalization mode + + , which gives you accurate indicator values to use in your universe selection after each corporate action. +

    +
    + 
    class EquityIndicatorUniverseSelectionAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.settings.seed_initial_prices = True
+        # Add a universe of US Equities based on an indicator.
+        self._selection_data_by_symbol = {}
+        self._universe = self.add_universe(self._select_assets)
+        # Add a warm-up period to warm up the indicators in the universe selection.
+        self.set_warm_up(timedelta(60))
+
+    def _select_assets(self, fundamentals):
+        # Update the indicator of all stocks in the universe dataset and
+        # get the subset of stocks that have their indicator ready.
+        ready_stocks = [
+            f for f in fundamentals
+            if self._selection_data_by_symbol.setdefault(f.symbol, SelectionData(self, f)).update(f)
+        ]
+        # As assests leave the Fundamental dataset, delete their SelectionData object.
+        for symbol in self._selection_data_by_symbol.keys() - {f.symbol for f in fundamentals}:
+            del self._selection_data_by_symbol[symbol]
+        # During warm-up, keep the universe empty.
+        if self.is_warming_up:
+            return []
+        # Select a subset of the stocks based on the indicator.
+        # Example: 10 stocks furthest above their SMA.
+        factor_by_symbol = {
+            f.symbol: f.price / self._selection_data_by_symbol[f.symbol].indicator.current.value 
+            for f in ready_stocks
+        }
+        return sorted(
+            {k: v for k, v in factor_by_symbol.items() if v > 0}, 
+            key=lambda symbol: factor_by_symbol[symbol]
+        )[-100:]
+
+
+class SelectionData:
+
+    def __init__(self, algorithm, f):
+        self._algorithm = algorithm
+        self._price_scale_factor = f.price_scale_factor
+        self.indicator = SimpleMovingAverage(21)
+
+    def update(self, f):
+        # If there hasn't been a split or dividend since the last trading
+        # day, just update the indicator like normal.
+        if f.price_scale_factor == self._price_scale_factor:
+            return self.indicator.update(f.end_time, f.price)
+        # Otherwise, reset the indicator and warm it up with the new 
+        # adjusted history.
+        self._price_scale_factor = f.price_scale_factor
+        self.indicator.reset()
+        history = self._algorithm.history[TradeBar](
+            f.symbol, 
+            self.indicator.warm_up_period, 
+            Resolution.DAILY, 
+            data_normalization_mode=DataNormalizationMode.SCALED_RAW
+        )
+        for bar in history:
+            self.indicator.update(bar)
+        return self.indicator.is_ready
    + 
    public class EquityIndicatorUniverseSelectionAlgorithm : QCAlgorithm
+{
+    private Dictionary<Symbol, SelectionData> _selectionDataBySymbol = new();
+    private Universe _universe;
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        Settings.SeedInitialPrices = true;
+        // Add a universe of US Equities based on an indicator.
+        _universe = AddUniverse(SelectAssets);
+        // Add a warm-up period to warm up the indicators in the universe selection.
+        SetWarmUp(TimeSpan.FromDays(60));
+    }
+
+    private IEnumerable<Symbol> SelectAssets(IEnumerable<Fundamental> fundamentals)
+    {
+        // Update the indicator of all stocks in the universe dataset and
+        // get the subset of stocks that have their indicator ready.
+        var readyStocks = new List<Fundamental>();
+        foreach (var f in fundamentals)
+        {
+            if (!_selectionDataBySymbol.TryGetValue(f.Symbol, out var sd))
+            {
+                sd = new SelectionData(this, f);
+                _selectionDataBySymbol[f.Symbol] = sd;
+            }
+            if (sd.Update(f))
+            {
+                readyStocks.Add(f);
+            }
+        }
+        // As assests leave the Fundamental dataset, delete their SelectionData object.
+        var activeStocks = fundamentals.Select(f => f.Symbol).ToHashSet();
+        foreach (var symbol in _selectionDataBySymbol.Keys.Where(s => !activeStocks.Contains(s)).ToList())
+        {
+            _selectionDataBySymbol.Remove(symbol);
+        }
+        // During warm-up, keep the universe empty.
+        if (IsWarmingUp)
+        {
+            return Enumerable.Empty<Symbol>();
+        }
+        // Select a subset of the stocks based on the indicator.
+        // Example: 10 stocks furthest above their SMA.
+        return readyStocks
+            .Select(f => (f.Symbol, Factor: f.Price / _selectionDataBySymbol[f.Symbol].Indicator.Current.Value))
+            .Where(t => t.Factor > 0)
+            .OrderBy(t => t.Factor)
+            .TakeLast(100)
+            .Select(t => t.Symbol);
+    }
+}
+
+public class SelectionData
+{
+    private QCAlgorithm _algorithm;
+    private decimal _priceScaleFactor;
+    public SimpleMovingAverage Indicator { get; }
+
+    public SelectionData(QCAlgorithm algorithm, Fundamental f)
+    {
+        _algorithm = algorithm;
+        _priceScaleFactor = f.PriceScaleFactor;
+        Indicator = new SimpleMovingAverage(21);
+    }
+
+    public bool Update(Fundamental f)
+    {
+        // If there hasn't been a split or dividend since the last trading
+        // day, just update the indicator like normal.
+        if (f.PriceScaleFactor == _priceScaleFactor)
+        {
+            return Indicator.Update(f.EndTime, f.Price);
+        }
+        // Otherwise, reset the indicator and warm it up with the new
+        // adjusted history.
+        _priceScaleFactor = f.PriceScaleFactor;
+        Indicator.Reset();
+        var history = _algorithm.History<TradeBar>(
+            f.Symbol,
+            Indicator.WarmUpPeriod,
+            Resolution.Daily,
+            dataNormalizationMode: DataNormalizationMode.ScaledRaw
+        );
+        foreach (var bar in history)
+        {
+            Indicator.Update(bar);
+        }
+        return Indicator.IsReady;
+    }
+}
    +

    Other Examples

    @@ -150471,7 +152169,7 @@

    The ETF ticker. To view the supported ETFs in the US ETF Constituents dataset, see - + Supported ETFs . @@ -150704,7 +152402,7 @@

    Historical Data

    For more information about ETF Constituents data, see - + US ETF Constituents . @@ -151198,91 +152896,97 @@

    Chain Fundamental and Alternative Data

    QuiverCNBCsUniverse alternative universe - . It first selects the 100 most liquid US Equities and then filters them down to those mentioned by CNBC commentator/trader Jim Cramer. The output of the alternative universe selection method is the output of the chained universe. + . It stores every US Equity fundamental, intersects them with the names CNBC commentator Jim Cramer mentions, and trades the 100 most liquid intersection members each morning. 

    public class ChainedUniverseAlgorithm : QCAlgorithm
 {
-    private List<Symbol> _fundamental = new();
+    private List<Fundamental> _fundamental = [];
+    private Universe _universe;
 
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
         SetCash(100000);
-
-        // Filter the top 100 liquid equities of the last trading day, and save the symbols for the next filtering.
+        Settings.SeedInitialPrices = true;
+        UniverseSettings.Resolution = Resolution.Minute;
+        // First universe: store all US Equity fundamentals; emits Universe.Unchanged.
         AddUniverse(fundamental =>
         {
-            _fundamental = (from c in fundamental
-                orderby c.DollarVolume descending
-                select c.Symbol).Take(100).ToList();
+            _fundamental = [..fundamental];
             return Universe.Unchanged;
         });
-        // Filter the equities being commented on by CNBC analyst Cramer, then select the ones that intersect with the fundamental universe.
-        AddUniverse<QuiverCNBCsUniverse>(altCoarse =>
-        {
-            var followers = from d in altCoarse.OfType<QuiverCNBCsUniverse>()
-                where d.Traders.ToLower().Contains("cramer")
-                select d.Symbol;
-            return _fundamental.Intersect(followers);
+        // Second universe: equities mentioned by Cramer, ranked by dollar volume.
+        _universe = AddUniverse<QuiverCNBCsUniverse>(altCoarse =>
+        {
+            var alt = altCoarse.OfType<QuiverCNBCsUniverse>()
+                .Where(d => d.Traders.ToLower().Contains("cramer"))
+                .Select(d => d.Symbol)
+                .ToHashSet();
+            Plot("Universe", "Raw", alt.Count);
+            return _fundamental
+                .Where(c => alt.Contains(c.Symbol))
+                .OrderByDescending(c => c.DollarVolume)
+                .Select(c => c.Symbol)
+                .Take(100);
         });
+        // Rebalance before market open to trade today's intersection.
+        Schedule.On(DateRules.EveryDay("SPY"), TimeRules.At(9, 0, 0), Rebalance);
     }
 
-    public override void OnSecuritiesChanged(SecurityChanges changes)
-    {
-        // Request CNBC data for the selected stocks.
-        foreach (var added in changes.AddedSecurities)
-        {
-            AddData<QuiverCNBCs>(added.Symbol);
-        }
-    }
-
-    public override void OnData(Slice data)
+    private void Rebalance()
     {
-        foreach (var dataPoint in data.Get<QuiverCNBCs>().SelectMany(x=> x.Value.OfType<QuiverCNBC>()))
+        if (_universe.Selected.Count == 0)
         {
-            Debug($"{dataPoint.Symbol} traders at {data.Time}: {dataPoint.Traders}");
+            return;
         }
+        var weight = _universe.Selected.Count >= 10 ? 1m / _universe.Selected.Count : 0.1m;
+        var targets = _universe.Selected
+            .Select(symbol => new PortfolioTarget(symbol, weight))
+            .ToList();
+        SetHoldings(targets, true);
     }
 }
    from AlgorithmImports import *
 
+
 class ChainedUniverseAlgorithm(QCAlgorithm):
 
-    _fundamental = []
+    _fundamental: List[Fundamental] = []
 
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
         self.set_cash(100000)
-        self.add_universe(self._fundamental_filter_function)
-        self.add_universe(QuiverCNBCsUniverse, self._mad_money_selection)
-    
-    def _fundamental_filter_function(self, fundamental: List[Fundamental]) -> List[Symbol]:
-        # Filter the top 100 liquid equities of the last trading day, and save the symbols for the next filtering.
-        sorted_by_dollar_volume = sorted(fundamental, key=lambda x: x.dollar_volume, reverse=True) 
-        self.fundamental = [c.symbol for c in sorted_by_dollar_volume[:100]]
+        self.settings.seed_initial_prices = True
+        self.universe_settings.resolution = Resolution.MINUTE
+        # First universe: store all US Equity fundamentals; emits Universe.UNCHANGED.
+        self.add_universe(self._fundamental_filter)
+        # Second universe: equities mentioned by Cramer, ranked by dollar volume.
+        self._universe = self.add_universe(QuiverCNBCsUniverse, self._select_assets)
+        # Rebalance before market open to trade today's intersection.
+        self.schedule.on(self.date_rules.every_day("SPY"), self.time_rules.at(9, 0, 0), self._rebalance)
+
+    def _fundamental_filter(self, fundamental: List[Fundamental]) -> Universe.UnchangedUniverse:
+        self._fundamental = fundamental
         return Universe.UNCHANGED
-    
-    def _mad_money_selection(self, alt_coarse: List[QuiverCNBCsUniverse]) -> List[Symbol]:
-        # Filter the equities being commented on by CNBC analyst Cramer, then select the ones that intersect with the fundamental universe.
-        madmoney = [d.symbol for d in alt_coarse if 'Cramer' in d.traders]
-        return list(set(self._fundamental) & set(madmoney))
-    
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
-        # Request CNBC data for the selected stocks.
-        for added in changes.added_securities:
-            self.add_data(QuiverCNBCs, added.symbol)
-    
-    def on_data(self, data: Slice) -> None:
-        # Prices in the slice from the universe selection
-        # Alternative data in a slice from OnSecuritiesChanged Addition
-        # for ticker,bar in data.bars.items():
-        #     pass
-        for dataset_symbol, data_points in data.get(QuiverCNBCs).items():
-            for data_point in data_points:
-                self.debug(f"{dataset_symbol} traders at {data.time}: {data_point.traders}")
+
+    def _select_assets(self, alt_coarse: List[QuiverCNBCsUniverse]) -> List[Symbol]:
+        # Keep symbols mentioned by Cramer.
+        alt = {d.symbol for d in alt_coarse if 'cramer' in d.traders.lower()}
+        self.plot('Universe', 'Raw', len(alt))
+        return [c.symbol for c in sorted(
+            [c for c in self._fundamental if c.symbol in alt],
+            key=lambda c: c.dollar_volume, reverse=True
+        )[:100]]
+
+    def _rebalance(self) -> None:
+        if not self._universe.selected:
+            return
+        weight = min(1 / len(self._universe.selected), 0.1)
+        targets = [PortfolioTarget(symbol, weight) for symbol in self._universe.selected]
+        self.set_holdings(targets, True)
    @@ -151494,92 +153198,100 @@

    Chain ETF and Alternative Data

    QuiverCNBCsUniverse alternative universe - . It first selects all constituents of SPY and then filters them down to those mentioned by CNBC commentator/trader Jim Cramer. The output of the alternative universe selection method is the output of the chained universe. + . It stores every SPY constituent, intersects them with the names CNBC commentator Jim Cramer mentions, and trades the 100 heaviest-weighted intersection members each morning. Names CNBC mentions but that aren't in SPY are dropped. 

    public class ChainedUniverseAlgorithm : QCAlgorithm
 {
-    private List<Symbol> _etf = new();
+    private List<ETFConstituentUniverse> _etf = [];
+    private Universe _universe;
 
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
         SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-
-        // Save all SPY constituents for the next filtering.
+        Settings.SeedInitialPrices = true;
+        UniverseSettings.Resolution = Resolution.Minute;
+        // First universe: store all SPY constituents; emits Universe.Unchanged.
         AddUniverse(Universe.ETF("SPY", Market.USA, UniverseSettings, constituents =>
         {
-            _etf = constituents.Select(c => c.Symbol).ToList();
+            _etf = [..constituents];
             return Universe.Unchanged;
         }));
-        // Filter the equities being commented on by CNBC analyst Cramer, then select the ones in SPY constituents.
-        AddUniverse<QuiverCNBCsUniverse>(altCoarse =>
-        {
-            var followers = from d in altCoarse.OfType<QuiverCNBCsUniverse>()
-                where d.Traders.ToLower().Contains("cramer")
-                select d.Symbol;
-            return _etf.Intersect(followers);
+        // Second universe: equities mentioned by Cramer that are in SPY, ranked by ETF weight.
+        _universe = AddUniverse<QuiverCNBCsUniverse>(altCoarse =>
+        {
+            var alt = altCoarse.OfType<QuiverCNBCsUniverse>()
+                .Where(d => d.Traders.ToLower().Contains("cramer"))
+                .Select(d => d.Symbol)
+                .ToHashSet();
+            Plot("Universe", "Raw", alt.Count);
+            // Names not in SPY cannot be traded, so intersect with the ETF list.
+            return _etf
+                .Where(c => alt.Contains(c.Symbol))
+                .OrderByDescending(c => c.Weight)
+                .Select(c => c.Symbol)
+                .Take(100);
         });
+        // Rebalance before market open to trade today's intersection.
+        Schedule.On(DateRules.EveryDay("SPY"), TimeRules.At(9, 0, 0), Rebalance);
     }
 
-    public override void OnSecuritiesChanged(SecurityChanges changes)
-    {
-        // Request CNBC data for the selected stocks.
-        foreach (var added in changes.AddedSecurities)
-        {
-            AddData<QuiverCNBCs>(added.Symbol);
-        }
-    }
-
-    public override void OnData(Slice data)
+    private void Rebalance()
     {
-        foreach (var dataPoint in data.Get<QuiverCNBCs>().SelectMany(x=> x.Value.OfType<QuiverCNBC>()))
+        if (_universe.Selected.Count == 0)
         {
-            Debug($"{dataPoint.Symbol} traders at {data.Time}: {dataPoint.Traders}");
+            return;
         }
+        var weight = _universe.Selected.Count >= 10 ? 1m / _universe.Selected.Count : 0.1m;
+        var targets = _universe.Selected
+            .Select(symbol => new PortfolioTarget(symbol, weight))
+            .ToList();
+        SetHoldings(targets, true);
     }
 }
    from AlgorithmImports import *
 
+
 class ChainedUniverseAlgorithm(QCAlgorithm):
 
-    _etf = []
+    _etf: List[ETFConstituentUniverse] = []
 
-    def initialize(self):
+    def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
         self.set_cash(100000)
-        self.universe_settings.asynchronous = True
-        # Save all SPY constituents for the next filtering.
-        self.add_universe(self.universe.etf("SPY", Market.USA, self.universe_settings, self._etf_constituents_filter))
-        # Next filtering based on CNBC data.
-        self.add_universe(QuiverCNBCsUniverse, self._mad_money_selection)
-
-    def _etf_constituents_filter(self, fundamental: List[Fundamental]) -> List[Symbol]:
-        # Save all SPY constituents for the next filtering.
-        self._etf = [c.symbol for c in constituents]
+        self.settings.seed_initial_prices = True
+        self.universe_settings.resolution = Resolution.MINUTE
+        # First universe: store all SPY constituents; emits Universe.UNCHANGED.
+        self.add_universe(self.universe.etf("SPY", Market.USA, self.universe_settings, self._etf_filter))
+        # Second universe: equities mentioned by Cramer that are in SPY, ranked by ETF weight.
+        self._universe = self.add_universe(QuiverCNBCsUniverse, self._select_assets)
+        # Rebalance before market open to trade today's intersection.
+        self.schedule.on(self.date_rules.every_day("SPY"), self.time_rules.at(9, 0, 0), self._rebalance)
+
+    def _etf_filter(self, constituents: List[ETFConstituentUniverse]) -> Universe.UnchangedUniverse:
+        self._etf = constituents
         return Universe.UNCHANGED
 
-    def _mad_money_selection(self, alt_coarse: List[QuiverCNBCsUniverse]) -> List[Symbol]:
-        # Filter the equities being commented on by CNBC analyst Cramer, then select the ones in SPY constituents.
-        madmoney = [d.symbol for d in alt_coarse if 'Cramer' in d.traders]
-        return list(set(self._etf) & set(madmoney))
-
-    def on_securities_changed(self, changes):
-        # Request CNBC data for the selected stocks.
-        for added in changes.added_securities:
-            self.add_data(QuiverCNBCs, added.symbol)
+    def _select_assets(self, alt_coarse: List[QuiverCNBCsUniverse]) -> List[Symbol]:
+        # Keep symbols mentioned by Cramer.
+        alt = {d.symbol for d in alt_coarse if 'cramer' in d.traders.lower()}
+        self.plot('Universe', 'Raw', len(alt))
+        # Names not in SPY cannot be traded, so intersect with the ETF list.
+        return [c.symbol for c in sorted(
+            [c for c in self._etf if c.symbol in alt],
+            key=lambda c: c.weight, reverse=True
+        )[:100]]
 
-    def on_data(self, data):
-        # Prices in the slice from the universe selection
-        # Alternative data in a slice from OnSecuritiesChanged Addition
-        # for ticker,bar in data.bars.items():
-        #     pass
-        for dataset_symbol, data_points in data.get(QuiverCNBCs).items():
-            for data_point in data_points:
-                self.debug(f"{dataset_symbol} traders at {data.time}: {data_point.traders}")
    + def _rebalance(self) -> None: + if not self._universe.selected: + return + weight = min(1 / len(self._universe.selected), 0.1) + targets = [PortfolioTarget(symbol, weight) for symbol in self._universe.selected] + self.set_holdings(targets, True) +
    @@ -152127,17 +153839,17 @@

    Supported Datasets

    		 - + Example - + Example - + Example - + Example - + Example - + Example
    + + self.date_rules.quarter_start(days_offset: int = 0) + + + DateRules.QuarterStart(int daysOffset = 0) + + + Trigger an event on the first day of each quarter plus an offset. +
    + + self.date_rules.quarter_start(symbol: Symbol, days_offset: int = 0) + + + DateRules.QuarterStart(Symbol symbol, int daysOffset = 0) + + + Trigger an event on the first tradable date of each quarter for a specific symbol plus an offset. +
    + + self.date_rules.quarter_end(days_offset: int = 0) + + + DateRules.QuarterEnd(int daysOffset = 0) + + + Trigger an event on the last day of each quarter minus an offset. +
    + + self.date_rules.quarter_end(symbol: Symbol, days_offset: int = 0) + + + DateRules.QuarterEnd(Symbol symbol, int daysOffset = 0) + + + Trigger an event on the last tradable date of each quarter for a specific symbol minus an offset. +
    @@ -161904,8 +163791,52 @@

    - +
    +
    +

    + Binance Crypto Future Price Data +

    +
    +
    +     +
    +
    + +
    +
    +

    + Binance Crypto Price Data +

    +
    +
    +     +
    +
    + +
    +
    +

    + Binance US Crypto Price Data +

    +
    +
    +     +
    +
    + +
    +
    +

    + Bitfinex Crypto Price Data +

    +
    +
    +     +
    +
    + +

    Bybit Crypto Future Margin Rate Data @@ -161914,9 +163845,31 @@

    +
    + +
    +
    +

    + Bybit Crypto Future Price Data +

    +
    +
    +     +
    +
    + +
    +
    +

    + Bybit Crypto Price Data +

    +
    +
    +     +
    -
    +

    CFD Data @@ -161927,7 +163880,7 @@

    -
    +

    Cash Indices @@ -161936,9 +163889,20 @@

    +
    + +
    +
    +

    + Coinbase Crypto Price Data +

    +
    +
    +     +
    -
    +

    FOREX Data @@ -161949,7 +163913,7 @@

    -
    +

    Fear and Greed @@ -161960,7 +163924,7 @@

    -
    +

    International Future Universe @@ -161969,9 +163933,20 @@

    +
    + +
    +
    +

    + Kraken Crypto Price Data +

    +
    +
    +     +
    -
    +

    US ETF Constituents @@ -161982,7 +163957,7 @@

    -
    +

    US Equities Short Availability @@ -161993,7 +163968,7 @@

    -
    +

    US Equity Coarse Universe @@ -162004,7 +163979,7 @@

    -
    +

    US Equity Option Universe @@ -162015,7 +163990,7 @@

    -
    +

    US Equity Security Master @@ -162026,7 +164001,7 @@

    -
    +

    US Future Option Universe @@ -162037,7 +164012,7 @@

    -
    +

    US Future Universe @@ -162048,7 +164023,7 @@

    -
    +

    US Futures Security Master @@ -162059,7 +164034,7 @@

    -
    +

    US Index Option Universe @@ -165143,29 +167118,29 @@

    Data Point Attributes

     

    -

    Datasets > QuantConnect > Bybit Crypto Future Margin Rate Data

    +

    Datasets > QuantConnect > Binance Crypto Future Price Data

    QuantConnect

    -

    Bybit Crypto Future Margin Rate Data

    +

    Binance Crypto Future Price Data

    Introduction

    - The Bybit Crypto Future Margin Rate Data by QuantConnect is for Cryptocurrency Futures margin interest data points. The data covers 631 Cryptocurrency pairs, starts in August 2020, and is delivered on a daily update frequency. This dataset is created by downloading data using Bybit API. + The Binance Crypto Future Price Data is for Crypto-currency futures price and volume data points. The data covers 718 Cryptocurrency pairs, starts in August 2020, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Binance.

    - This dataset is an important companion to the - - Bybit Crypto Future Price Data + The + + Binance Crypto Future Margin Rate Data - dataset because it contains information on margin interest data to model margin costs. + dataset provides margin interest data to model margin costs.

    - For more information about the Bybit Crypto Future Margin Rate Data dataset, including CLI commands and pricing, see the - + For more information about the Binance Crypto Future Price Data dataset, including CLI commands and pricing, see the + dataset listing . @@ -165179,7 +167154,7 @@

    About the Provider

    - QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month. + Binance was founded by Changpeng Zhao in 2017 with the goal to "increase the freedom of money globally". Binance provides access to trading Crypto through spot markets and perpetual Futures. They serve clients with no minimum deposit when depositing Crypto. Binance also provides an NFT marketplace, a mining pool, and services to deposit Crypto coins in liquidity pools to earn rewards.

    @@ -165188,28 +167163,27 @@

    Getting Started

    - The following snippet demonstrates how to request data from the Bybit Crypto Future Margin Rate dataset: + The following snippet demonstrates how to request data from the Binance Crypto Future Price dataset:

    - 
    def Initialize(self) -> None:
-    self.SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin)
+   
    def initialize(self) -> None:
+    self.set_brokerage_model(BrokerageName.BINANCE_FUTURES, AccountType.MARGIN)
+    #self.set_brokerage_model(BrokerageName.BINANCE_COIN_FUTURES, AccountType.MARGIN)
 
-    self.crypto_future_symbol = self.AddCryptoFuture("BTCBUSD", Resolution.Minute).Symbol
+    self.crypto_future_symbol = self.add_crypto_future("BTCBUSD", Resolution.MINUTE).symbol
 
    
    
    private Symbol _cryptoFutureSymbol;
 
 public override void Initialize
 {
-    SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin);
+    SetBrokerageModel(BrokerageName.BinanceFutures, AccountType.Margin);
+    //SetBrokerageModel(BrokerageName.BinanceCoinFutures, AccountType.Margin);
 
     // perpetual futures does not have a filter function
     _cryptoFutureSymbol = AddCryptoFuture("BTCBUSD", Resolution.Minute).Symbol;
 }
    -

    - The Bybit Crypto Future Margin Rate data is added to your algorithm along with the market data when you add a crypto future subscription. -

    @@ -165244,7 +167218,7 @@

    Data Summary

    Asset Coverage

    		- 631 Crypto Futures Pairs + 718 Crypto Futures Pairs
    - Regular + Dense
    - Daily + Tick, Second, Minute, Hourly, & Daily
    - Pairs Available (631) + Pairs Available (718)
    - 1CATUSDT + 0GUSDT + + 1000000BOBUSDT + + 1000000MOGUSDT + + 1000BONKUSDC + + 1000BONKUSDT + + 1000CATUSDT +
    + 1000CHEEMSUSDT + + 1000FLOKIUSDT + + 1000LUNCBUSD + + 1000LUNCUSDT + + 1000PEPEUSDC + + 1000PEPEUSDT +
    + 1000RATSUSDT + + 1000SATSUSDT + + 1000SHIBBUSD + + 1000SHIBUSDC + + 1000SHIBUSDT + + 1000WHYUSDT +
    + 1000XECUSDT + + 1000XUSDT 1INCHUSDT - A8USDT + 1MBABYDOGEUSDT + + 2ZUSDT + + 4USDT +
    + A2ZUSDT AAVEUSD + AAVEUSDC + AAVEUSDT ACEUSDT
    ACHUSDT
    ACTUSDT ACXUSDT + ADABUSD + ADAUSD - ADAUSDT + ADAUSDC - AERGOUSDT + ADAUSDT
    + AERGOUSDT + AEROUSDT - AGIUSDT + AGIXBUSD AGIXUSDT @@ -165588,17 +167710,28 @@

    Supported Assets

    		 AGLDUSDT
    + AGTUSDT + AI16ZUSDT
    - AIDOGEUSDT + AIAUSDT - AIOZUSDT + AINUSDT + + AIOTUSDT + + AIOUSDT
    AIUSDT - AKROUSDT + AKEUSDT AKTUSDT
    ALCHUSDT - ALEOUSDT + ALGOUSD
    ALGOUSDT ALICEUSDT + ALLUSDT + ALPACAUSDT ALPHAUSDT + ALPINEUSDT +
    ALTUSDT - ALUUSDT + AMBBUSD AMBUSDT + ANCBUSD + ANIMEUSDT ANKRUSDT
    ANTUSDT
    - APEUSDT + APEBUSD - APEXUSDT + APEUSD + + APEUSDT API3USDT + APTBUSD +
    APTUSD - APUUSDT + ARBUSDC
    ARBUSDT ARCUSDT + ARIAUSDT +
    ARKMUSDT ARUSDT + ASRUSDT + + ASTERUSDT +
    @@ -165702,48 +167861,68 @@

    Supported Assets

    		 ATHUSDT + ATOMUSD + ATOMUSDT + AUCTIONBUSD +
    AUCTIONUSDT AUDIOUSDT
    - AVAAIUSDT + AUSDT - AVAILUSDT + AVAAIUSDT AVAUSDT + AVAXBUSD +
    AVAXUSD + AVAXUSDC + AVAXUSDT - AVLUSDT + AVNTUSDT + + AWEUSDT + + AXLUSDT
    - AXLUSDT + AXSUSD AXSUSDT - B3USDT + B2USDT - BABYDOGEUSDT + B3USDT BABYUSDT @@ -165776,6 +167955,12 @@

    Supported Assets

    		 BANUSDT + BARDUSDT + + BASUSDT + BATUSDT BCHUSD
    - BCHUSDT + BCHUSDC - BEAMUSDT + BCHUSDT
    - BEERUSDT + BDXNUSDT - BELUSDT + BEAMXUSDT - BENDOGUSDT + BELUSDT BERAUSDT
    BICOUSDT + BIDUSDT + BIGTIMEUSDT
    - BILLYUSDT + BIOUSDC BIOUSDT - BLASTUSDT + BLESSUSDT +
    + BLUAIUSDT - BLUEUSDT + BLUEBIRDUSDT BLURUSDT @@ -165831,10 +168024,19 @@

    Supported Assets

    		 BLZUSDT + BMTUSDT + + BNBBUSD +
    - BMTUSDT + BNBUSD + + BNBUSDC BNBUSDT @@ -165846,50 +168048,67 @@

    Supported Assets

    BNXUSDT     		- BOBAUSDT + BOMEUSDC
    BOMEUSDT
    BONDUSDT - BONKUSDT + BRETTUSDT - BRETTUSDT + BROCCOLI714USDT - BROCCOLIUSDT + BROCCOLIF3BUSDT BRUSDT
    BSVUSDT
    BSWUSDT + BTCBUSD + + BTCDOMUSDT + + BTCSTUSDT + BTCUSD
    + BTCUSDC + BTCUSDT - BTTUSDT + BTRUSDT - BUSDUSDT + BTSUSDT - BUZZUSDT + BULLAUSDT + + BUSDT
    - CATSUSDT + CELOUSDT - CATUSDT + CELRUSDT
    - CEEKUSDT + CETUSUSDT - CELOUSDT + CFXUSDT - CELRUSDT + CGPTUSDT - CETUSUSDT + CHESSUSDT - CFXUSDT + CHILLGUYUSDT - CGPTUSDT + CHRUSDT
    - CHEEMSUSDT + CHZUSD - CHESSUSDT + CHZUSDT - CHILLGUYUSDT + CKBUSDT - CHRUSDT + CLOUSDT - CHZUSDT + COAIUSDT - CKBUSDT + COCOSUSDT
    - CLANKERUSDT + COMBOUSDT - CLOUDUSDT + COMPUSDT - COMBOUSDT + COOKIEUSDT - COMPUSDT + COSUSDT - COOKIEUSDT + COTIUSDT - COOKUSDT + COWUSDT
    - COQUSDT + CROSSUSDT - COREUSDT + CRVUSDC - COSUSDT + CRVUSDT - COTIUSDT + CTKUSDT - COVALUSDT + CTSIUSDT - COWUSDT + CUDISUSDT
    - CPOOLUSDT + CUSDT - CROUSDT + CVCUSDT - CRVUSDT + CVXBUSD - CTCUSDT + CVXUSDT - CTKUSDT + CYBERUSDT - CTSIUSDT + DAMUSDT
    - CVCUSDT + DARUSDT - CVXUSDT + DASHUSDT - CYBERUSDT + DEEPUSDT - DAOUSDT + DEFIUSDT - DARKUSDT + DEGENUSDT - DARUSDT + DEGOUSDT
    - DASHUSDT + DENTUSDT - DATAUSDT + DEXEUSDT - DBRUSDT + DFUSDT - DEEPUSDT + DGBUSDT - DEGENUSDT + DIAUSDT - DENTUSDT + DMCUSDT
    - DEXEUSDT + DODOBUSD - DGBUSDT + DODOXUSDT - DODOUSDT + DOGEBUSD DOGEUSD - DOGEUSDT + DOGEUSDC - DOGSUSDT + DOGEUSDT
    - DOGUSDT + DOGSUSD + + DOGSUSDT + + DOLOUSDT DOODUSDT - DOP1USDT + DOTBUSD DOTUSD
    DOTUSDT DRIFTUSDT
    - DUCKUSDT + DUSDT DUSKUSDT @@ -166105,37 +168330,48 @@

    Supported Assets

    		 DYMUSDT
    + EDENUSDT + EDUUSDT - EGLDUSDT + EGLDUSD
    - EIGENUSDT + EGLDUSDT - ELONUSDT + EIGENUSDT - ELXUSDT + ENAUSDC
    ENAUSDT ENJUSDT + ENSOUSDT + + ENSUSD + ENSUSDT
    EOSUSD
    EOSUSDT - ETCUSD + ERAUSDT - ETCUSDT + ESPORTSUSDT + + ETCBUSD
    - ETHBTCUSDT + ETCUSD + + ETCUSDT + + ETHBTC + + ETHBUSD + + ETHFIUSDC ETHFIUSDT
    ETHUSD + ETHUSDC + ETHUSDT - FARTCOINUSDT + EULUSDT + + EVAAUSDT
    - FBUSDT + FARTCOINUSDT - FDUSDUSDT + FETUSDT - FETUSDT + FFUSDT FHEUSDT @@ -166189,139 +168448,136 @@

    Supported Assets

    FIDAUSDT     		- FILUSD + FILBUSD
    - FILUSDT + FILUSD - FIOUSDT + FILUSDC - FIREUSDT + FILUSDT - FITFIUSDT + FIOUSDT - FLMUSDT + FISUSDT - FLOCKUSDT + FLMUSDT
    - FLOKIUSDT + FLOCKUSDT FLOWUSDT - FLRUSDT + FLUIDUSDT FLUXUSDT - FORMUSDT + FOOTBALLUSDT - FORTHUSDT + FORMUSDT
    - FOXYUSDT + FORTHUSDT FRONTUSDT - FTMUSDT + FTMBUSD - FTNUSDT + FTMUSD - FUELUSDT + FTMUSDT - FUNUSDT + FTTBUSD
    - FUSDT + FTTUSDT - FWOGUSDT + FUNUSDT - FXSUSDT + FUSDT - GALAUSDT + FXSUSDT - GALUSDT + GALABUSD - GASUSDT + GALAUSD
    - GEMSUSDT + GALAUSDT - GFTUSDT + GALBUSD - GIGAUSDT + GALUSDT - GLMRUSDT + GASUSDT - GLMUSDT + GHSTUSDT - GMEUSDT + GIGGLEUSDT
    - GMTUSDT + GLMRUSDT - GMXUSDT + GLMUSDT - GNOUSDT + GMTBUSD - GOATUSDT + GMTUSD - GODSUSDT + GMTUSDT - GOMININGUSDT + GMXUSDT
    - GORKUSDT + GOATUSDT GPSUSDT - GPTUSDT - GRASSUSDT GRTUSDT
    GTCUSDT
    GUNUSDT - HBARUSDT + HANAUSDT - HEIUSDT + HBARUSDC + + HBARUSDT
    + HEIUSDT + + HEMIUSDT + HFTUSDT HIPPOUSDT
    HIVEUSDT HMSTRUSDT
    HNTUSDT + HOLOUSDT + + HOMEUSDT + HOOKUSDT
    HOTUSDT - HPOS10IUSDT + HUMAUSDT + + HUSDT HYPERUSDT @@ -166391,17 +168667,31 @@

    Supported Assets

    		 HYPEUSDT + ICNTUSDT +
    + ICPBUSD + ICPUSDT + ICXUSD + ICXUSDT IDEXUSDT + IDOLUSDT +
    IDUSDT IMXUSDT
    INITUSDT INJUSDT + INUSDT +
    IOSTUSDT IOUSDT
    - IPUSDT + IPUSDC - IQ50USDT + IPUSDT
    JASMYUSDT JSTUSDT
    JTOUSDT JUPUSDT
    - JUSDT + KAIAUSDT - KAIAUSDT + KAITOUSDC KAITOUSDT @@ -166471,26 +168764,32 @@

    Supported Assets

    		 KASUSDT
    KAVAUSDT KDAUSDT
    KERNELUSDT KEYUSDT + KGENUSDT + KLAYUSDT KMNOUSDT + KNCUSD +
    @@ -166503,28 +168802,48 @@

    Supported Assets

    KSMUSDT     		- L3USDT + LABUSDT - LADYSUSDT + LAUSDT - LAIUSDT + LAYERUSDT
    + LDOBUSD + LDOUSDT + LEVERBUSD + LEVERUSDT + LIGHTUSDT + LINAUSDT
    + LINEAUSDT + + LINKBUSD + LINKUSD + LINKUSDC + LINKUSDT - LOOKSUSDT + LOKAUSDT LOOMUSDT @@ -166557,16 +168876,16 @@

    Supported Assets

    LSKUSDT     		- LTCUSD + LTCBUSD - LTCUSDT + LTCUSD - LTOUSDT + LTCUSDC - LUCEUSDT + LTCUSDT LUMIAUSDT @@ -166574,16 +168893,16 @@

    Supported Assets

    - LUNA2USDT + LUNA2BUSD - LUNCUSDT + LUNA2USDT - MAGICUSDT + LYNUSDT - MAJORUSDT + MAGICUSDT MANAUSD @@ -166594,19 +168913,19 @@

    Supported Assets

    - MANEKIUSDT + MANTAUSDT - MANTAUSDT + MASKUSDT - MAPOUSDT + MATICBUSD - MASAUSDT + MATICUSD - MASKUSDT + MATICUSDC MATICUSDT @@ -166619,26 +168938,20 @@

    Supported Assets

    		 MAVUSDT - MAXUSDT - MBLUSDT MBOXUSDT - MCUSDT -
    MDTUSDT MELANIAUSDT
    MEMEFIUSDT METISUSDT
    - MEUSDT + METUSDT - MEWUSDT + MEUSDT
    - MICHIUSDT + MEWUSDT MILKUSDT @@ -166669,30 +168982,22 @@

    Supported Assets

    MINAUSDT     		- MKRUSDT -
    - MLNUSDT + MIRAUSDT - MNTUSD + MITOUSDT - MNTUSDT + MKRUSDT
    - MOBILEUSDT + MLNUSDT MOCAUSDT - MOGUSDT -
    MONUSDT - MOTHER1USDT - - MOTHER2USDT - - MOTHERUSDT + MOVEUSDT
    - MOVEUSDT - MOVRUSDT - MULTIUSDT + MUSDT - MUMUUSDT + MYROUSDT + + MYXUSDT
    - MVLUSDT + NAORISUSDT - MYRIAUSDT + NEARBUSD - MYROUSDT + NEARUSD - NCUSDT + NEARUSDC - NEARUSD + NEARUSDT - NEARUSDT + NEIROETHUSDT
    - NEIROCTOUSDT + NEIROUSDT - NEIROETHUSDT + NEOUSDC NEOUSDT - NFPUSDT + NEWTUSDT - NFTUSDT + NFPUSDT NILUSDT @@ -166780,10 +169079,10 @@

    Supported Assets

    NMRUSDT     		- NOTUSDT + NOMUSDT - NSUSDT + NOTUSDT NTRNUSDT @@ -166794,14 +169093,11 @@

    Supported Assets

    - NYANUSDT + NXPCUSDT OBOLUSDT - OBTUSDT - OCEANUSDT OGUSDT
    - OKBUSDT - OLUSDT
    OMGUSDT ONDOUSDT
    ONEUSDT ONGUSDT
    ONTUSDT + OPENUSDT + OPUSD ORBSUSDT
    ORCAUSDT
    ORDERUSDT - ORDIUSDT - - ORNUSDT + ORDIUSDC - OSMOUSDT + ORDIUSDT OXTUSDT
    PARTIUSDT PAXGUSDT - PEAQUSDT - - PEIPEIUSDT -
    PENDLEUSDT - PENGUSDT + PENGUUSDC
    PENGUUSDT PEOPLEUSDT - PEPEUSDT - PERPUSDT PHAUSDT - PHBUSDT -
    - PIPPINUSDT + PHBBUSD - PIRATEUSDT + PHBUSDT + + PIPPINUSDT PIXELUSDT - PIXFIUSDT + PLAYUSDT PLUMEUSDT
    + PNUTUSDC + PNUTUSDT
    POLUSDT POPCATUSDT - PORTALUSDT - - POWRUSDT -
    - PRCLUSDT - - PRE05EOSUSDT + PORT3USDT - PRE08EOSUSDT + PORTALUSDT - PRIMEUSDT + POWRUSDT PROMPTUSDT @@ -166971,34 +169247,31 @@

    Supported Assets

    		 PROMUSDT + PROVEUSDT +
    - PROSUSDT + PTBUSDT PUFFERUSDT - PUMPUSDT + PUMPBTCUSDT - PUNDIXUSDT + PUMPUSDT - PUNDUUSDT + PUNDIXUSDT - PYRUSDT + PYTHUSDT
    - PYTHUSDT - - QIUSDT - QNTUSDT - QUBICUSDT + QUICKUSDT - QUICKUSDT + QUSDT
    RADUSDT RAREUSDT
    - RATSUSDT - - RAYDIUMUSDT + RAYSOLUSDT RAYUSDT @@ -167031,14 +169301,20 @@

    Supported Assets

    		 RDNTUSDT
    + RECALLUSDT + REDUSDT REEFUSDT
    + REIUSDT + RENDERUSDT - REQUSDT + RESOLVUSDT - REXUSDT + REZUSDT + + RIFUSDT
    - REZUSDT + RIVERUSDT - RFCUSDT + RLCUSDT - RIFSOLUSDT + RNDRUSDT - RIFUSDT + RONINUSDT - RLCUSDT + ROSEUSD - RNDRUSDT + ROSEUSDT
    - ROAMUSDT + RPLUSDT - RONINUSDT + RSRUSDT - RONUSDT + RUNEUSD - ROSEUSDT + RUNEUSDT - RPLUSDT + RVNUSDT - RSRUSDT + RVVUSDT
    - RSS3USDT + SAFEUSDT - RUNEUSDT + SAGAUSDT - RVNUSDT + SAHARAUSDT - SAFEUSDT + SANDBUSD - SAGAUSDT + SANDUSD SANDUSDT @@ -167114,10 +169393,10 @@

    Supported Assets

    - SATSUSDT + SANTOSUSDT - SCAUSDT + SAPIENUSDT SCRTUSDT @@ -167129,95 +169408,101 @@

    Supported Assets

    SCUSDT     		- SDUSDT + SEIUSDT
    - SEIUSDT + SFPUSDT - SENDUSDT + SHELLUSDT - SERAPHUSDT + SIGNUSDT - SFPUSDT + SIRENUSDT - SHELLUSDT + SKATEUSDT - SHIB1000USDT + SKLUSDT
    - SIGNUSDT + SKYAIUSDT - SILLYUSDT + SKYUSDT - SIRENUSDT + SLERFUSDT - SKLUSDT + SLPUSDT - SLERFUSDT + SNTUSDT - SLFUSDT + SNXUSDT
    - SLPUSDT + SOLBUSD - SNTUSDT + SOLUSD - SNXUSDT + SOLUSDC - SOLAYERUSDT + SOLUSDT - SOLOUSDT + SOLVUSDT - SOLUSD + SOMIUSDT
    - SOLUSDT + SONICUSDT - SOLVUSDT + SOONUSDT - SONICUSDT + SOPHUSDT - SPECUSDT + SPELLUSDT - SPELLUSDT + SPKUSDT SPXUSDT
    + SQDUSDT + + SRMUSDT + SSVUSDT - STARLUSDT + STBLUSDT STEEMUSDT @@ -167225,14 +169510,14 @@

    Supported Assets

    		 STGUSDT
    STMXUSDT STORJUSDT
    STOUSDT STRKUSDT
    STXUSDT SUIUSD
    - SUIUSDT + SUIUSDC - SUNDOGUSDT + SUIUSDT SUNUSDT @@ -167265,20 +169550,17 @@

    Supported Assets

    		 SUPERUSDT
    SUSDT SUSHIUSDT
    SWARMSUSDT - SWEATUSDT - SWELLUSDT SXTUSDT
    SYNUSDT
    + SYRUPUSDT + SYSUSDT + TACUSDT + + TAGUSDT + TAIKOUSDT
    - TAIUSDT + TAKEUSDT + + TANSSIUSDT TAOUSDT - TEST01SWELLUSDT + TAUSDT + + THETAUSD THETAUSDT @@ -167317,27 +169616,27 @@

    Supported Assets

    THEUSDT     		- TIAUSDT + TIAUSDC - TLMUSDT + TIAUSDT - TNSRUSDT + TLMBUSD - TOKENUSDT + TLMUSDT - TOMIUSDT + TNSRUSDT
    - TOMOUSDT + TOKENUSDT - TONUSD + TOMOUSDT TONUSDT @@ -167346,62 +169645,93 @@

    Supported Assets

    TOSHIUSDT     		- TRBUSDT + TOWNSUSDT - TROYUSDT + TRADOORUSDT
    + TRBUSDT + + TREEUSDT + + TROYUSDT + + TRUMPUSDC + TRUMPUSDT + TRUTHUSDT +
    TRUUSDT + TRXBUSD + + TRXUSD + TRXUSDT - TSTBSCUSDT + TSTUSDT TURBOUSDT
    TUSDT
    TUTUSDT TWTUSDT + UBUSDT + UMAUSDT UNFIUSDT
    + UNIBUSD + UNIUSD - UNIUSDT + UNIUSDC
    - UROUSDT + UNIUSDT USDCUSDT - USDEUSDT + USELESSUSDT
    USTCUSDT UXLINKUSDT
    VANAUSDT VELODROMEUSDT
    - VELOUSDT + VELVETUSDT + + VETUSD VETUSDT - VGXUSDT + VFYUSDT
    VICUSDT VIDTUSDT
    VINEUSDT - VINUUSDT - VIRTUALUSDT VOXELUSDT
    - VRAUSDT - - VRUSDT - VTHOUSDT WALUSDT - WAVESUSDT -
    - WAXPUSDT + WAVESBUSD - WCTUSDT + WAVESUSDT - WENUSDT + WAXPUSDT - WHYUSDT + WCTUSDT WIFUSD - WIFUSDT + WIFUSDC
    - WLDUSDT + WIFUSDT - WOOUSDT + WLDUSD - WSMUSDT + WLDUSDC - WUSDT + WLDUSDT - XAIUSDT + WLFIUSDC - XAUTUSDT + WLFIUSDT
    - XCHUSDT + WOOUSDT - XCNUSDT + WUSDT - XDCUSDT + XAIUSDT - XECUSDT + XANUSDT - XEMUSDT + XCNUSDT - XIONUSDT + XEMUSDT
    - XMRUSDT + XMRUSD - XNOUSDT + XMRUSDT - XRDUSDT + XNYUSDT - XRPUSD + XPINUSDT
    - XRPUSDT + XPLUSDT - XTERUSDT + XRPBUSD - XTZUSDT + XRPUSD - XUSDT + XRPUSDC - XVGUSDT + XRPUSDT - XVSUSDT + XTZUSD
    - YFIIUSDT + XTZUSDT - YFIUSDT + XVGUSDT - YGGUSDT + XVSUSDT - ZBCNUSDT + YALAUSDT - ZCXUSDT + YBUSDT - ZECUSDT + YFIUSDT
    - ZENTUSDT + YGGUSDT + + ZBTUSDT + + ZECUSDT ZENUSDT @@ -167605,16 +169930,16 @@

    Supported Assets

    		 ZETAUSDT
    - ZEUSUSDT + ZILUSD ZILUSDT
    - ZKFUSDT + ZKCUSDT ZKJUSDT @@ -167625,17 +169950,20 @@

    Supported Assets

    		 ZORAUSDT
    ZRCUSDT ZROUSDT
    ZRXUSDT + 币安人生USDT +
    @@ -167647,146 +169975,126 @@

    Example Applications

    - The Binance Crypto Future Margin Rate dataset enables correct margin cost so you can accurately design strategies for Cryptocurrencies with term structure. Examples include the following strategies: + The Binance Crypto Future Price dataset enables you to accurately design strategies for Cryptocurrencies with term structure. Examples include the following strategies:

    • - Horizontal/Diagonal arbitrage with the underlying Cryptocurrencies + Horizontal/Diagonal arbitrage with the underlying cryptocurrencies
    • Trade Contango/Backwardation predictions
    • - Hedge for illiquid Cryptocurrencies + Hedge for illiquid cryptocurrencies

    Classic Algorithm Example

    - The following example algorithm holds a long position in the BTCUSDT perpetual Future contract on the Bybit exchange. When the margin interest rate is relatively low, it flips to a short position. + The following example algorithm buys BTCUSDT perpetual future contract if the last day's close price was close to ask close price than bid close price, sells short of that in opposite, through the Binance exchange:

    - 
    from AlgorithmImports import *
-
+   
    from AlgorithmImports import *
 
-class BybitCryptoFutureDataAlgorithm(QCAlgorithm):
 
-    # Add an indicator to track the low of trailing margin interest rates.
-    _low_interest_rate = Minimum(10)
+class BinanceCryptoFutureDataAlgorithm(QCAlgorithm):
 
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        # Set Account Currency to USDT, since USD cannot be used to trade.
-        self.set_cash("USDT", 100_000)
-        # Bybit  accepts both Cash and Margin account types, select the one you need for the best reality modeling.
-        self.set_brokerage_model(BrokerageName.BYBIT, AccountType.MARGIN)
-        # Seed the price of each asset with its last known price to avoid trading errors.
-        self.settings.seed_initial_prices = True
-        # Requesting data, we only trade on BTCUSDT Future in Bybit  exchange
+        # Set the account currency to USDT, since you can't trade with 
+        # USD and the algorithm doesn't automatically convert USD & USDT
+        # holdings.
+        self.set_account_currency("USDT", 100000)
+        # Binance Futures Exchange accepts both Cash and Margin account 
+        # types. Select the one you need for the best reality modeling.
+        self.set_brokerage_model(BrokerageName.BINANCE_FUTURES, AccountType.MARGIN)
+        # Add BTCUSDT data from the Binance Future exchange.
         # Perpetual futures does not have a filter function.
-        self._future = self.add_crypto_future("BTCUSDT", Resolution.DAILY)
+        self._btc = self.add_crypto_future("BTCUSDT", Resolution.DAILY)
         # Historical data
-        history = self.history[MarginInterestRate](self._future.symbol, 10, Resolution.DAILY)
-        for data_point in history:
-            self._low_interest_rate.update(data_point.end_time, data_point.interest_rate)
+        history = self.history(self._btc, 10, Resolution.DAILY)
 
     def on_data(self, slice: Slice) -> None:
-        # Note that you may want to access the margin interest of the crypto future to calculate if it would impact a trade's PnL
-        # Or you can calculate the trade size on keeping the quote currency constant
-        if self._future.symbol not in slice.margin_interest_rates:
+        # Trade only based on updated price data.
+        if self._btc.symbol not in slice.bars or self._btc.symbol not in slice.quote_bars:
             return
-        interest_rate = slice.margin_interest_rates[self._future.symbol].interest_rate
-        self.plot('Margin Interest Rate', str(self._future.symbol), interest_rate)
-        # Short when margin interest rates are relatively low. Otherwise, long.
-        if interest_rate <= self._low_interest_rate.current.value:
-            if not self._future.holdings.is_short:
-                self.set_holdings(self._future.symbol, -0.5)
-        elif not self._future.holdings.is_long:
-            self.set_holdings(self._future.symbol, 1)
-        # Update the indicator.
-        self._low_interest_rate.update(self.time, interest_rate)
    
-   
    public class BybitCryptoFutureDataAlgorithm : QCAlgorithm
+        # Scalp-trade the bid-ask spread based on the supply-demand strength
+        if self._btc.price - self._btc.bid_price > self._btc.ask_price - self._btc.price:
+            self.set_holdings(self._btc, -0.5)
+        else:
+            self.set_holdings(self._btc, 1)
    
+   
    public class BinanceCryptoFutureDataAlgorithm : QCAlgorithm
 {
-    private CryptoFuture _future;
-    // Add an indicator to track the low of trailing margin interest rates.
-    private Minimum _lowInterestRate = new(10);
+    public CryptoFuture _btc;
 
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        // Set Account Currency to USDT, since USD cannot be used to trade.
-        SetCash("USDT", 100000);
-        // Bybit  accepts both Cash and Margin account types, select the one you need for the best reality modeling.
-        SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin);
-        // Seed the price of each asset with its last known price to avoid trading errors.
-        Settings.SeedInitialPrices = true;
-        // Requesting data, we only trade on BTCUSDT Future in Bybit  exchange
+        // Set the account currency to USDT, since you can't trade with 
+        // USD and the algorithm doesn't automatically convert USD & USDT
+        // holdings.
+        SetAccountCurrency("USDT", 100000);
+        // Binance Futures Exchange accepts both Cash and Margin account 
+        // types. Select the one you need for the best reality modeling.
+        SetBrokerageModel(BrokerageName.BinanceFutures, AccountType.Margin);
+        // Add BTCUSDT data from the Binance Future exchange.
         // Perpetual futures does not have a filter function.
-        _future = AddCryptoFuture("BTCUSDT", Resolution.Daily);
+        _btc = AddCryptoFuture("BTCUSDT", Resolution.Daily);
         // Historical data
-        var history = History<MarginInterestRate>(_future.Symbol, 10, Resolution.Daily);
-        foreach (var dataPoint in history)
-        {
-            _lowInterestRate.Update(dataPoint.EndTime, dataPoint.InterestRate);
-        }
+        var history = History(_btc, 10, Resolution.Daily);
     }
 
     public override void OnData(Slice slice)
     {
-        // Note that you may want to access the margin interest of the crypto future to calculate if it would impact a trade's PnL
-        // Or you can calculate the trade size on keeping the quote currency constant
-        if (!slice.MarginInterestRates.TryGetValue(_future.Symbol, out var dataPoint))
+        // Trade only based on updated price data.
+        if (!slice.Bars.ContainsKey(_btc) || !slice.QuoteBars.ContainsKey(_btc))
         {
             return;
         }
-        var interestRate = dataPoint.InterestRate;
-        Plot("Margin Interest Rate", _future.Symbol.ToString(), interestRate);
-        // Short when margin interest rates are relatively low. Otherwise, long.
-        if (interestRate <= _lowInterestRate)
+        // Scalp-trade the bid-ask spread based on the supply-demand strength
+        if (_btc.Price - _btc.BidPrice > _btc.AskPrice - _btc.Price)
         {
-            if (!_future.Holdings.IsShort)
-            {
-                SetHoldings(_future.Symbol, -0.5);
-            }
+            SetHoldings(_btc, -0.5m);
         }
-        else if (!_future.Holdings.IsLong)
+        else
         {
-            SetHoldings(_future.Symbol, 1);
+            SetHoldings(_btc, 1m);
         }
-        // Update the indicator.
-        _lowInterestRate.Update(Time, interestRate);
     }
-}
    
+}

    Framework Algorithm Example

    - The following example algorithm holds a long position in the BTCUSDT perpetual Future contract on the Bybit exchange. When the margin interest rate is relatively low, it flips to a short position. + The following example algorithm hold a long BTCUSDT Future portfolio if the last day's close price was close to ask close price than bid close price, while hold short of that in opposite, through the Binance exchange using the + + algorithm framework + + implementation:

    - 
    from AlgorithmImports import *
+   
    from AlgorithmImports import *
 
 
-class BybitCryptoFutureDataAlgorithm(QCAlgorithm):
+class BinanceCryptoFutureDataAlgorithm(QCAlgorithm):
 
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        self.settings.free_portfolio_value_percentage = 0.1
-        # Set Account Currency to USDT, since USD cannot be used to trade.
-        self.set_cash("USDT", 100000)
-        # Bybit accepts both Cash and Margin account types, select the one you need for the best reality modeling.
-        self.set_brokerage_model(BrokerageName.BYBIT, AccountType.MARGIN)
-        # Seed the price of each asset with its last known price to avoid trading errors.
-        self.settings.seed_initial_prices = True
+        # Set the account currency to USDT, since you can't trade with 
+        # USD and the algorithm doesn't automatically convert USD & USDT
+        # holdings.
+        self.set_account_currency("USDT", 100000)
+        # Binance Futures Exchange accepts both Cash and Margin account types, select the one you need for the best reality modeling.
+        self.set_brokerage_model(BrokerageName.BINANCE_FUTURES, AccountType.MARGIN)
         self.universe_settings.resolution = Resolution.DAILY
         self.universe_settings.leverage = 2
-        # We only trade on BTCUSDT Future in Bybit exchange
-        symbols = Symbol.create("BTCUSDT", SecurityType.CRYPTO_FUTURE, Market.BYBIT)
+        # We only trade on BTCUSDT Future in Binance Future exchange
+        symbols = Symbol.create("BTCUSDT", SecurityType.CRYPTO_FUTURE, Market.BINANCE)
         self.add_universe_selection(ManualUniverseSelectionModel(symbols))
         # Custom alpha model to emit insights based on the Crypto Future price data
         self.add_alpha(CryptoFutureAlphaModel())
@@ -167794,59 +170102,63 @@ 
    
         self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
         self.set_execution(ImmediateExecutionModel())
 
-
 class CryptoFutureAlphaModel(AlphaModel):
 
-    _min_by_symbol = {}
+    def __init__(self) -> None:
+        self.symbols = []
 
     def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
         insights = []
-        for symbol, low_interest_rate in self._min_by_symbol.items():
-            # Note that you may want to access the margin interest of the crypto future to calculate if it would impact a trade's PnL
-            # Or you can calculate the trade size on keeping the quote currency constant
-            if symbol not in slice.margin_interest_rates:
+
+        for symbol in self.symbols:
+            # Trade only based on updated price data
+            if not slice.bars.contains_key(symbol) or not slice.quote_bars.contains_key(symbol):
                 continue
-            interest_rate = slice.margin_interest_rates[symbol].interest_rate
-            algorithm.plot('Margin Interest Rate', str(symbol), interest_rate)
-            # Short when margin interest rates are relatively low. Otherwise, long.
-            if interest_rate <= low_interest_rate.current.value:
+
+            quote = slice.quote_bars[symbol]
+            price = slice.bars[symbol].price
+            
+            # Scalp-trade the bid-ask spread based on the supply-demand strength
+            if price - quote.bid.close > quote.ask.close - price:
                 direction = InsightDirection.DOWN
             else:
                 direction = InsightDirection.UP
             insights.append(Insight.price(symbol, timedelta(1), direction))
-            # Update the indicator.
-            low_interest_rate.update(slice.time, interest_rate)
+            
         return insights
 
     def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
         for security in changes.added_securities:
             symbol = security.symbol
-            # Add an indicator to track the low of trailing margin interest rates.
-            self._min_by_symbol[symbol] = Minimum(10)
+            self.symbols.append(symbol)
+
             # Historical data
-            history = algorithm.history[MarginInterestRate](symbol, 10, Resolution.DAILY)
-            for data_point in history:
-                self._min_by_symbol[symbol].update(data_point.end_time, data_point.interest_rate)
+            history = algorithm.history(symbol, 10, Resolution.DAILY)
+
         for security in changes.removed_securities:
-            self._min_by_symbol.pop(security.symbol, None)
    
-   
    public class BybitCryptoFutureDataAlgorithm : QCAlgorithm
+            symbol = security.symbol
+            if symbol in self.symbols:
+                self.symbols.remove(symbol)
+
    
+   
    public class BinanceCryptoFutureDataAlgorithm : QCAlgorithm
 {
+    public Symbol _symbol;
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        Settings.FreePortfolioValuePercentage = 0.1m;
-        // Set Account Currency to USDT, since USD cannot be used to trade.
-        SetCash("USDT", 100000);
-        // Bybit accepts both Cash and Margin account types, select the one you need for the best reality modeling.
-        SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin);
-        // Seed the price of each asset with its last known price to avoid trading errors.
-        Settings.SeedInitialPrices = true;
+        // Set the account currency to USDT, since you can't trade with 
+        // USD and the algorithm doesn't automatically convert USD & USDT
+        // holdings.
+        SetAccountCurrency("USDT", 100000);
+        // Binance Futures Exchange accepts both Cash and Margin account types, select the one you need for the best reality modeling.
+        SetBrokerageModel(BrokerageName.BinanceFutures, AccountType.Margin);
         UniverseSettings.Resolution = Resolution.Daily;
         UniverseSettings.Leverage = 2;
-        // We only trade on BTCUSDT Future in Bybit exchange
-        var symbols = new List<Symbol>{
-            QuantConnect.Symbol.Create("BTCUSDT", SecurityType.CryptoFuture, Market.Bybit)
+        // We only trade on BTCUSDT Future in Binance Future exchange
+        var symbols = new List<Symbol> {
+            QuantConnect.Symbol.Create("BTCUSDT", SecurityType.CryptoFuture, Market.Binance)
         };
         AddUniverseSelection(new ManualUniverseSelectionModel(symbols));
         // Custom alpha model to emit insights based on the Crypto Future price data
@@ -167859,28 +170171,32 @@ 
    
 
 public class CryptoFutureAlphaModel : AlphaModel
 {
-    private Dictionary<Symbol, Minimum> _minBySymbol = new();
+    private List<Symbol> _symbols = new();
 
     public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
     {
         var insights = new List<Insight>();
-        foreach (var kvp in _minBySymbol)
+
+        foreach (var symbol in _symbols)
         {
-            var symbol = kvp.Key;
-            var lowInterestRate = kvp.Value;
-            // Note that you may want to access the margin interest of the crypto future to calculate if it would impact a trade's PnL
-            // Or you can calculate the trade size on keeping the quote currency constant
-            if (!slice.MarginInterestRates.TryGetValue(symbol, out var dataPoint))
+            // Trade only based on updated price data
+            if (!slice.Bars.ContainsKey(symbol) || !slice.QuoteBars.TryGetValue(symbol, out var quote))
+            {
                 continue;
-            var interestRate = dataPoint.InterestRate;
-            algorithm.Plot("Margin Interest Rate", symbol.ToString(), interestRate);
-            // Short when margin interest rates are relatively low. Otherwise, long.
-            var direction = interestRate <= lowInterestRate.Current.Value
-                ? InsightDirection.Down
-                : InsightDirection.Up;
+            }
+            var price = slice.Bars[symbol].Price;
+            
+            // Scalp-trade the bid-ask spread based on the supply-demand strength
+            InsightDirection direction;
+            if (price - quote.Bid.Close > quote.Ask.Close - price)
+            {
+                direction = InsightDirection.Down;
+            }
+            else
+            {
+                direction = InsightDirection.Up;
+            }
             insights.Add(Insight.Price(symbol, TimeSpan.FromDays(1), direction));
-            // Update the indicator.
-            lowInterestRate.Update(slice.Time, interestRate);
         }
         return insights;
     }
@@ -167890,22 +170206,18 @@ 
    
         foreach (var security in changes.AddedSecurities)
         {
             var symbol = security.Symbol;
-            // Add an indicator to track the low of trailing margin interest rates.
-            var min = new Minimum(10);
-            _minBySymbol[symbol] = min;
+            _symbols.Add(symbol);
+
             // Historical data
-            var history = algorithm.History<MarginInterestRate>(symbol, 10, Resolution.Daily);
-            foreach (var dataPoint in history)
-            {
-                min.Update(dataPoint.EndTime, dataPoint.InterestRate);
-            }
+            var history = algorithm.History(symbol, 10, Resolution.Daily);
         }
+
         foreach (var security in changes.RemovedSecurities)
         {
-            _minBySymbol.Remove(security.Symbol);
+            _symbols.Remove(security.Symbol);
         }
     }
-}
    
+}
    @@ -167914,41 +170226,73 @@

    Data Point Attributes

    - The Binance Crypto Future Margin Rate dataset provides + The Binance Crypto Future Price dataset provides - MarginInterestRate + TradeBar - objects, which have the following attributes: + , + + QuoteBar + + , and + + Tick + + objects.

    -
    +

    + TradeBar Attributes +

    +

    + + TradeBar + + objects have the following attributes: +

    +
    +
    +

    + QuoteBar Attributes +

    +

    + + QuoteBar + + objects have the following attributes: +

    +
    +
    +

    + Tick Attributes +

    +

    + + Tick + + objects have the following attributes: +

    +

     

    -

    Datasets > QuantConnect > CFD Data

    +

    Datasets > QuantConnect > Binance Crypto Price Data

    QuantConnect

    -

    CFD Data

    +

    Binance Crypto Price Data

    Introduction

    - The CFD Data by QuantConnect serves 51 - - contracts for differences - - (CFD). The data starts as early as May 2002 and is delivered on any frequency from tick to daily. This dataset is created by QuantConnect processing raw tick data from OANDA. -

    -

    - CFD data does not include ask and bid sizes. + The Binance Crypto Price Data is for Cryptocurrency price and volume data points. The data covers 3,320 Cryptocurrency pairs, starts in July 2017, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Binance.

    - For more information about the CFD Data dataset, including CLI commands and pricing, see the - + For more information about the Binance Crypto Price Data dataset, including CLI commands and pricing, see the + dataset listing . @@ -167962,7 +170306,7 @@

    About the Provider

    - QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month. + Binance was founded by Changpeng Zhao in 2017 with the goal to "increase the freedom of money globally". Binance provides access to trading Crypto through spot markets and perpetual Futures. They serve clients with no minimum deposit when depositing Crypto. Binance also provides an NFT marketplace, a mining pool, and services to deposit Crypto coins in liquidity pools to earn rewards.

    @@ -167971,11 +170315,24 @@

    Getting Started

    - The following snippet demonstrates how to request data from the CFD dataset: + The following snippet demonstrates how to request data from the Binance Crypto Price dataset:

    - 
    self.xauusd = self.add_cfd("XAUUSD", Resolution.DAILY).symbol
    - 
    _symbol = AddCfd("XAUUSD", Resolution.Daily).Symbol;
    + 
    # Binance accepts both Cash and Margin account types only.
+self.set_brokerage_model(BrokerageName.BINANCE, AccountType.CASH)
+#self.set_brokerage_model(BrokerageName.BINANCE, AccountType.MARGIN)
+
+self.btcbusd = self.add_crypto("BTCBUSD", Resolution.MINUTE, Market.BINANCE).symbol
+
+self._universe = self.add_universe(CryptoUniverse.binance(self.universe_selection_filter))
+
    + 
    // Binance accepts both Cash and Margin account types only.
+SetBrokerageModel(BrokerageName.Binance, AccountType.Cash);
+//SetBrokerageModel(BrokerageName.Binance, AccountType.Margin);
+
+_symbol = AddCrypto("BTCBUSD", Resolution.Minute, Market.Binance).Symbol;
+
+_universe = AddUniverse(CryptoUniverse.Binance(UniverseSelectionFilter));
    @@ -168003,7 +170360,7 @@

    Data Summary

    Start Date - Mixed, earliest starts May 2002 + July 2017 @@ -168011,7 +170368,7 @@

    Data Summary

    Asset Coverage - 51 Contracts + 3,320 Currency Pairs @@ -168027,7 +170384,7 @@

    Data Summary

    Resolution - Tick, Second, Minute, Hour, & Daily + Tick, Second, Minute, Hourly, & Daily @@ -168035,7 +170392,7 @@

    Data Summary

    Timezone - Mixed, in which the contract is listed* + UTC @@ -168043,23 +170400,13 @@

    Data Summary

    Market Hours - + Always Open - , except from Friday 5 PM EST to Sunday 5 PM EST. -
    - Index CFDs depends on the underlying market hour* -

    - * E.g.: - - DE30EUR - - tracks DAX30 Index, which is listed in Europe/Berlin timezone. -

    @@ -168067,53 +170414,57 @@

    Requesting Data

    - To add CFD data to your algorithm, call the + To add Binance Crypto Price data to your algorithm, call the - AddCfd + AddCrypto - add_cfd + add_crypto - method. Save a reference to the CFD + method. Save a reference to the Crypto Symbol so you can access the data later in your algorithm.

    - 
    class CfdAlgorithm (QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_account_currency('EUR');
+   
    class CoinAPIDataAlgorithm(QCAlgorithm):
 
-        self.set_start_date(2019, 2, 20)
-        self.set_end_date(2019, 2, 21)
-        self.set_cash('EUR', 100000)
+    def initialize(self) -> None:
+        self.set_start_date(2020, 6, 1)
+        self.set_end_date(2021, 6, 1)
 
-        self.de30eur = self.add_cfd('DE30EUR').symbol
+        # Set Account Currency to Binance Stable Coin for USD
+        self.set_account_currency("BUSD")
+        self.set_cash(100000)
 
-        self.set_benchmark(self.de30eur)
-
    
-   
    public class CfdAlgorithm : QCAlgorithm
+        # Binance accepts both Cash and Margin account types.
+        self.set_brokerage_model(BrokerageName.BINANCE, AccountType.MARGIN)
+        
+        self.btcbusd = self.add_crypto("BTCBUSD", Resolution.MINUTE, Market.BINANCE).symbol
    
+   
    public class CoinAPIDataAlgorithm : QCAlgorithm
 {
     private Symbol _symbol;
-
+    
     public override void Initialize()
     {
-        SetAccountCurrency("EUR");
-
-        SetStartDate(2019, 2, 20);
-        SetEndDate(2019, 2, 21);
-        SetCash("EUR", 100000);
+        SetStartDate(2020, 6, 1);
+        SetEndDate(2021, 6, 1);
 
-        _symbol = AddCfd("DE30EUR").Symbol;
+        // Set Account Currency to Binance Stable Coin for USD
+        SetAccountCurrency("BUSD");
+        SetCash(100000);
 
-        SetBenchmark(_symbol);
+        // Binance accepts both Cash and Margin account types.
+        SetBrokerageModel(BrokerageName.Binance, AccountType.Margin);
+        
+        _symbol = AddCrypto("BTCBUSD", Resolution.Minute, Market.Binance).Symbol;
     }
 }

    - For more information about creating CFD subscriptions, see - + For more information about creating Crypto subscriptions, see + Requesting Data . @@ -168125,7 +170476,14 @@

    Accessing Data

    - To get the current CFD data, index the + To get the current Binance Crypto Price data, index the + + Bars + + + bars + + , QuoteBars @@ -168145,11 +170503,15 @@

    Accessing Data

    Slice     - with the CFD + with the Crypto Symbol - . Slice objects deliver unique events to your algorithm as they happen, but the + . + + Slice + + objects deliver unique events to your algorithm as they happen, but the Slice @@ -168161,16 +170523,26 @@

    Accessing Data

    def on_data(self, slice: Slice) -> None:
-    if self.de30eur in slice.quote_bars:
-        quote_bar = slice.quote_bars[self.de30eur]
-        self.log(f"{self.de30eur} bid at {slice.time}: {quote_bar.bid.close}")
+    if self.btcbusd in slice.bars:
+        trade_bar = slice.bars[self.btcbusd]
+        self.log(f"{self.btcbusd} close at {slice.time}: {trade_bar.close}")
 
-    if self.de30eur in slice.ticks:
-        ticks = slice.ticks[self.de30eur]
+    if self.btcbusd in slice.quote_bars:
+        quote_bar = slice.quote_bars[self.btcbusd]
+        self.log(f"{self.btcbusd} bid at {slice.time}: {quote_bar.bid.close}")
+
+    if self.btcbusd in slice.ticks:
+        ticks = slice.ticks[self.btcbusd]
         for tick in ticks:
-            self.log(f"{self.de30eur} price at {slice.time}: {tick.price}")
    + self.log(f"{self.btcbusd} price at {slice.time}: {tick.price}") 
    public override void OnData(Slice slice)
 {
+    if (slice.Bars.ContainsKey(_symbol))
+    {
+        var tradeBar = slice.Bars[_symbol];
+        Log($"{_symbol} price at {slice.Time}: {tradeBar.Close}");
+    }
+
     if (slice.QuoteBars.ContainsKey(_symbol))
     {
         var quoteBar = slice.QuoteBars[_symbol];
@@ -168197,6 +170569,9 @@ 
    Accessing Data
    
   
    
   
    
    
    def on_data(self, slice: Slice) -> None:
+    for symbol, trade_bar in slice.bars.items():
+        self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")
+
     for symbol, quote_bar in slice.quote_bars.items():
         self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
 
@@ -168205,6 +170580,13 @@ 
    Accessing Data
    
             self.log(f"{symbol} price at {slice.time}: {tick.price}")
    
    
    public override void OnData(Slice slice)
 {
+    foreach (var kvp in slice.Bars)
+    {
+        var symbol = kvp.Key;
+        var tradeBar = kvp.Value;
+        Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
+    }
+
     foreach (var kvp in slice.QuoteBars)
     {
         var symbol = kvp.Key;
@@ -168225,8 +170607,8 @@ 
    Accessing Data
    
 
    
   
    
   
    
-   For more information about accessing CFD data, see
-   
+   For more information about accessing Crypto data, see
+   
     Handling Data
    
    .
@@ -168238,14 +170620,14 @@ 
    Historical Data
    
 
  
   
    
-   To get historical CFD data, call the
+   To get historical Binance Crypto Price data, call the
    
     History
    
    
     history
    
-   method with the CFD
+   method with the Crypto
    
     Symbol
    
@@ -168253,14 +170635,20 @@ 
    Historical Data
    
   
    
   
    
    
    # DataFrame
-history_df = self.history(self.de30eur, 100, Resolution.MINUTE)
+history_df = self.history(self.btcbusd, 100, Resolution.DAILY)
+
+# TradeBar objects
+history_trade_bars = self.history[TradeBar](self.btcbusd, 100, Resolution.MINUTE)
 
 # QuoteBar objects
-history_quote_bars = self.history[QuoteBar](self.de30eur, 100, Resolution.MINUTE)
+history_quote_bars = self.history[QuoteBar](self.btcbusd, 100, Resolution.MINUTE)
 
 # Tick objects
-history_ticks = self.history[Tick](self.de30eur, timedelta(seconds=10), Resolution.TICK)
    
-   
    // QuoteBar objects 
+history_ticks = self.history[Tick](self.btcbusd, timedelta(seconds=10), Resolution.TICK)
    
+   
    // TradeBar objects 
+var historyTradeBars = History(_symbol, 100, Resolution.Daily);
+
+// QuoteBar objects 
 var historyQuoteBars = History<QuoteBar>(_symbol, 100, Resolution.Minute);
 
 // Tick objects 
@@ -168276,11 +170664,169 @@ 
    Historical Data
    
  
 
 
-
    Remove Subscriptions
    
+
    Universe Selection
    
 
  
   
    
-   To remove a CFD subscription, call the
+   To select a dynamic universe of Binance Crypto pairs, call the
+   
+    AddUniverse
+   
+   
+    add_universe
+   
+   method with a
+   
+    CryptoUniverse
+   
+   object. A
+   
+    Crypto universe
+   
+   uses a selection function to select Crypto pairs based on their OHLCV and dollar volume of the previous day as of midnight Coordinated Universal Time (UTC).
+  
    
+  
    
+   
    def initialize(self) -> None:
+    self.universe_settings.asynchronous = True
+    self.set_brokerage_model(BrokerageName.BINANCE, AccountType.MARGIN)
+    self._universe = self.add_universe(CryptoUniverse.binance(self.universe_selection_filter))
+
+def universe_selection_filter(self, universe_day):
+    return [c.symbol for c in universe_day if c.volume >= 100 and c.volume_in_usd > 10000]
    
+   
    using QuantConnect.Data.UniverseSelection;
+
+public override void Initialize()
+{
+    UniverseSettings.Asynchronous = true;
+    SetBrokerageModel(BrokerageName.Binance, AccountType.Margin);
+    _universe = AddUniverse(CryptoUniverse.Binance(UniverseSelectionFilter));
+}
+
+private IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
+{
+    return from c in universeDay
+           where c.Volume >= 100m && c.VolumeInUsd > 10000m
+           select c.Symbol;
+}
    
+  
    
+  
    
+   For more information about universe settings, see
+   
+    Settings
+   
+   .
+  
    
+ 
+
+
+
    Universe History
    
+
+ 
+  
    
+   You can get historical universe data in an algorithm and in the Research Environment.
+  
    
+  
    
+   Historical Universe Data in Algorithms
+  
    
+  
    
+   To get historical universe data in an algorithm, call the
+   
+    History
+   
+   
+    history
+   
+   method with the
+   
+    Universe
+   
+   object, and the lookback period. If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    var history = History(_universe, 30, Resolution.Daily);
+foreach (var universeDay in history)
+{
+    foreach (CryptoUniverse universeItem in universeDay)
+    {
+        Log($"{universeItem.Symbol} price at {universeItem.EndTime}: {universeItem.Close}");
+    }
+}
    
+   
    # DataFrame example where the columns are the CryptoUniverse attributes: 
+history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)
+
+# Series example where the values are lists of CryptoUniverse objects: 
+history = self.history(self._universe, 30, Resolution.DAILY)
+for (univere_symbol, time), universe_day in history.items():
+    for universe_item in universe_day:
+        self.log(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")
    
+  
    
+  
    
+   Historical Universe Data in Research
+  
    
+  
    
+   To get historical universe data in research, call the
+   
+    UniverseHistory
+   
+   
+    universe_history
+   
+   method with the
+   
+    Universe
+   
+   object, and the lookback period. The
+   
+    UniverseHistory
+   
+   
+    universe_history
+   
+   returns the filtered universe. If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var universeDay in universeHistory)
+{
+    foreach (CryptoUniverse universeItem in universeDay)
+    {
+        Console.WriteLine($"{universeItem.Symbol} price at {universeItem.EndTime}: {universeItem.Close}");
+    }
+}
    
+   
    # DataFrame example where the columns are the CryptoUniverse attributes: 
+history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
+
+# Series example where the values are lists of CryptoUniverse objects: 
+universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+for (univere_symbol, time), universe_day in universe_history.items():
+    for universe_item in universe_day:
+        print(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")
    
+  
    
+  
    
+   You can call the
+   
+    History
+   
+   
+    history
+   
+   method in Research.
+  
    
+ 
+
+
+
    Remove Subcriptions
    
+
+ 
+  
    
+   To unsubscribe from a Crypto pair that you added with the
+   
+    AddCrypto
+   
+   
+    add_crypto
+   
+   method, call the
    
     RemoveSecurity
    
@@ -168290,7 +170836,7 @@ 
    Remove Subscriptions
    
    method.
   
    
   
    
-   
    self.remove_security(self.de30eur)
    
+   
    self.remove_security(self.btcbusd)
    
    
    RemoveSecurity(_symbol);
    
   
    
   
    
@@ -168301,7 +170847,11 @@ 
    Remove Subscriptions
    
    
     remove_security
    
-   method cancels your open orders for the security and liquidates your holdings.
+   method cancels your open orders for the security and liquidates your holdings in the
+   
+    virtual pair
+   
+   .
   
    
  
 
@@ -168309,46332 +170859,14080 @@ 
    Remove Subscriptions
    
 
    Supported Assets
    
 
  
-  
    
-   The following table shows the available contracts:
-  
    
+  The following table shows the available Cryptocurrency pairs:
   
    
    
+      
+      
+      
-    
-   
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The CFD price data enables you to trade CFD assets in your algorithm. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Exploring the daily worldwide news cycles with CFDs that track international indices.
-   
      • 
-   
    • 
-    Trading price movements of commodities with no delivery of physical goods. For example, pairs trading between gold and silver, corn and wheat, brent and crude oil, etc.
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm implements a pairs trading strategy using Gold and Silver CFDs, XAUUSD and XAGUSD, respectively. When the spread is higher than one standard deviation above its mean, the algorithm buys the spread (buy XAUUSD and sell XAGUSD). When the spread is lower than one standard deviation below its mean, it sells the spread (buy XAGUSD and sell XAUUSD).
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class SMAPairsTrading(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-
-        # Request gold and sliver spot CFDs for trading their spread difference, assuming their spread series is cointegrated
-        self.add_cfd('XAUUSD', Resolution.HOUR)
-        self.add_cfd('XAGUSD', Resolution.HOUR)
-
-        # Use 500-step mean and SD indicator on determine the spread relative difference for trading signal generation
-        self.pair = [ ]
-        self.spread_mean = SimpleMovingAverage(500)
-        self.spread_std = StandardDeviation(500)
-        
-    def on_data(self, slice: Slice) -> None:
-        # Update the indicator with updated spread difference, such that the an updated cointegration threshold is calculated for trade inception
-        spread = self.pair[1].price - self.pair[0].price
-        self.spread_mean.update(self.time, spread)
-        self.spread_std.update(self.time, spread) 
-        
-        spread_mean = self.spread_mean.current.value
-        upperthreshold = spread_mean  + self.spread_std.current.value
-        lowerthreshold = spread_mean  - self.spread_std.current.value
-
-        # If the spread is higher than upper threshold, bet their spread series will revert to mean
-        if spread > upperthreshold:
-            self.set_holdings(self.pair[0].symbol, 1)
-            self.set_holdings(self.pair[1].symbol, -1)
-        elif spread < lowerthreshold:
-            self.set_holdings(self.pair[0].symbol, -1)
-            self.set_holdings(self.pair[1].symbol, 1)
-        # Close positions if mean reverted
-        elif (self.portfolio[self.pair[0].symbol].quantity > 0 and spread < spread_mean)\
-        or (self.portfolio[self.pair[0].symbol].quantity < 0 and spread > spread_mean):
-            self.liquidate()
-    
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
-        self.pair = [x for x in changes.added_securities]
-        
-        #1. Call for 500 bars of history data for each symbol in the pair and save to the variable history
-        history = self.history([x.symbol for x in self.pair], 500)
-        #2. Unstack the Pandas data frame to reduce it to the history close price
-        history = history.close.unstack(level=0)
-        #3. Iterate through the history tuple and update the mean and standard deviation with historical data 
-        for tuple in history.itertuples():
-            self.spread_mean.update(tuple[0], tuple[2]-tuple[1])
-            self.spread_std.update(tuple[0], tuple[2]-tuple[1])
-
    
-   
    public class GoldSilverPairsTradingAlgorithm : QCAlgorithm
-{
-    // Use 500-step mean and SD indicator on determine the spread relative difference for trading signal generation
-    private SimpleMovingAverage _spreadMean = new SimpleMovingAverage(500);
-    private StandardDeviation _spreadStd = new StandardDeviation(500);
-    private Security[] _pair = new Security[2];
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);  
-
-        // Request gold and sliver spot CFDs for trading their spread difference, assuming their spread series is cointegrated
-        AddCfd("XAUUSD", Resolution.Hour);
-        AddCfd("XAGUSD", Resolution.Hour);
-    }
-
-    public override void OnData(Slice slice) 
-    {
-        // Update the indicator with updated spread difference, such that the an updated cointegration threshold is calculated for trade inception
-        var spread = _pair[1].Price - _pair[0].Price;
-        _spreadMean.Update(Time, spread);
-        _spreadStd.Update(Time, spread);
-        
-        var upperthreshold = _spreadMean + _spreadStd;
-        var lowerthreshold = _spreadMean - _spreadStd;
-        
-        // If the spread is higher than upper threshold, bet their spread series will revert to mean
-        if (spread > upperthreshold)
-        {
-            SetHoldings(_pair[0].Symbol, 1);
-            SetHoldings(_pair[1].Symbol, -1);
-        }
-        else if (spread < lowerthreshold)
-        {
-            SetHoldings(_pair[0].Symbol, -1);
-            SetHoldings(_pair[1].Symbol, 1);
-        }
-        // Close positions if mean reverted
-        else if ((Portfolio[_pair[0].Symbol].Quantity > 0m && spread < _spreadMean)
-        || (Portfolio[_pair[0].Symbol].Quantity < 0m && spread > _spreadMean))
-        {
-            Liquidate();
-        }
-    }
-    
-    public override void OnSecuritiesChanged(SecurityChanges changes)
-    {    
-        _pair = changes.AddedSecurities.ToArray();
-        
-        //1. Call for 500 days of history data for each symbol in the pair and save to the variable history
-        var history = History(_pair.Select(x => x.Symbol), 500);
-        
-        //2. Iterate through the history tuple and update the mean and standard deviation with historical data 
-        foreach(var slice in history)
-        {
-            var spread = slice[_pair[1].Symbol].Close - slice[_pair[0].Symbol].Close;
-            _spreadMean.Update(slice.Time, spread);
-            _spreadStd.Update(slice.Time, spread);
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm implements a pairs trading strategy using Gold and Silver CFDs, XAUUSD and XAGUSD, respectively. When the spread is higher than one standard deviation above its mean, the algorithm buys the spread (buy XAUUSD and sell XAGUSD). When the spread is lower than one standard deviation below its mean, it sells the spread (buy XAGUSD and sell XAUUSD).
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class GoldSilverPairsTradingAlgorithm (QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        
-        self.universe_settings.resolution = Resolution.HOUR
-        # Custom universe contains only gold and sliver spot CFDs for trading their spread difference, assuming their spread series is cointegrated
-        self.set_universe_selection(ManualUniverseSelectionModel
-        (
-            [ Symbol.create(x, SecurityType.CFD, Market.OANDA) for x in ["XAUUSD", "XAGUSD"] ]
-        ))
-        # Custom alpha model to emit trade insights based on the gold-sliver price spread
-        self.add_alpha(PairsTradingAlphaModel())
-        # Equal weighting trades assuming the spread is cointegrated by 1:1 ratio
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
-
-class PairsTradingAlphaModel(AlphaModel):
-
-    def __init__(self) -> None:
-        # Use 500-step mean and SD indicator on determine the spread relative difference for trading signal generation
-        self.pair = [ ]
-        self.spread_mean = SimpleMovingAverage(500)
-        self.spread_std = StandardDeviation(500)
-        # Assume efficient mean reversal happens within 2 hours
-        self.period = timedelta(hours=2)
-        
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        # Update the indicator with updated spread difference, such that the an updated cointegration threshold is calculated for trade inception
-        spread = self.pair[1].price - self.pair[0].price
-        self.spread_mean.update(algorithm.time, spread)
-        self.spread_std.update(algorithm.time, spread) 
-        
-        upperthreshold = self.spread_mean.current.value + self.spread_std.current.value
-        lowerthreshold = self.spread_mean.current.value - self.spread_std.current.value
-
-        # If the spread is higher than upper threshold, bet their spread series will revert to mean
-        if spread > upperthreshold:
-            return Insight.group(
-                [
-                    Insight.price(self.pair[0].symbol, self.period, InsightDirection.UP),
-                    Insight.price(self.pair[1].symbol, self.period, InsightDirection.DOWN)
-                ])
-        elif spread < lowerthreshold:
-            return Insight.group(
-                [
-                    Insight.price(self.pair[0].symbol, self.period, InsightDirection.DOWN),
-                    Insight.price(self.pair[1].symbol, self.period, InsightDirection.UP)
-                ])
-
-        return []
-    
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        self.pair = [x for x in changes.added_securities]
-        
-        #1. Call for 500 bars of history data for each symbol in the pair and save to the variable history
-        history = algorithm.history([x.symbol for x in self.pair], 500)
-        #2. Unstack the Pandas data frame to reduce it to the history close price
-        history = history.close.unstack(level=0)
-        #3. Iterate through the history tuple and update the mean and standard deviation with historical data 
-        for tuple in history.itertuples():
-            self.spread_mean.update(tuple[0], tuple[2]-tuple[1])
-            self.spread_std.update(tuple[0], tuple[2]-tuple[1])
-
    
-   
    public class GoldSilverPairsTradingAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);  
-
-        UniverseSettings.Resolution = Resolution.Hour;
-        // Custom universe contains only gold and sliver spot CFDs for trading their spread difference, assuming their spread series is cointegrated
-        SetUniverseSelection(new ManualUniverseSelectionModel
-        (
-            new [] {"XAUUSD", "XAGUSD"}
-                .Select(x => QuantConnect.Symbol.Create(x, SecurityType.Cfd, Market.Oanda))
-        ));
-        // Custom alpha model to emit trade insights based on the gold-sliver price spread
-        AddAlpha(new PairsTradingAlphaModel());
-        // Equal weighting trades assuming the spread is cointegrated by 1:1 ratio
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel()); 
-    }
-}
-
-public partial class PairsTradingAlphaModel : AlphaModel
-{
-    // Use 500-step mean and SD indicator on determine the spread relative difference for trading signal generation
-    private SimpleMovingAverage _spreadMean = new SimpleMovingAverage(500);
-    private StandardDeviation _spreadStd = new StandardDeviation(500);
-    // Assume efficient mean reversal happens within 2 hours
-    private TimeSpan _period = TimeSpan.FromHours(2);
-    private Security[] _pair = new Security[2];
-
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice) 
-    {
-        // Update the indicator with updated spread difference, such that the an updated cointegration threshold is calculated for trade inception
-        var spread = _pair[1].Price - _pair[0].Price;
-        _spreadMean.Update(algorithm.Time, spread);
-        _spreadStd.Update(algorithm.Time, spread);
-        
-        var upperthreshold = _spreadMean + _spreadStd;
-        var lowerthreshold = _spreadMean - _spreadStd;
-
-        // If the spread is higher than upper threshold, bet their spread series will revert to mean
-        if (spread > upperthreshold)
-        {
-            return Insight.Group( 
-                Insight.Price(_pair[0].Symbol, _period, InsightDirection.Up),
-                Insight.Price(_pair[1].Symbol, _period, InsightDirection.Down)
-            );
-        }
-        else if (spread < lowerthreshold)
-        {
-            return Insight.Group( 
-                Insight.Price(_pair[0].Symbol, _period, InsightDirection.Down), 
-                Insight.Price(_pair[1].Symbol, _period, InsightDirection.Up) 
-            );
-        }
-
-        return Enumerable.Empty<Insight>();
-    }
-    
-    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-    {    
-        _pair = changes.AddedSecurities.ToArray();
-        
-        //1. Call for 500 days of history data for each symbol in the pair and save to the variable history
-        var history = algorithm.History(_pair.Select(x => x.Symbol), 500);
-        
-        //2. Iterate through the history tuple and update the mean and standard deviation with historical data 
-        foreach (var slice in history)
-        {
-            var spread = slice[_pair[1].Symbol].Close - slice[_pair[0].Symbol].Close;
-            _spreadMean.Update(slice.Time, spread);
-            _spreadStd.Update(slice.Time, spread);
-        }
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The CFD dataset provides
-   
-    QuoteBar
-   
-   and
-   
-    Tick
-   
-   objects.
-  
    
-  
    
-   QuoteBar Attributes
-  
    
-  
    
-   
-    QuoteBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   Tick Attributes
-  
    
-  
    
-   
-    Tick
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > Cash Indices
    
-
    
-    
    
-        
    QuantConnect
    
-        
    Cash Indices
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The Cash Indices dataset by QuantConnect covers 125 US Indices and 3 International indices. The data starts on various dates from January 1998 and is delivered on any frequency from minute to daily.
-  
    
-  
    
-   For more information about the Cash Indices dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
-  
    
- 
-
-
-
    Live Data
    
-
- 
-  
    
-   The Cash Indices dataset does not provide a live data feed. To receive Cash Indices' data in your live algorithm, you must add a brokerage or a third-party data provider (see
-   
-    Cloud Platform > Datasets
-   
-   ).
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the Cash Indices dataset:
-  
    
-  
    
-   
    self.vix = self.add_index("VIX", Resolution.DAILY).symbol
    
-   
    _symbol = AddIndex("VIX", Resolution.Daily).Symbol;
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 1998
-     
    
-      Coverage
-     
-      125 US Indices and 3 International indices (HSI, SX5E and N225)
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Minute, Hour, & Daily
-     
    
-      Timezone
-     
-      New York for US Indices, refer to
-      
-       Supported Assets
-      
-      below for International indices
-     
    
-      Market Hours
-     
-      
-       Regular Only
-      
-     
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add Cash Indices data to your algorithm, call the
-   
-    AddIndex
-   
-   
-    add_index
-   
-   method. Save a reference to the Index
-   
-    Symbol
-   
-   so you can access the data later in your algorithm.
-  
    
-  
    
-   
    class CashIndexAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2020, 6, 1)
-        self.set_end_date(2021, 6, 1)
-        self.set_cash(100000)
-
-        self.vix = self.add_index("VIX").symbol
-
    
-   
    public class CashIndexAlgorithm : QCAlgorithm
-{
-    private Symbol _symbol;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2020, 6, 1);
-        SetEndDate(2021, 6, 1);
-        SetCash(100000);
-        
-        _symbol = AddIndex("VIX").Symbol;
-    }
-}
    
-  
    
-  
    
-   For more information about creating Index subscriptions, see
-   
-    Requesting Data
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To get the current Cash Indices data, index the
-   
-    Bars
-   
-   
-    bars
-   
-   property of the current
-   
-    
-     Slice
-    
-   
-   with the Index
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your security at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    if self.vix in slice.bars:
-        trade_bar = slice.bars[self.vix]
-        self.log(f"{self.vix} close at {slice.time}: {trade_bar.close}")
    
-   
    public override void OnData(Slice slice)
-{
-    if (slice.Bars.ContainsKey(_symbol))
-    {
-        var tradeBar = slice.Bars[_symbol];
-        Log($"{_symbol} price at {slice.Time}: {tradeBar.Close}");
-    }
-}
-
    
-  
    
-  
    
-   You can also iterate through all of the data objects in the current
-   
-    Slice
-   
-   .
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    for symbol, trade_bar in slice.bars.items():
-        self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")
    
-   
    public override void OnData(Slice slice)
-{
-    foreach (var kvp in slice.Bars)
-    {
-        var symbol = kvp.Key;
-        var tradeBar = kvp.Value;
-        Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
-    }
-}
-
    
-  
    
-  
    
-   For more information about accessing Index data, see
-   
-    Handling Data
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   To get historical Cash Indices data, call the
-   
-    History
-   
-   
-    history
-   
-   method with the Index
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame
-history_df = self.history(self.vix, 100, Resolution.DAILY)
-
-# TradeBar objects
-history_bars = self.history[TradeBar](self.vix, 100, Resolution.DAILY)
    
-   
    // TradeBar objects
-var historyBars = History(_symbol, 100, Resolution.Daily);
    
-  
    
-  
    
-   For more information about historical data, see
-   
-    History Requests
-   
-   .
-  
    
- 
-
-
-
    Remove Subscriptions
    
-
- 
-  
    
-   To remove an Index subscription, call the
-   
-    RemoveSecurity
-   
-   
-    remove_security
-   
-   method.
-  
    
-  
    
-   
    self.remove_security(self.vix)
    
-   
    RemoveSecurity(_symbol);
    
-  
    
- 
-
-
-
    Supported Indices
    
-
- 
-  
    
-   US Cash Indices
-  
    
-  
    
-   The following table shows the US Cash indices with Index Option support:
-  
    
-  
-   
-    
-     
-     
-     
-     
-    
-   
-   
-    
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-    
-   
-  
    
-      Ticker
-     
-      Index
-     
-      Expiry
-     
-      Start Date
-     
    
-      VIX
-     
-      S&P500
-     
-      30 Days
-     
-      Jul 2003
-     
    
-      SPX
-     
-      S&P500
-     
-      -
-     
-      Jan 1998
-     
    
-      NDX
-     
-      NASDAQ-100
-     
-      -
-     
-      Jan 1998
-     
    
-      RUT
-     
-      Russell 2000
-     
-      -
-     
-      Jan 2008
-     
    
-  
    
-   VIX - CBOE Volatility Index
-  
    
-  
    
-   The Cboe Volatility Index (VIX) is a real-time index that represents the market's expectations for the relative strength of near-term price changes of the S&P 500 Index (SPX). Because it's derived from the prices of SPX index Options with near-term expiration dates, it generates a 30-day forward projection of volatility. Volatility, or how fast prices change, is often seen as a way to gauge market sentiment, and in particular, the degree of fear among market participants.
-  
    
-  
    
-   SPX - S&P 500 Index
-  
    
-  
    
-   The S&P 500 Index, or the Standard & Poor's 500 Index, is a market-capitalization-weighted index of the 500 largest publicly-traded companies in the U.S. It is not an exact list of the top 500 U.S. companies by market capitalization because there are other criteria included in the index. The index is widely regarded as the best gauge of large-cap U.S. Equities.
-  
    
-  
    
-   NDX - Nasdaq 100 Index
-  
    
-  
    
-   The Nasdaq-100 Index is a modified market-capitalization-weighted index composed of securities issued by 100 of the largest non-financial companies listed on the Nasdaq Stock Market (Nasdaq). The index includes companies from various industries except for the financial industry, like commercial and investment banks. These non-financial sectors include retail, biotechnology, industrial, technology, health care, and others.
-  
    
-  
    
-   RUT - Russell 2000 Index
-  
    
-  
    
-   The Russell 2000 Index is a small-cap U.S. stock market index that makes up the smallest 2,000 stocks in the Russell Index. It was started by the Frank Russell Company in 1984. The index is maintained by FTSE Russell, a subsidiary of the London Stock Exchange Group (LSEG).
-  
    
-  
    
-   All US Cash Indices
-  
    
-  The following table shows all US Cash indices:
-  
    
-   
-    
-      
+      
+      
+      
+      
+      
+      
-    
-    
+      
-    
-   
-  
    
-  
    
-   International Cash Indices
-  
    
-  
    
-   The following table shows the International Cash indices:
-  
    
-  
-   
-    
-     
-     
-     
-     
-     
-    
-   
-   
-    
-    
-    
-    
-    
-    
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-    
-   
-  
    
-      Ticker
-     
-      Index
-     
-      Start Date
-     
-      Time Zone
-     
-      Currency
-     
    
-     HSI
-    
-     Hang Seng Index
-    
-     Dec 2006
-    
-     Asia/Hong Kong
-    
-     HKD
-    
    
-      SX5E
-     
-      EUREX EU STOXX
-     
-      Jul 2003
-     
-      Europe/Berlin
-     
-      EUR
-     
    
-      N225
-     
-      Nikkei 225
-     
-      Jul 2003
-     
-      Asia/Tokyo
-     
-      JPY
-     
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The Cash Indices enable you to incorporate popular indices into your trading algorithms. Examples include the following use cases:
-  
    
-  
      
-   
    • 
-    Exploring the difference between the Index and the ETF that tracks it
-   
      • 
-   
    • 
-    Using these indices as the underlying asset fo Index Options strategies
-   
      • 
-   
    • 
-    Understanding the stock market's level of expected forward-looking volatility, also known as the "fear index". When the VIX starts moving higher, it tells you that traders are getting nervous. When the VIX starts moving lower, it tells you that traders are gaining confidence.
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm tracks the 80-day EMA and 200-day EMA of SPX. When the short EMA crosses above the long EMA, the algorithm buys SPY. Otherwise, it holds cash.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class IndexDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-
-        # Request SPY data as a trading vehicle for SPX
-        self.spy = self.add_equity("SPY").symbol
-
-        # Request SPX data for trade signal generation
-        spx = self.add_index("SPX").symbol
-
-        # Create short and long-term EMA indicators for trend estimation to trade
-        self.ema_fast = self.EMA(spx, 80, Resolution.DAILY)
-        self.ema_slow = self.EMA(spx, 200, Resolution.DAILY)
-        self.set_warm_up(200, Resolution.DAILY)
-
-        # Historical data
-        history = self.history(spx, 60, Resolution.DAILY)
-        self.debug(f'We got {len(history.index)} items from our history request')
-
-    def on_data(self, slice: Slice) -> None:
-        # Trade signals required indicators to be ready
-        if self.is_warming_up or not self.ema_slow.is_ready:
-            return
-
-        # If short-term EMA is above long-term, it indicates an up trend, so we buy SPY
-        if not self.portfolio.invested and self.ema_fast > self.ema_slow:
-            self.set_holdings(self.spy, 1)
-        # If it is the reverse, it indicates a downtrend, and we liquidate any position
-        elif self.ema_fast < self.ema_slow:
-            self.liquidate()
-
    
-   
    public class IndexDataAlgorithm : QCAlgorithm
-{
-    private Symbol _spy;
-    private ExponentialMovingAverage _emaSlow, _emaFast;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(1000000);
-
-        // Request SPY data as trading vehicle for SPX
-        _spy = AddEquity("SPY").Symbol;
-
-        // Request SPX data for trade signal generation
-        var spx = AddIndex("SPX").Symbol;
-
-        // Create short and long-term EMA indicators for trend estimation to trade
-        _emaFast = EMA(spx, 80, Resolution.Daily);
-        _emaSlow = EMA(spx, 200, Resolution.Daily);
-        SetWarmUp(200, Resolution.Daily);
-
-        // Historical data
-        var history = History(spx, 60, Resolution.Daily);
-        Debug($"We got {history.Count()} items from our history request");
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // Trade signals required indicators to be ready
-        if (IsWarmingUp || !_emaSlow.IsReady)
-        {
-            return;
-        }
-
-        // If short-term EMA is above long-term, it indicates an up trend, so we buy SPY
-        if (!Portfolio.Invested && _emaFast > _emaSlow)
-        {
-            SetHoldings(_spy, 1);
-        }
-        // If it is the reverse, it indicates a downtrend, and we liquidate any position
-        else if (_emaFast < _emaSlow)
-        {
-            Liquidate();
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm tracks the 80-day EMA and 200-day EMA of SPX. When the short EMA crosses above the long EMA, the algorithm buys SPY. Otherwise, it holds cash.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class IndexDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-
-        # Universe contains only SPY as a trading vehicle for SPX
-        self.set_universe_selection(ManualUniverseSelectionModel
-        (
-            Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
-        ))
-        # Custom alpha model that emits insights based on SPX index data
-        self.set_alpha(SpxEmaCrossAlphaModel(self))
-        # Equally investing can dissipate non-systematic risky event's capital concentration risk evenly
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel(Expiry.END_OF_MONTH))
-    
-
-class SpxEmaCrossAlphaModel(AlphaModel):
-
-    def __init__(self, algorithm: QCAlgorithm) -> None:
-        # Request SPX data for trade signal generation
-        spx = algorithm.add_index("SPX").symbol
-
-        # Create short and long-term EMA indicators for trend estimation to trade
-        self.ema_fast = algorithm.EMA(spx, 80, Resolution.DAILY)
-        self.ema_slow = algorithm.EMA(spx, 200, Resolution.DAILY)
-        algorithm.set_warm_up(200, Resolution.DAILY)
-
-        # Historical data
-        history = algorithm.history(spx, 60, Resolution.DAILY)
-        algorithm.debug(f'We got {len(history.index)} items from our history request')
-    
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        # Trade signals required indicators to be ready
-        if algorithm.is_warming_up or not self.ema_slow.is_ready:
-            return []
-
-        # If short-term EMA is above long-term, it indicates an up trend, so we buy SPY
-        if not algorithm.portfolio.invested and self.ema_fast > self.ema_slow:
-            return [Insight.price(kvp.key, Expiry.END_OF_MONTH, InsightDirection.UP)
-                for kvp in algorithm.active_securities if kvp.value.is_tradable]
-        # If it is the reverse, it indicates downtrend, and we liquidate any position
-        elif self.ema_fast < self.ema_slow:
-            return [Insight.price(kvp.key, Expiry.END_OF_MONTH, InsightDirection.FLAT)
-                for kvp in algorithm.active_securities if kvp.value.is_tradable]
-            
-        return []
-
    
-   
    public class IndexDataAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(1000000);
-
-        // Universe contains only SPY as a trading vehicle for SPX
-        SetUniverseSelection(new ManualUniverseSelectionModel
-        (
-            QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)
-        ));
-        // Custom alpha model that emits insights based on SPX index data
-        SetAlpha(new SpxEmaCrossAlphaModel(this));
-        // Equally invests can dissipate non-systematic risky event's capital concentration risk evenly
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(Expiry.EndOfMonth));
-    }
-}
-
-public class SpxEmaCrossAlphaModel : AlphaModel
-{
-    private ExponentialMovingAverage _emaSlow, _emaFast;
-
-    public SpxEmaCrossAlphaModel(QCAlgorithm algorithm)
-    {
-        // Request SPX data for trade signal generation
-        var spx = algorithm.AddIndex("SPX").Symbol;
-
-        // Create short and long-term EMA indicators for trend estimation to trade
-        _emaFast = algorithm.EMA(spx, 80, Resolution.Daily);
-        _emaSlow = algorithm.EMA(spx, 200, Resolution.Daily);
-        algorithm.SetWarmUp(200, Resolution.Daily);
-
-        // Historical data
-        var history = algorithm.History(spx, 60, Resolution.Daily);
-        algorithm.Debug($"We got {history.Count()} items from our history request");
-    }
-
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        // Trade signals required indicators to be ready
-        if (algorithm.IsWarmingUp || !_emaSlow.IsReady)
-        {
-            return Enumerable.Empty<Insight>();
-        }
-
-        // If short-term EMA is above long-term, it indicates an up trend, so we buy SPY
-        if (!algorithm.Portfolio.Invested && _emaFast > _emaSlow)
-        {
-            return algorithm.ActiveSecurities
-                .Where(kvp => kvp.Value.IsTradable)
-                .Select(kvp => Insight.Price(kvp.Key, Expiry.EndOfMonth, InsightDirection.Up));
-        }
-        // If it is the reverse, it indicates downtrend, and we liquidate any position
-        else if (_emaFast < _emaSlow)
-        {
-            return algorithm.ActiveSecurities
-                .Where(kvp => kvp.Value.IsTradable)
-                .Select(kvp => Insight.Price(kvp.Key, Expiry.EndOfMonth, InsightDirection.Flat));
-        }
-
-        return Enumerable.Empty<Insight>();
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The Cash Indices dataset provides
-   
-    TradeBar
-   
-   object.
-  
    
-  
    
-   TradeBar Attributes
-  
    
-  
    
-   
-    TradeBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > FOREX Data
    
-
    
-    
    
-        
    QuantConnect
    
-        
    FOREX Data
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The FOREX Data by QuantConnect serves 71
-   
-    foreign exchange
-   
-   (FOREX) pairs, starts on various dates from January 2007, and is delivered on any frequency from tick to daily. This dataset is created by QuantConnect processing raw tick data from OANDA.
-  
    
-  
    
-   FOREX data does not include ask and bid sizes.
-  
    
-  
    
-   For more information about the FOREX Data dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the FOREX dataset:
-  
    
-  
    
-   
    self.eurusd = self.add_forex("EURUSD", Resolution.DAILY).symbol
    
-   
    _symbol = AddForex("EURUSD", Resolution.Daily).Symbol;
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 2007
-     
    
-      Asset Coverage
-     
-      71 Currency pairs
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Tick, Second, Minute, Hour, & Daily
-     
    
-      Timezone
-     
-      UTC
-     
    
-      Market Hours
-     
-      
-       Always Open
-      
-      , except from Friday 5 PM EST to Sunday 5 PM EST
-     
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add FOREX data to your algorithm, call the
-   
-    AddForex
-   
-   
-    add_forex
-   
-   method. Save a reference to the Forex
-   
-    Symbol
-   
-   so you can access the data later in your algorithm.
-  
    
-  
    
-   
    -class ForexAlgorithm (QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2019, 2, 20)
-        self.set_end_date(2019, 2, 21)
-        self.set_cash(100000)
-
-        self.eurusd = self.add_forex('EURUSD', Resolution.MINUTE).symbol
-
-        self.set_benchmark(self.eurusd)
-
    
-   
    public class ForexAlgorithm : QCAlgorithm
-{
-    private Symbol _symbol;
-
-    public override void Initialize()
-    {
-        SetStartDate(2019, 2, 20);
-        SetEndDate(2019, 2, 21);
-        SetCash(100000);
-
-        _symbol = AddForex("EURUSD", Resolution.Minute).Symbol;
-
-        SetBenchmark(_symbol);
-    }
-}
    
-  
    
-  
    
-   For more information about creating Forex subscriptions, see
-   
-    Requesting Data
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To get the current Forex data, index the
-   
-    QuoteBars
-   
-   
-    quote_bars
-   
-   , or
-   
-    Ticks
-   
-   
-    ticks
-   
-   properties of the current
-   
-    
-     Slice
-    
-   
-   with the Forex
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your security at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    if self.eurusd in slice.quote_bars:
-        quote_bar = slice.quote_bars[self.eurusd]
-        self.log(f"{self.eurusd} bid at {slice.time}: {quote_bar.bid.close}")
-
-    if self.eurusd in slice.ticks:
-        ticks = slice.ticks[self.eurusd]
-        for tick in ticks:
-            self.log(f"{self.eurusd} price at {slice.time}: {tick.price}")
    
-   
    public override void OnData(Slice slice)
-{
-    if (slice.QuoteBars.ContainsKey(_symbol))
-    {
-        var quoteBar = slice.QuoteBars[_symbol];
-        Log($"{_symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
-    }
-
-    if (slice.Ticks.ContainsKey(_symbol))
-    {
-        var ticks = slice.Ticks[_symbol];
-        foreach (var tick in ticks)
-        {
-            Log($"{_symbol} price at {slice.Time}: {tick.Price}");
-        }
-    }
-}
-
    
-  
    
-  
    
-   You can also iterate through all of the data objects in the current
-   
-    Slice
-   
-   .
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    for symbol, quote_bar in slice.quote_bars.items():
-        self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
-
-    for symbol, ticks in slice.ticks.items():
-        for tick in ticks:
-            self.log(f"{symbol} price at {slice.time}: {tick.price}")
    
-   
    public override void OnData(Slice slice)
-{
-    foreach (var kvp in slice.QuoteBars)
-    {
-        var symbol = kvp.Key;
-        var quoteBar = kvp.Value;
-        Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
-    }
-
-    foreach (var kvp in slice.Ticks)
-    {
-        var symbol = kvp.Key;
-        var ticks = kvp.Value;
-        foreach (var tick in ticks)
-        {
-            Log($"{symbol} price at {slice.Time}: {tick.Price}");
-        }
-    }
-}
-
    
-  
    
-  
    
-   For more information about accessing Forex data, see
-   
-    Handling Data
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   To get historical Forex data, call the
-   
-    History
-   
-   
-    history
-   
-   method with the Forex
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame
-history_df = self.history(self.eurusd, 100, Resolution.MINUTE)
-
-# QuoteBar objects
-history_quote_bars = self.history[QuoteBar](self.eurusd, 100, Resolution.MINUTE)
-
-# Tick objects
-history_ticks = self.history[Tick](self.eurusd, timedelta(seconds=10), Resolution.TICK)
    
-   
    // QuoteBar objects 
-var historyQuoteBars = History<QuoteBar>(_symbol, 100, Resolution.Minute);
-
-// Tick objects 
-var historyTicks = History<Tick>(_symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
-  
    
-  
    
-   For more information about historical data, see
-   
-    History Requests
-   
-   .
-  
    
- 
-
-
-
    Remove Subscriptions
    
-
- 
-  
    
-   To remove a Forex pair subscription, call the
-   
-    RemoveSecurity
-   
-   
-    remove_security
-   
-   method.
-  
    
-  
    
-   
    self.remove_security(self.eurusd)
    
-   
    RemoveSecurity(_symbol);
    
-  
    
-  
    
-   The
-   
-    RemoveSecurity
-   
-   
-    remove_security
-   
-   method cancels your open orders for the security and liquidates your holdings.
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   The following table shows the available Forex pairs:
-  
    
-  
    
-   
-    
-      
+      
+      
+      
+      
+      
+      
-    
-    
-     
-    
-   
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The FOREX price data enables you to trade currency pairs in the global market. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Exploring the impact that daily worldwide news cycles have on international currencies
-   
      • 
-   
    • 
-    Carry Trade: borrow from a lower interest currency pair to fund the purchase of a currency pair with a higher interest rate
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm implements a FOREX carry trade. It buys the FOREX pair of the country with the lowest interest rate and sells the FOREX pair of the country with the highest interest rate.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class ForexCarryTradeAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(25000)
-        
-        # We will use hard-coded interest rates for each base currency
-        self.rates = {
-            "USDAUD": 1.5,    # Australia
-            "USDCAD": 0.5,    # Canada
-            "USDCNY": 4.35,   # China
-            "USDEUR": 0.0,    # Euro Area
-            "USDINR": 6.5,    # India
-            "USDJPY": -0.1,   # Japan
-            "USDMXN": 4.25,   # Mexico
-            "USDTRY": 7.5,    # Turkey
-            "USDZAR": 7.0     # South Africa
-        }
-
-        # Subscribe to forex data for trading
-        for ticker in self.rates:
-            self.add_forex(ticker, Resolution.DAILY)
-
-        # Use a month counter as variable to control rebalancing
-        self.month = -1
-
-    def on_data(self, slice: Slice) -> None:
-        # Monthly rebalance checker
-        if self.month == self.time.month:
-            return
-        self.month = self.time.month
-        
-        # Long the pair with highest interest rate and sell the pair with the lowest to earn the max monetary inflation difference between the two
-        sorted_rates = sorted(self.rates.items(), key = lambda x: x[1])
-        self.set_holdings(sorted_rates[0][0], -0.5)
-        self.set_holdings(sorted_rates[-1][0], 0.5)
-
    
-   
    public class ForexCarryTradeAlgorithm : QCAlgorithm
-{
-    // Use a month counter as variable to control rebalancing
-    private int _month = -1;
-    private Dictionary<string, decimal> _rates;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(25000);
-        
-        // We will use hard-coded interest rates for each base currency
-        _rates = new Dictionary<string, decimal>()
-        {
-            {"USDAUD", 1.5m},    // Australia
-            {"USDCAD", 0.5m},    // Canada
-            {"USDCNY", 4.35m},   // China
-            {"USDEUR", 0.0m},    // Euro Area
-            {"USDINR", 6.5m},    // India
-            {"USDJPY", -0.1m},   // Japan
-            {"USDMXN", 4.25m},   // Mexico
-            {"USDTRY", 7.5m},    // Turkey
-            {"USDZAR", 7.0m}     // South Africa
-        };
-
-        // Subscribe to forex data for trading
-        foreach (var ticker in _rates.Keys)
-        {
-            AddForex(ticker, Resolution.Daily);
-        }
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // Monthly rebalance checker
-        if (_month == Time.Month) return;
-        _month = Time.Month;
-
-        // Long the pair with highest interest rate and sell the pair with the lowest to earn the max monetary inflation difference between the two
-        var sortedRates = (from kvp in _rates orderby kvp.Value ascending select kvp.Key).ToArray();
-        SetHoldings(sortedRates[0], -0.5);
-        SetHoldings(sortedRates[sortedRates.Length-1], 0.5);
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm implements a FOREX carry trade. It buys the FOREX pair of the country with the lowest interest rate and sells the FOREX pair of the country with the highest interest rate.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class ForexCarryTradeAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(25000)
-        
-        def to_symbol(ticker: str) -> Symbol:
-            return Symbol.create(ticker, SecurityType.FOREX, Market.OANDA)
-        
-        # We will use hard-coded interest rates for each base currency
-        rates = {
-            to_symbol("USDAUD"): 1.5,    # Australia
-            to_symbol("USDCAD"): 0.5,    # Canada
-            to_symbol("USDCNY"): 4.35,   # China
-            to_symbol("USDEUR"): 0.0,    # Euro Area
-            to_symbol("USDINR"): 6.5,    # India
-            to_symbol("USDJPY"): -0.1,   # Japan
-            to_symbol("USDMXN"): 4.25,   # Mexico
-            to_symbol("USDTRY"): 7.5,    # Turkey
-            to_symbol("USDZAR"): 7.0     # South Africa
-        }
-        
-        self.set_universe_selection(ManualUniverseSelectionModel(list(rates.keys())))
-        # A custom alpha model to emit insight according to interest rate
-        self.set_alpha(IntestRatesAlphaModel(rates))
-        # Equal size to capitalize the monetary value of quote currency only based on interest rate difference
-        # For dollar-neutral to save margin cost
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel(Expiry.END_OF_MONTH))
-
-class IntestRatesAlphaModel(AlphaModel):
-    def __init__(self, rates: float) -> None:
-        self.rates = rates
-        # Variable to control the rebalancing time
-        self.month = -1
-                
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        # Monthly rebalance
-        if self.month == algorithm.time.month:
-            return []
-        self.month = algorithm.time.month
-
-        # # Long the pair with highest interest rate and sell the pair with the lowest to earn the max monetary inflation difference between the two
-        sorted_rates = sorted(self.rates.items(), key = lambda x: x[1])
-        return Insight.group(
-            Insight.price(sorted_rates[0][0], Expiry.END_OF_MONTH, InsightDirection.UP),
-            Insight.price(sorted_rates[-1][0], Expiry.END_OF_MONTH, InsightDirection.DOWN)
-        )
-
    
-   
    public class ForexCarryTradeAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(25000);
-        
-        Symbol toSymbol(string ticker)
-        {
-            return QuantConnect.Symbol.Create(ticker, SecurityType.Forex, Market.Oanda);
-        }
-        
-        // We will use hard-coded interest rates for each base currency
-        var rates = new Dictionary<Symbol, decimal>()
-        {
-            {toSymbol("USDAUD"), 1.5m},    // Australia
-            {toSymbol("USDCAD"), 0.5m},    // Canada
-            {toSymbol("USDCNY"), 4.35m},   // China
-            {toSymbol("USDEUR"), 0.0m},    // Euro Area
-            {toSymbol("USDINR"), 6.5m},    // India
-            {toSymbol("USDJPY"), -0.1m},   // Japan
-            {toSymbol("USDMXN"), 4.25m},   // Mexico
-            {toSymbol("USDTRY"), 7.5m},    // Turkey
-            {toSymbol("USDZAR"), 7.0m}     // South Africa
-        };
-
-        SetUniverseSelection(new ManualUniverseSelectionModel(rates.Keys));
-        // A custom alpha model to emit insight according to interest rate
-        SetAlpha(new IntestRatesAlphaModel(rates));
-        // Equal size to capitalize the monetary value of quote currency only based on interest rate difference
-        // For dollar-neutral to save margin cost
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(Expiry.EndOfMonth));
-    }
-}
-
-public class IntestRatesAlphaModel : AlphaModel
-{
-    // Variable to control the rebalancing time
-    private int _month = -1;
-    private Dictionary<Symbol, decimal> _rates;
-
-    public IntestRatesAlphaModel(Dictionary<Symbol, decimal> rates)
-    {
-        _rates = rates;
-    }
-
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        // Monthly rebalance
-        if (_month == algorithm.Time.Month)
-        {
-            return Enumerable.Empty<Insight>();
-        }
-        _month = algorithm.Time.Month;
-
-        // Long the pair with highest interest rate and sell the pair with the lowest to earn the max monetary inflation difference between the two
-        var sortedRates = (from kvp in _rates orderby kvp.Value ascending select kvp.Key).ToArray();
-        
-        return Insight.Group(
-            Insight.Price(sortedRates[0], Expiry.EndOfMonth, InsightDirection.Up),
-            Insight.Price(sortedRates[sortedRates.Length-1], Expiry.EndOfMonth, InsightDirection.Down)
-        );
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The FOREX dataset provides
-   
-    QuoteBar
-   
-   and
-   
-    Tick
-   
-   objects.
-  
    
-  
    
-   QuoteBar Attributes
-  
    
-  
    
-   
-    QuoteBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   Tick Attributes
-  
    
-  
    
-   
-    Tick
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > Fear and Greed
    
-
    
-    
    
-        
    QuantConnect
    
-        
    Fear and Greed
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The Fear and Greed dataset by QuantConnect provides data points to quantify the degree of fear and greed in the US Equity market. The data starts in 2014 and is delivered on a daily frequency, delayed by two days. This dataset is created by aggregating seven indicators, including market volatility, stock market breadth, and put-call ratios.
-  
    
-  
    
-   For more information about the Fear and Greed dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the Fear and Greed dataset:
-  
    
-  
    
-   
    self._dataset_symbol = self.add_data(FearGreedIndex, 'FG').symbol
    
-   
    _datasetSymbol = AddData<FearGreedIndex>("FG").Symbol;
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      July 2014
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Daily
-     
    
-      Timezone
-     
-      New York
-     
    
-  
    
-   The latest daily data points are about two days since the VIX and SPX prices are inputs into the index and their latest daily prices are also two days old. New data is available at 9 AM EST.
-  
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add Fear and Greed data to your algorithm, call the
-   
-    AddData<FearGreedIndex>
-   
-   
-    add_data
-   
-   method. Save a reference to the dataset
-   
-    Symbol
-   
-   so you can access the data later in your algorithm.
-  
    
-  
    
-   
    class FearAndGreedDataAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2019, 1, 1)
-        self.set_end_date(2020, 6, 1)
-        self.set_cash(100000)
-
-        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
-        self._dataset_symbol = self.add_data(FearGreedIndex, "FG").symbol
    
-   
    public class FearAndGreedDataAlgorithm : QCAlgorithm
-{
-    private Symbol _symbol, _datasetSymbol;
-
-    public override void Initialize()
-    {
-        SetStartDate(2019, 1, 1);
-        SetEndDate(2020, 6, 1);
-        SetCash(100000);
-        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
-        _datasetSymbol = AddData<FearGreedIndex>("FG").Symbol;
-    }
-}
    
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To get the current Fear and Greed data, index the current
-   
-    
-     Slice
-    
-   
-   with the dataset
-   
-    Symbol
-   
-   .
-   
-    Slice
-   
-   objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your dataset at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    if slice.contains_key(self._dataset_symbol):
-        data_point = slice[self._dataset_symbol]
-        self.log(f"{self._dataset_symbol} value at {slice.time}: {data_point.value}")
    
-   
    public override void OnData(Slice slice)
-{
-    if (slice.ContainsKey(_datasetSymbol))
-    {
-        var dataPoint = slice[_datasetSymbol];
-        Log($"{_datasetSymbol} value at {slice.Time}: {dataPoint.Value}");
-    }
-}
-
    
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   To get historical Fear and Greed data, call the
-   
-    History
-   
-   
-    history
-   
-   method with the dataset
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame
-history_df = self.history(self._dataset_symbol, 100, Resolution.DAILY)
-
-# Dataset objects
-history = self.history[FearGreedIndex](self._dataset_symbol, 100, Resolution.DAILY)
    
-   
    var history = History<FearGreedIndex>(_datasetSymbol, 100, Resolution.Daily);
    
-  
    
-  
    
-   For more information about historical data, see
-   
-    History Requests
-   
-   .
-  
    
- 
-
-
-
    Remove Subscription
    
-
- 
-  
    
-   To remove a subscription to the Fear and Greed dataset, call the
-   
-    RemoveSecurity
-   
-   
-    remove_security
-   
-   method.
-  
    
-  
    
-   
    self.remove_security(self._dataset_symbol)
    
-   
    RemoveSecurity(_datasetSymbol);
    
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The Fear and Greed dataset quantifies the degree of fear and greed in the US Equity market. Example use cases include the following strategies:
-  
    
-  
      
-   
    • 
-    Accumulate long-term positions when the market is fearful (the index is low).
-   
      • 
-   
    • 
-    Short risky assets when the market is greedy.
-   
      • 
-   
    • 
-    Trade
-    
-     butterfly Option strategies
-    
-    based on the market volatility indicator.
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm buys SPY when the Fear and Greed index is less than 25 (extreme fear) and sells when it's above 75 (extreme greed).
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class FearAndGreedExampleAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-
-        # Add the market index to trade.
-        self._equity = self.add_equity('SPY', Resolution.DAILY)
-        # Add the Fear and Greed dataset.
-        self._dataset_symbol = self.add_data(FearGreedIndex, 'FG').symbol
-    
-    def on_data(self, data: Slice) -> None:
-        # Wait until the Fear and Greed dataset has new data.
-        if self._dataset_symbol not in data:
-            return
-        value = data[self._dataset_symbol].value
-        # Buy when the market is fearful.
-        if not self._equity.invested and value < 25:
-            self.set_holdings(self._equity.symbol, 1)
-        # Sell when the market is greedy.
-        elif self._equity.invested and value > 75:
-            self.liquidate()
    
-   
    public class FearAndGreedExampleAlgorithm : QCAlgorithm
-{
-    private Equity _equity;
-    private Symbol _datasetSymbol;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-
-        // Add the market index to trade.
-        _equity = AddEquity("SPY", Resolution.Daily);
-        // Add the Fear and Greed dataset.
-        _datasetSymbol = AddData<FearGreedIndex>("FG").Symbol;
-    }
-
-    public override void OnData(Slice data)
-    {
-        // Wait until the Fear and Greed dataset has new data
-        if (!data.ContainsKey(_datasetSymbol))
-        {
-            return;
-        }
-        var value = data[_datasetSymbol].Value;
-        // Buy when the market is fearful.
-        if (!_equity.Invested && value < 25)
-        {
-            SetHoldings(_equity.Symbol, 1);
-        }
-        // Sell when the market is greedy.
-        else if (_equity.Invested && value > 75)
-        {
-            Liquidate();
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Example
-  
    
-  
    
-   The following example algorithm buys SPY when the Fear and Greed index is less than 25 (extreme fear) and sells when it's above 75 (extreme greed).
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class FearAndGreedExampleAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-
-        # Trade SPY on a daily basis.
-        self.universe_settings.resolution = Resolution.DAILY
-        symbols = [Symbol.create('SPY', SecurityType.EQUITY, Market.USA)]
-        self.add_universe_selection(ManualUniverseSelectionModel(symbols))
-        # Add a custom Alpha model that emits insights based on the Fear and Greed dataset.
-        self.add_alpha(FearAndGreedAlphaModel(self))
-        # Set the equal weighted PCM so the algorithm places trades.
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
-
-
-# Define the custom alpha model.
-class FearAndGreedAlphaModel(AlphaModel):
-
-    def __init__(self, algorithm):
-        # Create a list to track the current universe.
-        self._securities = []
-        # Add the Fear and Greed dataset. 
-        self._dataset_symbol = algorithm.add_data(FearGreedIndex, 'FG').symbol
-
-    def update(self, algorithm: QCAlgorithm, data: Slice) -> List[Insight]:
-        # Wait until the Fear and Greed dataset has new data.
-        if self._dataset_symbol not in data:
-            return []
-        # Wait until the Fear and Greed index is extreme.
-        value = data[self._dataset_symbol].value
-        if 25 <= value <= 75:
-            return []
-        insights = []
-        for security in self._securities:
-            # Buy when the market is fearful.
-            if not security.invested and value < 25:
-                direction = InsightDirection.UP
-            # Sell when the market is greedy.
-            elif security.invested and value > 75:
-                direction = InsightDirection.FLAT
-            else:
-                continue
-            insights.append(Insight.price(security.symbol, timedelta(2*365), direction))
-        return insights
-
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        # When assets enter the universe, save them.
-        for security in changes.added_securities:
-            self._securities.append(security)
-        # When assets leave the universe, remove them.
-        for security in changes.removed_securities:
-            if security in self._securities:
-                self._securities.remove(security)
    
-   
    public class FearAndGreedExampleAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-
-        // Trade SPY on a daily basis.
-        UniverseSettings.Resolution = Resolution.Daily;
-        var symbols = new[] { QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA) };
-        AddUniverseSelection(new ManualUniverseSelectionModel(symbols));
-        // Add a custom Alpha model that emits insights based on the Fear and Greed dataset.
-        AddAlpha(new FearAndGreedAlphaModel(this));
-        // Set the equal weighted PCM so the algorithm places trades.
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
-    }
-}
-
-// Define the custom Alpha model.
-public class FearAndGreedAlphaModel : AlphaModel
-{
-    // Create a list to track the current universe.
-    private List<Security> _securities = new List<Security>();
-    private Symbol _datasetSymbol;
-
-    public FearAndGreedAlphaModel(QCAlgorithm algorithm)
-    {
-        // Add the Fear and Greed dataset. 
-        _datasetSymbol = algorithm.AddData<FearGreedIndex>("FG").Symbol;
-    }
-
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data)
-    {
-        // Wait until the Fear and Greed dataset has new data.
-        if (!data.ContainsKey(_datasetSymbol))
-        {
-            return Enumerable.Empty<Insight>();
-        }
-        // Wait until the Fear and Greed index is extreme.
-        var value = data[_datasetSymbol].Value;
-        if (value >= 25 && value <= 75)
-        {
-            return Enumerable.Empty<Insight>();
-        }
-
-        var insights = new List<Insight>();
-        foreach (var security in _securities)
-        {
-            InsightDirection direction;
-            // Buy when the market is fearful.
-            if (!security.Invested && value < 25)
-            {
-                direction = InsightDirection.Up;
-            }
-            // Sell when the market is greedy.
-            else if (security.Invested && value > 75)
-            {
-                direction = InsightDirection.Flat;
-            }
-            else
-            {
-                continue;
-            }
-            insights.Add(Insight.Price(security.Symbol, TimeSpan.FromDays(2 * 365), direction));
-        }
-        return insights;
-    }
-
-    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-    {
-        // When assets enter the universe, save them.
-        foreach (var security in changes.AddedSecurities)
-        {
-            _securities.Add(security);
-        }
-        // When assets leave the universe, remove them.
-        foreach (var security in changes.RemovedSecurities)
-        {
-            _securities.Remove(security);
-        }
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The Fear and Greed dataset provides
-   
-    FearGreedIndex
-   
-   objects, which have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > International Future Universe
    
-
    
-    
    
-        
    QuantConnect
    
-        
    International Future Universe
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The International Future Universe dataset by QuantConnect lists the available International Future contracts, their daily trading volume, and Open Interest. The data covers 4 contracts (DAX, FESX, HSI, and NKD), starts in July 1998, and is delivered on daily frequency. This dataset is created by monitoring the trading activity on the EUREX, HKFE, and CME.
-  
    
-  
    
-   This dataset depends on the
-   
-    US Futures Security Master
-   
-   dataset because the US Futures Security Master dataset contains information on symbol changes of the contracts.
-  
    
-  
    
-   This dataset
-   
-    does not
-   
-   contain market data. For market data, see
-   
-    International Futures by TickData
-   
-   and
-   
-    US Futures by AlgoSeek
-   
-   for NKD.
-  
    
-  
    
-   For more information about the International Future Universe dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The International Futures Universe dataset provides data for contract filtering/selection:
-  
    
-  
    
-   
    hsi = self.add_future(Futures.Indices.HANG_SENG, Resolution.MINUTE)           # "HSI"
-hsi.set_filter(0, 90)
-dax = self.add_future(Futures.Indices.DAX, Resolution.MINUTE)                 # "DAX"
-dax.set_filter(0, 90)
-fesx = self.add_future(Futures.Indices.EURO_STOXX_50, Resolution.MINUTE)      # "FESX"
-fesx.set_filter(0, 90)
-nkd = self.add_future(Futures.Indices.NIKKEI_225_DOLLAR, Resolution.MINUTE)   # "NKD"
-nkd.set_filter(0, 90)
    
-   
    var hsi= AddFuture(Futures.Indices.HangSeng, Resolution.Minute)           // "HSI";
-hsi.SetFilter(0, 90);
-var dax = AddFuture(Futures.Indices.Dax, Resolution.Minute)               // "DAX";
-dax.SetFilter(0, 90);
-var fesx = AddFuture(Futures.Indices.EuroStoxx50, Resolution.Minute)      // "FESX";
-fesx.SetFilter(0, 90);
-var nkd = AddFuture(Futures.Indices.Nikkei225Dollar, Resolution.Minute)   // "NKD";
-nkd.SetFilter(0, 90);
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      July 1998 (for details, see
-      
-       Supported Assets
-      
-      )
-     
    
-      Coverage
-     
-      4 Contracts
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Daily
-     
    
-      Timezone
-     
-      Various (for details, see
-      
-       Supported Assets
-      
-      )
-     
    
-      Market Hours
-     
-      
-       Regular and Extended
-      
-     
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add International Future Universe data to your algorithm, call the
-   
-    AddFuture
-   
-   
-    add_future
-   
-   method. Save a reference to the Future object so you can access the data later in your algorithm. To define which contracts should be in your universe, specify the filter when requesting the Future data.
-  
    
-  
    
-   The
-   
-    AddFuture
-   
-   
-    add_future
-   
-   method provides a daily stream of Future chain data. To get the most recent daily chain, call the
-   
-    FuturesChain
-   
-   
-    futures_chain
-   
-   method with the underlying Future Symbol. The
-   
-    FuturesChain
-   
-   
-    futures_chain
-   
-   method returns data on all the tradable contracts, not just the contracts that pass your universe filter.
-  
    
-  
    
-   
    class InternationalFuturesDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2013, 12, 20) 
-        self.set_end_date(2014, 2, 20) 
-        self.set_cash(1000000) 
-        self.universe_settings.asynchronous = True
-        self._future = self.add_future(Futures.Indices.HANG_SENG)
-        # Set our contract filter for this Future chain.
-        self._future.set_filter(lambda universe: universe.standards_only().front_month())
-        # Get the entire Futures chain for the current day.
-        chain = self.futures_chain(self._future.symbol, flatten=True).data_frame
    
-   
    public class InternationalFuturesDataAlgorithm : QCAlgorithm
-{
-    private Future _future;
-        
-    public override void Initialize()
-    {
-        SetStartDate(2013, 12, 20);
-        SetEndDate(2014, 2, 20);
-        SetCash(1000000);
-        UniverseSettings.Asynchronous = true;
-        var future = AddFuture(Futures.Indices.HangSeng);
-        // Set our contract filter for this Future chain.
-        _future.SetFilter((universe) => universe.StandardsOnly().FrontMonth());
-        // Get the entire Futures chain for the current day.
-        var chain = FuturesChain(_future.Symbol);
-    }
-}
    
-  
    
-  
    
-   For more information about creating Future Universes, see
-   
-    Futures
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   For information about accessing International Future Universe data, see
-   
-    Futures
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical International Future Universe data in an algorithm and the Research Environment.
-  
    
-  
    
-   Historical Data In Algorithms
-  
    
-  
    
-   To get historical International Future Universe data in an algorithm, call the
-   
-    History<FutureUniverse>
-   
-   
-    history
-   
-   method with continuous Future
-   
-    Symbol
-   
-   and a lookback period. This method returns the all the available contracts for each trading day, not the subset of contracts that pass your universe filter. If there is no data for the period you requested, the history result is empty.
-  
    
-  
    
-   
    # Add the Future and save a reference to it.
-future = self.add_future(Futures.Indices.HANG_SENG)
-
-# DataFrame example where the columns are the FutureUniverse attributes:
-history_df = self.history(FutureUniverse, future.symbol, 5, flatten=True)
-
-# Series example where the values are lists of FutureUniverse objects: 
-history_series = self.history(FutureUniverse, future.symbol, 5, flatten=False)
-
-# FutureUniverse objects example:
-history = self.history[FutureUniverse](future.symbol, 5)
    
-   
    // Add the Future and save a reference to it.
-var future = AddFuture(Futures.Indices.HangSeng);
-
-// Get historical data.
-var history = History<FutureUniverse>(future.Symbol, 5);
    
-  
    
-  
    
-   For more information about historical International Future Universe data in algorithms, see
-   
-    Contracts
-   
-   .
-  
    
-  
    
-   Historical Data In Research
-  
    
-  
    
-   To get historical International Future Universe data in the Research Environment, call the
-   
-    History<FutureUniverse>
-   
-   
-    history
-   
-   method with continuous Future
-   
-    Symbol
-   
-   and a time period. This method returns the all the available contracts for each trading day, not the subset of contracts that pass your universe filter. If there is no data for the period you requested, the history result is empty.
-  
    
-  
    
-   
    # Add the Future and save a reference to it.
-future = qb.add_future(Futures.Indices.HANG_SENG)
-
-# DataFrame example where the columns are the FutureUniverse attributes:
-history_df = qb.history(FutureUniverse, future.symbol, datetime(2025, 1, 1), datetime(2025, 4, 1), flatten=True)
-
-# Series example where the values are lists of FutureUniverse objects: 
-history_series = qb.history(FutureUniverse, future.symbol, 5, flatten=False)
-
-# FutureUniverse objects example:
-history = qb.history[FutureUniverse](future.symbol, datetime(2025, 1, 1), datetime(2025, 4, 1))
    
-   
    // Add the Future and save a reference to it.
-var future = qb.AddFuture(Futures.Indices.HangSeng);
-
-// Get historical data.
-var history = qb.History<FutureUniverse>(future.Symbol, new DateTime(2025, 1, 1), new DateTime(2025, 4, 1));
    
-  
    
-  
    
-   For more information about historical International Future Universe data in the Research Environment, see
-   
-    Daily Prices History
-   
-   .
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   The following table shows the available Futures:
-  
    
-  
-   
-    
-     
-     
-     
-     
-     
-    
-   
-   
-    
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-    
-   
-  
    
-      Ticker
-     
-      Future
-     
-      Start Date
-     
-      Time Zone
-     
-      Currency
-     
    
-      HSI
-     
-      Hang Seng Index Futures
-     
-      Jan 2010
-     
-      Asia/Hong Kong
-     
-      HKD
-     
    
-      DAX
-     
-      German Index Futures
-     
-      Jan 2010
-     
-      Europe/Berlin
-     
-      EUR
-     
    
-      FESX
-     
-      EURO STOXX 50 Index Futures
-     
-      Jul 1998
-     
-      Europe/Berlin
-     
-      EUR
-     
    
-      NKD
-     
-      Nikkei 225 Index Futures
-     
-      Jan 2007
-     
-      America/Chicago
-     
-      USD
-     
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The International Futures Universe dataset enables you to design Futures strategies accurately. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Buying the Futures contract with the most open interest to reduce slippage and market impact
-   
      • 
-   
    • 
-    Trade speculation on an International Index
-   
      • 
-   
    • 
-    Trading bull calendar spreads to reduce volatility and margin requirements
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm uses the
-   
-    ZigZag indicator
-   
-   to determine the trend of Hang Seng Index. It then trades the Index with HSI Futures.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class InternationalFuturesDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        # Set the time zone to HKT to make it more comparable with the exchange.
-        self.set_time_zone(TimeZones.HONG_KONG)
-        # Set the account currency as HKD to trade HSI Futures.
-        self.set_account_currency("HKD", 1000000)
-        # Seed the last price of the contracts for filling.
-        self.settings.seed_initial_prices = True
-
-        # Request HSI Futures to trade. 
-        # Note that we will trade the contract with the highest open interest for liquidity.
-        self.hsi_future = self.add_future(
-            Futures.Indices.HANG_SENG,
-            extended_market_hours=True,
-            data_mapping_mode=DataMappingMode.LAST_TRADING_DAY,
-            contract_depth_offset=0
-        )
-        # Adds contracts that expiry within 90 days. We will trade the farthest contract
-        self.hsi_future.set_filter(0,90)
-        # Request the corresponding underlying Index for feeding indicator for trade signal generation.
-        hsi_index = self.add_index("HSI").symbol
-
-        # Create a ZigZag indicator to trade Hang Seng Index price pivot points.
-        self._zz = self.zz(hsi_index, 0.15, 5, Resolution.DAILY)
-        # Warm up indicator for immediate readiness to trade.
-        self.warm_up_indicator(hsi_index, self._zz, Resolution.DAILY)
-
-    def on_data(self, slice: Slice) -> None:
-        # Only place trade if the Future contracts is in market opening hours to avoid stale fills.
-        if self.is_market_open(self.hsi_future.symbol) and self._zz.is_ready:
-            pivot = self._zz.pivot_type
-            # If the last pivot point is a low point, the current trend is increasing after this low point.
-            if pivot == PivotPointType.LOW:
-                contracts = sorted([x.symbol for x in slice.future_chains.get(self.hsi_future.symbol)],
-                    key=lambda x: x.id.date)
-                self.set_holdings(contracts[-1], 0.2)
-            # If the last pivot point is a high point, the current trend is decreasing after this high point.
-            if pivot == PivotPointType.HIGH:
-                contracts = sorted([x.symbol for x in slice.future_chains.get(self.hsi_future.symbol)],
-                    key=lambda x: x.id.date)
-                self.set_holdings(contracts[-1], -0.2)
    
-   
    public class InternationalFuturesDataAlgorithm : QCAlgorithm
-{
-    private Future _hsiFuture;
-    private ZigZag _zz;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        // Set the time zone to HKT to make it more comparable with the exchange.
-        SetTimeZone(TimeZones.HongKong);
-        // Set the account currency as HKD to trade HSI Futures.
-        SetAccountCurrency("HKD", 1000000);
-
-        // Seed the last price of the contracts for filling.
-        Settings.SeedInitialPrices = true;
-
-        // Request HSI Futures to trade. 
-        // Note that we will trade the contract with the highest open interest for liquidity.
-        _hsiFuture = AddFuture(
-            Futures.Indices.HangSeng,
-            extendedMarketHours: true,
-            dataMappingMode: DataMappingMode.LastTradingDay,
-            contractDepthOffset: 0
-        );
-        // Adds contracts that expiry within 90 days. We will trade the farthest contract
-        _hsiFuture.SetFilter(0,90);
-        // Request the corresponding underlying index for feeding indicators for trade signal generation.
-        var hsiIndex = AddIndex("HSI").Symbol;
-
-        // Create a ZigZag indicator to trade Hang Seng Index price pivot points.
-        _zz = ZZ(hsiIndex, 0.15m, 5, Resolution.Daily);
-        // Warm up indicator for immediate readiness to trade.
-        WarmUpIndicator(hsiIndex, _zz, Resolution.Daily);
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // Only place trade if the Future contracts is in market opening hours to avoid stale fills.
-        if (IsMarketOpen(_hsiFuture.Symbol) && _zz.IsReady)
-        {
-            var pivot = _zz.PivotType;
-            FuturesChain chain;
-            // If the last pivot point is low, the current trend is increasing after this low point.
-            if (pivot == PivotPointType.Low && slice.FutureChains.TryGetValue(_hsiFuture.Symbol, out chain))
-            {
-                var contract = chain.OrderBy(x => x.Expiry).LastOrDefault()?.Symbol;                    
-                SetHoldings(contract, 0.2m);
-            }
-            // If the last pivot point is high, the current trend decreases after this high point.
-            if (pivot == PivotPointType.High && slice.FutureChains.TryGetValue(_hsiFuture.Symbol, out chain))
-            {
-                var contract = chain.OrderBy(x => x.Expiry).LastOrDefault()?.Symbol;                    
-                SetHoldings(contract, -0.2m);
-            }
-        }
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The International Future Universe dataset provides
-   
-    FutureFilterUniverse
-   
-   and
-   
-    FutureUniverse
-   
-   objects.
-  
    
-  
    
-   FutureFilterUniverse Attributes
-  
    
-  
    
-   
-    FutureFilterUniverse
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   FutureUniverse Attributes
-  
    
-  
    
-   
-    FutureUniverse
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > US ETF Constituents
    
-
    
-    
    
-        
    QuantConnect
    
-        
    US ETF Constituents
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US ETF Constituents dataset by QuantConnect tracks the constituents and weighting of US Equities in 2,650 ETF listings. The data starts in June 2009 and is delivered on a daily basis (monthly basis before January 2015). This dataset is created by tracking the host ETF websites and can be delayed by up to 1 week.
-  
    
-  
    
-   This dataset depends on the
-   
-    US Equity Security Master
-   
-   dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
-  
    
-  
    
-   For more information about the US ETF Constituents dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 160,000 quants are served every month.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the US ETF Constituents dataset:
-  
    
-  
    
-   
    def initialize(self) -> None:
-    self.universe_settings.asynchronous = True
-    # Use the following method for a Classic Algorithm
-    self._universe = self.add_universe(self.universe.etf("SPY", Market.USA, self.universe_settings, self.etf_constituents_filter))
-
-    symbol = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
-    # Use the following method for a Framework Algorithm
-    self.add_universe_selection(ETFConstituentsUniverseSelectionModel(symbol, self.universe_settings, self.etf_constituents_filter))
-
-    def etf_constituents_filter(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
-        # Add all Symbols of the ETFConstituentUniverse
-       return [x.symbol for x in constituents]
    
-   
    public override void Initialize()
-{
-    UniverseSettings.Asynchronous = true;
-    // Use the following method for a Classic Algorithm
-    _universe = AddUniverse(Universe.ETF("SPY", Market.USA, UniverseSettings, ETFConstituentsFilter));
-
-    var symbol = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
-    // Use the following method for a Framework Algorithm
-    AddUniverseSelection(new ETFConstituentsUniverseSelectionModel(symbol, UniverseSettings, ETFConstituentsFilter));
-}
-private IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable <ETFConstituentUniverse> constituents)
-{
-    // Add all Symbols of the ETFConstituentUniverse
-    return constituents.Select(x => x.Symbol);
-}
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      June 2009
-     
    
-      Asset Coverage
-     
-      2,650 US ETF Listings
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Daily (Monthly before Jan 2015)
-     
    
-      Timezone
-     
-      New York
-     
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add US ETF Constituents data to your algorithm, call the
-   
-    AddUniverse
-   
-   
-    add_universe
-   
-   and
-   
-    Universe.ETF
-   
-   
-    universe.etf
-   
-   methods. To select which constituents occupy the universe, provide the ETF
-   
-    Symbol
-   
-   and a selection function.
-  
    
-  
    
-   
    class ETFConstituentUniverseAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2018, 1, 1)
-        self.set_end_date(2020, 8, 25)
-        self.set_cash(100000)
-        self.universe_settings.asynchronous = True
-        self._universe = self.add_universe(self.universe.etf("SPY", self.universe_settings, self.etf_constituents_filter))
    
-   
    public class ETFConstituentUniverseAlgorithm : QCAlgorithm
-{
-    private Universe _universe;
-
-    public override void Initialize()
-    {
-        SetStartDate(2018, 1, 1);
-        SetEndDate(2020, 8, 25);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-        _universe = AddUniverse(Universe.ETF("SPY", UniverseSettings, ETFConstituentsFilter));
-    }
-}
-
    
-  
    
-  
    
-   For more information about universe settings, see
-   
-    Settings
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To access the US ETF Constituent data, use the
-   
-    ETFConstituentUniverse
-   
-   objects in your selection function. The data is available in daily resolution. The
-   
-    Symbol
-   
-   objects you return from your selection function defines the universe constituents.
-  
    
-  
    
-   
    def etf_constituents_filter(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
-    for c in constituents:
-        self.debug(f'{c.end_time} :: {c.last_update} :: {c.weight} :: {c.shares_held} :: {c.market_value}')
-    return [x.symbol for x in constituents]
    
-   
    -public IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentUniverse> constituents)
-{
-    foreach (var c in constituents)
-    {
-        Debug($"{c.EndTime} :: {c.LastUpdate} :: {c.Weight} :: {c.SharesHeld} :: {c.MarketValue}");
-    }
-
-    return constituents.Select(c => c.Symbol);
-}
-
    
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical universe data in an algorithm and in the Research Environment.
-  
    
-  
    
-   Historical Universe Data in Algorithms
-  
    
-  
    
-   To get historical universe data in an algorithm, call the
-   
-    History
-   
-   
-    history
-   
-   method with the
-   
-    Universe
-   
-   object and the lookback period. If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    var history = History(_universe, 30, Resolution.Daily);
-foreach (var constituents in history)
-{
-    foreach (ETFConstituentUniverse constituent in constituents)
-    {
-        Log($"{constituent.Symbol} weight at {constituent.EndTime}: {constituent.Weight}");
-    }
-}
    
-   
    # DataFrame example where the columns are the ETFConstituentUniverse attributes: 
-df_history = self.history(self.universe, 30, Resolution.DAILY, flatten=True)
-
-# Series example where the values are lists of ETFConstituentUniverse objects: 
-series_history = self.history(self.universe, 30, Resolution.DAILY)
-for (universe_symbol, time), constituents in series_history.items():
-    for constituent in constituents:
-        self.log(f'{constituent.symbol} weight at {constituent.end_time}: {constituent.weight}')
    
-  
    
-  
    
-   Historical Universe Data in Research
-  
    
-  
    
-   To get historical universe data in research, call the
-   
-    UniverseHistory
-   
-   
-    universe_history
-   
-   method with the
-   
-    Universe
-   
-   object and the lookback period. The
-   
-    UniverseHistory
-   
-   
-    universe_history
-   
-   returns the filtered universe. If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-foreach (var constituents in universeHistory )
-{
-    foreach (ETFConstituentUniverse constituent in constituents)
-    {
-        Console.WriteLine($"{constituent.Symbol} weight at {constituent.EndTime}: {constituent.Weight}");
-    }
-}
    
-   
    # DataFrame example where the columns are the ETFConstituentUniverse attributes: 
-df_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
-
-# Series example where the values are lists of ETFConstituentUniverse objects: 
-series_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (universe_symbol, time), constituents in series_history.items():
-    for constituent in constituents:
-       print(f"{constituent.symbol} weight at {constituent.end_time}: {constituent.weight}")
    
-  
    
-  
    
-   You can call the
-   
-    History
-   
-   
-    history
-   
-   method in Research.
-  
    
- 
-
-
-
    Supported ETFs
    
-
- 
-  
    
-   The following table shows the available ETFs:
-  
    
-  
-   
-    
-     
-    
-   
-   
-    
-     
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    Example Applications
    
-
- 
-  
    
-   The ETF Constituents dataset provides an excellent source of tradable universes for strategies without selection bias. When you use an ETF universe, the original ETF can serve as an excellent benchmark for your strategy performance. Other use cases include the following:
-  
    
-  
      
-   
    • 
-    Creating an index-tracking algorithm for customized passive portfolio management
-   
      • 
-   
    • 
-    Performing statistical arbitrage with the base ETF
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm creates a dynamic universe of the 10 largest US Equities in the SPY ETF. Each day, the algorithm forms a dollar-neutral and market-neutral portfolio by buying the 10 ETF constituents and shorting the SPY ETF.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class ETFConstituentUniverseAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        # Add the SPY to trade.
-        self._spy = self.add_equity("SPY").symbol
-        # Add an ETF constituents universe that selects the large caps 
-        # in SPY. Save the universe object so you can get history
-        # for the universe.
-        universe = self.add_universe(
-            self.universe.etf(
-                self._spy, 
-                universe_filter_func=self._select_assets
-            )
-        )
-        # Get historical universe data.
-        history = self.history(universe, 30, Resolution.DAILY, flatten=True)
-        # Show an example of wrangling the historical data.
-        etf_weights = history.weight.unstack(1)  
-        # Create a dictionary to store the ETF weights each day.
-        self._weight_by_symbol = {}
-        # Add a Scheduled Event to rebalance the portfolio each day.
-        self.schedule.on(
-            self.date_rules.every_day(self._spy),
-            self.time_rules.after_market_open(self._spy, 1),
-            self._rebalance
-        )
-
-    def _select_assets(
-            self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
-        # Select the 10 largest stocks in the SPY.
-        selected = sorted(
-            [c for c in constituents if c.weight], 
-            key=lambda c: c.weight
-        )[-10:]
-        # Save the weights for position sizing.
-        self._weight_by_symbol = {c.symbol: c.weight for c in selected}
-        # Return the selected assets.
-        return list(self._weight_by_symbol.keys())
-
-    def _rebalance(self) -> None:
-        # Create a long-short portfolio to earn the excess return of the 
-        # top 10 weighted stocks from SPY.
-        spy_weight = sum(self._weight_by_symbol.values())
-        targets = [PortfolioTarget(self._spy, -0.5)]
-        for symbol, weight in self._weight_by_symbol.items():
-            targets.append(PortfolioTarget(symbol, 0.5*weight/spy_weight))
-        # Liquidate the stocks that aren't in top 10.
-        self.set_holdings(targets, True)
-
    
-   
    public class ETFConstituentUniverseAlgorithm : QCAlgorithm
-{ 
-    private Symbol _spy;
-    // Create a dictionary to store the ETF weights each day.
-    private Dictionary<Symbol, decimal> _weightBySymbol = new();
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        // Add the SPY to trade.
-        _spy = AddEquity("SPY").Symbol;
-        // Add an ETF constituents universe that selects the large caps 
-        // in SPY. Save the universe object so you can get history
-        // for the universe.
-        var universe = AddUniverse(Universe.ETF(_spy, universeFilterFunc: SelectAssets));
-        // Get historical universe data.
-        var history = History(universe, 30, Resolution.Daily);
-        // Show an example of iterating through the historical data.
-        foreach (var constituents in history)
-        {
-            var t = constituents.EndTime;
-            foreach (ETFConstituentUniverse constituent in constituents)
-            {
-                var symbol = constituent.Symbol;
-                var weight = constituent.Weight;
-            }
-        }
-        // Add a Scheduled Event to rebalance the portfolio each day.
-        Schedule.On(DateRules.EveryDay(_spy), TimeRules.AfterMarketOpen(_spy, 1), Rebalance);
-    }
-    
-    private IEnumerable<Symbol> SelectAssets(IEnumerable<ETFConstituentUniverse> constituents)
-    {
-        // Select the 10 largest stocks in the SPY. 
-        _weightBySymbol = constituents.OrderByDescending(c => c.Weight).Take(10)
-            // Save the weights for position sizing.
-            .ToDictionary(c => c.Symbol, c => c.Weight ?? 0m);
-        // Return the selected assets.
-        return _weightBySymbol.Keys;
-    }
-    
-    private void Rebalance()
-    {
-        // Create a long-short portfolio to earn the excess return of the 
-        // top 10 weighted stocks from SPY.
-        var spyWeight = _weightBySymbol.Values.Sum();
-        var targets = new List<PortfolioTarget>() { new PortfolioTarget(_spy, -0.5m) };
-        foreach (var kvp in _weightBySymbol)
-        {
-            targets.Add(new PortfolioTarget(kvp.Key, 0.5m * kvp.Value / spyWeight));
-        }
-        // Liquidate the stocks that aren't in top 10.
-        SetHoldings(targets, true);
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm creates a dynamic universe of the 10 largest US Equities in the SPY ETF. Each day, the algorithm forms a dollar-neutral and market-neutral portfolio by buying the 10 ETF constituents and shorting the SPY ETF.
-  
    
-  
    
-   
    class ETFConstituentUniverseFrameworkAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        self.universe_settings.asynchronous = False
-        self.universe_settings.resolution = Resolution.MINUTE
-        self.weight_by_symbol = {}
-        
-        # Add universe selection on SPY's constituents to select only from large cap stocks
-        # Save the universe to access its members for historical data call
-        spy = self.add_equity("SPY").symbol
-        self.add_universe_selection(ETFConstituentsUniverseSelectionModel(spy, self.universe_settings, self.etf_constituents_filter))
-
-        # Add alpha model that set normalized weight as investment insight
-        self.add_alpha(ETFConstituentsAlphaModel(self, spy))
-
-        # Set up portfolio construction model that invest by the insight weights
-        pcm = InsightWeightingPortfolioConstructionModel()
-        # Avoid excessive rebalance on insight changes
-        pcm.rebalance_on_insight_changes = False
-        self.set_portfolio_construction(pcm)
-
-        self.add_risk_management(NullRiskManagementModel())
-
-        self.set_execution(ImmediateExecutionModel())
-
-    def etf_constituents_filter(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
-        # The top 10 weighted securities are considered better active selections
-        # Save the weights for position sizing
-        selected = sorted([c for c in constituents if c.weight],
-            key=lambda c: c.weight, reverse=True)[:10]
-        self.weight_by_symbol = {c.symbol: c.weight for c in selected}
-
-        return list(self.weight_by_symbol.keys())
-
-class ETFConstituentsAlphaModel(AlphaModel):
-
-    def __init__(self, algorithm: QCAlgorithm, etf: Symbol) -> None:
-        self.algorithm = algorithm
-        self.etf = etf
-        self.day = -1
-
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        # Rebalance daily since selection is on daily basis
-        if self.day == algorithm.time.day:
-            return []
-
-        self.day = algorithm.time.day
-
-        insights = []
-
-        # Create a long-short portfolio to earn excess return of the top 10 weighted stocks from SPY
-        etf_weight = sum(self.algorithm.weight_by_symbol.values())
-        if etf_weight> 0:
-            # Invest half the portfolio by normalized weights of the top 10 constituents
-            for symbol, weight in self.algorithm.weight_by_symbol.items():
-                if algorithm.securities.contains_key(symbol):
-                    insights.append(Insight.price(symbol, Expiry.END_OF_DAY, InsightDirection.UP, weight=0.5*weight/etf_weight))
-            
-            # Short the other half with SPY, looking to profit from the active selection
-            insights.append(Insight.price(self.etf, Expiry.END_OF_DAY, InsightDirection.DOWN, weight=0.5))
-
-        return insights
-
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        # Liquidate the ones not in top 10 weights
-        symbols = [x.symbol for x in changes.removed_securities if x.invested]
-        algorithm.liquidate(symbols, tag='Removed From Universe')
    
-   
    public class ETFConstituentUniverseFrameworkAlgorithm : QCAlgorithm
-{ 
-    public Dictionary<Symbol, decimal> WeightBySymbol = [];
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = false;
-        UniverseSettings.Resolution = Resolution.Minute;
-
-        // Add universe selection on SPY's constituents to select only from large cap stocks
-        // Save the universe to access its members for historical data call
-        var spy = AddEquity("SPY").Symbol;
-        AddUniverseSelection(new ETFConstituentsUniverseSelectionModel(spy, UniverseSettings, ETFConstituentsFilter));
-        
-        // Add alpha model that set normalized weight as investment insight
-        AddAlpha(new ETFConstituentsAlphaModel(this, spy));
-
-        // Set up portfolio construction model that invest by the insight weights
-        SetPortfolioConstruction(new InsightWeightingPortfolioConstructionModel
-        {
-            // Avoid excessive rebalance on insight changes
-            RebalanceOnInsightChanges = false
-        });
-        
-        AddRiskManagement(new NullRiskManagementModel());
-        
-        SetExecution(new ImmediateExecutionModel());
-    }
-    
-    private IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentUniverse> constituents)
-    {
-        // The top 10 weighted securities are considered better active selections
-        // Save the weights for position sizing
-        WeightBySymbol = constituents.Where(c=> c.Weight.HasValue).OrderByDescending(c => c.Weight).Take(10)
-            .ToDictionary(c => c.Symbol, c => c.Weight.Value);
-
-        return WeightBySymbol.Keys;
-    }
-}
-
-public class ETFConstituentsAlphaModel : AlphaModel
-{
-    private int _day = -1;
-    private Symbol _etf;
-    private ETFConstituentUniverseFrameworkAlgorithm _algorithm;
-    
-    public ETFConstituentsAlphaModel(ETFConstituentUniverseFrameworkAlgorithm algorithm, Symbol etf)
-    {
-        _etf = etf;
-        _algorithm = algorithm;
-    }
-    
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        // Rebalance daily since selection is on daily basis
-        if (_day == algorithm.Time.Day)
-        {
-            return [];
-        }
-        
-        _day = algorithm.Time.Day;
-        
-        var insights = new List<Insight>();
-        
-        // Create a long-short portfolio to earn excess return of the top 10 weighted stocks from SPY
-        var etfWeight = (double)_algorithm.WeightBySymbol.Values.Sum();
-        if (etfWeight > 0)
-        {
-            // Invest half the portfolio by normalized weights of the top 10 constituents
-            foreach(var kvp in _algorithm.WeightBySymbol)
-            {
-                insights.Add(Insight.Price(kvp.Key, Expiry.EndOfDay, InsightDirection.Up, weight: (double)kvp.Value/etfWeight * 0.5));
-            }
-
-            // Short the other half with SPY, looking to profit from the active selection
-            insights.Add(Insight.Price(_etf, Expiry.EndOfDay, InsightDirection.Down, weight: 0.5));
-        }
-        
-        return insights;
-    }
-    
-    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-    {
-        // Liquidate the ones not in top 10 weights
-        var symbols = changes.RemovedSecurities.Where(x => x.Invested).Select(x => x.Symbol);
-        algorithm.Liquidate(symbols, tag: "Removed From Universe");
-    }
-}
    
-  
    
-  
    
-   Research Example
-  
    
-  
    
-   The following example lists ETF constituents with the greatest weight in the SPY:
-  
    
-  
    
-   
    var qb = new QuantBook();
-
-// Add the ETF
-var symbol = qb.AddEquity("SPY").Symbol;
-
-// Add ETF Universe Selection
-IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentUniverse> constituents)
-{
-    // Take the top 10 weighted constituents
-    return constituents
-        .OrderByDescending(c => c.Weight)
-        .Take(10)
-        .Select(c => c.Symbol);
-}
-
-var universe = qb.AddUniverse(qb.Universe.ETF(spy, qb.UniverseSettings, ETFConstituentsFilter)); 
-
-// Historical Universe data
-var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-foreach (var constituents in universeHistory )
-{
-    foreach (ETFConstituentUniverse constituent in constituents)
-    {
-        Console.WriteLine($"{constituent.Symbol} weight at {constituent.EndTime}: {constituent.Weight}");
-    }
-}
    
-   
    qb = QuantBook()
-
-# Add the ETF
-qb.spy = qb.add_equity("SPY").symbol
-
-# Add ETF Universe Selection
-def etf_constituents_filter(constituents):
-    # Take the top 10 weighted constituents
-    selected = sorted([c for c in constituents if c.weight],
-        key=lambda c: c.weight, reverse=True)[:10]
-    return [c.symbol for c in selected]
-
-universe = qb.add_universe(qb.universe.etf(qb.spy, qb.universe_settings, etf_constituents_filter))
-
-# Historical Universe data
-universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (universe_symbol, time), constituents in universe_history.items():
-    for constituent in constituents:
-        print(f"{constituent.symbol} weight at {constituent.end_time}: {constituent.weight}")
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The ETF Constituents dataset provides
-   
-    ETFConstituentUniverse
-   
-   objects, which have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > US Equities Short Availability
    
-
    
-    
    
-        
    QuantConnect
    
-        
    US Equities Short Availability
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Equity Short Availability dataset provides the available shares for open short positions and their borrowing cost in the US Equity market. The data covers 10,500 US Equities, starts in January 2018, and is delivered on a daily frequency. This dataset is created using information from the exchanges.
-  
    
-  
    
-   This dataset depends on the
-   
-    US Equity Security Master
-   
-   dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
-  
    
-  
    
-   For more information about the US Equities Short Availability dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading.  Through the QuantConnect web platform, more than 50,000 quants are served every month.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippets demonstrate how to request data from the US Equities Short Availability dataset.
-  
    
-  
    
-   Interactive Brokers Data
-  
    
-  
    
-   
    security.set_shortable_provider(InteractiveBrokersShortableProvider())
    
-   
    security.SetShortableProvider(new InteractiveBrokersShortableProvider());
    
-  
    
-  
    
-   Axos Clearing Data
-  
    
-  
    
-   
    security.set_shortable_provider(LocalDiskShortableProvider("axos"))
    
-   
    security.SetShortableProvider(new LocalDiskShortableProvider("axos"));
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 2018
-     
    
-      Asset Coverage
-     
-      10,500 US Equities
-     
    
-      Data Density
-     
-      Sparse
-     
    
-      Resolution
-     
-      Daily
-     
    
-      Timezone
-     
-      New York
-     
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add US Equities Short Availability data to your algorithm, set the
-   
-    shortable provider
-   
-   of each US Equity in your algorithm.
-  
    
-  
    
-   
    class ShortAvailabilityDataAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2019, 1, 1)
-        security = self.add_equity("AAPL")
-        # Set shortable provider as IB
-        security.set_shortable_provider(InteractiveBrokersShortableProvider())
-        self._symbol = security.symbol
    
-   
    public class ShortAvailabilityDataAlgorithm : QCAlgorithm
-{
-    private Symbol _symbol;
-
-    public override void Initialize()
-    {
-        SetStartDate(2019, 1, 1);
-        var security = AddEquity("AAPL");
-        // Set shortable provider as IB
-        security.SetShortableProvider(new InteractiveBrokersShortableProvider());
-        _symbol = security.Symbol;
-    }
-}
    
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To check how many shares are available for a security to short, call the
-   
-    ShortableQuantity
-   
-   
-    shortable_quantity
-   
-   method of the
-   
-    ShortableProvider
-   
-   
-    shortable_provider
-   
-  
    
-  
    
-   
    var shortableProvider = Securities[_symbol].ShortableProvider;
-// Get the shortable quantity of the selected symbol at the selected time
-var shortableQuantity = shortableProvider.ShortableQuantity(_symbol, Time);
-
-// Check if there are a certain quantity of shares available
-var quantity = 100;
-var isShortableQuantity = Shortable(_symbol, quantity);
    
-   
    shortable_provider = self.securities[self._symbol].shortable_provider
-# Get the shortable quantity of the selected symbol at the selected time
-shortable_quantity = shortable_provider.shortable_quantity(self._symbol, self.time)
-
-# Check if there are a certain quantity of shares available
-quantity = 100;
-is_shortable_quantity = self.shortable(self._symbol, quantity)
    
-  
    
-  
    
-   To check borrowing cost, call the
-   
-    FeeRate
-   
-   
-    fee_rate
-   
-   or
-   
-    RebateRate
-   
-   
-    rebate_rate
-   
-   method of the
-   
-    ShortableProvider
-   
-   
-    shortable_provider
-   
-  
    
-  
    
-   
    var feeRate = shortableProvider.FeeRate(_symbol, Time);
-var rebateRate = shortableProvider.RebateRate(_symbol, Time);
    
-   
    fee_rate = shortable_provider.fee_rate(self._symbol, self.time);
-rebate_rate = shortable_provider.rebate_rate(self._symbol, self.time);
    
-  
    
-  To get valid borrowing rates, use the
-  
-   InteractiveBrokersShortableProvider
-  
-  
-   .
-  
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Equities Short Availability dataset enables you to accurately design strategies harnessing information about short availability. Examples include the following use cases:
-  
    
-  
      
-   
    • 
-    Avoiding short orders that the brokerage will reject
-   
      • 
-   
    • 
-    Selecting securities based on how many shares are available to short
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm shorts GameStop every day there are shares available to short. If the algorithm receives a margin call, it liquidates the position and start again on the next day.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class ShortAvailabilityDataAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(1000)
-        # Seed the security price as the last known price, such that the price data is immediately available at initial rebalance
-        self.settings.seed_initial_prices = True
-        self.add_security_initializer(self._custom_security_initializer)
-
-        self.equity = self.add_equity("GME")
-
-        # Set up daily rebalance scheduled event, since shortable quantity is updated daily
-        self.schedule.on(
-            self.date_rules.every_day(self.equity.symbol),
-            self.time_rules.after_market_open(self.equity.symbol, 10),
-            self.rebalance)
-
-    def rebalance(self) -> None:
-        symbol = self.equity.symbol
-        
-        # You can obtain the shortable quantity to decide the submission of a short order
-        shortable_quantity = self.equity.shortable_provider.shortable_quantity(symbol, self.time)
-        if not shortable_quantity:
-            shortable_quantity = 0
-        # Fee and rebate rate is also available, such that you can calculate the expected return and decide if the margin is worthwhile
-        self.plot('Total Shortable Quantity', symbol, shortable_quantity)
-        self.plot('Borrowing Cost', "Fee Rate", self.equity.shortable_provider.fee_rate(symbol, self.time))
-        self.plot('Borrowing Cost', "Rebate Rate", self.equity.shortable_provider.rebate_rate(symbol, self.time))
-
-        # Test whether we can short the desired quantity
-        quantity = self.calculate_order_quantity(symbol, -1)
-        if quantity and self.shortable(symbol, quantity):
-            self.market_order(symbol, quantity)
-
-    def on_margin_call_warning(self) -> None:
-        self.liquidate()
-
-    def _custom_security_initializer(self, security: Security) -> None:
-        # Set the shortable provider as your broker for accurate short reality modeling
-        security.set_shortable_provider(InteractiveBrokersShortableProvider())
    
-   
    public class ShortAvailabilityDataAlgorithm : QCAlgorithm
-{
-    private Equity _equity;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(1000);
-        // Seed the security price as the last known price, such that the price data is immediately available at initial rebalance
-        Settings.SeedInitialPrices = true;
-        AddSecurityInitializer(security =>
-        {
-            // Set the shortable provider as your broker for accurate short reality modeling
-            security.SetShortableProvider(new InteractiveBrokersShortableProvider());    
-        });
-        
-        _equity = AddEquity("GME");
-        
-        // Set up daily rebalance scheduled event, since shortable quantity is updated daily
-        Schedule.On(
-            DateRules.EveryDay(_equity.Symbol),
-            TimeRules.AfterMarketOpen(_equity.Symbol, 10),
-            Rebalance);
-    }
-    
-    public void Rebalance()
-    {
-        var symbol = _equity.Symbol;
-
-        // You can obtain the shortable quantity to decide the submission of a short order
-        // Fee and rebate rate is also available, such that you can calculate the expected return and decide if the margin is worthwhile
-        Plot("Total Shortable Quantity", symbol, _equity.ShortableProvider.ShortableQuantity(symbol, Time) ?? 0m);
-        Plot("Borrowing Cost", "Fee Rate", _equity.ShortableProvider.FeeRate(symbol, Time));
-        Plot("Borrowing Cost", "Rebate Rate", _equity.ShortableProvider.RebateRate(symbol, Time));
-
-        // Test whether we can short the desired quantity
-        var quantity = CalculateOrderQuantity(symbol, -1m);
-        if (quantity != 0 && Shortable(symbol, quantity))
-        {
-            MarketOrder(symbol, quantity);
-        }
-    }
-
-    public override void OnMarginCallWarning()
-    {
-        Liquidate();
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm shorts GameStop every day there are shares available to short. If the algorithm receives a margin call, it liquidates the position and start again on the next day.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class ShortAvailabilityDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(1000)
-        # Seed the security price as the last known price, such that the price data is immediately available at initial rebalance
-        self.settings.seed_initial_prices = True
-        self.add_security_initializer(self._custom_security_initializer)
-
-        self.set_universe_selection(ManualUniverseSelectionModel(
-            [Symbol.create("GME", SecurityType.EQUITY, Market.USA)]))           
-
-        # Emit down-direction insights to short all securities in the universe            
-        self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.DOWN, timedelta(1)))
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
-        self.set_execution(ShortableExecutionModel())
-
-    # On Margin Call, emit flat-direction insights to liquidate the positions
-    def on_margin_call_warning(self) -> None:
-        self.emit_insights([Insight.price(kvp.Key, timedelta(1), InsightDirection.FLAT)
-            for kvp in self.securities if kvp.Value.invested])
-
-    def _custom_security_initializer(self, security: Security) -> None:
-        # Set the shortable provider as IB
-        security.set_shortable_provider(InteractiveBrokersShortableProvider())
-
-class ShortableExecutionModel(ExecutionModel):
-    def __init__(self) -> None:
-        self.targets_collection = PortfolioTargetCollection()
-
-    def execute(self, algorithm: QCAlgorithm, targets: List[PortfolioTarget]) -> None:
-        '''Immediately submits orders for the specified portfolio targets.
-        Args:
-            algorithm: The algorithm instance
-            targets: The portfolio targets to be ordered'''
-
-        # for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
-        self.targets_collection.add_range(targets)
-        if self.targets_collection.count > 0:
-            for target in self.targets_collection.order_by_margin_impact(algorithm):
-                # calculate remaining quantity to be ordered
-                quantity = OrderSizing.get_unordered_quantity(algorithm, target)
-                # If the quantity is negative, ensure that the security is shortable
-                if quantity > 0 or algorithm.shortable(target.symbol, quantity):
-                    algorithm.market_order(target.symbol, quantity)
-
-            self.targets_collection.clear_fulfilled(algorithm)
    
-   
    public class ShortAvailabilityDataAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(1000);
-        // Seed the security price as the last known price, such that the price data is immediately available at initial rebalance
-        Settings.SeedInitialPrices = true;
-        AddSecurityInitializer(security =>
-        {
-            // Set the shortable provider as your broker for accurate short reality modeling
-            security.SetShortableProvider(new InteractiveBrokersShortableProvider());    
-        });
-
-        SetUniverseSelection(new ManualUniverseSelectionModel(
-            QuantConnect.Symbol.Create("GME", SecurityType.Equity, Market.USA)));            
-        
-        // Emit down-direction insights to short all securities in the universe            
-        SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Down, TimeSpan.FromDays(1)));
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
-        SetExecution(new ShortableExecutionModel());
-    }
-
-    // On Margin Call, emit flat-direction insights to liquidate the positions
-    public override void OnMarginCallWarning()
-    {
-        EmitInsights(Securities
-            .Where(kvp => kvp.Value.Invested)
-            .Select(kvp => Insight.Price(kvp.Key, TimeSpan.FromDays(1), InsightDirection.Flat))
-            .ToArray());
-    }
-
-    public class ShortableExecutionModel : ImmediateExecutionModel
-    {
-        private readonly PortfolioTargetCollection _targetsCollection = new PortfolioTargetCollection();
-
-        public override void Execute(QCAlgorithm algorithm, IPortfolioTarget[] targets)
-        {
-            _targetsCollection.AddRange(targets);
-            // for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
-            if (_targetsCollection.Count > 0)
-            {
-                foreach (var target in _targetsCollection.OrderByMarginImpact(algorithm))
-                {
-                    // calculate remaining quantity to be ordered
-                    var quantity = OrderSizing.GetUnorderedQuantity(algorithm, target);
-                    // If the quantity is negative, ensure that the security is shortable
-                    if (quantity > 0 || algorithm.Shortable(target.Symbol, quantity))
-                    {
-                        algorithm.MarketOrder(target.Symbol, quantity);
-                    }
-                }
-
-                _targetsCollection.ClearFulfilled(algorithm);
-            }
-        }
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Equities Short Availability data is a
-   
-    Symbol
-   
-   /decimal pair for
-   
-    ShortQuantity
-   
-   ,
-   
-    FeeRate
-   
-   , 
- and
-   
-    RebateRate
-   
-   .
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > US Equity Coarse Universe
    
-
    
-    
    
-        
    QuantConnect
    
-        
    US Equity Coarse Universe
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Equity Coarse Universe dataset by QuantConnect is a daily universe of all trading stocks in the US for a given day with the end of day price and volume. The data covers 30,000 US Equities in total, with approximately 8,000 Equities per day. The data starts in January 1998 and is delivered each trading day. This dataset is created by taking the closing auction price tick from the daily L1 trade and quote exchange dumps.
-  
    
-  
    
-   This dataset depends on the
-   
-    US Equities by AlgoSeek
-   
-   dataset because
-   
-    universe selection
-   
-   adds and removes market data subscriptions and on the
-   
-    US Equity Security Master
-   
-   dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
-  
    
-  
    
-   QuantConnect/LEAN combines the data of this dataset with
-   
-    MorningStar Fundamental data
-   
-   in runtime. You can use this dataset in local development without the Morningstar counterpart.
-  
    
-  
    
-   For more information about the US Equity Coarse Universe dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the US Equity Coarse Universe dataset:
-  
    
-  
      
-   
    • 
-    Direct Access using the Security object
-   
      • 
-  
    
-  
    
-   
    equity = self.add_equity("IBM")
-ibm_fundamental = equity.fundamentals
    
-   
    var equity = AddEquity("IBM");
-var ibmFundamental = equity.Fundamentals;
    
-  
    
-  
      
-   
    • 
-    Direct Access using the Symbol object
-   
      • 
-  
    
-  
    
-   
    ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
-ibm_fundamental = self.fundamentals
    
-   
    var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
-var ibmFundamental = Fundamentals(ibm);
    
-  
    
-  
      
-   
    • 
-    Universe Selection
-   
      • 
-  
    
-  
    
-   
    def initialize(self) -> None:
-    # Universe selection
-    self._universe = self.add_universe(self.fundamental_filter_function)
-
-def fundamental_filter_function(self, fundamental: List[Fundamental]):
-    # Sort all equities with price above $10 by dollar volume
-    sorted_by_dollar_volume = sorted([f for f in fundamental if f.price > 10],
-                                key=lambda f: f.dollar_volume, reverse=True)
-    # Take the top 10
-    return [f.symbol for f in sorted_by_dollar_volume[:10]]
    
-   
    public override void Initialize()
-{
-    // Universe selection
-    _universe = AddUniverseSelection(new FundamentalUniverseSelectionModel(FundamentalFilterFunction));
-}
-
-public override List<Symbol> FundamentalFilterFunction(List<Fundamental> fundamental)
-{
-    // Sort all equities with price above $10 by dollar volume, take the top 10
-    return (from f in fundamental
-            where f.Price > 10
-            orderby f.DollarVolume descending
-            select f.Symbol).Take(10);
-}
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 1998
-     
    
-      Asset Coverage
-     
-      30,000 US Equities
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Daily
-     
    
-      Timezone
-     
-      New York
-     
    
-  
    
-   This dataset doesn't include Over-the-Counter (OTC) stocks.
-  
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   You don't need any special code to request US Coarse Fundamental Data. You can access the current and historical fundamental data for any of the US Equities that this dataset includes.
-  
    
-  
    
-   
    class CoarseFundamentalDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2021, 1, 1)
-        self.set_end_date(2021, 7, 1)
-        self.set_cash(100000) 
-        self.universe_settings.asynchronous = True
-
-        # Option 1: Subscribe to individual US Equity assets
-        self.add_equity("IBM")  
-
-        # Option 2A: Create a fundamental universe (classic version)
-        self._universe = self.add_universe(self.fundamental_function)
-
-        # Option 2B: Create a fundamental universe (framework version)
-        self.add_universe_selection(FundamentalUniverseSelectionModel(self.fundamental_function))
    
-   
    public class CoarseFundamentalDataAlgorithm : QCAlgorithm
-{
-    private Universe _universe;
-    public override void Initialize()
-    {
-        SetStartDate(2021, 1, 1);
-        SetEndDate(2021, 7, 1);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-        
-        // Option 1: Subscribe to individual US Equity assets
-        AddEquity("IBM");
-
-        // Option 2A: Create a fundamental universe (classic version)
-        _universe = AddUniverse(FundamentalFilterFunction);
-
-        // Option 2B: Create a fundamental universe (framework version)
-        AddUniverseSelection(new FundamentalUniverseSelectionModel(FundamentalFilterFunction));
-    }
-}
    
-  
    
-  
    
-   For more information about universe settings, see
-   
-    Settings
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   If you add a
-   
-    fundamental universe
-   
-   to your algorithm, you can access fundamental data in the universe selection function.
-  
    
-  
    
-   
    def fundamental_function(self, fundamental: List[Fundamental]) -> List[Symbol]:
-    sorted_by_dollar_volume = sorted(fundamental, key=lambda x: x.dollar_volume, reverse=True)[:3]
-    for cf in sorted_by_dollar_volume:
-        self.debug(f"{cf.end_time} :: {cf.symbol} : {cf.adjusted_price} :: {cf.dollar_volume}")
-
-    return [ x.symbol for x in sorted_by_dollar_volume]
    
-   
    public IEnumerable<Symbol> FundamentalFunction(IEnumerable<Fundamental> fundamental)
-{
-    var sortedByDollarVolume = fundamental
-        .OrderByDescending(x => x.DollarVolume)
-        .Take(3).ToList();
-
-    foreach (var cf in sortedByDollarVolume)
-    {
-        Debug($"{cf.EndTime} :: {cf.Symbol} : {cf.AdjustedPrice} :: {cf.DollarVolume}");
-    }
-
-    return sortedByDollarVolume.Select(x => x.Symbol);
-}
    
-  
    
-  
    
-   To get fundamental data for Equities in your algorithm, use the
-   
-    Fundamentals
-   
-   
-    fundamentals
-   
-   property of the
-   
-    Equity
-   
-   objects. The fundamental data represent the corporate fundamentals for the current algorithm time.
-  
    
-  
    
-   
    fundamentals = self.securities[symbol].fundamentals
    
-   
    var fundamentals = Securities[symbol].Fundamentals;
    
-  
    
-  
    
-   To get fundamental data for Equities, regardless of whether or not you have subscribed to them in your algorithm, call the
-   
-    Fundamentals
-   
-   
-    fundamentals
-   
-   method. If you pass one
-   
-    Symbol
-   
-   , the method returns a
-   
-    Fundamental
-   
-   object. If you pass a list of
-   
-    Symbol
-   
-   objects, the method returns a list of
-   
-    Fundamental
-   
-   objects.
-  
    
-  
    
-   
    // Single asset 
-var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
-var ibmFundamental = Fundamentals(ibm);
-
-// Multiple assets
-var nb = QuantConnect.Symbol.Create("NB", SecurityType.Equity, Market.USA);
-var fundamentals = Fundamentals(new List<Symbol>{ nb, ibm }).ToList();
    
-   
    # Single asset
-ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
-ibm_fundamental = self.fundamentals(ibm)
-
-# Multiple assets
-nb = Symbol.create("NB", SecurityType.EQUITY, Market.USA)
-fundamentals = self.fundamentals([ nb, ibm ])
    
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical fundamental data in an algorithm and in the Research Environment.
-  
    
-  
    
-   Historical Data in Algorithms
-  
    
-  
    
-   To get historical fundamental data in an algorithm, call the
-   
-    History
-   
-   
-    history
-   
-   method with
-   
-    Fundamental
-   
-   type and the Equity
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
-
-// Fundamental objects
-var fundamentalHistory = History<Fundamental>(ibm, TimeSpan.FromDays(30));
-
-// Fundamentals objects for all US Equities (including delisted companies)
-var fundamentalsHistory = History<Fundamentals>(TimeSpan.FromDays(30));
-
-// Collection of Fundamental objects for all US Equities (including delisted companies)
-var collectionHistory = History(_universe, 30, Resolution.Daily);
-foreach (var fundamental in collectionHistory)
-{
-    // Cast to Fundamental is required
-    var mostLiquid = fundamental.OfType<Fundamental>().OrderByDescending(x => x.DollarVolume).Take(5);
-}
    
-   
    ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
-
-# DataFrame of fundamental data for a given asset
-df_history = self.history(Fundamental, ibm, timedelta(30), flatten=True)
-
-# Fundamental objects
-fundamental_history = self.history[Fundamental](ibm, timedelta(30))
-
-# Fundamentals objects for all US Equities (including delisted companies)
-fundamentals_history = self.history[Fundamentals](timedelta(30))
-
-# DataFrame of fundamental data for universe constituents
-df_history = self.history(self._universe, 30, Resolution.DAILY, flatten=True)
-
-# Series of fundamental data for universe constituents
-series_history = self.history(self._universe, 30, Resolution.DAILY)
-for (universe_symbol, time), fundamental in series_history.items():
-    most_liquid = sorted(fundamental, key=lambda x: x.dollar_volume)[-5:]
    
-  
    
-  
    
-   Historical Universe Data in Research
-  
    
-  
    
-   To get historical universe data in the Research Environment, call the
-   
-    UniverseHistory
-   
-   
-    universe_history
-   
-   method with the
-   
-    Universe
-   
-   object and the lookback period. This method returns the filtered universe. If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-foreach (var fundamentals in universeHistory)
-{
-    foreach (Fundamental fundamental in fundamentals)
-    {
-        Console.WriteLine($"{fundamental.Symbol} dollar volume at {fundamental.EndTime}: {fundamental.DollarVolume}");
-    }
-}
    
-   
    # DataFrame of fundamental data for universe constituents
-df_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
-
-# Series of fundamental data for universe constituents
-series_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (universe_symbol, time), fundamentals in series_history.items():
-    for fundamental in fundamentals:
-        print(f"{fundamental.symbol} dollar volume at {fundamental.end_time}: {fundamental.dollar_volume}")
    
-  
    
-  
    
-   For more information about historical US Equity fundamental data, see
-   
-    Equity Fundamental Data
-   
-   .
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Equity Coarse Universe dataset enables you to accurately design a universe of US Equities. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Selecting securities with the largest dollar volume
-   
      • 
-   
    • 
-    Selecting securities within a specific price range
-   
      • 
-   
    • 
-    Selecting securities that have fundamental data available (together with
-    
-     MorningStar Fundamental data
-    
-    in
-    
-     Fundamental
-    
-    objects)
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm creates a dynamic universe of the three most liquid US Equities. Each time the universe changes, the algorithm forms an equal-weighted portfolio of the three companies.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class USEquityCoarseUniverseConstituentsDataAlgorithm(QCAlgorithm):
-
-    _number_of_symbols = 3
-    _changes = None
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        # Set Asynchronous to True to improve speed performance.
-        self.universe_settings.asynchronous = True
-        # Add a universe of US Equities.
-        universe = self.add_universe(self._select_assets)
-        # Get historical data for the universe.
-        history = self.history(universe, 30, Resolution.DAILY, flatten=True)
-
-    def _select_assets(self, fundamental: List[Fundamental]) -> List[Symbol]:
-        # Select the top traded stocks, since they are the most popular 
-        # with high capital flow.
-        return [ 
-            x.symbol for x in sorted(
-                fundamental, key=lambda x: x.dollar_volume
-            )[-self._number_of_symbols:] 
-        ]
-
-    def on_data(self, slice: Slice) -> None:
-        # If we have no changes, do nothing.
-        if not slice.bars or self._changes is None: 
-            return
-        # Liquidate removed securities, since they are not the most 
-        # popular stocks.
-        for security in self._changes.removed_securities:
-            if security.invested:
-                self.liquidate(security.symbol)
-        # We want 1/N allocation for each security in our universe to
-        # evenly dissipate risk.
-        for security in self._changes.added_securities:
-            self.set_holdings(security.symbol, 1 / self._number_of_symbols)
-        self._changes = None
-
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
-        self._changes = changes
-
    
-   
    public class USEquityCoarseUniverseConstituentsDataAlgorithm : QCAlgorithm
-{
-    private int _numberOfSymbols = 3;
-    private SecurityChanges _changes = SecurityChanges.None;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-    
-        // Set Asynchronous to true to improve speed performance 
-        UniverseSettings.Asynchronous = true;
-        // Requesting data
-        var universe = AddUniverse(SelectAssets);
-        // Get historical data for the universe.
-        var history = History(universe, 30, Resolution.Daily);
-        foreach (var fundamentals in history)
-        {
-            // Iterate through each asset in the universe on this day 
-            // and access its data point attributes.
-            foreach (Fundamental f in fundamentals)
-            {
-                var symbol = f.Symbol;
-                var t = f.EndTime;
-                var dollarVolume = f.DollarVolume;
-            }
-        }
-    }
-
-    public IEnumerable<Symbol> SelectAssets(IEnumerable<Fundamental> fundamental)
-    {
-        // Select the top traded stocks, since they are the most popular 
-        // with high capital flow.
-        return fundamental.OrderByDescending(x => x.DollarVolume)
-            .Take(_numberOfSymbols).Select(x => x.Symbol);
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // If we have no changes, do nothing.
-        if (slice.Bars.Count == 0 || _changes == SecurityChanges.None) return;
-        // Liquidate removed securities, since they are not the most 
-        // popular stocks.
-        foreach (var security in _changes.RemovedSecurities)
-        {
-            if (security.Invested)
-            {
-                Liquidate(security.Symbol);
-            }
-        }
-        // We want 1/N allocation for each security in our universe to
-        // evenly dissipate risk.
-        foreach (var security in _changes.AddedSecurities)
-        {
-            SetHoldings(security.Symbol, 1m / _numberOfSymbols);
-        }
-        _changes = SecurityChanges.None;
-    }
-    
-    public override void OnSecuritiesChanged(SecurityChanges changes)
-    {
-        _changes = changes;
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm creates a dynamic universe of the three most liquid US Equities and forms an equal-weighted portfolio with them.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class USEquityCoarseUniverseConstituentsDataAlgorithm(QCAlgorithm):
-
-    _number_of_symbols = 3
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-
-        # Set Asynchronous to True to improve speed performance
-        self.universe_settings.asynchronous = True
-            
-        # Requesting data
-        self.set_universe_selection(
-            FundamentalUniverseSelectionModel(self.fundamental_selection_function))
-        
-        # we want 1/N allocation in each security in our universe to evenly dissipate risk
-        self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(1)))
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
-         
-        self.add_risk_management(NullRiskManagementModel())
-        
-        self.set_execution(ImmediateExecutionModel())
-
-    def fundamental_selection_function(self, fundamental: List[Fundamental]) -> List[Symbol]:
-        # Select the top traded stocks, since they are the most popular with high capital flow
-        sorted_by_dollar_volume = sorted(fundamental, key=lambda x: x.dollar_volume, reverse=True)
-        return [ x.symbol for x in sorted_by_dollar_volume[:self._number_of_symbols] ]
    
-   
    public class USEquityCoarseUniverseConstituentsDataAlgorithm : QCAlgorithm
-{
-    private int _numberOfSymbols = 3;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-
-        // Set Asynchronous to true to improve speed performance
-        UniverseSettings.Asynchronous = true;
-
-        // Requesting data
-        SetUniverseSelection(
-            new FundamentalUniverseSelectionModel(FundamentalSelectionFunction));
-
-        // we want 1/N allocation in each security in our universe to evenly dissipate risk
-        SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
-        
-        AddRiskManagement(new NullRiskManagementModel());
-        
-        SetExecution(new ImmediateExecutionModel());
-    }
-
-    public IEnumerable<Symbol> FundamentalSelectionFunction(IEnumerable<Fundamental> fundamental)
-    {
-        // Select the top traded stocks, since they are the most popular with high capital flow
-        return fundamental.OrderByDescending(x => x.DollarVolume)
-            .Take(_numberOfSymbols).Select(x => x.Symbol);
-    }
-}
    
-  
    
-  
    
-   Research Example
-  
    
-  
    
-   The following example lists the three most liquid US Equities:
-  
    
-  
    
-   
    var qb = new QuantBook();
-
-// Add Fundamental Universe Selection
-IEnumerable<Symbol> FundamentalSelectionFunction(IEnumerable<Fundamental> fundamentals)
-{
-    return fundamentals.OrderByDescending(x => c.DollarVolume).Take(10).Select(x => c.Symbol);
-}
-
-var universe = qb.AddUniverse(FundamentalSelectionFunction); 
-
-// Historical Universe data
-var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-foreach (var fundamentals in universeHistory)
-{
-    foreach (Fundamental fundamental in fundamentals)
-    {
-        Console.WriteLine($"{fundamental.Symbol} dollar volume at {fundamental.EndTime}: {fundamental.DollarVolume}");
-    }
-}
    
-   
    qb = QuantBook()
-
-# Add Fundamental Universe Selection
-def fundamental_selection_function(fundamentals):
-    selected = sorted(fundamentals, key=lambda x: x.dollar_volume, reverse=True)[:10]
-    return [x.symbol for x in selected]
-
-universe = qb.add_universe(fundamental_selection_function)
-
-# Historical Universe data
-universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (universe_symbol, time), fundamentals in universe_history.items():
-    for fundamental in fundamentals:
-        print(f"{fundamental.symbol} dollar volume at {fundamental.end_time}: {fundamental.dollar_volume}")
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Equity Coarse Universe dataset provides its data within
-   
-    Fundamental
-   
-   objects, which have the following . The attributes from
-   
-    market_cap
-   
-   
-    MarketCap
-   
-   to
-   
-    asset_classification
-   
-   
-    AssetClassification
-   
-   listed in the following widget are NaN until this dataset is combined with the
-   
-    MorningStar Fundamental
-   
-   dataset.
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > US Equity Option Universe
    
-
    
-    
    
-        
    QuantConnect
    
-        
    US Equity Option Universe
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Equity Option Universe dataset by QuantConnect lists the available US Equity Options contracts and the current Implied Volatility and Greeks. The data covers 4,000 Symbols, starts in January 2012, and is delivered on a daily update frequency. To create this dataset, we use
-   
-    our implementation
-   
-   of the forward tree pricing model, which accounts for the interest rate, dividend payments, and daily closing prices. The values in this dataset are the same values you can get from daily
-   
-    indicators
-   
-   with mirror Options.
-  
    
-  
    
-   This dataset depends on the
-   
-    US Equity Security Master
-   
-   dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes of the underlying security.
-  
    
-  
    
-   This dataset
-   
-    does not
-   
-   contain market data. For market data, see
-   
-    US Equity Options by AlgoSeek
-   
-   .
-  
    
-  
    
-   For more information about the US Equity Option Universe dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the US Equity Options Universe dataset:
-  
    
-  
    
-   
    option = self.add_option("GOOG")
-self.option_symbol = option.symbol
-option.set_filter(lambda universe: universe.delta(0.4, 0.6))
    
-   
    var option = AddOption("GOOG");
-_optionSymbol = option.Symbol;
-option.SetFilter(universe => universe.delta(0.4m, 0.6m));
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 2012
-     
    
-      Asset Coverage
-     
-      4,000 Symbols
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Daily
-     
    
-      Timezone
-     
-      New York
-     
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add US Equity Options Universe data to your algorithm, call the
-   
-    AddOption
-   
-   
-    add_option
-   
-   method. Save a reference to the Equity Option
-   
-    Symbol
-   
-   so you can access the data later in your algorithm. To define which contracts should be in your universe, call the
-   
-    SetFilter
-   
-   
-    set_filter
-   
-   method of the Option object.
-  
    
-  
    
-   The
-   
-    AddOption
-   
-   
-    add_option
-   
-   method provides a daily stream of Option chain data. To get the most recent daily chain, call the
-   
-    OptionChain
-   
-   
-    option_chain
-   
-   method with the underlying Equity Symbol. The
-   
-    OptionChain
-   
-   
-    option_chain
-   
-   method returns data on all the tradable contracts, not just the contracts that pass your universe filter.
-  
    
-  
    
-   
    class USEquityOptionsDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2020, 6, 1)
-        self.set_end_date(2021, 6, 1)
-        self.set_cash(100000)
-        self.universe_settings.asynchronous = True
-        option = self.add_option("GOOG")
-        self.option_symbol = option.symbol
-        # Set our strike/expiry filter for this option chain
-        option.set_filter(self._option_filter)
-        # Get the entire Option chain for the current day.
-        chain = self.option_chain(option.symbol.underlying, flatten=True).data_frame
-
-    def _option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
-        # Contracts can be filtered by greeks, implied volatility, open interest:
-        return universe \
-            .delta(0.5, 1.5) \
-            .gamma(0.0001, 0.0006) \
-            .vega(0.01, 1.5) \
-            .theta(-2.0, -0.5) \
-            .rho(0.5, 3.0) \
-            .implied_volatility(1, 3) \
-            .open_interest(100,500)
-
    
-   
    public class USEquityOptionsDataAlgorithm : QCAlgorithm
-{
-    private Symbol _optionSymbol;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2020, 6, 1);
-        SetEndDate(2021, 6, 1);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-        // Requesting data
-        var option = AddOption("GOOG");
-        _optionSymbol = option.Symbol;
-        // Set our strike/expiry filter for this option chain
-        option.SetFilter(OptionFilter);
-        // Get the entire Option chain for the current day.
-        var chain = OptionChain(option.Symbol.Underlying);
-    }
-
-    private OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
-    {
-        // Contracts can be filtered by greeks, implied volatility, open interest:
-        return universe
-            .Delta(0.5m, 1.5m)
-            .Gamma(0.0001m, 0.0006m)
-            .Vega(0.01m, 1.5m)
-            .Theta(-2.0m, -0.5m)
-            .Rho(0.5m, 3.0m)
-            .ImpliedVolatility(1.0m, 3.0m)
-            .OpenInterest(100, 500);
-    }
-}
    
-  
    
-  
    
-   The Equity resolution must be less than or equal to the Equity Option resolution. For example, if you set the Equity resolution to minute, then you must set the Equity Option resolution to minute, hour, or daily.
-  
    
-  
    
-   For more information about creating US Equity Option Universes, see
-   
-    Equity Options
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   For information about accessing US Equity Options Universe data, see
-   
-    Equity Options
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical US Equity Options Universe data in an algorithm and the Research Environment.
-  
    
-  
    
-   Historical Data In Algorithms
-  
    
-  
    
-   To get historical US Equity Options Universe data in an algorithm, call the
-   
-    History<OptionUniverse>
-   
-   
-    history
-   
-   method with the canonical Equity Option
-   
-    Symbol
-   
-   . This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter. If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame
-history_df = self.history(self.option_symbol, timedelta(3), flatten=True)
-
-# OptionUniverse objects
-history = self.history[OptionUniverse](self.option_symbol, timedelta(3))
    
-   
    // OptionUniverse objects 
-var history = History<OptionUniverse>(_optionSymbol, TimeSpan.FromDays(3)).ToList();
    
-  
    
-  
    
-   For more information about historical Equity Options Universe data in algorithms, see
-   
-    Historical Data
-   
-   .
-  
    
-  
    
-   Historical Data In Research
-  
    
-  
    
-   To get historical US Equity Options Universe data in the Research Environment, call the
-   
-    History<OptionUniverse>
-   
-   
-    history
-   
-   method with the canonical Option
-   
-    Symbol
-   
-   . This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter.
-  
    
-  
    
-   
    qb = QuantBook()
-option = qb.add_option("GOOG")
-history = qb.history(option.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5), flatten=True)
    
-   
    var qb = new QuantBook();
-var option = qb.AddOption("GOOG");
-var history = qb.History<OptionUniverse>(option.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 6));
-foreach (var chain in history)
-{
-    var endTime = chain.EndTime;
-    var filteredContracts = chain.Data
-        .Select(contract => contract as OptionUniverse)
-        .Where(contract => contract.Greeks.Delta > 0.3m);
-    foreach (var contract in filteredContracts)
-    {
-        var price = contract.Price;
-        var iv = contract.ImpliedVolatility;
-    }
-}
    
-  
    
-  
    
-   For more information about historical Equity Options Universe data in the Research Environment, see
-   
-    Universes
-   
-   .
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   To view the supported assets in the US Equity Options Universe dataset, see the
-   
-    Data Explorer
-   
-   .
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Equity Options Universe dataset enables you to accurately design Option strategies. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Buying put Options to hedge against downward price movement in positive Equity positions
-   
      • 
-   
    • 
-    Exploiting arbitrage opportunities that arise when the price of Option contracts deviate from their theoretical value
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm subscribes to Google put options that fall within delta range between -1 and -0.95, open interest range between 10 and 1000, and expire within seven days. Within this Option chain, the algorithm holds the put Option contract that has the minimum delta (closest to -1) during market hour to hedge the underlying intra-day movement completely. It avoid volatility from sentiment and only earns from inter-day movement from longer-term factors. When the contract expires, the algorithm rolls over to the next contract that meets this criteria.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class USEquityOptionsUniverseAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(10000000)
-
-        # Asynchronous can use computational resources efficiently
-        self.universe_settings.asynchronous = True
-        # Subscribe to the underlying for the underlying position
-        # Set the data normalization mode to raw for strike price comparability
-        self.equity = self.add_equity("GOOG", data_normalization_mode=DataNormalizationMode.RAW).symbol
-        # Requesting option data and filter for the hedge candidates
-        option = self.add_option(self.equity)
-        option.set_filter(self.option_filter)
-        self.option_symbol = option.symbol
-
-        # Set scheduled event to buy a hedge option contract at market open to eliminate the intra-day movement
-        self.schedule.on(
-            self.date_rules.every_day(self.equity),
-            self.time_rules.after_market_open(self.equity, 1),
-            self.buy_hedge_contract
-        )
-
-        # Set a scheduled event to sell the hedge contract before market close, since we want to earn from inter-day movement
-        # Leave 2 minutes contingency to fill
-        self.schedule.on(
-            self.date_rules.every_day(self.equity),
-            self.time_rules.before_market_close(self.equity, 2),
-            self.sell_hedge_contract
-        )
-
-        self.hedge = None
-        
-    def option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
-        # Select the contracts with delta very close to -1 and high open interest
-        # This can effectively hedge most of the price change of the underlying and ensure the liquidity
-        # Make sure the contract is expiring close for its tradbility
-        return universe.puts_only().expiration(2, 7).delta(-1, -0.95).open_interest(10, 1000)
-
-    def buy_hedge_contract(self) -> None:
-        chain = self.current_slice.option_chains.get(self.option_symbol)
-        if chain:
-            # Order the underlying if not hold, the order size should match the option contract
-            # Order only if option chain data ready for hedging
-            if not self.portfolio[self.equity].invested:
-                self.market_order(self.equity, self.securities[self.option_symbol].symbol_properties.contract_multiplier)
-
-            # Get the contract with delta closest to -1 (lowest possible delta)
-            contract = sorted(chain, key=lambda x: x.greeks.delta)[0]
-            self.hedge = contract.symbol
-            # Buy 1 deep ITM put with delta close to -1 to eliminate the intraday movement
-            self.market_order(self.hedge, 1)
-        
-    def sell_hedge_contract(self) -> None:
-        # Check if any hedge contract position, if so, liquidate before market close to expose to underlying overnight movement
-        if self.hedge:
-            self.liquidate(self.hedge)
-            self.hedge = None
    
-   
    public class USEquityOptionsUniverseAlgorithm : QCAlgorithm
-{
-    private Symbol _equity, _optionSymbol, _hedge;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(10000000);
-        // Asynchronous can use computational resources efficiently
-        UniverseSettings.Asynchronous = true;
-
-        // Subscribe to the underlying for the underlying position
-        // Set the data normalization mode to raw for strike price comparability
-        _equity = AddEquity("GOOG", dataNormalizationMode: DataNormalizationMode.Raw).Symbol;
-        // Requesting option data and filter for the hedge candidates
-        var option = AddOption(_equity);
-        _optionSymbol = option.Symbol;
-        option.SetFilter(OptionFilter);
-
-        // Set scheduled event to buy a hedge option contract at market open to eliminate the intra-day movement
-        Schedule.On(
-            DateRules.EveryDay(_equity),
-            TimeRules.AfterMarketOpen(_equity, 1),
-            BuyHedgeContract
-        );
-
-        // Set a scheduled event to sell the hedge contract before market close, since we want to earn from inter-day movement
-        // Leave 2 minutes contingency to fill
-        Schedule.On(
-            DateRules.EveryDay(_equity),
-            TimeRules.BeforeMarketClose(_equity, 2),
-            SellHedgeContract
-        );
-    }
-
-    private OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
-    {
-        // Select the contracts with delta very close to -1 and high open interest
-        // This can effectively hedge most of the price change of the underlying and ensure the liquidity
-        // Make sure the contract is expiring close for its tradbility
-        return universe
-            .PutsOnly()
-            .Expiration(2, 7)
-            .Delta(-1m, -0.95m)
-            .OpenInterest(10, 1000);
-    }
-
-    private void BuyHedgeContract()
-    {
-        if (CurrentSlice.OptionChains.TryGetValue(_optionSymbol, out var chain))
-        {
-            // Order the underlying if not hold, the order size should match the option contract
-            // Order only if option chain data ready for hedging
-            if (!Portfolio[_equity].Invested)
-            {
-                MarketOrder(_equity, Securities[_optionSymbol].SymbolProperties.ContractMultiplier);
-            }
-
-            // Get the contract with delta closest to -1 (lowest possible delta)
-            var contract = chain.MinBy(x => x.Greeks.Delta);
-            _hedge = contract.Symbol;
-            // Buy 1 deep ITM put with delta close to -1 to eliminate the intraday movement
-            MarketOrder(_hedge, 1);
-        }
-    }
-
-    private void SellHedgeContract()
-    {
-        // Check if any hedge contract position, if so, liquidate before market close to expose to underlying overnight movement
-        if (_hedge != null)
-        {
-            Liquidate(_hedge);
-            _hedge = null;
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm demonstrating a Gamma Scalping strategy through framework algorithm. It filters NVDA options with expiration between 30 to 90 days and open interest between 50 and 1000, due to liquidity concern and lower Gamma fluctuation. Assuming a significant upward trend is expecting, it orders a long straddle strategy from the strike price with Delta-neutral and the highest Gamma to earn the highest profit from upward underlying movement.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class USEquityOptionsUniverseFrameworkAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        # Asynchronous can use computational resources efficiently
-        self.universe_settings.asynchronous = True
-
-        # Universe selection that select based on liquidity of the option contracts (by open interest)
-        self.add_universe_selection(EquityOptionsUniverseSelectionModel())
-        # Custom alpha model that use Delta and Gamma to signal insights
-        self.add_alpha(OptionDeltaGammaAlphaModel())
-        # To maintain long-short size equal, use a PCM that order a single contract
-        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
-
-class EquityOptionsUniverseSelectionModel(OptionUniverseSelectionModel):
-    def __init__(self) -> None:
-        # Daily update with the select_option_chain_symbols function
-        super().__init__(timedelta(1), self.select_option_chain_symbols)
-
-    def select_option_chain_symbols(self, dt: datetime) -> List[Symbol]:
-        # Select only NVDA options as our focus, which is a highly traded volatile equity with great upward momentum
-        return [Symbol.create("NVDA", SecurityType.OPTION, Market.USA)]
-
-    def filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
-        # To ensure the liquidity and tradability, make sure the option is not 0-DTE and have a fair open interest
-        # A longer TTM will have lower Gamma thus more stable in the local delta-neutral position
-        return universe.expiration(30, 90).open_interest(50, 1000)
-
-class OptionDeltaGammaAlphaModel(AlphaModel):
-    def __init__(self) -> None:
-        # A day count variable to control the alpha model only trade once a day
-        self._day = -1
-
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        insights = []
-
-        if self._day != slice.time.day:
-            # We open position for each option underlying as separate bet
-            for _, chain in slice.option_chains.items():
-                # For theta-neutral, select the same expiry for both call and put
-                expiry = min(x.expiry for x in chain)
-                contracts = [x for x in chain if x.expiry == expiry]
-
-                # Calculate delta and gamma per strike price group for later filtering, ensure both call and put available for option strategy
-                delta_gamma_symbols = []
-                strikes = set(x.strike for x in contracts if len([y for y in contracts if y.strike == x.strike]) == 2)
-                for strike in strikes:
-                    # Get both call and put for their aggregated delta and gamma
-                    call = next(filter(lambda x: x.right == OptionRight.CALL and x.strike == strike, contracts))
-                    put = next(filter(lambda x: x.right == OptionRight.PUT and x.strike == strike, contracts))
-                    delta_gamma_symbols.append((call.greeks.delta + put.greeks.delta, call.greeks.gamma + put.greeks.gamma, call.symbol, put.symbol))
-                
-                if len(delta_gamma_symbols) == 0:
-                    continue
-
-                # We want a delta-neutral position, so it is likely to be ATM (like a long straddle)
-                # Less than 2d.p. difference is non-significant, which we can risk for better reward
-                # Assuming the market direction is up in most scenario, we try to get the strike providing max overall Gamma
-                # Make sure the aggregated gamma to be positive to bet on large uptrend
-                # So it will earn more when the price really go up higher and higher, but locally immune for small noise
-                filtered = [item for item in delta_gamma_symbols if round(item[0], 2) <= 0.01 and item[1] > 0]
-                if len(filtered) == 0:
-                    continue
-                selected = sorted(filtered, key=lambda x: x[1], reverse=True)[0]
-
-                # Provide trade signal and roll the day count to let the position stay for the whole day
-                selected_call = selected[2]
-                selected_put = selected[3]
-                insights.extend([
-                    Insight.price(selected_call, Expiry.END_OF_DAY, InsightDirection.UP),
-                    Insight.price(selected_put, Expiry.END_OF_DAY, InsightDirection.UP)
-                ])
-                self._day = slice.time.day
-        
-        return insights
-
-class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
-    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
-        targets = []
-        for insight in insights:
-            if algorithm.securities[insight.symbol].is_tradable:
-                # Use a whole number target to order the exact number of share for size-matching
-                targets.append(PortfolioTarget(insight.symbol, insight.direction))
-        return targets
    
-   
    public class USEquityOptionsUniverseFrameworkAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        // Asynchronous can use computational resources efficiently
-        UniverseSettings.Asynchronous = true;
-
-        // Universe selection that select based on liquidity of the option contracts (by open interest)
-        AddUniverseSelection(new EquityOptionsUniverseSelectionModel());
-        // Custom alpha model that use Delta and Gamma to signal insights
-        AddAlpha(new OptionDeltaGammaAlphaModel());
-        // To maintain long-short size equal, use a PCM that order a single contract
-        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
-    }
-}       
-    
-class EquityOptionsUniverseSelectionModel : OptionUniverseSelectionModel
-{
-    // Daily update with the SelectOptionChainSymbols function
-    public EquityOptionsUniverseSelectionModel()
-            : base(TimeSpan.FromDays(1), SelectOptionChainSymbols) {}
-    
-    private static IEnumerable<Symbol> SelectOptionChainSymbols(DateTime utcTime)
-    {
-        // Select only NVDA options as our focus, which is a highly traded volatile equity with great upward momentum
-        return new[] {QuantConnect.Symbol.Create("NVDA", SecurityType.Option, Market.USA)};
-    }
-
-    protected override OptionFilterUniverse Filter(OptionFilterUniverse filter)
-    {
-        // To ensure the liquidity and tradability, make sure the option is not 0-DTE and have a fair open interest
-        // A longer TTM will have lower Gamma thus more stable in the local delta-neutral position
-        return filter
-            .Expiration(30, 90)
-            .OpenInterest(50, 1000);
-    }
-}
-    
-class OptionDeltaGammaAlphaModel : AlphaModel
-{
-    // A day count variable to control the alpha model only trade once a day
-    private int _day = -1;
-
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        var insights = new List<Insight>();
-        
-        if (_day != slice.Time.Day)
-        {
-            // We open position for each option underlying as separate bet
-            foreach (var kvp in slice.OptionChains)
-            {
-                var chain = kvp.Value;
-
-                // For theta-neutral, select the same expiry for both call and put
-                var expiry = chain.Min(x => x.Expiry);
-                var contracts = chain.Where(x => x.Expiry == expiry).ToList();
-
-                // Calculate delta and gamma per strike price group for later filtering, ensure both call and put available for option strategy
-                var deltaGammaSymbols = new List<(decimal, decimal, Symbol, Symbol)>();
-                var strikes = contracts.Select(x => x.Strike)
-                    .Where(x => contracts.Count(y => y.Strike == x) == 2)
-                    .Distinct();
-                foreach (var strike in strikes)
-                {
-                    // Get both call and put for their aggregated delta and gamma
-                    var call = contracts.Single(x => x.Right == OptionRight.Call && x.Strike == strike);
-                    var put = contracts.Single(x => x.Right == OptionRight.Put && x.Strike == strike);
-                    deltaGammaSymbols.Add((call.Greeks.Delta + put.Greeks.Delta, call.Greeks.Gamma + put.Greeks.Gamma, call.Symbol, put.Symbol));
-                }
-
-                if (deltaGammaSymbols.Count == 0)
-                {
-                    continue;
-                }
-
-                // We want a delta-neutral position, so it is likely to be ATM (like a long straddle)
-                // Less than 2d.p. difference is non-significant, which we can risk for better reward
-                // Assuming the market direction is up in most scenario, we try to get the strike providing max overall Gamma
-                // Make sure the aggregated gamma to be positive to bet on large uptrend
-                // So it will earn more when the price really go up higher and higher, but locally immune for small noise
-                var filtered = deltaGammaSymbols.Where(item => Math.Round(item.Item1, 2) <= 0.01m && item.Item2 > 0).ToList();
-                if (filtered.Count == 0)
-                {
-                    continue;
-                }
-                var selected = filtered.OrderByDescending(item => item.Item2).First();
-
-                // Provide trade signal and roll the day count to let the position stay for the whole day
-                var selectedCall = selected.Item3;
-                var selectedPut = selected.Item4;
-                insights.AddRange(new[] {
-                    Insight.Price(selectedCall, Expiry.EndOfDay, InsightDirection.Up),
-                    Insight.Price(selectedPut, Expiry.EndOfDay, InsightDirection.Up)
-                });
-                _day = slice.Time.Day;
-            }
-        }
-
-        return insights;
-    }
-}
-    
-class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
-{
-    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
-    {
-        var targets = new List<PortfolioTarget>();
-        foreach (var insight in insights)
-        {
-            if (algorithm.Securities[insight.Symbol].IsTradable)
-            {
-                // Use a whole number target to order the exact number of share for size-matching
-                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
-            }
-        }
-        return targets;
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Equity Options Universe dataset provides
-   
-    OptionFilterUniverse
-   
-   ,
-   
-    OptionUniverse
-   
-   , and
-   
-    OptionChain
-   
-   objects.
-  
    
-  
    
-   OptionFilterUniverse Attributes
-  
    
-  
    
-   
-    OptionFilterUniverse
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   OptionUniverse Attributes
-  
    
-  
    
-   
-    OptionUniverse
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   OptionChain Attributes
-  
    
-  
    
-   
-    OptionChain
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > US Equity Security Master
    
-
    
-    
    
-        
    QuantConnect
    
-        
    US Equity Security Master
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Equity Security Master dataset by QuantConnect tracks US Equity corporate actions, including splits, dividends, delistings, mergers, and ticker changes through history. The data covers approximately 27,500 US Equities, starts in January 1998, and is delivered on a daily update frequency. You can easily download and install the dataset with the LEAN CLI so it's ready to use by LEAN. LEAN automatically handles all corporate actions and passes them into your algorithm as
-   
-    events
-   
-   .
-  
    
-  
    
-   This is
-   
-    NOT
-   
-   the underlying Equity data (
-   
-    US Equities
-   
-   dataset), which you need to purchase separately with a license from AlgoSeek. This security master dataset is required to purchase the US Equities or US Equities Options datasets.
-  
    
-  
    
-   For more information about the US Equity Security Master dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading.  Through the QuantConnect web platform, more than 50,000 quants are served every month.
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   Data is delivered as a daily updated zip archive of map and factor files. The data is designed to be used in the LEAN Engine and cannot be consumed another way. The following table shows the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 1998
-     
    
-      Data Points
-     
-      Splits, Dividends, Mergers, IPO, & Delistings
-     
    
-      Asset Coverage
-     
-      27,500 US Equities
-     
    
-      Resolution
-     
-      Daily
-     
    
-      Timezone
-     
-      New York
-     
    
-  
    
-   This is
-   
-    not
-   
-   the underlying Equity data (
-   
-    US Equities
-   
-   dataset), which you need to purchase separately with a license from AlgoSeek. This security master dataset is required to purchase the US Equities or US Equities Options datasets.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   You don't need any special code to utilize the US Equity Security Master. It automatically loads when you
-   
-    request US Equities data
-   
-   .
-  
    
- 
-
-
-
    Accessing Splits
    
-
- 
-  
    
-   To get the current split data, index the
-   
-    Splits
-   
-   
-    splits
-   
-   property of the current
-   
-    
-     Slice
-    
-   
-   with the Equity
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your security at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Check if any splits for the symbol
-    if slice.splits.contains_key(self._symbol):
-        # If so, get the mapped split object
-        split = slice.splits[self._symbol]
-        split_type = {0: "Warning", 1: "SplitOccurred"}.get(split.type)
-        self.log(f"Split: {split.symbol}\t{split.split_factor}\t{split.reference_price}\t{split_type}")
    
-   
    public override void OnData(Slice slice)
-{
-    // Check if any splits for the symbol
-    if (slice.Splits.ContainsKey(_symbol))
-    {
-        // If so, get the mapped split object
-        var split = slice.Splits[_symbol];
-        Log($"Split: {split.Symbol}\t{split.SplitFactor}\t{split.ReferencePrice}\t{split.Type}");
-    }
-}
    
-  
    
-  
    
-   For more information about accessing splits, see
-   
-    Splits
-   
-   .
-  
    
- 
-
-
-
    Accessing Dividends
    
-
- 
-  
    
-   To get the current dividend data, index the
-   
-    Dividends
-   
-   
-    dividends
-   
-   property of the current
-   
-    Slice
-   
-   with the Equity
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your security at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Check if any dividend for the symbol
-    if slice.dividends.contains_key(self._symbol):
-        # If so, get the mapped dividend object
-        dividend = slice.dividends[self._symbol]
-        self.log(f'Dividend: {dividend.symbol}\t{dividend.distribution}\t{dividend.reference_price}')
    
-   
    public override void OnData(Slice slice)
-{
-    // Check if any dividend for the symbol
-    if (slice.Dividends.ContainsKey(_symbol))
-    {
-        // If so, get the mapped dividend object
-        var dividend = slice.Dividends[_symbol];
-        Log($"Dividend: {dividend.Symbol}\t{dividend.Distribution}\t{dividend.ReferencePrice}");
-    }
-}
    
-  
    
-  
    
-   For more information about accessing dividends, see
-   
-    Dividends
-   
-   .
-  
    
- 
-
-
-
    Accessing Delistings
    
-
- 
-  
    
-   To get the current Delistings data, index the
-   
-    Delistings
-   
-   
-    delistings
-   
-   property of the current
-   
-    Slice
-   
-   with the Equity
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your security at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Check if any delisting for the symbol
-    if slice.delistings.contains_key(self._symbol):
-        # If so, get the mapped delisting object
-        delisting = slice.delistings[self._symbol]
-        delisting_type = {0: "Warning", 1: "Delisted"}.get(delisting.type)
-        self.log(f'Delistings: {delisting_type}')
    
-   
    public override void OnData(Slice slice)
-{
-    // Check if any delisting for the symbol
-    if (slice.Delistings.ContainsKey(_symbol))
-    {
-        // If so, get the mapped delisting object
-        var delisting = slice.Delistings[_symbol];
-        Log($"Delistings: {delisting.Type}");
-    }
-}
    
-  
    
-  
    
-   For more information about accessing delistings, see
-   
-    Delistings
-   
-   .
-  
    
- 
-
-
-
    Accessing Symbol Change Events
    
-
- 
-  
    
-   To get the current Symbol change events, index the
-   
-    SymbolChangedEvents
-   
-   
-    symbol_changed_events
-   
-   property of the current
-   
-    Slice
-   
-   with the Equity
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your security at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Check if any symbol change event for the symbol
-    if slice.symbol_changed_events.contains_key(self._symbol):
-        # If so, get the mapped SymbolChangeEvent object
-        symbol_changed_event = slice.symbol_changed_events[self._symbol]
-        self.log(f"Symbol changed: {symbol_changed_event.old_symbol} -> {symbol_changed_event.new_symbol}")
-
    
-   
    -public override void OnData(Slice slice){
-    // Check if any symbol change event for the symbol
-    if (slice.SymbolChangedEvents.ContainsKey(_symbol))
-    {
-        // If so, get the mapped SymbolChangeEvent object
-        var symbolChangedEvent = slice.SymbolChangedEvents[_symbol];
-        Log($"Symbol changed: {symbolChangedEvent.OldSymbol} -> {symbolChangedEvent.NewSymbol}");
-    }
-}
    
-  
    
-  
    
-   For more information about accessing Symbol change events, see
-   
-    Symbol Changes
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   To get historical US Equity Security Master data, call the
-   
-    History
-   
-   
-    history
-   
-   method with the data type and the Equity
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # Splits
-split_history_df = self.history(Split, self._symbol, timedelta(5*365))
-split_history = self.history[Split](self._symbol, timedelta(5*365))
-
-# Dividends
-dividend_history_df = self.history(Dividend, self._symbol, timedelta(5*365))
-dividend_history = self.history[Dividend](self._symbol, timedelta(5*365))
-
-# Symbol Changes
-symbol_change_history_df = self.history(SymbolChangedEvent, self._symbol, timedelta(5*365))
-symbol_change_history = self.history[SymbolChangedEvent](self._symbol, timedelta(5*365))
-
-# Delistings
-delisting_history_df = self.history(Delisting, self._symbol, timedelta(5*365))
-delisting_history = self.history[Delisting](self._symbol, timedelta(5*365))
    
-   
    // Splits
-var splitHistory = History<Split>(_symbol, TimeSpan.FromDays(5*365));
-
-// Dividends 
-var dividendHistory = History<Dividend>(_symbol, TimeSpan.FromDays(5*365));
-
-// Symbol Changes
-var symbolChangeHistory = History<SymbolChangedEvent>(_symbol, TimeSpan.FromDays(5*365));
-
-// Delistings
-var delistingHistory = History<Delisting>(_symbol, TimeSpan.FromDays(5*365));
    
-  
    
-  
    
-   For more information about historical data, see
-   
-    History Requests
-   
-   .
-  
    
- 
-
-
-
    Live Trading Considerations
    
-
- 
-  
    
-   In backtesting, corporate actions occurs at midnight (ET). In live trading, the live data for corporate actions arrives at 6/7 AM ET, so that's when they occur.
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   To view the supported assets in the US Equity Security Master dataset, see the
-   
-    Data Explorer
-   
-   . This dataset doesn't include Over-the-Counter (OTC) stocks.
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Security Master enables you to accurately design strategies harnessing any core corporate actions. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Post-dividend announcement trading strategies.
-   
      • 
-   
    • 
-    Trading on new Equities by monitoring for IPOs.
-   
      • 
-   
    • 
-    Harnessing split announcements for reverse-split announcement momentum.
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm logs the
-   
-    Split
-   
-   ,
-   
-    Dividend
-   
-   ,
-   
-    Delisting
-   
-   , and
-   
-    SymbolChangedEvent
-   
-   objects of Apple:
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class USEquitySecurityMasterAlgorithm (QCAlgorithm):
-
-    def initialize(self):
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(1000000)
-        
-        self.equity = self.add_equity("AAPL", Resolution.DAILY).symbol
-        
-    def on_data(self, slice: Slice) -> None:
-        # Accessing Data - Splits
-        split = slice.splits.get(self.equity)
-        if split:
-            self.debug(f"{self.time} >> SPLIT >> {split.symbol} - {split.split_factor} - {self.portfolio.cash} - {self.portfolio[self.equity].price}")
-        
-        # Accessing Data - Dividends
-        dividend = slice.dividends.get(self.equity)
-        if dividend:
-            self.debug(f"{self.time} >> DIVIDEND >> {dividend.symbol} - {dividend.distribution} - {self.portfolio.cash} - {self.portfolio[self.equity].price}")
-
-        # Accessing Data - Delisting
-        delisting = slice.delistings.get(self.equity)
-        if delisting:
-            delistingType = {0: "Warning", 1: "Delisted"}.get(delisting.type)
-            self.debug(f"{self.time} >> DELISTING >> {delisting.symbol} - {delistingType}")
-            
-        # Accessing Data - Symbol Changed Event
-        symbolChangedEvent = slice.symbol_changed_events.get(self.equity)
-        if symbolChangedEvent:
-            self.debug(f"{self.time} >> SYMBOL CHANGED >> {symbolChangedEvent.old_symbol} -> {symbolChangedEvent.new_symbol}")
-
    
-   
    public class USEquitySecurityMasterAlgorithm : QCAlgorithm
-{
-    private Symbol _equity;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(1000000);
-        
-        _equity = AddEquity("AAPL", Resolution.Daily).Symbol;
-    }
-    
-    public override void OnData(Slice slice)
-    {
-        // Accessing Data - Splits
-        if (slice.Splits.ContainsKey(_equity))
-        {
-            var split = slice.Splits[_equity];
-            Debug($"Split: {split.Symbol}\t{split.SplitFactor}\t{split.ReferencePrice}\t{split.Type}");
-        }
-        
-        // Accessing Data - Dividends
-        if (slice.Dividends.ContainsKey(_equity))
-        {
-            var dividend = slice.Dividends[_equity];
-            Log($"Dividend: {dividend.Symbol}\t{dividend.Distribution}\t{dividend.ReferencePrice}");
-        }
-        
-        // Accessing Data - Delisting
-        if (slice.Delistings.ContainsKey(_equity))
-        {
-            var delisting = slice.Delistings[_equity];
-            Log($"Delistings: {delisting.Type}");
-        }
-        
-        // Accessing Data - Symbol Changed Event
-        if (slice.SymbolChangedEvents.ContainsKey(_equity))
-        {
-            var symbolChangedEvent = slice.SymbolChangedEvents[_equity];
-            Log($"Symbol changed: {symbolChangedEvent.OldSymbol} -> {symbolChangedEvent.NewSymbol}");
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following algorithm demonstrates the payments for cash dividends in backtesting. When the data normalization mode is
-   
-    Raw
-   
-   , your portfolio receives cash dividends.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class PaymentAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-
-        # this will use the Tradier Brokerage open order split behavior
-        # forward split will modify open order to maintain order value
-        # reverse split open orders will be canceled
-        self.set_brokerage_model(BrokerageName.TRADIER_BROKERAGE)
-
-        self.universe_settings.resolution = Resolution.DAILY
-        self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
-        
-        # MSFT: Splits and Dividends
-        # GOOG: Symbol Changed Event
-        # AAA.1: Delisting
-        
-        self.set_universe_selection(ManualUniverseSelectionModel(
-            Symbol.create("MSFT", SecurityType.EQUITY, Market.USA)))
-
-        self.set_alpha(PaymentAlphaModel())
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
-        self.set_execution(BracketExecutionModel())
-            
-class PaymentAlphaModel(AlphaModel):
-
-    symbol = Symbol.EMPTY
-    
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        # Accessing Data - Splits
-        split = slice.splits.get(self.symbol)
-        if split:
-            algorithm.debug(f"{algorithm.time} >> SPLIT >> {split.symbol} - {split.split_factor} - {algorithm.portfolio.cash} - {algorithm.portfolio[self.symbol].price}")
-
-        # Accessing Data - Dividends
-        dividend = slice.dividends.get(self.symbol)
-        if dividend:
-            algorithm.debug(f"{algorithm.time} >> DIVIDEND >> {dividend.symbol} - {dividend.distribution} - {algorithm.portfolio.cash} - {algorithm.portfolio[self.symbol].price}")
-
-        # Accessing Data - Delistings
-        delisting = slice.delistings.get(self.symbol)
-        if delisting:
-            delistingType = {0: "Warning", 1: "Delisted"}.get(delisting.type)
-            algorithm.debug(f"{algorithm.time} >> DELISTING >> {delisting.symbol} - {delistingType}")
-
-        # Accessing Data - Symbol Changed Events
-        symbolChangedEvent = slice.symbol_changed_events.get(self.symbol)
-        if symbolChangedEvent:
-            algorithm.debug(f"{algorithm.time} >> SYMBOL CHANGED >> {symbolChangedEvent.old_symbol} -> {symbolChangedEvent.new_symbol}")
-
-        bar = slice.bars.get(self.symbol)
-        return [Insight.price(self.symbol, timedelta(1), InsightDirection.UP)] if bar else []
-
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        self.symbol = list(changes.added_securities)[0].symbol
-
-class BracketExecutionModel(ExecutionModel):
-    
-    def __init__(self) -> None:
-        '''Initializes a new instance of the ImmediateExecutionModel class'''
-        self.targets_collection = PortfolioTargetCollection()
-
-    def execute(self, algorithm: QCAlgorithm, targets: List[PortfolioTarget]) -> None:
-
-        # for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
-        self.targets_collection.add_range(targets)
-        if self.targets_collection.count > 0:
-            for target in self.targets_collection.order_by_margin_impact(algorithm):
-                # calculate remaining quantity to be ordered
-                quantity = OrderSizing.get_unordered_quantity(algorithm, target)
-                if quantity != 0 and algorithm.transactions.orders_count == 0:
-                    bar = algorithm.securities[target.symbol].get_last_data()
-                    algorithm.market_order(target.symbol, quantity)
-                    # place some orders that won't fill, when the split comes in they'll get modified to reflect the split
-                    algorithm.stop_market_order(target.symbol, -quantity, bar.low/2)
-                    algorithm.limit_order(target.symbol, -quantity, bar.high*2)
-
-            self.targets_collection.clear_fulfilled(algorithm)
-
    
-   
    public class PaymentsAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-            
-        // this will use the Tradier Brokerage open order split behavior
-        // forward split will modify open order to maintain order value
-        // reverse split open orders will be canceled
-        SetBrokerageModel(BrokerageName.TradierBrokerage);
-        
-        UniverseSettings.Resolution = Resolution.Daily;
-        UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
-        
-        // MSFT: Splits and Dividends
-        // GOOG: Symbol Changed Event
-        // AAA.1: Delisting
-        SetUniverseSelection(new ManualUniverseSelectionModel(
-            QuantConnect.Symbol.Create("MSFT", SecurityType.Equity, Market.USA)));
-
-        SetAlpha(new PaymentAlphaModel());
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
-        SetExecution(new BracketExecutionModel());
-    }
-}
-
-public class PaymentAlphaModel : AlphaModel
-{
-    private Symbol _symbol = Symbol.Empty;
-    
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        // Accessing Data - Splits
-        if (slice.Splits.ContainsKey(_symbol))
-        {
-            var split = slice.Splits[_symbol];
-            algorithm.Debug($"{split.Time.ToIso8601Invariant()} >> SPLIT >> {split.Symbol} - " +
-                $"{split.SplitFactor.ToStringInvariant()} - " +
-                $"{algorithm.Portfolio.Cash.ToStringInvariant()} - " +
-                $"{algorithm.Portfolio[_symbol].Quantity.ToStringInvariant()}");
-        }
-        
-        // Accessing Data - Dividends
-        if (slice.Dividends.ContainsKey(_symbol))
-        {
-            var dividend = slice.Dividends[_symbol];
-            algorithm.Debug($"{dividend.Time.ToStringInvariant("o")} >> DIVIDEND >> {dividend.Symbol} - " +
-                $"{dividend.Distribution.ToStringInvariant("C")} - {algorithm.Portfolio.Cash} - " +
-                $"{algorithm.Portfolio[_symbol].Price.ToStringInvariant("C")}");
-        }
-
-        // Accessing Data - Delisting
-        if (slice.Delistings.ContainsKey(_symbol))
-        {
-            var delisting = slice.Delistings[_symbol];
-            algorithm.Debug($"{delisting.Time.ToStringInvariant("o")} >> DELISTING >> {delisting.Type}");
-        }
-
-        // Accessing Data - Symbol Changed Event
-        if (slice.SymbolChangedEvents.ContainsKey(_symbol))
-        {
-            var symbolChangedEvent = slice.SymbolChangedEvents[_symbol];
-            algorithm.Debug($"{symbolChangedEvent.Time.ToStringInvariant("o")} >> Symbol Changed Event >> " +
-                $"{symbolChangedEvent.OldSymbol} -> {symbolChangedEvent.OldSymbol}");
-        }
-        
-        return slice.Bars.ContainsKey(_symbol)
-            ? new [] { Insight.Price(_symbol, TimeSpan.FromDays(1), InsightDirection.Up) }
-            : Enumerable.Empty<Insight>();
-    }
-    
-    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-    {
-        _symbol = changes.AddedSecurities.First().Symbol;
-    }
-}
-
-public class BracketExecutionModel : ExecutionModel
-{
-    private readonly PortfolioTargetCollection _targetsCollection = new PortfolioTargetCollection();
-
-    public override void Execute(QCAlgorithm algorithm, IPortfolioTarget[] targets)
-    {
-        _targetsCollection.AddRange(targets);
-        // for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
-        if (_targetsCollection.Count > 0)
-        {
-            foreach (var target in _targetsCollection.OrderByMarginImpact(algorithm))
-            {
-                // calculate remaining quantity to be ordered
-                var quantity = OrderSizing.GetUnorderedQuantity(algorithm, target);
-                if (quantity != 0 && algorithm.Transactions.OrdersCount == 0)
-                {
-                    var bar = algorithm.Securities[target.Symbol].GetLastData() as TradeBar;
-                    algorithm.MarketOrder(target.Symbol, quantity);
-                    // place some orders that won't fill, when the split comes in they'll get modified to reflect the split
-                    algorithm.StopMarketOrder(target.Symbol, -quantity, bar.Low/2);
-                    algorithm.LimitOrder(target.Symbol, -quantity, bar.High*2);
-                }
-            }
-
-            _targetsCollection.ClearFulfilled(algorithm);
-        }
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Equity Security Master dataset provides
-   
-    Split
-   
-   ,
-   
-    Dividend
-   
-   ,
-   
-    Delisting
-   
-   , and
-   
-    SymbolChangedEvent
-   
-   objects.
-  
    
-  
    
-   Split Attributes
-  
    
-  
    
-   When a split or merger occurs, we pass the previous
-   
-    Symbol
-   
-   data into your algorithm.
-   
-    Split
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   Dividend Attributes
-  
    
-  
    
-   Dividend events are triggered on the payment date.
-   
-    Dividend
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   Delisting Attributes
-  
    
-  
    
-   When a security is delisted, we notify your algorithm.
-   
-    Delisting
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   SymbolChangedEvent Attributes
-  
    
-  
    
-   When a security changes their ticker, we notify your algorithm.
-   
-    SymbolChangedEvent
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > US Future Option Universe
    
-
    
-    
    
-        
    QuantConnect
    
-        
    US Future Option Universe
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Future Option Universe dataset by QuantConnect lists the available US Future Options contracts and the current open interest. The data covers 15 Monthly Future contracts, starts in January 2012, and is delivered on a daily update frequency. This dataset is created by monitoring the trading activity on the CME, CBOT, NYMEX, and COMEX markets.
-  
    
-  
    
-   The US Future Option Universe dataset depends on the
-   
-    US Future Universe
-   
-   dataset because the US Future Universe dataset contains the universe of underlying Futures contracts. 
-  This dataset also depends on the
-   
-    US Futures Security Master
-   
-   dataset because the US Futures Security Master dataset contains information on symbol changes of the contracts.
-  
    
-  
    
-   This dataset
-   
-    does not
-   
-   contain market data. For market data, see
-   
-    US Future Options by AlgoSeek
-   
-   .
-  
    
-  
    
-   For more information about the US Future Option Universe dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the US Future Options Universe dataset:
-  
    
-  
    
-   
    future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
-future.set_filter(0, 90)
-self.add_future_option(future.symbol, lambda universe: universe.strikes(-1, 1))
    
-   
    var future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
-future.SetFilter(0, 90);
-AddFutureOption(future.Symbol, universe => universe.Strikes(-1, 1));
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 2012
-     
    
-      Asset Coverage
-     
-      15 Monthly Future Contracts. Standard expires only. No weeklies or 0DTE contracts.
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Daily
-     
    
-      Timezone
-     
-      New York
-     
    
-      Market Hours
-     
-      
-       Regular and Extended
-      
-     
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add US Future Options Universe data to your algorithm, call the
-   
-    AddFutureOption
-   
-   
-    add_future_option
-   
-   method. To define which contracts should be in your universe, specify the filter when requesting the Future Option data.
-  
    
-  
    
-   The
-   
-    AddFutureOption
-   
-   
-    add_future_option
-   
-   method provides a daily stream of Option chain data. To get the most recent daily chain, call the
-   
-    OptionChain
-   
-   
-    option_chain
-   
-   method with the underlying Future Symbol. The
-   
-    OptionChain
-   
-   
-    option_chain
-   
-   method returns data on all the tradable contracts, not just the contracts that pass your universe filter.
-  
    
-  
    
-   
    class USFutureOptionsDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2020, 6, 1)
-        self.set_end_date(2021, 6, 1)
-        self.set_cash(100000)
-        self.universe_settings.asynchronous = True
-        
-        self.future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
-        self.future.set_filter(0, 90)
-        # Set our strike/expiry filter for this option chain
-        self.add_future_option(self.future.symbol, self._option_filter)
-
-    def on_data(self, slice: Slice) -> None:
-        # Get the entire Option chain for the current day.
-        symbol = Symbol.create_canonical_option(self.future.mapped)
-        chain = self.option_chain(symbol, flatten=True).data_frame
-
-    def _option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
-        # Contracts can be filtered by strike, and expiration
-        return universe.strikes(-1, 1)
    
-   
    public class USFutureOptionsDataAlgorithm : QCAlgorithm
-{
-    private Future _future;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2020, 6, 1);
-        SetEndDate(2021, 6, 1);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-        
-        _future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
-        _future.SetFilter(0, 90);
-        // Set our strike/expiry filter for this option chain
-        AddFutureOption(_future.Symbol, OptionFilter);
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // Create canonical symbol for the mapped future contract, since option chains are mapped by canonical symbol.
-        var symbol = QuantConnect.Symbol.CreateCanonicalOption(_future.Mapped);
-        // Get the entire Option chain for the current day.
-        var chain = OptionChain(symbol);
-    }
-
-    private OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
-    {
-        // Contracts can be filtered by strike, and expiration
-        return universe.Strikes(-1, 1);
-    }
-}
    
-  
    
-  
    
-   The Future resolution must be less than or equal to the Future Option resolution. For example, if you set the Future resolution to minute, then you must set the Future Option resolution to minute, hour, or daily.
-  
    
-  
    
-   For more information about creating US Future Option universes, see
-   
-    Future Options
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   For information about accessing US Future Options Universe data, see
-   
-    Future Options
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical US Future Options Universe data in an algorithm and the Research Environment.
-  
    
-  
    
-   Historical Data In Algorithms
-  
    
-  
    
-   To get historical US Future Options Universe data in an algorithm, call the
-   
-    History<OptionUniverse>
-   
-   
-    history
-   
-   method with the canonical mapped Future Option
-   
-    Symbol
-   
-   . This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter. If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    future_option_symbol = Symbol.create_canonical_option(self.future.mapped)
-# DataFrame
-history_df = self.history(future_option_symbol, timedelta(10), flatten=True)
-# OptionUniverse objects
-history = self.history[OptionUniverse](future_option_symbol, timedelta(10))
    
-   
    var futureOptionSymbol = QuantConnect.Symbol.CreateCanonicalOption(_future.Mapped);
-var history = History<OptionUniverse>(futureOptionSymbol, TimeSpan.FromDays(10)).ToList();
    
-  
    
-  
    
-   For more information about historical US Future Options Universe data in algorithms, see
-   
-    Historical Data
-   
-   .
-  
    
-  
    
-   Historical Data In Research
-  
    
-  
    
-   To get historical US Future Options Universe data in the Research Environment, call the
-   
-    History<OptionUniverse>
-   
-   
-    history
-   
-   method with the canonical Option
-   
-    Symbol
-   
-   . This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter.
-  
    
-  
    
-   
    qb = QuantBook()
-future = qb.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
-future.set_filter(0, 90)
-symbol = Symbol.create_canonical_option(future.mapped)
-history = qb.history(symbol, datetime(2020, 6, 1), datetime(2020, 6, 5), flatten=True)
    
-   
    var qb = new QuantBook();
-var future = qb.AddFuture(Futures.Metals.Gold, Resolution.Minute);
-var symbol = QuantConnect.Symbol.CreateCanonicalOption(future.Mapped);
-var history = qb.History<OptionUniverse>(symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 6));
-foreach (var chain in history)
-{
-    var endTime = chain.EndTime;
-    var filteredContracts = chain.Data
-        .Select(contract => contract as OptionUniverse)
-        .Where(contract => contract.Greeks.Delta > 0.3m);
-    foreach (var contract in filteredContracts)
-    {
-        var price = contract.Price;
-        var iv = contract.ImpliedVolatility;
-    }
-}
    
-  
    
-  
    
-   For more information about historical Future Options Universe data in the Research Environment, see
-   
-    Universes
-   
-   .
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   The following list shows the available (15) Futures Options:
-  
    
-  
      
-   
    • 
-    
-     Futures.Energy.CrudeOilWTI
-    
-    
-     Futures.Energy.CRUDE_OIL_WTI
-    
-    : Crude Oil WTI Futures (NYMEX: LO | Underlying: CL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.Gasoline
-    
-    
-     Futures.Energy.GASOLINE
-    
-    : Gasoline RBOB Futures (NYMEX: OB | Underlying: RB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.HeatingOil
-    
-    
-     Futures.Energy.HEATING_OIL
-    
-    : Heating Oil Futures (NYMEX: OH | Underlying: HO)
-   
      • 
-   
    • 
-    
-     Futures.Energy.NaturalGas
-    
-    
-     Futures.Energy.NATURAL_GAS
-    
-    : Natural Gas Futures (NYMEX: ON | Underlying: NG)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y10TreasuryNote
-    
-    
-     Futures.Financials.Y_10_TREASURY_NOTE
-    
-    : 10Y U.S. Treasury Note Futures (CBOT: OZN | Underlying: ZN)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y2TreasuryNote
-    
-    
-     Futures.Financials.Y_2_TREASURY_NOTE
-    
-    : 2Y U.S. Treasury Note Futures (CBOT: OZT | Underlying: ZT)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y30TreasuryBond
-    
-    
-     Futures.Financials.Y_30_TREASURY_BOND
-    
-    : 30Y U.S. Treasury Bond Futures (CBOT: OZB | Underlying: ZB)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Corn
-    
-    
-     Futures.Grains.CORN
-    
-    : Corn Futures (CBOT: OZC | Underlying: ZC)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Soybeans
-    
-    
-     Futures.Grains.SOYBEANS
-    
-    : Soybeans Futures (CBOT: OZS | Underlying: ZS)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Wheat
-    
-    
-     Futures.Grains.WHEAT
-    
-    : Default wheat contract is SRWWheat (CBOT: OZW | Underlying: ZW)
-   
      • 
-   
    • 
-    
-     Futures.Indices.NASDAQ100EMini
-    
-    
-     Futures.Indices.NASDAQ_100_E_MINI
-    
-    : E-mini NASDAQ 100 Futures (CME: NQ)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SP500EMini
-    
-    
-     Futures.Indices.SP_500_E_MINI
-    
-    : E-mini S&P 500 Futures (CME: ES)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Copper
-    
-    
-     Futures.Metals.COPPER
-    
-    : Copper Futures (COMEX: HXE | Underlying: HG)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Gold
-    
-    
-     Futures.Metals.GOLD
-    
-    : Gold Futures (COMEX: OG | Underlying: GC)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Silver
-    
-    
-     Futures.Metals.SILVER
-    
-    : Silver Futures (COMEX: SO | Underlying: SI)
-   
      • 
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Future Options dataset enables you to accurately design Future Option strategies. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Selling out of the money Future Option contracts to collect the premium that the Option buyer pays
-   
      • 
-   
    • 
-    Buying put Options to hedge against downward price movement in Future contracts you bought
-   
      • 
-   
    • 
-    Exploiting arbitrage opportunities that arise when the price of Option contracts deviate from their theoretical value
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example demonstrates a bull call spread Option strategy using universe filtering.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class FutureOptionAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        # Filter the underlying continuous Futures to narrow the FOP spectrum.
-        self.underlying = self.add_future(Futures.Indices.SP_500_E_MINI,
-            extended_market_hours=True,
-            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
-            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
-            contract_depth_offset=0)
-        self.underlying.set_filter(0, 182)
-        # Use CallSpread filter to obtain the 2 best-matched contracts that forms a call spread.
-        # It simplifies from further filtering and reduce computation on redundant subscription.
-        self.add_future_option(self.underlying.symbol, lambda u: u.call_spread(5, 5, -5))
-    
-    def on_data(self, slice: Slice) -> None:
-        if self.portfolio.invested:
-            return
-        # Create canonical symbol for the mapped future contract, since we need that to access the option chain.
-        symbol = Symbol.create_canonical_option(self.underlying.mapped)
-    
-        # Get option chain data for the mapped future only.
-        # It requires 2 contracts with different strikes to form a call spread, so we make sure at least 2 contracts are present.
-        chain = slice.option_chains.get(symbol)
-        if not chain or len(list(chain)) < 2:
-            return
-            
-        # Separate the contracts by strike, as we need to access their strike.
-        expiry = min([x.expiry for x in chain])
-        sorted_by_strike = sorted([x.strike for x in chain])
-        itm_strike = sorted_by_strike[0]
-        otm_strike = sorted_by_strike[-1]
-        
-        # Use abstraction method to order a bull call spread to avoid manual error.
-        option_strategy = OptionStrategies.bull_call_spread(symbol, itm_strike, otm_strike, expiry)
-        self.buy(option_strategy, 1)
    
-   
    public class FutureOptionAlgorithm : QCAlgorithm
-{
-    private Future _underlying;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        // Filter the underlying continuous Futures to narrow the FOP spectrum.
-        _underlying = AddFuture(Futures.Indices.SP500EMini,
-            extendedMarketHours: true,
-            dataMappingMode: DataMappingMode.OpenInterest,
-            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
-            contractDepthOffset: 0);
-        _underlying.SetFilter(0, 182);
-        // Use CallSpread filter to obtain the 2 best-matched contracts that forms a call spread.
-        // It simplifies from further filtering and reduce computation on redundant subscription.
-        AddFutureOption(_underlying.Symbol, (u) => u.CallSpread(5, 5, -5));
-    }
-    
-    public override void OnData(Slice slice)
-    {
-        if (Portfolio.Invested)
-            return;
-
-        // Create canonical symbol for the mapped future contract, since we need that to access the option chain.
-        var symbol = QuantConnect.Symbol.CreateCanonicalOption(_underlying.Mapped);
-    
-        // Get option chain data for the mapped future only.
-        // It requires 2 contracts with different strikes to form a call spread, so we make sure at least 2 contracts are present.
-        if (!slice.OptionChains.TryGetValue(symbol, out var chain) || chain.Count() < 2)
-            return;
-    
-        // Separate the contracts by strike, as we need to access their strike.
-        var expiry = chain.Min(x => x.Expiry);
-        var itmStrike = chain.Min(x => x.Strike);
-        var otmStrike = chain.Max(x => x.Strike);
-    
-        // Use abstraction method to order a bull call spread to avoid manual error.
-        var optionStrategy = OptionStrategies.BullCallSpread(symbol, itmStrike, otmStrike, expiry);
-        Buy(optionStrategy, 1);
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Future Options Universe dataset provides
-   
-    OptionFilterUniverse
-   
-   and
-   
-    OptionUniverse
-   
-   objects.
-  
    
-  
    
-   OptionFilterUniverse Attributes
-  
    
-  
    
-   
-    OptionFilterUniverse
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   OptionUniverse Attributes
-  
    
-  
    
-   
-    OptionUniverse
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > US Future Universe
    
-
    
-    
    
-        
    QuantConnect
    
-        
    US Future Universe
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Future Universe dataset by QuantConnect lists the available US Future contracts, their daily trading volume, and Open Interest. The data covers the 157 most liquid contracts, starts in May 2009, and is delivered on daily frequency. This dataset is created by monitoring the trading activity on the CFE, CBOT, CME, COMEX, NYMEX, and ICE
-   
-    *
-   
-   .
-  
    
-  
    
-   This dataset depends on the
-   
-    US Futures Security Master
-   
-   dataset because the US Futures Security Master dataset contains information on symbol changes of the contracts.
-  
    
-  
    
-   This dataset
-   
-    does not
-   
-   contain market data. For market data, see
-   
-    US Futures by AlgoSeek
-   
-   .
-  
    
-  
    
-   For more information about the US Future Universe dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the US Future Universe dataset:
-  
    
-  
    
-   
    future = self.add_future(Futures.Metals.GOLD)
-future.set_filter(0, 90)
    
-   
    var future = AddFuture(Futures.Metals.Gold);
-future.SetFilter(0, 90);
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      May 2009
-     
    
-      Asset Coverage
-     
-      157 Futures
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Daily
-     
    
-      Timezone
-     
-      
      
-       
    • 
-        Chicago (CFE, CME or CBOT)
-       
      • 
-       
    • 
-        New York (ICE, NYMEX or COMEX)
-       
      • 
-      
    
-     
    
-      Market Hours
-     
-      
-       Regular and Extended
-      
-     
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add US Future Universe data to your algorithm, call the
-   
-    AddFuture
-   
-   
-    add_future
-   
-   method. Save a reference to the Future object so you can access the data later in your algorithm. To define which contracts should be in your universe, specify the filter when requesting the Future data.
-  
    
-  
    
-   The
-   
-    AddFuture
-   
-   
-    add_future
-   
-   method provides a daily stream of Future chain data. To get the most recent daily chain, call the
-   
-    FuturesChain
-   
-   
-    futures_chain
-   
-   method with the underlying Future Symbol. The
-   
-    FuturesChain
-   
-   
-    futures_chain
-   
-   method returns data on all the tradable contracts, not just the contracts that pass your universe filter.
-  
    
-  
    
-   
    class USFutureDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2020, 6, 1)
-        self.set_end_date(2021, 6, 1)
-        self.set_cash(100000)
-        self.universe_settings.asynchronous = True
-        self._future = self.add_future(Futures.Metals.GOLD)
-        # Set our contract filter for this Future chain.
-        self._future.set_filter(lambda universe: universe.standards_only().front_month())
-        # Get the entire Futures chain for the current day.
-        chain = self.futures_chain(self._future.symbol, flatten=True).data_frame
    
-   
    public class USFutureDataAlgorithm : QCAlgorithm
-{
-    private Future _future;
-        
-    public override void Initialize()
-    {
-        SetStartDate(2020, 6, 1);
-        SetEndDate(2021, 6, 1);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-        _future = AddFuture(Futures.Metals.Gold);
-        // Set our contract filter for this Future chain.
-        _future.SetFilter((universe) => universe.StandardsOnly().FrontMonth());
-        // Get the entire Futures chain for the current day.
-        var chain = FuturesChain(_future.Symbol);
-    }
-}
    
-  
    
-  
    
-   For more information about creating US Future universes, see
-   
-    Futures
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   For information about accessing US Future Universe data, see
-   
-    Futures
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical US Future Universe data in an algorithm and the Research Environment.
-  
    
-  
    
-   Historical Data In Algorithms
-  
    
-  
    
-   To get historical US Future Universe data in an algorithm, call the
-   
-    History<FutureUniverse>
-   
-   
-    history
-   
-   method with continuous Future
-   
-    Symbol
-   
-   and a lookback period. This method returns the all the available contracts for each trading day, not the subset of contracts that pass your universe filter. If there is no data for the period you requested, the history result is empty.
-  
    
-  
    
-   
    # Add the Future and save a reference to it.
-future = self.add_future(Futures.Metals.GOLD)
-
-# DataFrame example where the columns are the FutureUniverse attributes:
-history_df = self.history(FutureUniverse, future.symbol, 5, flatten=True)
-
-# Series example where the values are lists of FutureUniverse objects: 
-history_series = self.history(FutureUniverse, future.symbol, 5, flatten=False)
-
-# FutureUniverse objects example:
-history = self.history[FutureUniverse](future.symbol, 5)
    
-   
    // Add the Future and save a reference to it.
-var future = AddFuture(Futures.Metals.Gold);
-
-// Get historical data.
-var history = History<FutureUniverse>(future.Symbol, 5);
    
-  
    
-  
    
-   For more information about historical US Future Universe data in algorithms, see
-   
-    Contracts
-   
-   .
-  
    
-  
    
-   Historical Data In Research
-  
    
-  
    
-   To get historical US Future Universe data in the Research Environment, call the
-   
-    History<FutureUniverse>
-   
-   
-    history
-   
-   method with continuous Future
-   
-    Symbol
-   
-   and a time period. This method returns the all the available contracts for each trading day, not the subset of contracts that pass your universe filter. If there is no data for the period you requested, the history result is empty.
-  
    
-  
    
-   
    # Add the Future and save a reference to it.
-future = qb.add_future(Futures.Metals.GOLD)
-
-# DataFrame example where the columns are the FutureUniverse attributes:
-history_df = qb.history(FutureUniverse, future.symbol, datetime(2025, 1, 1), datetime(2025, 4, 1), flatten=True)
-
-# Series example where the values are lists of FutureUniverse objects: 
-history_series = qb.history(FutureUniverse, future.symbol, 5, flatten=False)
-
-# FutureUniverse objects example:
-history = qb.history[FutureUniverse](future.symbol, datetime(2025, 1, 1), datetime(2025, 4, 1))
    
-   
    // Add the Future and save a reference to it.
-var future = qb.AddFuture(Futures.Metals.Gold);
-
-// Get historical data.
-var history = qb.History<FutureUniverse>(future.Symbol, new DateTime(2025, 1, 1), new DateTime(2025, 4, 1));
    
-  
    
-  
    
-   For more information about historical US Future Universe data in the Research Environment, see
-   
-    Daily Prices History
-   
-   .
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   The following list shows the available (157) Futures:
-  
    
-  
      
-   
    • 
-    
-     Futures.Currencies.AUD
-    
-    : Australian Dollar Futures (CME: 6A)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.AUDCAD
-    
-    : Australian Dollar/Canadian Dollar Futures (CME: ACD)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.AUDJPY
-    
-    : Australian Dollar/Japanese Yen Futures (CME: AJY)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.AUDNZD
-    
-    : Australian Dollar/New Zealand Dollar Futures (CME: ANE)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.BRL
-    
-    : Brazillian Real Futures (CME: 6L)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.BTC
-    
-    : Bitcoin Futures (CME: BTC)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.BTICMicroBTC
-    
-    
-     Futures.Currencies.BTIC_MICRO_BTC
-    
-    : BTIC on Micro Bitcoin Futures (CME: MIB)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.BTICMicroEther
-    
-    
-     Futures.Currencies.BTIC_MICRO_ETHER
-    
-    : BTIC on Micro Ether Futures (CME: MRB)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.CAD
-    
-    : Canadian Dollar Futures (CME: 6C)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.CADJPY
-    
-    : Canadian Dollar/Japanese Yen Futures (CME: CJY)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.CHF
-    
-    : Swiss Franc Futures (CME: 6S)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.ETH
-    
-    : Ether Futures (CME: ETH)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EUR
-    
-    : Euro FX Futures (CME: 6E)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EURAUD
-    
-    : Euro/Australian Dollar Futures (CME: EAD)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EURCAD
-    
-    : Euro/Canadian Dollar Futures (CME: ECD)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EuroFXEmini
-    
-    
-     Futures.Currencies.EURO_FX_EMINI
-    
-    : E-mini Euro FX Futures (CME: E7)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EURSEK
-    
-    : Euro/Swedish Krona Futures (CME: ESK)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.GBP
-    
-    : British Pound Futures (CME: 6B)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.JapaneseYenEmini
-    
-    
-     Futures.Currencies.JAPANESE_YEN_EMINI
-    
-    : E-mini Japanese Yen Futures (CME: J7)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.JPY
-    
-    : Japanese Yen Futures (CME: 6J)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroAUD
-    
-    
-     Futures.Currencies.MICRO_AUD
-    
-    : Micro AUD/USD Futures (CME: M6A)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroBTC
-    
-    
-     Futures.Currencies.MICRO_BTC
-    
-    : Micro Bitcoin Futures (CME: MBT)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroCAD
-    
-    
-     Futures.Currencies.MICRO_CAD
-    
-    : Micro USD/CAD Futures (CME: M6C)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroCADUSD
-    
-    
-     Futures.Currencies.MICRO_CADUSD
-    
-    : Micro CAD/USD Futures (CME: MCD)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroCHF
-    
-    
-     Futures.Currencies.MICRO_CHF
-    
-    : Micro CHF/USD Futures (CME: MSF)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroEther
-    
-    
-     Futures.Currencies.MICRO_ETHER
-    
-    : Micro Ether Futures (CME: MET)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroEUR
-    
-    
-     Futures.Currencies.MICRO_EUR
-    
-    : Micro EUR/USD Futures (CME: M6E)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroGBP
-    
-    
-     Futures.Currencies.MICRO_GBP
-    
-    : Micro GBP/USD Futures (CME: M6B)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroINRUSD
-    
-    
-     Futures.Currencies.MICRO_INRUSD
-    
-    : Micro INR/USD Futures (CME: MIR)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroJPY
-    
-    
-     Futures.Currencies.MICRO_JPY
-    
-    : Micro JPY/USD Futures (CME: MJY)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroUSDCHF
-    
-    
-     Futures.Currencies.MICRO_USDCHF
-    
-    : Micro USD/CHF Futures (CME: M6S)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroUSDCNH
-    
-    
-     Futures.Currencies.MICRO_USDCNH
-    
-    : Micro USD/CNH Futures (CME: MNH)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroUSDJPY
-    
-    
-     Futures.Currencies.MICRO_USDJPY
-    
-    : Micro USD/JPY Futures (CME: M6J)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MXN
-    
-    : Mexican Peso Futures (CME: 6M)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.NZD
-    
-    : New Zealand Dollar Futures (CME: 6N)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.RUB
-    
-    : Russian Ruble Futures (CME: 6R)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.StandardSizeUSDOffshoreRMBCNH
-    
-    
-     Futures.Currencies.STANDARD_SIZE_USD_OFFSHORE_RMBCNH
-    
-    : Standard-Size USD/Offshore RMB (CNH) Futures (CME: CNH)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.ZAR
-    
-    : South African Rand Futures (CME: 6Z)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ArgusLLSvsWTIArgusTradeMonth
-    
-    
-     Futures.Energy.ARGUS_LL_SVS_WTI_ARGUS_TRADE_MONTH
-    
-    : Argus LLS vs. WTI (Argus) Trade Month Futures (NYMEX: AE5)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ArgusPropaneFarEastIndex
-    
-    
-     Futures.Energy.ARGUS_PROPANE_FAR_EAST_INDEX
-    
-    : Argus Propane Far East Index Futures (NYMEX: A7E)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ArgusPropaneSaudiAramco
-    
-    
-     Futures.Energy.ARGUS_PROPANE_SAUDI_ARAMCO
-    
-    : Argus Propane (Saudi Aramco) Futures (NYMEX: A9N)
-   
      • 
-   
    • 
-    
-     Futures.Energy.BrentCrudeOilVsDubaiCrudeOilPlatts
-    
-    
-     Futures.Energy.BRENT_CRUDE_OIL_VS_DUBAI_CRUDE_OIL_PLATTS
-    
-    : Brent Crude Oil vs. Dubai Crude Oil (Platts) Futures (NYMEX: ADB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.BrentLastDayFinancial
-    
-    
-     Futures.Energy.BRENT_LAST_DAY_FINANCIAL
-    
-    : Brent Last Day Financial Futures (NYMEX: BZ)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ChicagoEthanolPlatts
-    
-    
-     Futures.Energy.CHICAGO_ETHANOL_PLATTS
-    
-    : Chicago Ethaanol (Platts) Futures (NYMEX: CU)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ConwayPropaneOPIS
-    
-    
-     Futures.Energy.CONWAY_PROPANE_OPIS
-    
-    : Conway Propane (OPIS) Futures (NYMEX: A8K)
-   
      • 
-   
    • 
-    
-     Futures.Energy.CrudeOilWTI
-    
-    
-     Futures.Energy.CRUDE_OIL_WTI
-    
-    : Crude Oil WTI Futures (NYMEX: CL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.DubaiCrudeOilPlattsFinancial
-    
-    
-     Futures.Energy.DUBAI_CRUDE_OIL_PLATTS_FINANCIAL
-    
-    : Dubai Crude Oil (Platts) Financial Futures (NYMEX: DCB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EastWestGasolineSpreadPlattsArgus
-    
-    
-     Futures.Energy.EAST_WEST_GASOLINE_SPREAD_PLATTS_ARGUS
-    
-    : East-West Gasoline Spread (Platts-Argus) Futures (NYMEX: EWG)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EastWestNaphthaJapanCFvsCargoesCIFNWESpreadPlatts
-    
-    
-     Futures.Energy.EAST_WEST_NAPHTHA_JAPAN_C_FVS_CARGOES_CIFNWE_SPREAD_PLATTS
-    
-    : East-West Naphtha: Japan C&F vs. Cargoes CIF NWE Spread (Platts) Futures (NYMEX: EWN)
-   
      • 
-   
    • 
-    
-     Futures.Energy.Ethanol
-    
-    
-     Futures.Energy.ETHANOL
-    
-    : Ethanol Futures (CBOT: EH)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EthanolT2FOBRdamIncludingDutyPlatts
-    
-    
-     Futures.Energy.ETHANOL_T_2_FOB_RDAM_INCLUDING_DUTY_PLATTS
-    
-    : Ethanol T2 FOB Rdam Including Duty (Platts) Futures (NYMEX: AZ1)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EuropeanNaphthaPlattsCrackSpread
-    
-    
-     Futures.Energy.EUROPEAN_NAPHTHA_PLATTS_CRACK_SPREAD
-    
-    : European Naphtha (Platts) Crack Spread Futures (NYMEX: EN)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EuropeanPropaneCIFARAArgus
-    
-    
-     Futures.Energy.EUROPEAN_PROPANE_CIFARA_ARGUS
-    
-    : European Propane CIF ARA (Argus) Futures (NYMEX: APS)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EuropeanPropaneCIFARAArgusVsNaphthaCargoesCIFNWEPlatts
-    
-    
-     Futures.Energy.EUROPEAN_PROPANE_CIFARA_ARGUS_VS_NAPHTHA_CARGOES_CIFNWE_PLATTS
-    
-    : European Propane CIF ARA (Argus) vs. Naphtha Cargoes CIF NWE (Platts) Futures (NYMEX: EPN)
-   
      • 
-   
    • 
-    
-     Futures.Energy.FreightRouteTC14Baltic
-    
-    
-     Futures.Energy.FREIGHT_ROUTE_TC_14_BALTIC
-    
-    : Freight Route TC14 (Baltic) Futures (NYMEX: FRC)
-   
      • 
-   
    • 
-    
-     Futures.Energy.Gasoline
-    
-    
-     Futures.Energy.GASOLINE
-    
-    : Gasoline RBOB Futures (NYMEX: RB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GasolineEurobobOxyNWEBargesArgus
-    
-    
-     Futures.Energy.GASOLINE_EUROBOB_OXY_NWE_BARGES_ARGUS
-    
-    : Gasoline Euro-bob Oxy NWE Barges (Argus) Futures (NYMEX: B7H)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GroupThreeSuboctaneGasolinePlattsVsRBOB
-    
-    
-     Futures.Energy.GROUP_THREE_SUBOCTANE_GASOLINE_PLATTS_VS_RBOB
-    
-    : Group Three Sub-octane Gasoliine (Platts) vs. RBOB Futures (NYMEX: AA8)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GroupThreeULSDPlattsVsNYHarborULSD
-    
-    
-     Futures.Energy.GROUP_THREE_ULSD_PLATTS_VS_NY_HARBOR_ULSD
-    
-    : Group Three ULSD (Platts) vs. NY Harbor ULSD Futures (NYMEX: AA6)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GulfCoastCBOBGasolineA2PlattsVsRBOBGasoline
-    
-    
-     Futures.Energy.GULF_COAST_CBOB_GASOLINE_A_2_PLATTS_VS_RBOB_GASOLINE
-    
-    : Gulf Coast CBOB Gasoline A2 (Platts) vs. RBOB Gasoline Futures (NYMEX: CRB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GulfCoastHSFOPlattsVsEuropeanThreePointFivePercentFuelOilBargesFOBRdamPlatts
-    
-    
-     Futures.Energy.GULF_COAST_HSFO_PLATTS_VS_EUROPEAN_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
-    
-    : Gulf Coast HSFO (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: GCU)
-   
      • 
-   
    • 
-    
-     Futures.Energy.HeatingOil
-    
-    
-     Futures.Energy.HEATING_OIL
-    
-    : Heating Oil Futures (NYMEX: HO)
-   
      • 
-   
    • 
-    
-     Futures.Energy.LosAngelesCARBOBGasolineOPISvsRBOBGasoline
-    
-    
-     Futures.Energy.LOS_ANGELES_CARBOB_GASOLINE_OPI_SVS_RBOB_GASOLINE
-    
-    : Los Angeles CARBOB Gasoline (OPIS) vs. RBOB Gasoline Futures (NYMEX: AJL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.LosAngelesCARBDieselOPISvsNYHarborULSD
-    
-    
-     Futures.Energy.LOS_ANGELES_CARB_DIESEL_OPI_SVS_NY_HARBOR_ULSD
-    
-    : Los Angeles CARB Diesel (OPIS) vs. NY Harbor ULSD Futures (NYMEX: AKL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.LosAngelesJetOPISvsNYHarborULSD
-    
-    
-     Futures.Energy.LOS_ANGELES_JET_OPI_SVS_NY_HARBOR_ULSD
-    
-    : Los Angeles Jet (OPIS) vs. NY Harbor ULSD Futures (NYMEX: AJS)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MarsArgusVsWTIFinancial
-    
-    
-     Futures.Energy.MARS_ARGUS_VS_WTI_FINANCIAL
-    
-    : Mars (Argus) vs. WTI Financial Futures (NYMEX: AYX)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MarsArgusVsWTITradeMonth
-    
-    
-     Futures.Energy.MARS_ARGUS_VS_WTI_TRADE_MONTH
-    
-    : Mars (Argus) vs. WTI Trade Month Futures (NYMEX: AYV)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroCrudeOilWTI
-    
-    
-     Futures.Energy.MICRO_CRUDE_OIL_WTI
-    
-    : Micro WTI Crude Oil Futures (NYMEX: MCL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroEuropeanFOBRdamMarineFuelZeroPointFivePercentBargesPlatts
-    
-    
-     Futures.Energy.MICRO_EUROPEAN_FOB_RDAM_MARINE_FUEL_ZERO_POINT_FIVE_PERCENT_BARGES_PLATTS
-    
-    : Micro European FOB Rdam Marine Fuel 0.5% Barges (Platts) Futures (NYMEX: R5O)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroEuropeanThreePointFivePercentOilBargesFOBRdamPlatts
-    
-    
-     Futures.Energy.MICRO_EUROPEAN_THREE_POINT_FIVE_PERCENT_OIL_BARGES_FOB_RDAM_PLATTS
-    
-    : Micro European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: MEF)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroGasoilZeroPointOnePercentBargesFOBARAPlatts
-    
-    
-     Futures.Energy.MICRO_GASOIL_ZERO_POINT_ONE_PERCENT_BARGES_FOBARA_PLATTS
-    
-    : Micro Gasoil 0.1% Barges FOB ARA (Platts) Futures (NYMEX: M1B)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroSingaporeFOBMarineFuelZeroPointFivePercetPlatts
-    
-    
-     Futures.Energy.MICRO_SINGAPORE_FOB_MARINE_FUEL_ZERO_POINT_FIVE_PERCET_PLATTS
-    
-    : Micro Singapore FOB Marine Fuel 0.5% (Platts) Futures (NYMEX: S5O)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroSingaporeFuelOil380CSTPlatts
-    
-    
-     Futures.Energy.MICRO_SINGAPORE_FUEL_OIL_380_CST_PLATTS
-    
-    : Micro Singapore Fuel Oil 380CST (Platts) Futures (NYMEX: MAF)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MiniEuropeanThreePointPercentFiveFuelOilBargesPlatts
-    
-    
-     Futures.Energy.MINI_EUROPEAN_THREE_POINT_PERCENT_FIVE_FUEL_OIL_BARGES_PLATTS
-    
-    : Mini European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: A0D)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MiniSingaporeFuelOil180CstPlatts
-    
-    
-     Futures.Energy.MINI_SINGAPORE_FUEL_OIL_180_CST_PLATTS
-    
-    : Mini Singapore Fuel Oil 180 cst (Platts) Futures (NYMEX: A0F)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MontBelvieuEthaneOPIS
-    
-    
-     Futures.Energy.MONT_BELVIEU_ETHANE_OPIS
-    
-    : Mont Belvieu Ethane (OPIS) Futures (NYMEX: AC0)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MontBelvieuLDHPropaneOPIS
-    
-    
-     Futures.Energy.MONT_BELVIEU_LDH_PROPANE_OPIS
-    
-    : Mont Belvieu LDH Propane (OPIS) Futures (NYMEX: B0)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MontBelvieuNaturalGasolineOPIS
-    
-    
-     Futures.Energy.MONT_BELVIEU_NATURAL_GASOLINE_OPIS
-    
-    : Mont Belvieu Natural Gasoline (OPIS) Futures (NYMEX: A7Q)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MontBelvieuNormalButaneOPIS
-    
-    
-     Futures.Energy.MONT_BELVIEU_NORMAL_BUTANE_OPIS
-    
-    : Mont Belvieu Normal Butane (OPIS) Futures (NYMEX: AD0)
-   
      • 
-   
    • 
-    
-     Futures.Energy.NaturalGas
-    
-    
-     Futures.Energy.NATURAL_GAS
-    
-    : Natural Gas Futures (NYMEX: NG)
-   
      • 
-   
    • 
-    
-     Futures.Energy.NaturalGasHenryHubLastDayFinancial
-    
-    
-     Futures.Energy.NATURAL_GAS_HENRY_HUB_LAST_DAY_FINANCIAL
-    
-    : Natural Gas (Henry Hub) Last-day Financial Futures (NYMEX: HH)
-   
      • 
-   
    • 
-    
-     Futures.Energy.NaturalGasHenryHubPenultimateFinancial
-    
-    
-     Futures.Energy.NATURAL_GAS_HENRY_HUB_PENULTIMATE_FINANCIAL
-    
-    : Natural Gas (Henry Hub) Penultimate Financial Futures (NYMEX: HP)
-   
      • 
-   
    • 
-    
-     Futures.Energy.OnePercentFuelOilCargoesFOBNWEPlattsVsThreePointFivePercentFuelOilBargesFOBRdamPlatts
-    
-    
-     Futures.Energy.ONE_PERCENT_FUEL_OIL_CARGOES_FOBNWE_PLATTS_VS_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
-    
-    : 1% Fuel Oil Cargoes FOB NWE (Platts) vs. 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: FSS)
-   
      • 
-   
    • 
-    
-     Futures.Energy.PremiumUnleadedGasoline10ppmFOBMEDPlatts
-    
-    
-     Futures.Energy.PREMIUM_UNLEADED_GASOLINE_10_PPM_FOBMED_PLATTS
-    
-    : Premium Unleaded Gasoline 10 ppm FOB MED (Platts) Futures (NYMEX: A3G)
-   
      • 
-   
    • 
-    
-     Futures.Energy.PropaneNonLDHMontBelvieuOPIS
-    
-    
-     Futures.Energy.PROPANE_NON_LDH_MONT_BELVIEU_OPIS
-    
-    : Propane Non-LDH Mont Belvieu (OPIS) Futures (NYMEX: A1R)
-   
      • 
-   
    • 
-    
-     Futures.Energy.RBOBGasolineCrackSpread
-    
-    
-     Futures.Energy.RBOB_GASOLINE_CRACK_SPREAD
-    
-    : RBOB Gasoline Crack Spread Futures (NYMEX: ARE)
-   
      • 
-   
    • 
-    
-     Futures.Energy.RBOBGasolineVsEurobobOxyNWEBargesArgusThreeHundredFiftyThousandGallons
-    
-    
-     Futures.Energy.RBOB_GASOLINE_VS_EUROBOB_OXY_NWE_BARGES_ARGUS_THREE_HUNDRED_FIFTY_THOUSAND_GALLONS
-    
-    : RBOB Gasoline vs. Euro-bob Oxy NWE Barges (Argus) (350,000 gallons) Futures (NYMEX: EXR)
-   
      • 
-   
    • 
-    
-     Futures.Energy.SingaporeFuelOil380cstPlattsVsEuropeanThreePointFivePercentFuelOilBargesFOBRdamPlatts
-    
-    
-     Futures.Energy.SINGAPORE_FUEL_OIL_380_CST_PLATTS_VS_EUROPEAN_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
-    
-    : Singapore Fuel Oil 380 cst (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: EVC)
-   
      • 
-   
    • 
-    
-     Futures.Energy.SingaporeGasoilPlattsVsLowSulphurGasoilFutures
-    
-    
-     Futures.Energy.SINGAPORE_GASOIL_PLATTS_VS_LOW_SULPHUR_GASOIL_FUTURES
-    
-    : Singapore Gasoil (Platts) vs. Low Sulphur Gasoil Futures (NYMEX: AGA)
-   
      • 
-   
    • 
-    
-     Futures.Energy.SingaporeMogas92UnleadedPlattsBrentCrackSpread
-    
-    
-     Futures.Energy.SINGAPORE_MOGAS_92_UNLEADED_PLATTS_BRENT_CRACK_SPREAD
-    
-    : Singapore Mogas 92 Unleaded (Platts) Brent Crack Spread Futures (NYMEX: D1N)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ThreePointFivePercentFuelOilBargesFOBRdamPlattsCrackSpread
-    
-    
-     Futures.Energy.THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS_CRACK_SPREAD
-    
-    : 3.5% Fuel Oil Barges FOB Rdam (Platts) Crack Spread Futures (NYMEX: FO)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ThreePointFivePercentFuelOilBargesFOBRdamPlattsCrackSpread1000mt
-    
-    
-     Futures.Energy.THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS_CRACK_SPREAD_1000_MT
-    
-    : 3.5% Fuel Oil Barges FOB Rdam (Platts) Crack Spread (1000mt) Futures (NYMEX: BOO)
-   
      • 
-   
    • 
-    
-     Futures.Energy.WTIBrentFinancial
-    
-    
-     Futures.Energy.WTI_BRENT_FINANCIAL
-    
-    : WTI-Brent Financial Futures (NYMEX: BK)
-   
      • 
-   
    • 
-    
-     Futures.Energy.WTIFinancial
-    
-    
-     Futures.Energy.WTI_FINANCIAL
-    
-    : WTI Financial Futures (NYMEX: CSX)
-   
      • 
-   
    • 
-    
-     Futures.Energy.WTIHoustonArgusVsWTITradeMonth
-    
-    
-     Futures.Energy.WTI_HOUSTON_ARGUS_VS_WTI_TRADE_MONTH
-    
-    : WTI Houston (Argus) vs. WTI Trade Month Futures (NYMEX: HTT)
-   
      • 
-   
    • 
-    
-     Futures.Energy.WTIHoustonCrudeOil
-    
-    
-     Futures.Energy.WTI_HOUSTON_CRUDE_OIL
-    
-    : WTI Houston Crude Oil Futures (NYMEX: HCL)
-   
      • 
-   
    • 
-    
-     Futures.Financials.EuroDollar
-    
-    
-     Futures.Financials.EURO_DOLLAR
-    
-    : EuroDollar Futures (CME: GE)
-   
      • 
-   
    • 
-    
-     Futures.Financials.FiveYearUSDMACSwap
-    
-    
-     Futures.Financials.FIVE_YEAR_USDMAC_SWAP
-    
-    : 5-Year USD MAC Swap Futures (CBOT: F1U)
-   
      • 
-   
    • 
-    
-     Futures.Financials.MicroY10TreasuryNote
-    
-    
-     Futures.Financials.MICRO_Y_10_TREASURY_NOTE
-    
-    : Micro 10-Year Yield Futures (CBOT: 10Y)
-   
      • 
-   
    • 
-    
-     Futures.Financials.MicroY2TreasuryBond
-    
-    
-     Futures.Financials.MICRO_Y_2_TREASURY_BOND
-    
-    : Micro 2-Year Yield Futures (CBOT: 2YY)
-   
      • 
-   
    • 
-    
-     Futures.Financials.MicroY30TreasuryBond
-    
-    
-     Futures.Financials.MICRO_Y_30_TREASURY_BOND
-    
-    : Micro 30-Year Yield Futures (CBOT: 30Y)
-   
      • 
-   
    • 
-    
-     Futures.Financials.MicroY5TreasuryBond
-    
-    
-     Futures.Financials.MICRO_Y_5_TREASURY_BOND
-    
-    : Micro 5-Year Yield Futures (CBOT: 5YY)
-   
      • 
-   
    • 
-    
-     Futures.Financials.UltraTenYearUSTreasuryNote
-    
-    
-     Futures.Financials.ULTRA_TEN_YEAR_US_TREASURY_NOTE
-    
-    : Ultra 10-Year U.S. Treasury Note Futures (CBOT: TN)
-   
      • 
-   
    • 
-    
-     Futures.Financials.UltraUSTreasuryBond
-    
-    
-     Futures.Financials.ULTRA_US_TREASURY_BOND
-    
-    : Ultra U.S. Treasury Bond Futures (CBOT: UB)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y10TreasuryNote
-    
-    
-     Futures.Financials.Y_10_TREASURY_NOTE
-    
-    : 10Y U.S. Treasury Note Futures (CBOT: ZN)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y2TreasuryNote
-    
-    
-     Futures.Financials.Y_2_TREASURY_NOTE
-    
-    : 2Y U.S. Treasury Note Futures (CBOT: ZT)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y30TreasuryBond
-    
-    
-     Futures.Financials.Y_30_TREASURY_BOND
-    
-    : 30Y U.S. Treasury Bond Futures (CBOT: ZB)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y5TreasuryNote
-    
-    
-     Futures.Financials.Y_5_TREASURY_NOTE
-    
-    : 5Y U.S. Treasury Note Futures (CBOT: ZF)
-   
      • 
-   
    • 
-    
-     Futures.Forestry.Lumber
-    
-    
-     Futures.Forestry.LUMBER
-    
-    : Lumber Futures (CME: LBR)
-   
      • 
-   
    • 
-    
-     Futures.Forestry.RandomLengthLumber
-    
-    
-     Futures.Forestry.RANDOM_LENGTH_LUMBER
-    
-    : Random Length Lumber Futures (CME: LBS)
-   
      • 
-   
    • 
-    
-     Futures.Grains.BlackSeaCornFinanciallySettledPlatts
-    
-    
-     Futures.Grains.BLACK_SEA_CORN_FINANCIALLY_SETTLED_PLATTS
-    
-    : Black Sea Corn Financially Settled (Platts) Futures (CBOT: BCF)
-   
      • 
-   
    • 
-    
-     Futures.Grains.BlackSeaWheatFinanciallySettledPlatts
-    
-    
-     Futures.Grains.BLACK_SEA_WHEAT_FINANCIALLY_SETTLED_PLATTS
-    
-    : Black Sea Wheat Financially Settled (Platts) Futures (CBOT: BWF)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Corn
-    
-    
-     Futures.Grains.CORN
-    
-    : Corn Futures (CBOT: ZC)
-   
      • 
-   
    • 
-    
-     Futures.Grains.HRWWheat
-    
-    
-     Futures.Grains.HRW_WHEAT
-    
-    : KC HRW Wheat Futures (CBOT: KE)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Oats
-    
-    
-     Futures.Grains.OATS
-    
-    : Oats Futures (CBOT: ZO)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Soybeans
-    
-    
-     Futures.Grains.SOYBEANS
-    
-    : Soybeans Futures (CBOT: ZS)
-   
      • 
-   
    • 
-    
-     Futures.Grains.SoybeanMeal
-    
-    
-     Futures.Grains.SOYBEAN_MEAL
-    
-    : Soybean Meal Futures (CBOT: ZM)
-   
      • 
-   
    • 
-    
-     Futures.Grains.SoybeanOil
-    
-    
-     Futures.Grains.SOYBEAN_OIL
-    
-    : Soybean Oil Futures (CBOT: ZL)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Wheat
-    
-    
-     Futures.Grains.WHEAT
-    
-    : Default wheat contract is SRWWheat (CBOT: ZW)
-   
      • 
-   
    • 
-    
-     Futures.Indices.BloombergCommodityIndex
-    
-    
-     Futures.Indices.BLOOMBERG_COMMODITY_INDEX
-    
-    : Bloomberg Commodity Index Futures (CBOT: AW)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Dow30EMini
-    
-    
-     Futures.Indices.DOW_30_E_MINI
-    
-    : E-mini Dow Indu 30 Futures (CBOT: YM)
-   
      • 
-   
    • 
-    
-     Futures.Indices.DowJonesRealEstate
-    
-    
-     Futures.Indices.DOW_JONES_REAL_ESTATE
-    
-    : Dow Jones Real Estate futures on CME (CME: RX)
-   
      • 
-   
    • 
-    
-     Futures.Indices.FTSEEmergingEmini
-    
-    
-     Futures.Indices.FTSE_EMERGING_EMINI
-    
-    : E-mini FTSE Emerging Index Futures (CME: EI)
-   
      • 
-   
    • 
-    
-     Futures.Indices.MicroDow30EMini
-    
-    
-     Futures.Indices.MICRO_DOW_30_E_MINI
-    
-    : Micro E-mini Dow Jones Industrial Average Index Futures (CBOT: MYM)
-   
      • 
-   
    • 
-    
-     Futures.Indices.MicroNASDAQ100EMini
-    
-    
-     Futures.Indices.MICRO_NASDAQ_100_E_MINI
-    
-    : Micro E-mini Nasdaq-100 Index Futures (CME: MNQ)
-   
      • 
-   
    • 
-    
-     Futures.Indices.MicroRussell2000EMini
-    
-    
-     Futures.Indices.MICRO_RUSSELL_2000_E_MINI
-    
-    : Micro E-mini Russell 2000 Index Futures (CME: M2K)
-   
      • 
-   
    • 
-    
-     Futures.Indices.MicroSP500EMini
-    
-    
-     Futures.Indices.MICRO_SP_500_E_MINI
-    
-    : Micro E-mini S&P 500 Index Futures (CME: MES)
-   
      • 
-   
    • 
-    
-     Futures.Indices.NASDAQ100BiotechnologyEMini
-    
-    
-     Futures.Indices.NASDAQ_100_BIOTECHNOLOGY_E_MINI
-    
-    : E-mini Nasdaq-100 Biotechnology Index Futures (CME: BIO)
-   
      • 
-   
    • 
-    
-     Futures.Indices.NASDAQ100EMini
-    
-    
-     Futures.Indices.NASDAQ_100_E_MINI
-    
-    : E-mini NASDAQ 100 Futures (CME: NQ)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Nikkei225Dollar
-    
-    
-     Futures.Indices.NIKKEI_225_DOLLAR
-    
-    : Nikkei-225 Dollar Futures (CME: NKD)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Nikkei225YenCME
-    
-    
-     Futures.Indices.NIKKEI_225_YEN_CME
-    
-    : Nikkei-225 Yen denominated Futures on CME (CME: NIY)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Russell1000EMini
-    
-    
-     Futures.Indices.RUSSELL_1000_E_MINI
-    
-    : E-mini Russell 1000 futures on CME (CME: RS1)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Russell2000EMini
-    
-    
-     Futures.Indices.RUSSELL_2000_E_MINI
-    
-    : E-mini Russell 2000 Futures (CME: RTY)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SPGSCICommodity
-    
-    
-     Futures.Indices.SPGSCI_COMMODITY
-    
-    : S&P-GSCI Commodity Index Futures (CME: GD)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SP400MidCapEmini
-    
-    
-     Futures.Indices.SP_400_MID_CAP_EMINI
-    
-    : E-mini S&P MidCap 400 Futures (CME: EMD)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SP500AnnualDividendIndex
-    
-    
-     Futures.Indices.SP_500_ANNUAL_DIVIDEND_INDEX
-    
-    :  (CME: SDA)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SP500EMini
-    
-    
-     Futures.Indices.SP_500_E_MINI
-    
-    : E-mini S&P 500 Futures (CME: ES)
-   
      • 
-   
    • 
-    
-     Futures.Indices.TOPIXYEN
-    
-    : YEN Denominated Topix Index Futures on CME (CME: TPY)
-   
      • 
-   
    • 
-    
-     Futures.Indices.USDDenominatedIbovespa
-    
-    
-     Futures.Indices.USD_DENOMINATED_IBOVESPA
-    
-    : USD-Denominated Ibovespa Index Futures (CME: IBV)
-   
      • 
-   
    • 
-    
-     Futures.Indices.VIX
-    
-    : CBOE Volatility Index Futures (CFE: VX)
-   
      • 
-   
    • 
-    
-     Futures.Meats.FeederCattle
-    
-    
-     Futures.Meats.FEEDER_CATTLE
-    
-    : Feeder Cattle Futures (CME: GF)
-   
      • 
-   
    • 
-    
-     Futures.Meats.LeanHogs
-    
-    
-     Futures.Meats.LEAN_HOGS
-    
-    : Lean Hogs Futures (CME: HE)
-   
      • 
-   
    • 
-    
-     Futures.Meats.LiveCattle
-    
-    
-     Futures.Meats.LIVE_CATTLE
-    
-    : Live Cattle Futures (CME: LE)
-   
      • 
-   
    • 
-    
-     Futures.Metals.AluminiumEuropeanPremiumDutyPaidMetalBulletin
-    
-    
-     Futures.Metals.ALUMINIUM_EUROPEAN_PREMIUM_DUTY_PAID_METAL_BULLETIN
-    
-    : Aluminium European Premium Duty-Paid (Metal Bulletin) Futures (COMEX: EDP)
-   
      • 
-   
    • 
-    
-     Futures.Metals.AluminumMWUSTransactionPremiumPlatts25MT
-    
-    
-     Futures.Metals.ALUMINUM_MWUS_TRANSACTION_PREMIUM_PLATTS_25_MT
-    
-    : Aluminum MW U.S. Transaction Premium Platts (25MT) Futures (COMEX: AUP)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Copper
-    
-    
-     Futures.Metals.COPPER
-    
-    : Copper Futures (COMEX: HG)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Gold
-    
-    
-     Futures.Metals.GOLD
-    
-    : Gold Futures (COMEX: GC)
-   
      • 
-   
    • 
-    
-     Futures.Metals.MicroGold
-    
-    
-     Futures.Metals.MICRO_GOLD
-    
-    : Micro Gold Futures (COMEX: MGC)
-   
      • 
-   
    • 
-    
-     Futures.Metals.MicroGoldTAS
-    
-    
-     Futures.Metals.MICRO_GOLD_TAS
-    
-    : Micro Gold TAS Futures (COMEX: MGT)
-   
      • 
-   
    • 
-    
-     Futures.Metals.MicroPalladium
-    
-    
-     Futures.Metals.MICRO_PALLADIUM
-    
-    : Micro Palladium Futures (NYMEX: PAM)
-   
      • 
-   
    • 
-    
-     Futures.Metals.MicroSilver
-    
-    
-     Futures.Metals.MICRO_SILVER
-    
-    : Micro Silver Futures (COMEX: SIL)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Palladium
-    
-    
-     Futures.Metals.PALLADIUM
-    
-    : Palladium Futures (NYMEX: PA)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Platinum
-    
-    
-     Futures.Metals.PLATINUM
-    
-    : Platinum Futures (NYMEX: PL)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Silver
-    
-    
-     Futures.Metals.SILVER
-    
-    : Silver Futures (COMEX: SI)
-   
      • 
-   
    • 
-    
-     Futures.Metals.USMidwestDomesticHotRolledCoilSteelCRUIndex
-    
-    
-     Futures.Metals.US_MIDWEST_DOMESTIC_HOT_ROLLED_COIL_STEEL_CRU_INDEX
-    
-    : U.S. Midwest Domestic Hot-Rolled Coil Steel (CRU) Index Futures (NYMEX: HRC)
-   
      • 
-   
    • 
-    
-     Futures.Softs.Sugar11
-    
-    
-     Futures.Softs.SUGAR_11
-    
-    : Sugar #11 Futures ICE (ICE: SB)
-   
      • 
-   
    • 
-    
-     Futures.Softs.Sugar11CME
-    
-    
-     Futures.Softs.SUGAR_11_CME
-    
-    : Sugar #11 Futures CME (NYMEX: YO)
-   
      • 
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Future Universe dataset enables you to accurately design Futures strategies. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Buying the Futures contract with the most open interest to reduce slippage and market impact
-   
      • 
-   
    • 
-    Trading bull calendar spreads to reduce volatility and margin requirements
-   
      • 
-   
    • 
-    Trading bull calendar spreads to speculate contango
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm selects and buys the front-month Mini Gold Futures contract and sells the front-month Micro Gold Futures contract. When the front-month contract changes, the algorithm rebalances the portfolio.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class USFuturesDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(1000000) 
-        self.universe_settings.asynchronous = True
-        # Requesting data on gold and micro-gold contracts.
-        # Filter the universe to trade only the contracts expiring within 90 days to ensure liquidity.
-        self.mini_gold = self.add_future(Futures.Metals.GOLD) 
-        self.mini_gold.set_filter(0, 90)
-        
-        self.micro_gold = self.add_future(Futures.Metals.MICRO_GOLD) 
-        self.micro_gold.set_filter(0, 90)
-        
-        # Save a cache of the mapped symbol to trade.
-        self.contract = {self.mini_gold.symbol: None, self.micro_gold.symbol: None}
-    
-    def on_data(self, slice: Slice) -> None:
-        for symbol, chain in slice.future_chains.items():
-            if symbol in self.contract:
-                # Select the contract with the greatest open interest to trade with the most efficiency.
-                most_liquid_contract = sorted(chain, key=lambda contract: contract.open_interest, reverse=True)[0]
-                
-                if self.contract[symbol] is None or most_liquid_contract.symbol != self.contract[symbol].symbol:
-                    # Liquidate any unmapped contracts.
-                    if self.contract[symbol] is not None:
-                        self.liquidate(self.contract[symbol].symbol)
-                    self.contract[symbol] = most_liquid_contract
-                    
-                    # Buy mini-gold and short micro-gold contracts as planned.
-                    if symbol == self.mini_gold.symbol:
-                        self.market_order(self.contract[symbol].symbol, 1)
-                    elif symbol == self.micro_gold.symbol:
-                        self.market_order(self.contract[symbol].symbol, -1)
    
-   
    public class USFuturesDataAlgorithm : QCAlgorithm
-{
-    private Future _miniGold;
-    private Future _microGold;
-    // Save a cache of the mapped symbol to trade.
-    private Dictionary<Symbol, FuturesContract?> _contract = new ();
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(1000000);
-        UniverseSettings.Asynchronous = true;
-
-        // Requesting data on gold and micro-gold contracts.
-        // Filter the universe to trade only the contracts expiring within 90 days to ensure liquidity.
-        _miniGold = AddFuture(Futures.Metals.Gold);
-        _miniGold.SetFilter(0, 90);
-        _contract.Add(_miniGold.Symbol, null);
-        
-        _microGold = AddFuture(Futures.Metals.MicroGold);
-        _microGold.SetFilter(0, 90);
-        _contract.Add(_microGold.Symbol, null);
-    }
-
-    public override void OnData(Slice slice)
-    {
-        foreach (var (symbol, chain) in slice.FutureChains)
-        {
-            if (_contract.ContainsKey(symbol))
-            {
-                // Select the contract with the greatest open interest to trade with the most efficiency.
-                var mostLiquidContract = chain.OrderBy(x => x.OpenInterest).Last();
-                
-                if (_contract[symbol] == null || mostLiquidContract.Symbol != _contract[symbol].Symbol)
-                {
-                    // Liquidate any unmapped contracts.
-                    if (_contract[symbol] != null)
-                    {
-                        Liquidate(_contract[symbol].Symbol);
-                    }
-                    _contract[symbol] = mostLiquidContract;
-
-                    // Buy mini-gold and short micro-gold contracts as planned.
-                    if (symbol == _miniGold.Symbol)
-                    {
-                        MarketOrder(_contract[symbol].Symbol, 1);
-                    }
-                    else if (symbol == _microGold.Symbol)
-                    {
-                        MarketOrder(_contract[symbol].Symbol, -1);
-                    }
-                }
-            }
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm selects and buys the front-month Mini Gold Futures contract and sells the front-month Micro Gold Futures contract. When the front-month contract changes, the algorithm rebalances the portfolio.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class USFuturesDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        self.universe_settings.asynchronous = True
-        # Set up an universe selection model that selects the front month contract
-        self.set_universe_selection(FrontMonthFutureUniverseSelectionModel())
-        self.add_alpha(ConstantFuturesAlphaModel())
-        # A portfolio construction model that only order a single share per insight signal
-        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
-        
-class FrontMonthFutureUniverseSelectionModel(FutureUniverseSelectionModel):
-    def __init__(self,) -> None:
-        # Daily updating with select_future_chain_symbols function
-        super().__init__(timedelta(1), self.select_future_chain_symbols)
-
-    def select_future_chain_symbols(self, utcTime: datetime) -> List[Symbol]:
-        # Select gold and micro gold contracts for the strategy need
-        future_pairs = [
-            (Futures.Metals.GOLD, Market.COMEX),
-            (Futures.Metals.MICRO_GOLD, Market.COMEX)
-        ]
-        return [Symbol.create(pair[0], SecurityType.FUTURE, pair[1]) for pair in future_pairs]
-
-    def filter(self, filter: FutureFilterUniverse) -> FutureFilterUniverse:
-        # Filter only front month contract for liquidity and most informed information
-        return filter.front_month().only_apply_filter_at_market_open()
-
-class ConstantFuturesAlphaModel(AlphaModel):
-    # Long gold and short micro gold in this strategy
-    long_symbol = Symbol.create(Futures.Metals.GOLD, SecurityType.FUTURE, Market.COMEX)
-    short_symbol = Symbol.create(Futures.Metals.MICRO_GOLD, SecurityType.FUTURE, Market.COMEX)
-        
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        if algorithm.portfolio.invested:
-            return []
-        
-        insights = []
-        # For both gold and micro gold, select the front month contract (only contract) in the chain
-        for kvp in slice.future_chains:
-            chain = [contract for contract in kvp.Value]
-            contract = chain[0]
-            
-            # Long gold and short micro gold as planned
-            if kvp.Key == self.long_symbol:
-                insights.append(Insight.price(contract.symbol, contract.expiry + timedelta(days=1), InsightDirection.UP))
-            elif kvp.Key == self.short_symbol:
-                insights.append(Insight.price(contract.symbol, contract.expiry + timedelta(days=1), InsightDirection.DOWN))
-        
-        return insights
-        
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            # Historical data
-            history = algorithm.history(security.symbol, 10, Resolution.MINUTE)
-            algorithm.debug(f"We got {len(history)} from our history request for {security.symbol}")
-
-class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
-    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
-        targets = []
-        for insight in insights:
-            if algorithm.securities[insight.symbol].is_tradable:
-                # Single share only using integer portfolio target
-                targets.append(PortfolioTarget(insight.symbol, insight.direction))
-        return targets
    
-   
    public class USFuturesDataAlgorithm : QCAlgorithm
-{        
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(1000000);
-        UniverseSettings.Asynchronous = true;
-        // Set up an universe selection model that selects the front month contract
-        SetUniverseSelection(new FrontMonthFutureUniverseSelectionModel());
-        SetAlpha(new ConstantFuturesAlphaModel());
-        // A portfolio construction model that only order a single share per insight signal
-        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
-    }
-}
-    
-class FrontMonthFutureUniverseSelectionModel : FutureUniverseSelectionModel
-{
-    // Daily updating with select_future_chain_symbols function
-    public FrontMonthFutureUniverseSelectionModel()
-        : base(TimeSpan.FromDays(1), SelectFutureChainSymbols) {}
-
-    private static IEnumerable<Symbol> SelectFutureChainSymbols(DateTime utcTime)
-    {
-        //Select gold and micro gold contracts for the strategy need
-        return new List<Symbol> {
-            QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX),
-            QuantConnect.Symbol.Create(Futures.Metals.MicroGold, SecurityType.Future, Market.COMEX)
-        };
-    }
-
-    protected override FutureFilterUniverse Filter(FutureFilterUniverse filter)
-    {
-        // Filter only front month contract for liquidity and most informed information
-        return filter.FrontMonth().OnlyApplyFilterAtMarketOpen();
-    }
-}
-
-
-class ConstantFuturesAlphaModel : AlphaModel
-{
-    // Long gold and short micro gold in this strategy
-    private Symbol 
-        _longSymbol = QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX), 
-        _shortSymbol = QuantConnect.Symbol.Create(Futures.Metals.MicroGold, SecurityType.Future, Market.COMEX);
-    
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        var insights = new List<Insight>();
-
-        if (algorithm.Portfolio.Invested)
-        {
-            return insights;
-        }
-        
-        // For both gold and micro gold, select the front month contract (only contract) in the chain
-        foreach (var kvp in slice.FutureChains)
-        {
-            var symbol = kvp.Key;
-            var chain = kvp.Value;
-            var contract = chain.First();
-            
-            // Long gold and short micro gold as planned
-            if (symbol == _longSymbol)
-            {
-                insights.Add(Insight.Price(contract.Symbol, contract.Expiry + TimeSpan.FromDays(1), InsightDirection.Up));
-            }
-            else if (symbol == _shortSymbol)
-            {
-                insights.Add(Insight.Price(contract.Symbol, contract.Expiry + TimeSpan.FromDays(1), InsightDirection.Down));
-            }
-        }
-
-        return insights;
-    }
-    
-    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-    {
-        foreach (var security in changes.AddedSecurities)
-        {
-            // Historical data
-            var history = algorithm.History(security.Symbol, 100, Resolution.Minute);
-            algorithm.Debug($"We got {history.Count()} from our history request for {security.Symbol}");
-        }
-    }
-}
-    
-    
-class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
-{
-    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
-    {
-        var targets = new List<PortfolioTarget>();
-        foreach (var insight in insights)
-        {
-            if (algorithm.Securities[insight.Symbol].IsTradable)
-            {
-                // Single share only using integer portfolio target
-                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
-            }
-        }
-        return targets;
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Future Universe dataset provides
-   
-    FutureFilterUniverse
-   
-   and
-   
-    FutureUniverse
-   
-   objects.
-  
    
-  
    
-   FutureFilterUniverse Attributes
-  
    
-  
    
-   
-    FutureFilterUniverse
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   FutureUniverse Attributes
-  
    
-  
    
-   
-    FutureUniverse
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > US Futures Security Master
    
-
    
-    
    
-        
    QuantConnect
    
-        
    US Futures Security Master
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Futures Security Master dataset by QuantConnect provides mapping reference data for the most liquid contracts of the CME Group exchanges, calculated with popular rolling techniques. The data covers 157 root Future contracts, starts in May 2009, and is delivered on a daily frequency with a zip file with all the contract mappings. This dataset is created by daily processing of the US historical Future chains.
-  
    
-  
    
-   This dataset, paired with the US Futures dataset, supports the following rolling techniques: forward panama canal, backwards panama canal, and backwards ratio. You can set the specific rolling date to occur on the last trading day, first day month, or the day when the contract with the greatest open interest changes.
-  
    
-  
    
-   VIX Futures don't support continous contract rolling with open interest.
-  
    
-  
    
-   This is
-   
-    NOT
-   
-   the underlying Futures data (
-   
-    US Futures
-   
-   dataset), which you need to purchase separately with a license from AlgoSeek. This security master dataset is required to purchase the US Futures or US Future Options datasets.
-  
    
-  
    
-   For more information about the US Futures Security Master dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   You don't need any special code to utilize the US Futures Security Master. It automatically loads when you
-   
-    request US Futures data
-   
-   .
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      May 2009
-     
    
-      Asset Coverage
-     
-      157 Liquid Futures
-     
    
-      Data Density
-     
-      Regular
-     
    
-      Resolution
-     
-      Daily
-     
    
-  
    
-   VIX Futures don't support continous contract rolling with open interest.
-  
    
- 
-
-
-
    Key Concept
    
-
- 
-  
    
-   The US Futures Security Master lets you to construct continuous Futures, allowing the access of normalized historical data of the underlying assets, as well as trading the “lead” Future contracts for those assets.
-  
    
-  
    
-   Continuous Futures refer to sets of rolling lead Future contracts during their actively trading periods. Since Future contracts expire at their maturities, to analyze the historical price of a Future and to apply technical indicators, you need the continuous Future price series.
-  
    
-  
    
-   To access the continuous Future, use the Future's
-   
-    Symbol
-   
-   
-    symbol
-   
-   property.
-  
    
-  
    
-   
    # Set the data normalization mode and data mapping mode to select the contract as the continuous contract with price adjustment for smooth price series
-future = self.add_future(Futures.Energy.CRUDE_OIL_WTI,
-    data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
-    data_mapping_mode=DataMappingMode.OPEN_INTEREST,
-    contract_depth_offset=0)
-self.continuous_contract_symbol = future.symbol
    
-   
    // Set the data normalization mode and data mapping mode to select the contract as the continuous contract with price adjustment for smooth price series
-var future = AddFuture(Futures.Energy.CrudeOilWTI,
-    dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
-    dataMappingMode: DataMappingMode.OpenInterest,
-    contractDepthOffset: 0
-);
-_continuousFutureSymbol = future.Symbol;
    
-  
    
-  
    
-   The
-   
-    dataNormalizationMode
-   
-   and
-   
-    dataMappingMode
-   
-   arguments makes the transition of the underlying contracts seemless. However, the Future
-   
-    Symbol
-   
-   doesn't map to an underlying Future contract. It works fine to trade within backtests, but could be subjected to friction costs during live trading since the order price could be a normalized price. For more information about this topic, see the
-   
-    Live Trading Considerations
-   
-   section.
-  
    
- 
-
-
-
    Data Normalization Modes
    
-
- 
-  
    
-   The data normalization mode defines how the price series of two contracts are stitched together when the contract rollovers occur. The
-   
-    DataNormalizatoinMode
-   
-   enumeration has the following members available for continuous contracts:
-  
    
-  
    
-  
    
-  
    
-   If you use a data normalization mode that's not in the preceding list, LEAN automatically converts it to
-   
-    DataNormalizationMode.BackwardsRatio
-   
-   .
-  
    
- 
-
-
-
    Data Mapping Modes
    
-
- 
-  
    
-   The data mapping mode defines when contract rollovers occur. The
-   
-    DataMappingMode
-   
-   enumeration has the following members:
-  
    
-  
    
-  
    
-  
    
-   VIX Futures don't support continous contract rolling with
-   
-    DataMappingMode.OpenInterest
-   
-   
-    DataMappingMode.OPEN_INTEREST
-   
-   and
-   
-    DataMappingMode.OpenInterestAnnuak
-   
-   
-    DataMappingMode.OPEN_INTEREST_ANNUAL
-   
-   .
-  
    
- 
-
-
-
    Tracking Contract Changes
    
-
- 
-  
    
-   As the contracts roll over, the
-   
-    Mapped
-   
-   
-    mapped
-   
-   property of the
-   
-    Future
-   
-   object references the next contract in the series and you receive a
-   
-    SymbolChangedEvent
-   
-   . To get the current
-   
-    Symbol
-   
-   change events, index the
-   
-    SymbolChangedEvents
-   
-   
-    symbol_changed_events
-   
-   property of the current
-   
-    
-     Slice
-    
-   
-   with the continuous Futures
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your Future at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Get Symbol Change Event of the Continuous Future (change in mapped contract)
-    changed_event = slice.symbol_changed_events.get(self.continuous_future_symbol)
-    if changed_event:
-        old_symbol = changed_event.old_symbol
-        new_symbol = self.add_future_contract(changed_event.new_symbol).symbol
-        tag = f"Rollover - Symbol changed at {self.time}: {old_symbol} -> {new_symbol}"
-        quantity = self.portfolio[old_symbol].quantity
-
-        # Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
-        self.liquidate(old_symbol, tag = tag)
-        if quantity != 0: self.market_order(new_symbol, quantity, tag = tag)
-        self.log(tag)
    
-   
    public override void OnData(Slice slice)
-{
-    // Get Symbol Change Event of the Continuous Future (change in mapped contract)
-    if (slice.SymbolChangedEvents.TryGetValue(_continuousFutureSymbol, out var changedEvent))
-    {
-        var oldSymbol = changedEvent.OldSymbol;
-        var newSymbol = AddFutureContract(changedEvent.NewSymbol).Symbol;
-        var tag = $"Rollover - Symbol changed at {Time}: {oldSymbol} -> {newSymbol}";
-        var quantity = Portfolio[oldSymbol].Quantity;
-        // Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
-        Liquidate(oldSymbol, tag: tag);
-        if (quantity != 0) MarketOrder(newSymbol, quantity, tag: tag);
-        Log(tag);
-    }
-}
    
-  
    
-  
    
-   
-    SymbolChangedEvent
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
    Live Trading Considerations
    
-
- 
-  
    
-   You can trade continuous Futures, but the continuous Future
-   
-    Symbol
-   
-   doesn't map to a single underlying Future contract. Instead, it represents a set of rolling contracts. Thus, the prices could be frictional during a contract rollover, which could be catastrophic in live trading! For live trading, you should place your orders directly on the underlying contracts. To get the current underlying contract in the continuous Future series, use the
-   
-    Mapped
-   
-   
-    mapped
-   
-   property.
-  
    
-  
    
-   
    # Get the mapped symbol
-current_contract = self.continuous_contract.mapped
-self.buy(current_contract, 1)
    
-   
    // Get the mapped symbol
-var currentContract = _continuousContract.Mapped;
-Buy(currentContract, 1);
    
-  
    
- 
-
-
-
    Data Format
    
-
- 
-  
    
-   If you download the files in the US Futures Security Master, you get a factor file and a map file for each of the exchanges with supported continuous Futures. To view the files, see the
-   
-    \data\future\<exchange_name>
-   
-   directory under your LEAN CLI base directory.
-  
    
-  
    
-   For the factor file, it is a
-   
-    .zip
-   
-   collection of REST API styled
-   
-    .csv
-   
-   files for each Future
-   
-    Symbol
-   
-   , including the date, scaling factors for each type of data normalization and the data mapping mode that indicates the Symbol changing event is on that day for that mapping mode. The following line is an example line in the
-   
-    .csv
-   
-   file:
-  
    
-  
-   {"Date":"2009-10-31T00:00:00","BackwardsRatioScale":[0.9914163090128755364806866953,1.0,1.0],"BackwardsPanamaCanalScale":[-2.0,0.0,0.0],"ForwardPanamaCanalScale":[0.0,0.0,0.0],"DataMappingMode":1}
-  
-  
    
-  
    
-   For the map file, it is a
-   
-    .zip
-   
-   collection of
-   
-    .csv
-   
-   files for each Future
-   
-    Symbol
-   
-   , including the date, new underlying contract Symbol, the exchange code, and the data mapping mode that indicates the Symbol changing event is on that day for that mapping mode. The following line is an example line in the
-   
-    .csv
-   
-   file:
-  
    
-  
-   20091130,aw uii3j0m6zbj9,CBOT,1
-  
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical US Future Security Master data in an algorithm and the Research Environment.
-  
    
-  
    
-   Historical Data In Algorithms
-  
    
-  
    
-   To get historical US Future Security Master data in an algorithm, call the
-   
-    History<SymbolChangedEvent>
-   
-   
-    history
-   
-   method with continuous Future
-   
-    Symbol
-   
-   and a lookback period. This method returns the old and new contract of each rollover. If there is no data for the period you requested, the history result is empty.
-  
    
-  
    
-   
    # Add the Future and define its rollover settings.
-future = self.add_future(
-    Futures.Indices.SP_500_E_MINI,
-    data_mapping_mode=DataMappingMode.OPEN_INTEREST,
-    data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
-    contract_depth_offset=0
-)
-
-# DataFrame example where the columns are the SymbolChangedEvent attributes:
-history_df = self.history(SymbolChangedEvent, future.symbol, timedelta(365))
-
-# SymbolChangedEvent objects example:
-history = self.history[SymbolChangedEvent](future.symbol, timedelta(365))
    
-   
    // Add the Future and define its rollover settings.
-var future = AddFuture(
-    Futures.Indices.SP500EMini,
-    dataMappingMode: DataMappingMode.OpenInterest,
-    dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
-    contractDepthOffset: 0
-);
-
-// Get historical data.
-var history = History<SymbolChangedEvent>(future.Symbol, TimeSpan.FromDays(365));
    
-  
    
-  
    
-   For more information about historical US Future Security Master data in algorithms, see
-   
-    Symbol Changes
-   
-   .
-  
    
-  
    
-   Historical Data In Research
-  
    
-  
    
-   To get historical US Future Security Master data in the Research Environment, call the
-   
-    History<SymbolChangedEvent>
-   
-   
-    history
-   
-   method with continuous Future
-   
-    Symbol
-   
-   and a time period. This method returns the old and new contract of each rollover. If there is no data for the period you requested, the history result is empty.
-  
    
-  
    
-   
    # Add the Future and define its rollover settings.
-future = qb.add_future(
-    Futures.Indices.SP_500_E_MINI,
-    data_mapping_mode=DataMappingMode.OPEN_INTEREST,
-    data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
-    contract_depth_offset=0
-)
-
-# DataFrame example where the columns are the SymbolChangedEvent attributes:
-history_df = qb.history(SymbolChangedEvent, future.symbol, datetime(2023, 1, 1), datetime(2025, 1, 1))
-
-# SymbolChangedEvent objects example:
-history = qb.history[SymbolChangedEvent](future.symbol, datetime(2023, 1, 1), datetime(2025, 1, 1))
    
-   
    // Add the Future and define its rollover settings.
-var future = qb.AddFuture(
-    Futures.Indices.SP500EMini,
-    dataMappingMode: DataMappingMode.OpenInterest,
-    dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
-    contractDepthOffset: 0
-);
-
-// Get historical data.
-var history = qb.History<SymbolChangedEvent>(future.Symbol, new DateTime(2023, 1, 1), new DateTime(2025, 1, 1));
    
-  
    
-  
    
-   For more information about historical US Future Security Master data in the Research Environment, see
-   
-    Symbol Changes History
-   
-   .
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   The following list shows the available (157) Futures:
-  
    
-  
      
-   
    • 
-    
-     Futures.Currencies.AUD
-    
-    : Australian Dollar Futures (CME: 6A)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.AUDCAD
-    
-    : Australian Dollar/Canadian Dollar Futures (CME: ACD)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.AUDJPY
-    
-    : Australian Dollar/Japanese Yen Futures (CME: AJY)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.AUDNZD
-    
-    : Australian Dollar/New Zealand Dollar Futures (CME: ANE)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.BRL
-    
-    : Brazillian Real Futures (CME: 6L)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.BTC
-    
-    : Bitcoin Futures (CME: BTC)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.BTICMicroBTC
-    
-    
-     Futures.Currencies.BTIC_MICRO_BTC
-    
-    : BTIC on Micro Bitcoin Futures (CME: MIB)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.BTICMicroEther
-    
-    
-     Futures.Currencies.BTIC_MICRO_ETHER
-    
-    : BTIC on Micro Ether Futures (CME: MRB)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.CAD
-    
-    : Canadian Dollar Futures (CME: 6C)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.CADJPY
-    
-    : Canadian Dollar/Japanese Yen Futures (CME: CJY)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.CHF
-    
-    : Swiss Franc Futures (CME: 6S)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.ETH
-    
-    : Ether Futures (CME: ETH)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EUR
-    
-    : Euro FX Futures (CME: 6E)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EURAUD
-    
-    : Euro/Australian Dollar Futures (CME: EAD)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EURCAD
-    
-    : Euro/Canadian Dollar Futures (CME: ECD)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EuroFXEmini
-    
-    
-     Futures.Currencies.EURO_FX_EMINI
-    
-    : E-mini Euro FX Futures (CME: E7)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EURSEK
-    
-    : Euro/Swedish Krona Futures (CME: ESK)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.GBP
-    
-    : British Pound Futures (CME: 6B)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.JapaneseYenEmini
-    
-    
-     Futures.Currencies.JAPANESE_YEN_EMINI
-    
-    : E-mini Japanese Yen Futures (CME: J7)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.JPY
-    
-    : Japanese Yen Futures (CME: 6J)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroAUD
-    
-    
-     Futures.Currencies.MICRO_AUD
-    
-    : Micro AUD/USD Futures (CME: M6A)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroBTC
-    
-    
-     Futures.Currencies.MICRO_BTC
-    
-    : Micro Bitcoin Futures (CME: MBT)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroCAD
-    
-    
-     Futures.Currencies.MICRO_CAD
-    
-    : Micro USD/CAD Futures (CME: M6C)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroCADUSD
-    
-    
-     Futures.Currencies.MICRO_CADUSD
-    
-    : Micro CAD/USD Futures (CME: MCD)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroCHF
-    
-    
-     Futures.Currencies.MICRO_CHF
-    
-    : Micro CHF/USD Futures (CME: MSF)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroEther
-    
-    
-     Futures.Currencies.MICRO_ETHER
-    
-    : Micro Ether Futures (CME: MET)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroEUR
-    
-    
-     Futures.Currencies.MICRO_EUR
-    
-    : Micro EUR/USD Futures (CME: M6E)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroGBP
-    
-    
-     Futures.Currencies.MICRO_GBP
-    
-    : Micro GBP/USD Futures (CME: M6B)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroINRUSD
-    
-    
-     Futures.Currencies.MICRO_INRUSD
-    
-    : Micro INR/USD Futures (CME: MIR)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroJPY
-    
-    
-     Futures.Currencies.MICRO_JPY
-    
-    : Micro JPY/USD Futures (CME: MJY)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroUSDCHF
-    
-    
-     Futures.Currencies.MICRO_USDCHF
-    
-    : Micro USD/CHF Futures (CME: M6S)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroUSDCNH
-    
-    
-     Futures.Currencies.MICRO_USDCNH
-    
-    : Micro USD/CNH Futures (CME: MNH)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroUSDJPY
-    
-    
-     Futures.Currencies.MICRO_USDJPY
-    
-    : Micro USD/JPY Futures (CME: M6J)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MXN
-    
-    : Mexican Peso Futures (CME: 6M)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.NZD
-    
-    : New Zealand Dollar Futures (CME: 6N)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.RUB
-    
-    : Russian Ruble Futures (CME: 6R)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.StandardSizeUSDOffshoreRMBCNH
-    
-    
-     Futures.Currencies.STANDARD_SIZE_USD_OFFSHORE_RMBCNH
-    
-    : Standard-Size USD/Offshore RMB (CNH) Futures (CME: CNH)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.ZAR
-    
-    : South African Rand Futures (CME: 6Z)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ArgusLLSvsWTIArgusTradeMonth
-    
-    
-     Futures.Energy.ARGUS_LL_SVS_WTI_ARGUS_TRADE_MONTH
-    
-    : Argus LLS vs. WTI (Argus) Trade Month Futures (NYMEX: AE5)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ArgusPropaneFarEastIndex
-    
-    
-     Futures.Energy.ARGUS_PROPANE_FAR_EAST_INDEX
-    
-    : Argus Propane Far East Index Futures (NYMEX: A7E)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ArgusPropaneSaudiAramco
-    
-    
-     Futures.Energy.ARGUS_PROPANE_SAUDI_ARAMCO
-    
-    : Argus Propane (Saudi Aramco) Futures (NYMEX: A9N)
-   
      • 
-   
    • 
-    
-     Futures.Energy.BrentCrudeOilVsDubaiCrudeOilPlatts
-    
-    
-     Futures.Energy.BRENT_CRUDE_OIL_VS_DUBAI_CRUDE_OIL_PLATTS
-    
-    : Brent Crude Oil vs. Dubai Crude Oil (Platts) Futures (NYMEX: ADB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.BrentLastDayFinancial
-    
-    
-     Futures.Energy.BRENT_LAST_DAY_FINANCIAL
-    
-    : Brent Last Day Financial Futures (NYMEX: BZ)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ChicagoEthanolPlatts
-    
-    
-     Futures.Energy.CHICAGO_ETHANOL_PLATTS
-    
-    : Chicago Ethaanol (Platts) Futures (NYMEX: CU)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ConwayPropaneOPIS
-    
-    
-     Futures.Energy.CONWAY_PROPANE_OPIS
-    
-    : Conway Propane (OPIS) Futures (NYMEX: A8K)
-   
      • 
-   
    • 
-    
-     Futures.Energy.CrudeOilWTI
-    
-    
-     Futures.Energy.CRUDE_OIL_WTI
-    
-    : Crude Oil WTI Futures (NYMEX: CL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.DubaiCrudeOilPlattsFinancial
-    
-    
-     Futures.Energy.DUBAI_CRUDE_OIL_PLATTS_FINANCIAL
-    
-    : Dubai Crude Oil (Platts) Financial Futures (NYMEX: DCB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EastWestGasolineSpreadPlattsArgus
-    
-    
-     Futures.Energy.EAST_WEST_GASOLINE_SPREAD_PLATTS_ARGUS
-    
-    : East-West Gasoline Spread (Platts-Argus) Futures (NYMEX: EWG)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EastWestNaphthaJapanCFvsCargoesCIFNWESpreadPlatts
-    
-    
-     Futures.Energy.EAST_WEST_NAPHTHA_JAPAN_C_FVS_CARGOES_CIFNWE_SPREAD_PLATTS
-    
-    : East-West Naphtha: Japan C&F vs. Cargoes CIF NWE Spread (Platts) Futures (NYMEX: EWN)
-   
      • 
-   
    • 
-    
-     Futures.Energy.Ethanol
-    
-    
-     Futures.Energy.ETHANOL
-    
-    : Ethanol Futures (CBOT: EH)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EthanolT2FOBRdamIncludingDutyPlatts
-    
-    
-     Futures.Energy.ETHANOL_T_2_FOB_RDAM_INCLUDING_DUTY_PLATTS
-    
-    : Ethanol T2 FOB Rdam Including Duty (Platts) Futures (NYMEX: AZ1)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EuropeanNaphthaPlattsCrackSpread
-    
-    
-     Futures.Energy.EUROPEAN_NAPHTHA_PLATTS_CRACK_SPREAD
-    
-    : European Naphtha (Platts) Crack Spread Futures (NYMEX: EN)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EuropeanPropaneCIFARAArgus
-    
-    
-     Futures.Energy.EUROPEAN_PROPANE_CIFARA_ARGUS
-    
-    : European Propane CIF ARA (Argus) Futures (NYMEX: APS)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EuropeanPropaneCIFARAArgusVsNaphthaCargoesCIFNWEPlatts
-    
-    
-     Futures.Energy.EUROPEAN_PROPANE_CIFARA_ARGUS_VS_NAPHTHA_CARGOES_CIFNWE_PLATTS
-    
-    : European Propane CIF ARA (Argus) vs. Naphtha Cargoes CIF NWE (Platts) Futures (NYMEX: EPN)
-   
      • 
-   
    • 
-    
-     Futures.Energy.FreightRouteTC14Baltic
-    
-    
-     Futures.Energy.FREIGHT_ROUTE_TC_14_BALTIC
-    
-    : Freight Route TC14 (Baltic) Futures (NYMEX: FRC)
-   
      • 
-   
    • 
-    
-     Futures.Energy.Gasoline
-    
-    
-     Futures.Energy.GASOLINE
-    
-    : Gasoline RBOB Futures (NYMEX: RB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GasolineEurobobOxyNWEBargesArgus
-    
-    
-     Futures.Energy.GASOLINE_EUROBOB_OXY_NWE_BARGES_ARGUS
-    
-    : Gasoline Euro-bob Oxy NWE Barges (Argus) Futures (NYMEX: B7H)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GroupThreeSuboctaneGasolinePlattsVsRBOB
-    
-    
-     Futures.Energy.GROUP_THREE_SUBOCTANE_GASOLINE_PLATTS_VS_RBOB
-    
-    : Group Three Sub-octane Gasoliine (Platts) vs. RBOB Futures (NYMEX: AA8)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GroupThreeULSDPlattsVsNYHarborULSD
-    
-    
-     Futures.Energy.GROUP_THREE_ULSD_PLATTS_VS_NY_HARBOR_ULSD
-    
-    : Group Three ULSD (Platts) vs. NY Harbor ULSD Futures (NYMEX: AA6)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GulfCoastCBOBGasolineA2PlattsVsRBOBGasoline
-    
-    
-     Futures.Energy.GULF_COAST_CBOB_GASOLINE_A_2_PLATTS_VS_RBOB_GASOLINE
-    
-    : Gulf Coast CBOB Gasoline A2 (Platts) vs. RBOB Gasoline Futures (NYMEX: CRB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GulfCoastHSFOPlattsVsEuropeanThreePointFivePercentFuelOilBargesFOBRdamPlatts
-    
-    
-     Futures.Energy.GULF_COAST_HSFO_PLATTS_VS_EUROPEAN_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
-    
-    : Gulf Coast HSFO (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: GCU)
-   
      • 
-   
    • 
-    
-     Futures.Energy.HeatingOil
-    
-    
-     Futures.Energy.HEATING_OIL
-    
-    : Heating Oil Futures (NYMEX: HO)
-   
      • 
-   
    • 
-    
-     Futures.Energy.LosAngelesCARBOBGasolineOPISvsRBOBGasoline
-    
-    
-     Futures.Energy.LOS_ANGELES_CARBOB_GASOLINE_OPI_SVS_RBOB_GASOLINE
-    
-    : Los Angeles CARBOB Gasoline (OPIS) vs. RBOB Gasoline Futures (NYMEX: AJL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.LosAngelesCARBDieselOPISvsNYHarborULSD
-    
-    
-     Futures.Energy.LOS_ANGELES_CARB_DIESEL_OPI_SVS_NY_HARBOR_ULSD
-    
-    : Los Angeles CARB Diesel (OPIS) vs. NY Harbor ULSD Futures (NYMEX: AKL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.LosAngelesJetOPISvsNYHarborULSD
-    
-    
-     Futures.Energy.LOS_ANGELES_JET_OPI_SVS_NY_HARBOR_ULSD
-    
-    : Los Angeles Jet (OPIS) vs. NY Harbor ULSD Futures (NYMEX: AJS)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MarsArgusVsWTIFinancial
-    
-    
-     Futures.Energy.MARS_ARGUS_VS_WTI_FINANCIAL
-    
-    : Mars (Argus) vs. WTI Financial Futures (NYMEX: AYX)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MarsArgusVsWTITradeMonth
-    
-    
-     Futures.Energy.MARS_ARGUS_VS_WTI_TRADE_MONTH
-    
-    : Mars (Argus) vs. WTI Trade Month Futures (NYMEX: AYV)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroCrudeOilWTI
-    
-    
-     Futures.Energy.MICRO_CRUDE_OIL_WTI
-    
-    : Micro WTI Crude Oil Futures (NYMEX: MCL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroEuropeanFOBRdamMarineFuelZeroPointFivePercentBargesPlatts
-    
-    
-     Futures.Energy.MICRO_EUROPEAN_FOB_RDAM_MARINE_FUEL_ZERO_POINT_FIVE_PERCENT_BARGES_PLATTS
-    
-    : Micro European FOB Rdam Marine Fuel 0.5% Barges (Platts) Futures (NYMEX: R5O)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroEuropeanThreePointFivePercentOilBargesFOBRdamPlatts
-    
-    
-     Futures.Energy.MICRO_EUROPEAN_THREE_POINT_FIVE_PERCENT_OIL_BARGES_FOB_RDAM_PLATTS
-    
-    : Micro European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: MEF)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroGasoilZeroPointOnePercentBargesFOBARAPlatts
-    
-    
-     Futures.Energy.MICRO_GASOIL_ZERO_POINT_ONE_PERCENT_BARGES_FOBARA_PLATTS
-    
-    : Micro Gasoil 0.1% Barges FOB ARA (Platts) Futures (NYMEX: M1B)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroSingaporeFOBMarineFuelZeroPointFivePercetPlatts
-    
-    
-     Futures.Energy.MICRO_SINGAPORE_FOB_MARINE_FUEL_ZERO_POINT_FIVE_PERCET_PLATTS
-    
-    : Micro Singapore FOB Marine Fuel 0.5% (Platts) Futures (NYMEX: S5O)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroSingaporeFuelOil380CSTPlatts
-    
-    
-     Futures.Energy.MICRO_SINGAPORE_FUEL_OIL_380_CST_PLATTS
-    
-    : Micro Singapore Fuel Oil 380CST (Platts) Futures (NYMEX: MAF)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MiniEuropeanThreePointPercentFiveFuelOilBargesPlatts
-    
-    
-     Futures.Energy.MINI_EUROPEAN_THREE_POINT_PERCENT_FIVE_FUEL_OIL_BARGES_PLATTS
-    
-    : Mini European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: A0D)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MiniSingaporeFuelOil180CstPlatts
-    
-    
-     Futures.Energy.MINI_SINGAPORE_FUEL_OIL_180_CST_PLATTS
-    
-    : Mini Singapore Fuel Oil 180 cst (Platts) Futures (NYMEX: A0F)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MontBelvieuEthaneOPIS
-    
-    
-     Futures.Energy.MONT_BELVIEU_ETHANE_OPIS
-    
-    : Mont Belvieu Ethane (OPIS) Futures (NYMEX: AC0)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MontBelvieuLDHPropaneOPIS
-    
-    
-     Futures.Energy.MONT_BELVIEU_LDH_PROPANE_OPIS
-    
-    : Mont Belvieu LDH Propane (OPIS) Futures (NYMEX: B0)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MontBelvieuNaturalGasolineOPIS
-    
-    
-     Futures.Energy.MONT_BELVIEU_NATURAL_GASOLINE_OPIS
-    
-    : Mont Belvieu Natural Gasoline (OPIS) Futures (NYMEX: A7Q)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MontBelvieuNormalButaneOPIS
-    
-    
-     Futures.Energy.MONT_BELVIEU_NORMAL_BUTANE_OPIS
-    
-    : Mont Belvieu Normal Butane (OPIS) Futures (NYMEX: AD0)
-   
      • 
-   
    • 
-    
-     Futures.Energy.NaturalGas
-    
-    
-     Futures.Energy.NATURAL_GAS
-    
-    : Natural Gas Futures (NYMEX: NG)
-   
      • 
-   
    • 
-    
-     Futures.Energy.NaturalGasHenryHubLastDayFinancial
-    
-    
-     Futures.Energy.NATURAL_GAS_HENRY_HUB_LAST_DAY_FINANCIAL
-    
-    : Natural Gas (Henry Hub) Last-day Financial Futures (NYMEX: HH)
-   
      • 
-   
    • 
-    
-     Futures.Energy.NaturalGasHenryHubPenultimateFinancial
-    
-    
-     Futures.Energy.NATURAL_GAS_HENRY_HUB_PENULTIMATE_FINANCIAL
-    
-    : Natural Gas (Henry Hub) Penultimate Financial Futures (NYMEX: HP)
-   
      • 
-   
    • 
-    
-     Futures.Energy.OnePercentFuelOilCargoesFOBNWEPlattsVsThreePointFivePercentFuelOilBargesFOBRdamPlatts
-    
-    
-     Futures.Energy.ONE_PERCENT_FUEL_OIL_CARGOES_FOBNWE_PLATTS_VS_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
-    
-    : 1% Fuel Oil Cargoes FOB NWE (Platts) vs. 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: FSS)
-   
      • 
-   
    • 
-    
-     Futures.Energy.PremiumUnleadedGasoline10ppmFOBMEDPlatts
-    
-    
-     Futures.Energy.PREMIUM_UNLEADED_GASOLINE_10_PPM_FOBMED_PLATTS
-    
-    : Premium Unleaded Gasoline 10 ppm FOB MED (Platts) Futures (NYMEX: A3G)
-   
      • 
-   
    • 
-    
-     Futures.Energy.PropaneNonLDHMontBelvieuOPIS
-    
-    
-     Futures.Energy.PROPANE_NON_LDH_MONT_BELVIEU_OPIS
-    
-    : Propane Non-LDH Mont Belvieu (OPIS) Futures (NYMEX: A1R)
-   
      • 
-   
    • 
-    
-     Futures.Energy.RBOBGasolineCrackSpread
-    
-    
-     Futures.Energy.RBOB_GASOLINE_CRACK_SPREAD
-    
-    : RBOB Gasoline Crack Spread Futures (NYMEX: ARE)
-   
      • 
-   
    • 
-    
-     Futures.Energy.RBOBGasolineVsEurobobOxyNWEBargesArgusThreeHundredFiftyThousandGallons
-    
-    
-     Futures.Energy.RBOB_GASOLINE_VS_EUROBOB_OXY_NWE_BARGES_ARGUS_THREE_HUNDRED_FIFTY_THOUSAND_GALLONS
-    
-    : RBOB Gasoline vs. Euro-bob Oxy NWE Barges (Argus) (350,000 gallons) Futures (NYMEX: EXR)
-   
      • 
-   
    • 
-    
-     Futures.Energy.SingaporeFuelOil380cstPlattsVsEuropeanThreePointFivePercentFuelOilBargesFOBRdamPlatts
-    
-    
-     Futures.Energy.SINGAPORE_FUEL_OIL_380_CST_PLATTS_VS_EUROPEAN_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
-    
-    : Singapore Fuel Oil 380 cst (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: EVC)
-   
      • 
-   
    • 
-    
-     Futures.Energy.SingaporeGasoilPlattsVsLowSulphurGasoilFutures
-    
-    
-     Futures.Energy.SINGAPORE_GASOIL_PLATTS_VS_LOW_SULPHUR_GASOIL_FUTURES
-    
-    : Singapore Gasoil (Platts) vs. Low Sulphur Gasoil Futures (NYMEX: AGA)
-   
      • 
-   
    • 
-    
-     Futures.Energy.SingaporeMogas92UnleadedPlattsBrentCrackSpread
-    
-    
-     Futures.Energy.SINGAPORE_MOGAS_92_UNLEADED_PLATTS_BRENT_CRACK_SPREAD
-    
-    : Singapore Mogas 92 Unleaded (Platts) Brent Crack Spread Futures (NYMEX: D1N)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ThreePointFivePercentFuelOilBargesFOBRdamPlattsCrackSpread
-    
-    
-     Futures.Energy.THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS_CRACK_SPREAD
-    
-    : 3.5% Fuel Oil Barges FOB Rdam (Platts) Crack Spread Futures (NYMEX: FO)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ThreePointFivePercentFuelOilBargesFOBRdamPlattsCrackSpread1000mt
-    
-    
-     Futures.Energy.THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS_CRACK_SPREAD_1000_MT
-    
-    : 3.5% Fuel Oil Barges FOB Rdam (Platts) Crack Spread (1000mt) Futures (NYMEX: BOO)
-   
      • 
-   
    • 
-    
-     Futures.Energy.WTIBrentFinancial
-    
-    
-     Futures.Energy.WTI_BRENT_FINANCIAL
-    
-    : WTI-Brent Financial Futures (NYMEX: BK)
-   
      • 
-   
    • 
-    
-     Futures.Energy.WTIFinancial
-    
-    
-     Futures.Energy.WTI_FINANCIAL
-    
-    : WTI Financial Futures (NYMEX: CSX)
-   
      • 
-   
    • 
-    
-     Futures.Energy.WTIHoustonArgusVsWTITradeMonth
-    
-    
-     Futures.Energy.WTI_HOUSTON_ARGUS_VS_WTI_TRADE_MONTH
-    
-    : WTI Houston (Argus) vs. WTI Trade Month Futures (NYMEX: HTT)
-   
      • 
-   
    • 
-    
-     Futures.Energy.WTIHoustonCrudeOil
-    
-    
-     Futures.Energy.WTI_HOUSTON_CRUDE_OIL
-    
-    : WTI Houston Crude Oil Futures (NYMEX: HCL)
-   
      • 
-   
    • 
-    
-     Futures.Financials.EuroDollar
-    
-    
-     Futures.Financials.EURO_DOLLAR
-    
-    : EuroDollar Futures (CME: GE)
-   
      • 
-   
    • 
-    
-     Futures.Financials.FiveYearUSDMACSwap
-    
-    
-     Futures.Financials.FIVE_YEAR_USDMAC_SWAP
-    
-    : 5-Year USD MAC Swap Futures (CBOT: F1U)
-   
      • 
-   
    • 
-    
-     Futures.Financials.MicroY10TreasuryNote
-    
-    
-     Futures.Financials.MICRO_Y_10_TREASURY_NOTE
-    
-    : Micro 10-Year Yield Futures (CBOT: 10Y)
-   
      • 
-   
    • 
-    
-     Futures.Financials.MicroY2TreasuryBond
-    
-    
-     Futures.Financials.MICRO_Y_2_TREASURY_BOND
-    
-    : Micro 2-Year Yield Futures (CBOT: 2YY)
-   
      • 
-   
    • 
-    
-     Futures.Financials.MicroY30TreasuryBond
-    
-    
-     Futures.Financials.MICRO_Y_30_TREASURY_BOND
-    
-    : Micro 30-Year Yield Futures (CBOT: 30Y)
-   
      • 
-   
    • 
-    
-     Futures.Financials.MicroY5TreasuryBond
-    
-    
-     Futures.Financials.MICRO_Y_5_TREASURY_BOND
-    
-    : Micro 5-Year Yield Futures (CBOT: 5YY)
-   
      • 
-   
    • 
-    
-     Futures.Financials.UltraTenYearUSTreasuryNote
-    
-    
-     Futures.Financials.ULTRA_TEN_YEAR_US_TREASURY_NOTE
-    
-    : Ultra 10-Year U.S. Treasury Note Futures (CBOT: TN)
-   
      • 
-   
    • 
-    
-     Futures.Financials.UltraUSTreasuryBond
-    
-    
-     Futures.Financials.ULTRA_US_TREASURY_BOND
-    
-    : Ultra U.S. Treasury Bond Futures (CBOT: UB)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y10TreasuryNote
-    
-    
-     Futures.Financials.Y_10_TREASURY_NOTE
-    
-    : 10Y U.S. Treasury Note Futures (CBOT: ZN)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y2TreasuryNote
-    
-    
-     Futures.Financials.Y_2_TREASURY_NOTE
-    
-    : 2Y U.S. Treasury Note Futures (CBOT: ZT)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y30TreasuryBond
-    
-    
-     Futures.Financials.Y_30_TREASURY_BOND
-    
-    : 30Y U.S. Treasury Bond Futures (CBOT: ZB)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y5TreasuryNote
-    
-    
-     Futures.Financials.Y_5_TREASURY_NOTE
-    
-    : 5Y U.S. Treasury Note Futures (CBOT: ZF)
-   
      • 
-   
    • 
-    
-     Futures.Forestry.Lumber
-    
-    
-     Futures.Forestry.LUMBER
-    
-    : Lumber Futures (CME: LBR)
-   
      • 
-   
    • 
-    
-     Futures.Forestry.RandomLengthLumber
-    
-    
-     Futures.Forestry.RANDOM_LENGTH_LUMBER
-    
-    : Random Length Lumber Futures (CME: LBS)
-   
      • 
-   
    • 
-    
-     Futures.Grains.BlackSeaCornFinanciallySettledPlatts
-    
-    
-     Futures.Grains.BLACK_SEA_CORN_FINANCIALLY_SETTLED_PLATTS
-    
-    : Black Sea Corn Financially Settled (Platts) Futures (CBOT: BCF)
-   
      • 
-   
    • 
-    
-     Futures.Grains.BlackSeaWheatFinanciallySettledPlatts
-    
-    
-     Futures.Grains.BLACK_SEA_WHEAT_FINANCIALLY_SETTLED_PLATTS
-    
-    : Black Sea Wheat Financially Settled (Platts) Futures (CBOT: BWF)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Corn
-    
-    
-     Futures.Grains.CORN
-    
-    : Corn Futures (CBOT: ZC)
-   
      • 
-   
    • 
-    
-     Futures.Grains.HRWWheat
-    
-    
-     Futures.Grains.HRW_WHEAT
-    
-    : KC HRW Wheat Futures (CBOT: KE)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Oats
-    
-    
-     Futures.Grains.OATS
-    
-    : Oats Futures (CBOT: ZO)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Soybeans
-    
-    
-     Futures.Grains.SOYBEANS
-    
-    : Soybeans Futures (CBOT: ZS)
-   
      • 
-   
    • 
-    
-     Futures.Grains.SoybeanMeal
-    
-    
-     Futures.Grains.SOYBEAN_MEAL
-    
-    : Soybean Meal Futures (CBOT: ZM)
-   
      • 
-   
    • 
-    
-     Futures.Grains.SoybeanOil
-    
-    
-     Futures.Grains.SOYBEAN_OIL
-    
-    : Soybean Oil Futures (CBOT: ZL)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Wheat
-    
-    
-     Futures.Grains.WHEAT
-    
-    : Default wheat contract is SRWWheat (CBOT: ZW)
-   
      • 
-   
    • 
-    
-     Futures.Indices.BloombergCommodityIndex
-    
-    
-     Futures.Indices.BLOOMBERG_COMMODITY_INDEX
-    
-    : Bloomberg Commodity Index Futures (CBOT: AW)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Dow30EMini
-    
-    
-     Futures.Indices.DOW_30_E_MINI
-    
-    : E-mini Dow Indu 30 Futures (CBOT: YM)
-   
      • 
-   
    • 
-    
-     Futures.Indices.DowJonesRealEstate
-    
-    
-     Futures.Indices.DOW_JONES_REAL_ESTATE
-    
-    : Dow Jones Real Estate futures on CME (CME: RX)
-   
      • 
-   
    • 
-    
-     Futures.Indices.FTSEEmergingEmini
-    
-    
-     Futures.Indices.FTSE_EMERGING_EMINI
-    
-    : E-mini FTSE Emerging Index Futures (CME: EI)
-   
      • 
-   
    • 
-    
-     Futures.Indices.MicroDow30EMini
-    
-    
-     Futures.Indices.MICRO_DOW_30_E_MINI
-    
-    : Micro E-mini Dow Jones Industrial Average Index Futures (CBOT: MYM)
-   
      • 
-   
    • 
-    
-     Futures.Indices.MicroNASDAQ100EMini
-    
-    
-     Futures.Indices.MICRO_NASDAQ_100_E_MINI
-    
-    : Micro E-mini Nasdaq-100 Index Futures (CME: MNQ)
-   
      • 
-   
    • 
-    
-     Futures.Indices.MicroRussell2000EMini
-    
-    
-     Futures.Indices.MICRO_RUSSELL_2000_E_MINI
-    
-    : Micro E-mini Russell 2000 Index Futures (CME: M2K)
-   
      • 
-   
    • 
-    
-     Futures.Indices.MicroSP500EMini
-    
-    
-     Futures.Indices.MICRO_SP_500_E_MINI
-    
-    : Micro E-mini S&P 500 Index Futures (CME: MES)
-   
      • 
-   
    • 
-    
-     Futures.Indices.NASDAQ100BiotechnologyEMini
-    
-    
-     Futures.Indices.NASDAQ_100_BIOTECHNOLOGY_E_MINI
-    
-    : E-mini Nasdaq-100 Biotechnology Index Futures (CME: BIO)
-   
      • 
-   
    • 
-    
-     Futures.Indices.NASDAQ100EMini
-    
-    
-     Futures.Indices.NASDAQ_100_E_MINI
-    
-    : E-mini NASDAQ 100 Futures (CME: NQ)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Nikkei225Dollar
-    
-    
-     Futures.Indices.NIKKEI_225_DOLLAR
-    
-    : Nikkei-225 Dollar Futures (CME: NKD)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Nikkei225YenCME
-    
-    
-     Futures.Indices.NIKKEI_225_YEN_CME
-    
-    : Nikkei-225 Yen denominated Futures on CME (CME: NIY)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Russell1000EMini
-    
-    
-     Futures.Indices.RUSSELL_1000_E_MINI
-    
-    : E-mini Russell 1000 futures on CME (CME: RS1)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Russell2000EMini
-    
-    
-     Futures.Indices.RUSSELL_2000_E_MINI
-    
-    : E-mini Russell 2000 Futures (CME: RTY)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SPGSCICommodity
-    
-    
-     Futures.Indices.SPGSCI_COMMODITY
-    
-    : S&P-GSCI Commodity Index Futures (CME: GD)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SP400MidCapEmini
-    
-    
-     Futures.Indices.SP_400_MID_CAP_EMINI
-    
-    : E-mini S&P MidCap 400 Futures (CME: EMD)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SP500AnnualDividendIndex
-    
-    
-     Futures.Indices.SP_500_ANNUAL_DIVIDEND_INDEX
-    
-    :  (CME: SDA)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SP500EMini
-    
-    
-     Futures.Indices.SP_500_E_MINI
-    
-    : E-mini S&P 500 Futures (CME: ES)
-   
      • 
-   
    • 
-    
-     Futures.Indices.TOPIXYEN
-    
-    : YEN Denominated Topix Index Futures on CME (CME: TPY)
-   
      • 
-   
    • 
-    
-     Futures.Indices.USDDenominatedIbovespa
-    
-    
-     Futures.Indices.USD_DENOMINATED_IBOVESPA
-    
-    : USD-Denominated Ibovespa Index Futures (CME: IBV)
-   
      • 
-   
    • 
-    
-     Futures.Indices.VIX
-    
-    : CBOE Volatility Index Futures (CFE: VX)
-   
      • 
-   
    • 
-    
-     Futures.Meats.FeederCattle
-    
-    
-     Futures.Meats.FEEDER_CATTLE
-    
-    : Feeder Cattle Futures (CME: GF)
-   
      • 
-   
    • 
-    
-     Futures.Meats.LeanHogs
-    
-    
-     Futures.Meats.LEAN_HOGS
-    
-    : Lean Hogs Futures (CME: HE)
-   
      • 
-   
    • 
-    
-     Futures.Meats.LiveCattle
-    
-    
-     Futures.Meats.LIVE_CATTLE
-    
-    : Live Cattle Futures (CME: LE)
-   
      • 
-   
    • 
-    
-     Futures.Metals.AluminiumEuropeanPremiumDutyPaidMetalBulletin
-    
-    
-     Futures.Metals.ALUMINIUM_EUROPEAN_PREMIUM_DUTY_PAID_METAL_BULLETIN
-    
-    : Aluminium European Premium Duty-Paid (Metal Bulletin) Futures (COMEX: EDP)
-   
      • 
-   
    • 
-    
-     Futures.Metals.AluminumMWUSTransactionPremiumPlatts25MT
-    
-    
-     Futures.Metals.ALUMINUM_MWUS_TRANSACTION_PREMIUM_PLATTS_25_MT
-    
-    : Aluminum MW U.S. Transaction Premium Platts (25MT) Futures (COMEX: AUP)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Copper
-    
-    
-     Futures.Metals.COPPER
-    
-    : Copper Futures (COMEX: HG)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Gold
-    
-    
-     Futures.Metals.GOLD
-    
-    : Gold Futures (COMEX: GC)
-   
      • 
-   
    • 
-    
-     Futures.Metals.MicroGold
-    
-    
-     Futures.Metals.MICRO_GOLD
-    
-    : Micro Gold Futures (COMEX: MGC)
-   
      • 
-   
    • 
-    
-     Futures.Metals.MicroGoldTAS
-    
-    
-     Futures.Metals.MICRO_GOLD_TAS
-    
-    : Micro Gold TAS Futures (COMEX: MGT)
-   
      • 
-   
    • 
-    
-     Futures.Metals.MicroPalladium
-    
-    
-     Futures.Metals.MICRO_PALLADIUM
-    
-    : Micro Palladium Futures (NYMEX: PAM)
-   
      • 
-   
    • 
-    
-     Futures.Metals.MicroSilver
-    
-    
-     Futures.Metals.MICRO_SILVER
-    
-    : Micro Silver Futures (COMEX: SIL)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Palladium
-    
-    
-     Futures.Metals.PALLADIUM
-    
-    : Palladium Futures (NYMEX: PA)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Platinum
-    
-    
-     Futures.Metals.PLATINUM
-    
-    : Platinum Futures (NYMEX: PL)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Silver
-    
-    
-     Futures.Metals.SILVER
-    
-    : Silver Futures (COMEX: SI)
-   
      • 
-   
    • 
-    
-     Futures.Metals.USMidwestDomesticHotRolledCoilSteelCRUIndex
-    
-    
-     Futures.Metals.US_MIDWEST_DOMESTIC_HOT_ROLLED_COIL_STEEL_CRU_INDEX
-    
-    : U.S. Midwest Domestic Hot-Rolled Coil Steel (CRU) Index Futures (NYMEX: HRC)
-   
      • 
-   
    • 
-    
-     Futures.Softs.Sugar11
-    
-    
-     Futures.Softs.SUGAR_11
-    
-    : Sugar #11 Futures ICE (ICE: SB)
-   
      • 
-   
    • 
-    
-     Futures.Softs.Sugar11CME
-    
-    
-     Futures.Softs.SUGAR_11_CME
-    
-    : Sugar #11 Futures CME (NYMEX: YO)
-   
      • 
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Futures Security Master enables you to design strategies harnessing continuous Futures contracts. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Trading cyclical patterns in commodity Futures.
-   
      • 
-   
    • 
-    Buying gold Futures as an inflation hedge with automatic contract roll-overs.
-   
      • 
-   
    • 
-    Detecting arbitrage opportunities between index Futures and Equities.
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm buys the continuous Future contract for Crude Oil when its price rises above its simple moving average. When its price drops below its simple moving average, the algorithm sells the continuous contract. Each time the continuous Future contract rolls over, the algorithm logs the event.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class USFuturesSecurityMasterDataClassicAlgorithm(QCAlgorithm):
-    # Define a threshold for the SMA cross.
-    _threshold = 0.01
-    
-    def initialize(self) -> None:
-        self.set_cash(1000000)
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        # Seed the price of each asset with its last known price to 
-        # avoid trading errors.
-        self.settings.seed_initial_prices = True
-        # Add a Future. Set the continuous contract mapping criteria to
-        # the contract with highest open interest to provide the best 
-        # price information for trend estimation.
-        self._future = self.add_future(
-            Futures.Energy.CRUDE_OIL_WTI,
-            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
-            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
-            contract_depth_offset=0
-        )                      
-        # Historical data: Get the contract rollovers for the trailing 
-        # year.
-        history = self.history(
-            SymbolChangedEvent, self._future.symbol, timedelta(365)
-        )
-        # Set up an SMA indicator for to estimate the trend direction.
-        self._sma = self.sma(self._future.symbol, 10, Resolution.DAILY)
-        # Warm up the SMA indicator so we can trade right away.
-        self.warm_up_indicator(self._future.symbol, self._sma)
-
-    def on_data(self, slice: Slice) -> None:
-        # Wait until we have the latest TradeBar and the indicator is 
-        # ready.
-        if not (self._future.symbol in slice.bars and self._sma.is_ready):
-            return
-        contract = self.portfolio[self._future.mapped]
-        # Long during an uptrend.
-        if (not contract.is_long and 
-            self._future.price > self._sma.current.value * (1+self._threshold)):
-            quantity = 1
-        # Short during an downtrend.
-        elif (not contract.is_short and 
-            self._future.price < self._sma.current.value * (1-self._threshold)):
-            quantity = -1
-        else:
-            return
-        self.market_order(self._future.mapped, quantity - contract.quantity)
-
-    def on_symbol_changed_events(self, symbol_changed_events):
-        for symbol, changed_event in symbol_changed_events.items():
-            old_symbol = changed_event.old_symbol
-            new_symbol = changed_event.new_symbol
-            # The quantity to roll over should be consistent.
-            quantity = self.portfolio[old_symbol].quantity
-            if not quantity:
-                continue
-            # Rolling over: Liquidate the old mapped contract and switch
-            # to the new mapped contract.
-            tag = f"Rollover: {old_symbol} -> {new_symbol}"
-            self.liquidate(old_symbol, tag=tag)
-            self.market_order(new_symbol, quantity, tag=tag)
    
-   
    public class USFuturesSecurityMasterDataClassicAlgorithm  : QCAlgorithm
-{
-    private Future _future;
-    // Define a threshold for the SMA cross.
-    private decimal _threshold = 0.01m;
-    private SimpleMovingAverage _sma;
-    
-    public override void Initialize()
-    {
-        SetCash(1000000);
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        // Seed the price of each asset with its last known price to 
-        // avoid trading errors.
-        Settings.SeedInitialPrices = true;
-        // Add a Future. Set the continuous contract mapping criteria to
-        // the contract with highest open interest to provide the best 
-        // price information for trend estimation.
-        _future = AddFuture(
-            Futures.Energy.CrudeOilWTI,
-            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
-            dataMappingMode: DataMappingMode.OpenInterest,
-            contractDepthOffset: 0
-        );
-        // Historical data: Get the contract rollovers for the trailing 
-        // year.
-        var history = History<SymbolChangedEvent>(_future.Symbol, TimeSpan.FromDays(365));
-        foreach (var symbolChangedEvent in history)
-        {
-            var t = symbolChangedEvent.EndTime;
-            var oldSymbol = symbolChangedEvent.OldSymbol;
-            var newSymbol = symbolChangedEvent.NewSymbol;
-        }
-        // Set up an SMA indicator for to estimate the trend direction.
-        _sma = SMA(_future.Symbol, 10, Resolution.Daily);
-        // Warm up the SMA indicator so we can trade right away.
-        WarmUpIndicator(_future.Symbol, _sma);
-    }
-    
-    public override void OnData(Slice slice)
-    {       
-        // Wait until we have the latest TradeBar and the indicator is 
-        // ready.
-        if (!(slice.Bars.ContainsKey(_future.Symbol) && _sma.IsReady))
-        {
-            return;
-        }
-        var contract = Portfolio[_future.Mapped];
-        int quantity;
-        // Long during an uptrend.
-        if (!contract.IsLong && _future.Price > _sma.Current.Value * (1+_threshold))
-        {
-            quantity = 1;
-        }
-        // Short during an downtrend.
-        else if (!contract.IsShort && _future.Price < _sma.Current.Value * (1-_threshold))
-        {
-            quantity = -1;
-        }
-        else
-        {
-            return;
-        }
-        MarketOrder(_future.Mapped, quantity - contract.Quantity);
-    }
-
-    public override void OnSymbolChangedEvents(SymbolChangedEvents symbolChangedEvents)
-    {
-        foreach (var (symbol, changedEvent) in symbolChangedEvents)
-        {
-            var oldSymbol = changedEvent.OldSymbol;
-            var newSymbol = changedEvent.NewSymbol;
-            // The quantity to roll over should be consistent.
-            var quantity = Portfolio[oldSymbol].Quantity;
-            if (quantity == 0)
-            {
-                continue;
-            }
-            // Rolling over: Liquidate the old mapped contract and switch 
-            // to the new mapped contract.
-            var tag = $"Rollover: {oldSymbol} -> {newSymbol}";
-            Liquidate(oldSymbol, tag: tag);
-            MarketOrder(newSymbol, quantity, tag: tag);
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm buys the continuous Future contract for Crude Oil when its price rises above its simple moving average. When its price drops below its simple moving average, the algorithm sells the continuous contract. Each time the contract rolls over, the algorithm logs the event.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class USFuturesSecurityMasterDataFrameworkAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_cash(1000000)
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-
-        # Setting the continuous contract mapping criteria, the contract with highest open interest provide the best price information for trend estimation
-        self.add_future(Futures.Energies.CRUDE_OIL_WTI,
-                       data_normalization_mode = DataNormalizationMode.BACKWARDS_RATIO,
-                       data_mapping_mode = DataMappingMode.OPEN_INTEREST,
-                       contract_depth_offset = 0)
-        self.add_alpha(ContinuousFuturesAlphaModel())
-        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
-
-class ContinuousFuturesAlphaModel(AlphaModel):
-    # 1% margin to reassure trend direction
-    threshold = 0.01
-    symbol = None
-    continuous_contract = None
-    
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        insights = []
-        # Check if contract data available, since all trades are based on that only
-        if self.continuous_contract_symbol is None or self.continuous_contract is None:
-            return insights 
-        
-        # Up-to-date handling of switching the mapped contract for trade liquidity
-        for symbol, changed_event in slice.symbol_changed_events.items():
-            old_symbol = changed_event.old_symbol
-            if algorithm.insights.has_active_insights(old_symbol, algorithm.utc_time):
-                new_symbol = changed_event.new_symbol
-                tag = f"Rollover - Symbol changed at {algorithm.time}: {old_symbol} -> {new_symbol}"
-                last_insight = sorted(algorithm.insights[old_symbol], key=lambda x: x.close_time_utc)[-1]
-                insights.append(Insight.price(new_symbol, last_insight.close_time_utc, last_insight.direction, tag= tag))
-                algorithm.insights.clear([old_symbol])
-                algorithm.log(tag)
-         
-        mapped_symbol = self.continuous_contract.mapped
-        # Make sure trade decisions are based on newly received data
-        if not slice.bars.contains_key(self.continuous_contract_symbol) or not self.sma.is_ready or not mapped_symbol:
-            return insights 
-        
-        direction = None
-        # Long if trend up by threshold to follow the trend
-        if slice.bars[self.continuous_contract_symbol].price > self.sma.current.value * (1+self.threshold) and not algorithm.portfolio[mapped_symbol].is_long:
-            direction = InsightDirection.UP
-        # Short if trend down by threshold to follow the trend
-        elif slice.bars[self.continuous_contract_symbol].price < self.sma.current.value * (1-self.threshold) and not algorithm.portfolio[mapped_symbol].is_short:
-            direction = InsightDirection.DOWN
-        
-        if direction:
-            insights.append(Insight.price(mapped_symbol, timedelta(days=14), direction))
-        
-        return insights
-        
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            symbol = security.symbol
-            # Set up SMA indicator for trend direction estimator, only for the canonical symbol
-            if symbol.is_canonical():
-                self.continuous_contract = security
-                self.continuous_contract_symbol = symbol
-                self.sma = algorithm.SMA(self.continuous_contract_symbol, 10, Resolution.DAILY)
-            
-                # Historical data
-                history = algorithm.history(symbol, 60*24*10, Resolution.MINUTE)
-                algorithm.debug(f"We got {len(history)} from our history request for {symbol}")
-                
-                if history.empty:
-                    continue
-                # Warm up the SMA indicator for its readiness for immediate use
-                for time, row in history.droplevel(0).loc[self.continuous_contract_symbol].iterrows():
-                    self.sma.update(IndicatorDataPoint(time, row.close))
-
-class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
-    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
-        targets = []
-        for insight in insights:
-            if algorithm.securities[insight.symbol].is_tradable:
-                targets.append(PortfolioTarget(insight.symbol, insight.direction))
-        return targets
    
-   
    public class USFuturesSecurityMasterDataFrameworkAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetCash(1000000);
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-
-        // Setting the continuous contract mapping criteria, the contract with highest open interest provide the best price information for trend estimation
-        AddFuture(Futures.Energies.CrudeOilWTI,
-            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
-            dataMappingMode: DataMappingMode.OpenInterest,
-            contractDepthOffset: 0
-        );
-        AddAlpha(new ContinuousFuturesAlphaModel());
-        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
-    }
-    
-    class ContinuousFuturesAlphaModel : AlphaModel
-    {
-        private Future _continuousContract;
-        private Symbol _symbol;
-        // 1% margin to reassure trend direction
-        private decimal _threshold = 0.01m;
-        private SimpleMovingAverage _sma;
-    
-        public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-        {
-            var insights = new List<Insight>();
-            
-            //  Check if contract data available, since all trades are based on that only
-            if (_symbol == null || _continuousContract == null)
-            {
-                return insights;
-            }
-            
-            // Up-to-date handling of switching the mapped contract for trade liquidity
-            foreach (var (symbol, changedEvent) in slice.SymbolChangedEvents)
-            {
-                var oldSymbol = changedEvent.OldSymbol;
-                if (algorithm.Insights.HasActiveInsights(oldSymbol, algorithm.UtcTime))
-                {
-                    var newSymbol = changedEvent.NewSymbol;
-                    var tag = $"Rollover - Symbol changed at {algorithm.Time}: {oldSymbol} -> {newSymbol}";
-                    var lastInsight = algorithm.Insights[oldSymbol].OrderBy(x => x.CloseTimeUtc).LastOrDefault();
-                    insights.Add(Insight.Price(newSymbol, lastInsight.CloseTimeUtc, lastInsight.Direction, tag: tag));
-                    algorithm.Insights.Clear(new Symbol[] { oldSymbol });
-                    algorithm.Log(tag);
-                }
-            }
-            
-            var mappedSymbol = _continuousContract.Mapped;
-            // Make sure trade decisions are based on newly received data
-            if (!slice.Bars.ContainsKey(_symbol) || !_sma.IsReady || mappedSymbol == null)
-            {
-                return insights;
-            }
-            
-            // Long if trend up by threshold to follow the trend
-            if (slice.Bars[_symbol].Price > _sma.Current.Value * (1+_threshold) && !algorithm.Portfolio[mappedSymbol].IsLong)
-            {
-                insights.Add(Insight.Price(mappedSymbol, TimeSpan.FromDays(14), InsightDirection.Up));
-            }
-            // Short if trend down by threshold to follow the trend
-            else if (slice.Bars[_symbol].Price < _sma.Current.Value * (1-_threshold) && !algorithm.Portfolio[mappedSymbol].IsShort)
-            {
-                insights.Add(Insight.Price(mappedSymbol, TimeSpan.FromDays(14), InsightDirection.Down));
-            }
-            
-            return insights;
-        }
-    
-        public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-        {
-            foreach (var security in changes.AddedSecurities)
-            {
-                var symbol = security.Symbol;
-                // Set up SMA indicator for trend direction estimator, only for the canonical symbol
-                if (symbol.IsCanonical())
-                {
-                    _continuousContract = (Future)security;
-                    _symbol = symbol;
-                    _sma = algorithm.SMA(_symbol, 10, Resolution.Daily);
-                    
-                    // Historical data 
-                    var history = algorithm.History(symbol, 60*24*10, Resolution.Minute);
-                    algorithm.Debug($"We got {history.Count()} from our history request for {symbol}");
-                    
-                    // Warm up the SMA indicator for its readiness for immediate use
-                    foreach (var bar in history)
-                    {
-                        _sma.Update(new IndicatorDataPoint(bar.Time, bar.Close));
-                    }
-                }
-            }
-        }
-    }
-    
-    class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
-    {
-        public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
-        {
-            var targets = new List<PortfolioTarget>();
-            foreach (var insight in insights)
-            {
-                if (algorithm.Securities[insight.Symbol].IsTradable)
-                {
-                    targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
-                }
-            }
-            return targets;
-        }
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Futures Security Master dataset provides
-   
-    SymbolChangedEvent
-   
-   objects, which have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > QuantConnect > US Index Option Universe
    
-
    
-    
    
-        
    QuantConnect
    
-        
    US Index Option Universe
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Index Option Universe dataset by QuantConnect lists the available US Index Options contracts and the current Implied Volatility and Greeks. The data covers European Option contracts for 3 US Indices: SPX, VIX, and NDX. It starts in January 2012 and is delivered on a daily update frequency. To create this dataset, we use
-   
-    our implementation
-   
-   of the forward tree pricing model, which accounts for the interest rate, dividend payments, and daily closing prices. The values in this dataset are the same values you can get from daily
-   
-    indicators
-   
-   with mirror Options.
-  
    
-  
    
-   This dataset
-   
-    does not
-   
-   contain market data. For market data, see
-   
-    US Index Options by AlgoSeek
-   
-   .
-  
    
-  
    
-   For more information about the US Index Option Universe dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the US Index Options Universe dataset:
-  
    
-  
    
-   
    option = self.add_index_option('VIX')
-option.set_filter(lambda universe: universe.delta(0.4, 0.6))
-self.option_symbol = option.symbol
    
-   
    var option = AddIndexOption("VIX");
-option.SetFilter(universe => universe.delta(0.4m, 0.6m));
-_optionSymbol = option.Symbol;
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 2012
-     
    
-      Asset Coverage
-     
-      7 Index Options
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Daily
-     
    
-      Timezone
-     
-      New York
-     
    
- 
-
-
-
    Specification Over Time
    
-
- 
-  
    
-   According to the SPX Options
-   
-    contract specification
-   
-   , some SPX contracts expire every month and SPXW contracts expires every day. Before 2021, you could only trade SPX contracts with the following expiration dates:
-  
    
-  
      
-   
    • 
-    Expires within the next 4 months
-   
      • 
-   
    • 
-    Expires in September within the next 14 months
-   
      • 
-   
    • 
-    Expires in January, March, or June within the next 2 years
-   
      • 
-   
    • 
-    Expires in December within the next 3 years
-   
      • 
-  
    
-  
    
-   During this time, SPXW didn't have 0DTE every day.
-  
    
-  
    
-   
-    Sources
-   
-   :
-   
    
-   -
-   
-    Cboe Options Exchange to List Three Long-Dated SPX Options Expirations, Beginning November 1, 2021
-   
-   
    
-   -
-   
-    S&P 500 Weekly Options Now Expire Five Days a Week
-   
-  
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add US Index Options Universe data to your algorithm, call the
-   
-    AddIndexOption
-   
-   
-    add_index_option
-   
-   method. Save a reference to the Index Option
-   
-    Symbol
-   
-   so you can access the data later in your algorithm. To define which contracts should be in your universe, call the
-   
-    SetFilter
-   
-   
-    set_filter
-   
-   method of the
-   
-    IndexOption
-   
-   object.
-  
    
-  
    
-   The
-   
-    AddIndexOption
-   
-   
-    add_index_option
-   
-   method provides a daily stream of Option chain data. To get the most recent daily chain, call the
-   
-    OptionChain
-   
-   
-    option_chain
-   
-   method with the canonical Index Option Symbol. The
-   
-    OptionChain
-   
-   
-    option_chain
-   
-   method returns data on all the tradable contracts, not just the contracts that pass your universe filter.
-  
    
-  
    
-   
    class IndexOptionsDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2021, 1, 1)
-        self.set_end_date(2021, 6, 1)
-        self.set_cash(1_000_000)
-        self.universe_settings.asynchronous = True
-        self.index_symbol = self.add_index('VIX').symbol
-        
-        standard_option = self.add_index_option(self.index_symbol)
-        standard_option.set_filter(self._option_filter)
-        self.standard_option_symbol = standard_option.symbol
-        standard_chain = self.option_chain(standard_option.symbol, flatten=True).data_frame
-
-        weekly_option = self.add_index_option(self.index_symbol, "VIXW")
-        weekly_option.set_filter(self._option_filter)
-        self.weekly_option_symbol = weekly_option.symbol
-        weekly_chain = self.option_chain(weekly_option.symbol, flatten=True).data_frame
-        weekly_chain = weekly_chain[
-            weekly_chain.index.map(lambda symbol: not IndexOptionSymbol.is_standard(symbol))
-        ]
-        
-
-    def _option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
-        # Contracts can be filtered by greeks, implied volatility, open interest:
-        return universe \
-            .delta(0.5, 1.5) \
-            .gamma(0.0001, 0.0006) \
-            .vega(0.01, 1.5) \
-            .theta(-2.0, -0.5) \
-            .rho(0.5, 3.0) \
-            .implied_volatility(1, 3) \
-            .open_interest(100,500)
    
-   
    public class IndexOptionsDataAlgorithm : QCAlgorithm
-{
-    private Symbol _indexSymbol, _standardOptionSymbol, _weeklyOptionSymbol;
-
-    public override void Initialize()
-    {
-        SetStartDate(2021, 1, 1);
-        SetEndDate(2021, 6, 1);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-        _indexSymbol = AddIndex("VIX").Symbol;
-
-        var standardOption = AddIndexOption(_indexSymbol);
-        standardOption.SetFilter(OptionFilter);
-        _standardOptionSymbol = standardOption.Symbol;
-        var standardChain = OptionChain(_standardOptionSymbol);
-
-        var weeklyOption = AddIndexOption(_indexSymbol, "VIXW");
-        weeklyOption.SetFilter(OptionFilter);
-        _weeklyOptionSymbol = weeklyOption.Symbol;
-        var weeklyChain = OptionChain(_weeklyOptionSymbol)
-            .Where(contract => !IndexOptionSymbol.IsStandard(contract.Symbol));
-    }
-
-    private OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
-    {
-        // Contracts can be filtered by greeks, implied volatility, open interest:
-        return universe
-            .Delta(0.5m, 1.5m)
-            .Gamma(0.0001m, 0.0006m)
-            .Vega(0.01m, 1.5m)
-            .Theta(-2.0m, -0.5m)
-            .Rho(0.5m, 3.0m)
-            .ImpliedVolatility(1.0m, 3.0m)
-            .OpenInterest(100, 500);
-    }
-}
    
-  
    
-  
    
-   The Index resolution must be less than or equal to the Index Option resolution. For example, if you set the Index resolution to minute, then you must set the Index Option resolution to minute, hour, or daily.
-  
    
-  
    
-   For more information about creating US Index Option subscriptions, see
-   
-    Index Options
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   For information about accessing US Equity Options data, see
-   
-    Handling Data
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical US Index Options data in an algorithm and the Research Environment.
-  
    
-  
    
-   Historical Data In Algorithms
-  
    
-  
    
-   To get historical US Index Options Universe data in an algorithm, call the
-   
-    History<OptionUniverse>
-   
-   
-    history
-   
-   method with the canonical Index Option
-   
-    Symbol
-   
-   . This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter. If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame
-history_df = self.history(self.standard_option_symbol, timedelta(3))
-
-# OptionUniverse objects
-history = self.history[OptionUniverse](self.standard_option_symbol, timedelta(3))
    
-   
    // OptionUniverse objects 
-var history = History<OptionUniverse>(_standardOptionSymbol, TimeSpan.FromDays(3)).ToList();
    
-  
    
-  
    
-   For more information about Index Options Universe data in algorithms, see
-   
-    Historical Data
-   
-   .
-  
    
-  
    
-   Historical Data In Research
-  
    
-  
    
-   To get historical US Index Options Universe data in the Research Environment, call the
-   
-    History<OptionUniverse>
-   
-   
-    history
-   
-   method with the canonical Option
-   
-    Symbol
-   
-   . This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter.
-  
    
-  
    
-   
    qb = QuantBook()
-index_symbol = qb.add_index('VIX').symbol
-option = qb.add_index_option(index_symbol) # or qb.add_index_option(index_symbol, "VIXW")
-history = qb.history(option.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5))
    
-   
    var qb = new QuantBook();
-var indexSymbol = qb.AddIndex("VIX").Symbol;
-var option = qb.AddIndexOption(indexSymbol); // or qb.AddIndexOption(indexSymbol, "VIXW");
-var history = qb.History<OptionUniverse>(option.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 6));
-foreach (var chain in history)
-{
-    var endTime = chain.EndTime;
-    var filteredContracts = chain.Data
-        .Select(contract => contract as OptionUniverse)
-        .Where(contract => contract.Greeks.Delta > 0.3m);
-    foreach (var contract in filteredContracts)
-    {
-        var price = contract.Price;
-        var iv = contract.ImpliedVolatility;
-    }
-}
    
-  
    
-  
    
-   For more information about historical Index Options Universe data in the Research Environment, see
-   
-    Universes
-   
-   .
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   The following table shows the available Index Options:
-  
    
-  
-   
-    
-     
-     
-     
-     
-     
-     
-    
-   
-   
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-   
-  
    
-      Underlying Index
-     
-      Underlying Ticker
-     
-      Target Ticker
-     
-      Standard Contracts
-     
-      Weekly Contracts
-     
-      Tradable on Expiry Day
-     
    
-      NASDAQ-100
-     
-      NDX
-     
-     
-      green check
-     
-     
-     
    
-      NASDAQ-100
-     
-      NDX
-     
-      NDXP
-     
-      green check
-     
-     
-      green check
-     
    
-      NASDAQ-100
-     
-      NDX
-     
-      NQX
-     
-      green check
-     
-      green check
-     
-      green check
-     
    
-      Russell 2000
-     
-      RUT
-     
-     
-      green check
-     
-     
-     
    
-      Russell 2000
-     
-      RUT
-     
-      RUTW
-     
-     
-      green check
-     
-      green check
-     
    
-      S&P500
-     
-      SPX
-     
-     
-      green check
-     
-     
-     
    
-      S&P500
-     
-      SPX
-     
-      SPXW
-     
-     
-      green check
-     
-      green check
-     
    
-      S&P500
-     
-      VIX
-     
-     
-      green check
-     
-     
-     
    
-      S&P500
-     
-      VIX
-     
-      VIXW
-     
-     
-      green check
-     
-     
    
-  
    
-   For more information about each underlying Index, see
-   
-    Supported Indices
-   
-   .
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Index Options Universe dataset enables you to accurately design strategies for Index Options. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Buying VIX call Options to hedge against upcoming volatility
-   
      • 
-   
    • 
-    Buying VIX put Options to capture the natural downward price movement in the VIX index
-   
      • 
-   
    • 
-    Buying SPX put Options to protect against downward price movement in the S&P 500
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm subscribes to SPX put options that fall within delta range between -1 and -0.95, open interest range between 10 and 1000, and expire within seven days. Within this Option chain, the algorithm holds the put Option contract that has the minimum delta (closest to -1) during market hour to hedge the underlying intra-day movement completely. It avoid volatility from sentiment and only earns from inter-day movement from longer-term factors. When the contract expires, the algorithm rolls over to the next contract that meets this criteria.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class IndexOptionsUniverseAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(10000000)
-
-        # Asynchronous can use computational resources efficiently
-        self.universe_settings.asynchronous = True
-        # Subscribe to the underlying for the underlying position
-        # Set the data normalization mode to raw for strike price comparability
-        self.index = self.add_index("SPX").symbol
-        # Requesting option data and filter for the hedge candidates
-        option = self.add_index_option(self.index)
-        option.set_filter(self.option_filter)
-        self.option_symbol = option.symbol
-
-        # Set scheduled event to buy a hedge option contract at market open to eliminate the intra-day movement
-        self.schedule.on(
-            self.date_rules.every_day(self.index),
-            self.time_rules.after_market_open(self.index, 1),
-            self.buy_hedge_contract
-        )
-
-        # Set a scheduled event to sell the hedge contract before market close, since we want to earn from inter-day movement
-        # Leave 2 minutes contingency to fill
-        self.schedule.on(
-            self.date_rules.every_day(self.index),
-            self.time_rules.before_market_close(self.index, 2),
-            self.sell_hedge_contract
-        )
-
-        self.hedge = None
-        
-    def option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
-        # Select the contracts with delta very close to -1 and high open interest
-        # This can effectively hedge most of the price change of the underlying and ensure the liquidity
-        # Make sure the contract is expiring close for its tradbility
-        return universe.puts_only().expiration(2, 7).delta(-1, -0.95).open_interest(10, 1000)
-
-    def buy_hedge_contract(self) -> None:
-        chain = self.current_slice.option_chains.get(self.option_symbol)
-        if chain:
-            # Order the underlying if not hold, the order size should match the option contract
-            # Order only if option chain data ready for hedging
-            if not self.portfolio[self.index].invested:
-                self.market_order(self.index, self.securities[self.option_symbol].symbol_properties.contract_multiplier)
-
-            # Get the contract with delta closest to -1 (lowest possible delta)
-            contract = sorted(chain, key=lambda x: x.greeks.delta)[0]
-            self.hedge = contract.symbol
-            # Buy 1 deep ITM put with delta close to -1 to eliminate the intraday movement
-            self.market_order(self.hedge, 1)
-        
-    def sell_hedge_contract(self) -> None:
-        # Check if any hedge contract position, if so, liquidate before market close to expose to underlying overnight movement
-        if self.hedge:
-            self.liquidate(self.hedge)
-            self.hedge = None
    
-   
    public class IndexOptionsUniverseAlgorithm : QCAlgorithm
-{
-    private Symbol _index, _optionSymbol, _hedge;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(10000000);
-        // Asynchronous can use computational resources efficiently
-        UniverseSettings.Asynchronous = true;
-
-        // Subscribe to the underlying for the underlying position
-        // Set the data normalization mode to raw for strike price comparability
-        _index = AddIndex("SPX").Symbol;
-        // Requesting option data and filter for the hedge candidates
-        var option = AddIndexOption(_index);
-        _optionSymbol = option.Symbol;
-        option.SetFilter(OptionFilter);
-
-        // Set scheduled event to buy a hedge option contract at market open to eliminate the intra-day movement
-        Schedule.On(
-            DateRules.EveryDay(_index),
-            TimeRules.AfterMarketOpen(_index, 1),
-            BuyHedgeContract
-        );
-
-        // Set a scheduled event to sell the hedge contract before market close, since we want to earn from inter-day movement
-        // Leave 2 minutes contingency to fill
-        Schedule.On(
-            DateRules.EveryDay(_index),
-            TimeRules.BeforeMarketClose(_index, 2),
-            SellHedgeContract
-        );
-    }
-
-    private OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
-    {
-        // Select the contracts with delta very close to -1 and high open interest
-        // This can effectively hedge most of the price change of the underlying and ensure the liquidity
-        // Make sure the contract is expiring close for its tradbility
-        return universe
-            .PutsOnly()
-            .Expiration(2, 7)
-            .Delta(-1m, -0.95m)
-            .OpenInterest(10, 1000);
-    }
-
-    private void BuyHedgeContract()
-    {
-        if (CurrentSlice.OptionChains.TryGetValue(_optionSymbol, out var chain))
-        {
-            // Order the underlying if not hold, the order size should match the option contract
-            // Order only if option chain data ready for hedging
-            if (!Portfolio[_index].Invested)
-            {
-                MarketOrder(_index, Securities[_optionSymbol].SymbolProperties.ContractMultiplier);
-            }
-
-            // Get the contract with delta closest to -1 (lowest possible delta)
-            var contract = chain.MinBy(x => x.Greeks.Delta);
-            _hedge = contract.Symbol;
-            // Buy 1 deep ITM put with delta close to -1 to eliminate the intraday movement
-            MarketOrder(_hedge, 1);
-        }
-    }
-
-    private void SellHedgeContract()
-    {
-        // Check if any hedge contract position, if so, liquidate before market close to expose to underlying overnight movement
-        if (_hedge != null)
-        {
-            Liquidate(_hedge);
-            _hedge = null;
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm demonstrating a Gamma Scalping strategy through framework algorithm. It filters SPX options with expiration between 30 to 90 days and open interest between 50 and 1000, due to liquidity concern and lower Gamma fluctuation. Assuming a significant upward trend is expecting, it orders a long straddle strategy from the strike price with Delta-neutral and the highest Gamma to earn the highest profit from upward underlying movement.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class IndexOptionsUniverseFrameworkAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2024, 12, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(1000000)
-        # Asynchronous can use computational resources efficiently
-        self.universe_settings.asynchronous = True
-
-        # Universe selection that select based on liquidity of the option contracts (by open interest)
-        self.add_universe_selection(IndexOptionsUniverseSelectionModel())
-        # Custom alpha model that use Delta and Gamma to signal insights
-        self.add_alpha(OptionDeltaGammaAlphaModel())
-        # To maintain long-short size equal, use a PCM that order a single contract
-        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
-
-class IndexOptionsUniverseSelectionModel(OptionUniverseSelectionModel):
-    def __init__(self) -> None:
-        # Daily update with the select_option_chain_symbols function
-        super().__init__(timedelta(1), self.select_option_chain_symbols)
-
-    def select_option_chain_symbols(self, dt: datetime) -> List[Symbol]:
-        # Select only SPX options as our focus, which is a highly traded volatile index with great upward momentum
-        return [Symbol.create("SPX", SecurityType.INDEX_OPTION, Market.USA)]
-
-    def filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
-        # To ensure the liquidity and tradability, make sure the option is not 0-DTE and have a fair open interest
-        # A longer TTM will have lower Gamma thus more stable in the local delta-neutral position
-        return universe.expiration(30, 90).open_interest(50, 1000)
-
-class OptionDeltaGammaAlphaModel(AlphaModel):
-    def __init__(self) -> None:
-        # A day count variable to control the alpha model only trade once a day
-        self._day = -1
-
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        insights = []
-
-        if self._day != slice.time.day:
-            # We open position for each option underlying as separate bet
-            for _, chain in slice.option_chains.items():
-                # For theta-neutral, select the same expiry for both call and put
-                expiry = min(x.expiry for x in chain)
-                contracts = [x for x in chain if x.expiry == expiry]
-
-                # Calculate delta and gamma per strike price group for later filtering, ensure both call and put available for option strategy
-                delta_gamma_symbols = []
-                strikes = set(x.strike for x in contracts if len([y for y in contracts if y.strike == x.strike]) == 2)
-                for strike in strikes:
-                    # Get both call and put for their aggregated delta and gamma
-                    call = next(filter(lambda x: x.right == OptionRight.CALL and x.strike == strike, contracts))
-                    put = next(filter(lambda x: x.right == OptionRight.PUT and x.strike == strike, contracts))
-                    delta_gamma_symbols.append((call.greeks.delta + put.greeks.delta, call.greeks.gamma + put.greeks.gamma, call.symbol, put.symbol))
-                
-                if len(delta_gamma_symbols) == 0:
-                    continue
-
-                # We want a delta-neutral position, so it is likely to be ATM (like a long straddle)
-                # Less than 2d.p. difference is non-significant, which we can risk for better reward
-                # Assuming the market direction is up in most scenario, we try to get the strike providing max overall Gamma
-                # Make sure the aggregated gamma to be positive to bet on large uptrend
-                # So it will earn more when the price really go up higher and higher, but locally immune for small noise
-                filtered = [item for item in delta_gamma_symbols if round(item[0], 2) <= 0.01 and item[1] > 0]
-                if len(filtered) == 0:
-                    continue
-                selected = sorted(filtered, key=lambda x: x[1])[0]
-
-                # Provide trade signal and roll the day count to let the position stay for the whole day
-                selected_call = selected[2]
-                selected_put = selected[3]
-                insights.extend([
-                    Insight.price(selected_call, Expiry.END_OF_DAY, InsightDirection.UP),
-                    Insight.price(selected_put, Expiry.END_OF_DAY, InsightDirection.UP)
-                ])
-                self._day = slice.time.day
-        
-        return insights
-
-class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
-    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
-        targets = []
-        for insight in insights:
-            if algorithm.securities[insight.symbol].is_tradable:
-                # Use a whole number target to order the exact number of share for size-matching
-                targets.append(PortfolioTarget(insight.symbol, insight.direction))
-        return targets
    
-   
    public class IndexOptionsUniverseFrameworkAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 12, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(1000000);
-        // Asynchronous can use computational resources efficiently
-        UniverseSettings.Asynchronous = true;
-
-        // Universe selection that select based on liquidity of the option contracts (by open interest)
-        AddUniverseSelection(new IndexOptionsUniverseSelectionModel());
-        // Custom alpha model that use Delta and Gamma to signal insights
-        AddAlpha(new OptionDeltaGammaAlphaModel());
-        // To maintain long-short size equal, use a PCM that order a single contract
-        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
-    }
-}       
-    
-class IndexOptionsUniverseSelectionModel : OptionUniverseSelectionModel
-{
-    // Daily update with the SelectOptionChainSymbols function
-    public IndexOptionsUniverseSelectionModel()
-            : base(TimeSpan.FromDays(1), SelectOptionChainSymbols) {}
-    
-    private static IEnumerable<Symbol> SelectOptionChainSymbols(DateTime utcTime)
-    {
-        // Select only SPX options as our focus, which is a highly traded volatile index with great upward momentum
-        return new[] {QuantConnect.Symbol.Create("SPX", SecurityType.IndexOption, Market.USA)};
-    }
-
-    protected override OptionFilterUniverse Filter(OptionFilterUniverse filter)
-    {
-        // To ensure the liquidity and tradability, make sure the option is not 0-DTE and have a fair open interest
-        // A longer TTM will have lower Gamma thus more stable in the local delta-neutral position
-        return filter
-            .Expiration(30, 90)
-            .OpenInterest(50, 1000);
-    }
-}
-    
-class OptionDeltaGammaAlphaModel : AlphaModel
-{
-    // A day count variable to control the alpha model only trade once a day
-    private int _day = -1;
-
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        var insights = new List<Insight>();
-        
-        if (_day != slice.Time.Day)
-        {
-            // We open position for each option underlying as separate bet
-            foreach (var kvp in slice.OptionChains)
-            {
-                var chain = kvp.Value;
-
-                // For theta-neutral, select the same expiry for both call and put
-                var expiry = chain.Min(x => x.Expiry);
-                var contracts = chain.Where(x => x.Expiry == expiry).ToList();
-
-                // Calculate delta and gamma per strike price group for later filtering, ensure both call and put available for option strategy
-                var deltaGammaSymbols = new List<(decimal, decimal, Symbol, Symbol)>();
-                var strikes = contracts.Select(x => x.Strike)
-                    .Where(x => contracts.Count(y => y.Strike == x) == 2)
-                    .Distinct();
-                foreach (var strike in strikes)
-                {
-                    // Get both call and put for their aggregated delta and gamma
-                    var call = contracts.Single(x => x.Right == OptionRight.Call && x.Strike == strike);
-                    var put = contracts.Single(x => x.Right == OptionRight.Put && x.Strike == strike);
-                    deltaGammaSymbols.Add((call.Greeks.Delta + put.Greeks.Delta, call.Greeks.Gamma + put.Greeks.Gamma, call.Symbol, put.Symbol));
-                }
-
-                if (deltaGammaSymbols.Count == 0)
-                {
-                    continue;
-                }
-
-                // We want a delta-neutral position, so it is likely to be ATM (like a long straddle)
-                // Less than 2d.p. difference is non-significant, which we can risk for better reward
-                // Assuming the market direction is up in most scenario, we try to get the strike providing max overall Gamma
-                // Make sure the aggregated gamma to be positive to bet on large uptrend
-                // So it will earn more when the price really go up higher and higher, but locally immune for small noise
-                var filtered = deltaGammaSymbols.Where(item => Math.Round(item.Item1, 2) <= 0.01m && item.Item2 > 0).ToList();
-                if (filtered.Count == 0)
-                {
-                    continue;
-                }
-                var selected = filtered.OrderByDescending(item => item.Item2).First();
-
-                // Provide trade signal and roll the day count to let the position stay for the whole day
-                var selectedCall = selected.Item3;
-                var selectedPut = selected.Item4;
-                insights.AddRange(new[] {
-                    Insight.Price(selectedCall, Expiry.EndOfDay, InsightDirection.Up),
-                    Insight.Price(selectedPut, Expiry.EndOfDay, InsightDirection.Up)
-                });
-                _day = slice.Time.Day;
-            }
-        }
-
-        return insights;
-    }
-}
-    
-class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
-{
-    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
-    {
-        var targets = new List<PortfolioTarget>();
-        foreach (var insight in insights)
-        {
-            if (algorithm.Securities[insight.Symbol].IsTradable)
-            {
-                // Use a whole number target to order the exact number of share for size-matching
-                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
-            }
-        }
-        return targets;
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Index Options Universe dataset provides
-   
-    OptionFilterUniverse
-   
-   ,
-   
-    OptionUniverse
-   
-   , and
-   
-    OptionChain
-   
-   objects.
-  
    
-  
    
-   OptionFilterUniverse Attributes
-  
    
-  
    
-   
-    OptionFilterUniverse
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   OptionUniverse Attributes
-  
    
-  
    
-   
-    OptionUniverse
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   OptionChain Attributes
-  
    
-  
    
-   
-    OptionChain
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > AlgoSeek
    
-
    
-    
    
-        
    Datasets
    
-        
    AlgoSeek
    
-    
    
-
    
-
- 
-  
    
-   
    
-    
    
-     AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering Equities, Futures, Options, cash FOREX, and Cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning.  For more information about AlgoSeek, visit
-     
-      algoseek.com
-     
-     .
-    
    
-   
    
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-      
    
-       
    
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-         US Equities
-        
    
-       
    
-      
    
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-      
    
-       
    
-        
    
-         US Equity Options
-        
    
-       
    
-      
    
-         
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-      
    
-       
    
-        
    
-         US Future Options
-        
    
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-      
    
-         
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-    
    
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-      
    
-       
    
-        
    
-         US Futures
-        
    
-       
    
-      
    
-         
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-    
    
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-      
    
-       
    
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-         US Index Options
-        
    
-       
    
-      
    
-         
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-  
    
- 
-
-
-
     
    
-
    Datasets > AlgoSeek > US Equities
    
-
    
-    
    
-        
    AlgoSeek
    
-        
    US Equities
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Equities dataset by AlgoSeek is survivorship bias-free daily coverage of every stock traded in the US Securities Information Processors (SIP) CTA/UTP feed since 1998. The dataset covers approximately 27,500 securities, starts in January 1998, and is delivered in any resolution from tick to daily. The Data is collected from the full SIP feed via our Equinix co-located servers, including all trades and quotes published to every exchange as well as FINRA. Over-the-Counter (OTC) trades are not included.
-  
    
-  
    
-   This dataset depends on the
-   
-    US Equity Security Master
-   
-   dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
-  
    
-  
    
-   For more information about the US Equities dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering Equities, Futures, Options, cash FOREX, and Cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning.  For more information about AlgoSeek, visit
-   
-    algoseek.com
-   
-   .
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   AlgoSeek is the default US Equities dataset on QuantConnect. The following snippet demonstrates how to request data from the US Equities dataset:
-  
    
-  
    
-   
    self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
    
-   
    _aapl = AddEquity("AAPL", Resolution.Daily).Symbol;
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 1998
-     
    
-      Asset Coverage
-     
-      27,500 US Equities
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Tick, Second, Minute, Hourly, & Daily
-     
    
-      Timezone
-     
-      New York
-     
    
-      Market Hours
-     
-      
-       Regular and Extended
-      
-     
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add US Equities data to your algorithm, call the
-   
-    AddEquity
-   
-   
-    add_equity
-   
-   method. Save a reference to the Equity
-   
-    Symbol
-   
-   so you can access the data later in your algorithm.
-  
    
-  
    
-   
    class USEquityDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2018, 1, 1)
-        self.set_end_date(2021, 6, 1)
-        self.set_cash(100000) 
-        # Subscribe to AAPL data
-        self.aapl = self.add_equity("AAPL", Resolution.MINUTE).symbol
    
-   
    public class USEquityDataAlgorithm : QCAlgorithm
-{
-    private Symbol _symbol; 
-    
-    public override void Initialize()
-    {
-        SetStartDate(2018, 1, 1);
-        SetEndDate(2021, 6, 1);
-        SetCash(100000);
-        // Subscribe to AAPL data
-        _symbol = AddEquity("AAPL", Resolution.Minute).Symbol;
-    }
-}
    
-  
    
-  
    
-   For more information about creating US Equity subscriptions, see
-   
-    Requesting Data
-   
-   or
-   
-    US Equity Universes
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To get the current US Equities data, index the
-   
-    Bars
-   
-   
-    bars
-   
-   ,
-   
-    QuoteBars
-   
-   
-    quote_bars
-   
-   , or
-   
-    Ticks
-   
-   
-    ticks
-   
-   properties of the current
-   
-    
-     Slice
-    
-   
-   with the Equity
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your security at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Access data: TradeBar data
-    if self.aapl in slice.bars:
-        trade_bar = slice.bars[self.aapl]
-        self.log(f"{self.aapl} close at {slice.time}: {trade_bar.close}")
-
-    # Access data: QuoteBar data
-    if self.aapl in slice.quote_bars:
-        quote_bar = slice.quote_bars[self.aapl]
-        self.log(f"{self.aapl} bid at {slice.time}: {quote_bar.bid.close}")
-
-    # Access data: Ticks data
-    if self.aapl in slice.ticks:
-        ticks = slice.ticks[self.aapl]
-        for tick in ticks:
-            self.log(f"{self.aapl} price at {slice.time}: {tick.price}")
    
-   
    public override void OnData(Slice slice)
-{
-    // Access data: TradeBar data
-    if (slice.Bars.ContainsKey(_symbol))
-    {
-        var tradeBar = slice.Bars[_symbol];
-        Log($"{_symbol} price at {slice.Time}: {tradeBar.Close}");
-    }
-
-    // Access data: QuoteBar data
-    if (slice.QuoteBars.ContainsKey(_symbol))
-    {
-        var quoteBar = slice.QuoteBars[_symbol];
-        Log($"{_symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
-    }
-
-    // Access data: Ticks data
-    if (slice.Ticks.ContainsKey(_symbol))
-    {
-        var ticks = slice.Ticks[_symbol];
-        foreach (var tick in ticks)
-        {
-            Log($"{_symbol} price at {slice.Time}: {tick.Price}");
-        }
-    }
-}
-
    
-  
    
-  
    
-   You can also iterate through all of the data objects in the current
-   
-    Slice
-   
-   .
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Iterate all TradeBar received
-    for symbol, trade_bar in slice.bars.items():
-        self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")
-
-    # Iterate all QuoteBar received
-    for symbol, quote_bar in slice.quote_bars.items():
-        self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
-
-    # Iterate all Ticks received
-    for symbol, ticks in slice.ticks.items():
-        for tick in ticks:
-            self.log(f"{symbol} price at {slice.time}: {tick.price}")
    
-   
    public override void OnData(Slice slice)
-{
-    // Iterate all TradeBar received
-    foreach (var kvp in slice.Bars)
-    {
-        var symbol = kvp.Key;
-        var tradeBar = kvp.Value;
-        Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
-    }
-
-    // Iterate all QuoteBar received
-    foreach (var kvp in slice.QuoteBars)
-    {
-        var symbol = kvp.Key;
-        var quoteBar = kvp.Value;
-        Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
-    }
-
-    // Iterate all Ticks received
-    foreach (var kvp in slice.Ticks)
-    {
-        var symbol = kvp.Key;
-        var ticks = kvp.Value;
-        foreach (var tick in ticks)
-        {
-            Log($"{symbol} price at {slice.Time}: {tick.Price}");
-        }
-    }
-}
-
    
-  
    
-  
    
-   For more information about accessing US Equities data, see
-   
-    Handling Data
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   To get historical US Equity data, call the
-   
-    History
-   
-   
-    history
-   
-   method with the Equity
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame
-history_df = self.history(self.aapl, 100, Resolution.DAILY)
-
-# TradeBar objects
-history_trade_bars = self.history[TradeBar](self.aapl, 100, Resolution.DAILY)
-
-# QuoteBar objects
-history_quote_bars = self.history[QuoteBar](self.aapl, 100, Resolution.MINUTE)
-
-# Tick objects
-history_ticks = self.history[Tick](self.aapl, timedelta(seconds=10), Resolution.TICK)
    
-   
    // TradeBar objects 
-var historyTradeBars = History(_symbol, 100, Resolution.Daily);
-
-// QuoteBar objects 
-var historyQuoteBars = History<QuoteBar>(_symbol, 100, Resolution.Minute);
-
-// Tick objects 
-var historyTicks = History<Tick>(_symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
-  
    
-  
    
-   For more information about historical data, see
-   
-    History Requests
-   
-   .
-  
    
- 
-
-
-
    Universe Selection
    
-
- 
-  
    
-   To select a universe of US Equities, see
-   
-    Equity Universes
-   
-   .
-  
    
- 
-
-
-
    Remove Subscriptions
    
-
- 
-  
    
-   To unsubscribe from a US Equity that you added with the
-   
-    AddEquity
-   
-   
-    add_equity
-   
-   method, call the
-   
-    RemoveSecurity
-   
-   
-    remove_security
-   
-   method.
-  
    
-  
    
-   
    self.remove_security(self.aapl)
    
-   
    RemoveSecurity(_symbol);
    
-  
    
-  
    
-   The
-   
-    RemoveSecurity
-   
-   
-    remove_security
-   
-   method cancels your open orders for the security and liquidates your holdings.
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   To view the supported assets in the US Equities dataset, see the
-   
-    Data Explorer
-   
-   . This dataset doesn't include Over-the-Counter (OTC) stocks.
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Equities dataset enables you to accurately design Equity trading strategies. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Momentum strategies using historical returns on the premise that the momentum will continue
-   
      • 
-   
    • 
-    Value strategies using fundamental factors on the premise that the price of undervalued securities will rise
-   
      • 
-   
    • 
-    Factor investing with periodic rebalancing
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm buys and holds Apple stock:
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class USEquityDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        
-        # Requesting single equity data, since we only trade AAPL
-        self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
-        
-        # Historical data
-        history = self.history(self.aapl, 60, Resolution.DAILY)
-        self.debug(f"We got {len(history)} items from our history request")
-
-    def on_data(self, slice: Slice) -> None:
-        # Check if the current slice containing AAPL and if we hold any position
-        # As we make use of the most updated price data to decide the order size
-        if slice.contains_key(self.aapl) and slice[self.aapl] is not None and not self.portfolio.invested:
-            self.set_holdings(self.aapl, 1)
    
-   
    public class USEquityDataAlgorithm : QCAlgorithm
-{
-    private Symbol _symbol;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-
-        // Requesting single equity data, since we only trade AAPL
-        _symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
-        
-        // Historical data
-        var history = History(_symbol, 60, Resolution.Daily);
-        Debug($"We got {history.Count()} items from our history request");
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // Check if the current slice containing AAPL and if we hold any position
-        // As we make use of the most updated price data to decide the order size
-        if (slice.ContainsKey(_symbol) && slice[_symbol] != null && !Portfolio.Invested)
-        {
-            SetHoldings(_symbol, 1);
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm buys and holds Apple stock:
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class USEquityDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        
-        self.universe_settings.resolution = Resolution.DAILY
-        # To select only AAPL, use a manual selection universe
-        symbols = [Symbol.create("AAPL", SecurityType.EQUITY, Market.USA)]
-        self.add_universe_selection(ManualUniverseSelectionModel(symbols))    
-        
-        # Constant investment signal
-        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(days=7), 0.025, None))
-        
-        # Invest in all members equally
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
    
-   
    public class USEquityDataAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        
-        UniverseSettings.Resolution = Resolution.Daily;
-        // To select only AAPL, use a manual selection universe
-        var symbols = new[] {QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA)};
-        AddUniverseSelection(new ManualUniverseSelectionModel(symbols));
-        
-        // Constant investment signal
-        AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(7), 0.025, null));
-
-        // Invest in all members equally
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Equities dataset provides
-   
-    TradeBar
-   
-   ,
-   
-    QuoteBar
-   
-   , and
-   
-    Tick
-   
-   objects.
-  
    
-  
    
-   TradeBar Attributes
-  
    
-  
    
-   
-    TradeBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   QuoteBar Attributes
-  
    
-  
    
-   
-    QuoteBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   Tick Attributes
-  
    
-  
    
-   
-    Tick
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > AlgoSeek > US Equity Options
    
-
    
-    
    
-        
    AlgoSeek
    
-        
    US Equity Options
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Equity Options data by AlgoSeek provides Option data, including prices, strikes, expires, and open interest. The data covers 4,000 Symbols, starts in January 2012, and is delivered on a minute frequency. This dataset is created by monitoring Options Price Reporting Authority (OPRA) data feed, which consolidates last sale and quotation information originating from the national securities exchanges that have been approved by the Securities and Exchange Commission.
-  
    
-  
    
-   This dataset depends on the following datasets:
-  
    
-  
-  
    
-   For more information about the US Equity Options dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   
-    AlgoSeek
-   
-   was in 2014 with the goal of providing the highest quality, most accurate, ready-to-use data in the financial data industry. AlgoSeek provides access to Equities, ETFs, ETNs, Equity Indices, Equity Options, Futures, and Future Options for quantitative firms and traders.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the US Equity Options dataset:
-  
    
-  
    
-   
    option = self.add_option("GOOG")
-self.option_symbol = option.symbol
-option.set_filter(-2, +2, 0, 180)
    
-   
    var option = AddOption("GOOG");
-_optionSymbol = option.Symbol;
-option.SetFilter(-2, +2, 0, 180);
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 2012*
-     
    
-      Asset Coverage
-     
-      4,000 Symbols
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Minute, Hourly, & Daily
-     
    
-      Timezone
-     
-      New York
-     
    
-      Market Hours
-     
-      
-       Regular Only
-      
-     
    
-  
    
-   * Some data is available before this date. In 2012, AlgoSeek started to fetch data from 48 OPRA channels instead of 24, increasing the quality of the data.
-  
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add US Equity Options data to your algorithm, call the
-   
-    AddOption
-   
-   
-    add_option
-   
-   method. Save a reference to the Equity Option
-   
-    Symbol
-   
-   so you can access the data later in your algorithm.
-  
    
-  
    
-   
    class USEquityOptionsDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2020, 6, 1)
-        self.set_end_date(2021, 6, 1)
-        self.set_cash(100000)
-        self.universe_settings.asynchronous = True
-        # Request GOOG option data
-        option = self.add_option("GOOG")
-        self.option_symbol = option.symbol
-        # Set our strike/expiry filter for this option chain
-        option.set_filter(-2, +2, 0, 180)
-
    
-   
    public class USEquityOptionsDataAlgorithm : QCAlgorithm
-{
-    private Symbol _optionSymbol;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2020, 6, 1);
-        SetEndDate(2021, 6, 1);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-        // Request GOOG option data
-        var option = AddOption("GOOG");
-        _optionSymbol = option.Symbol;
-        // Set our strike/expiry filter for this option chain
-        option.SetFilter(-2, +2, 0, 180);
-    }
-}
    
-  
    
-  
    
-   The Equity resolution must be less than or equal to the Equity Option resolution. For example, if you set the Equity resolution to minute, then you must set the Equity Option resolution to minute, hour, or daily.
-  
    
-  
    
-   For more information about creating US Equity Option subscriptions, see
-   
-    Requesting Data
-   
-   or
-   
-    Equity Options Universes
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To get the current US Equity Options data, index the
-   
-    OptionChains
-   
-   
-    option_chains
-   
-   property of the current
-   
-    
-     Slice
-    
-   
-   with the canonical Equity Option
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your Index Option at every time step. To avoid issues, call the
-   
-    Get
-   
-   
-    get
-   
-   method.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Get the wanted option chain with the canonical symbol
-    chain = slice.option_chains.get(self.option_symbol)
-    if chain:
-        # Iterate the option contracts in chain
-        for contract in chain:
-            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
-
    
-   
    public override void OnData(Slice slice)
-{
-    // Get the wanted option chain with the canonical symbol
-    if (slice.OptionChains.TryGetValue(_optionSymbol, out var chain))
-    {
-        // Iterate the option contracts in chain
-        foreach (var contract in chain)
-        {
-            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
-        }
-    }
-}
    
-  
    
-  
    
-   You can also iterate through all of the
-   
-    OptionChain
-   
-   objects in the current
-   
-    Slice
-   
-   .
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Iterate all option chains of all symbols
-    for canonical_symbol, chain in slice.option_chains.items():
-        # Iterate the option contracts in chain
-        for contract in chain:
-            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
-
    
-   
    public override void OnData(Slice slice)
-{
-    // Iterate all option chains of all symbols
-    foreach (var kvp in slice.OptionChains)
-    {
-        var canonicalSymbol = kvp.Key;
-        var chain = kvp.Value;
-        // Iterate the option contracts in chain
-        foreach (var contract in chain)
-        {
-            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
-        }
-    }
-}
    
-  
    
-  
    
-   For more information about accessing US Equity Options data, see
-   
-    Handling Data
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical US Equity Options data in an algorithm and the Research Environment.
-  
    
-  
    
-   Historical Data In Algorithms
-  
    
-  
    
-   To get historical US Equity Options data in an algorithm, call the
-   
-    History
-   
-   
-    history
-   
-   method with the Equity Option contract
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame of trade and quote data
-history_df = self.history(contract.symbol, 100, Resolution.MINUTE)
-
-# DataFrame of open interest data
-history_oi_df = self.history(OpenInterest, contract.symbol, 100, Resolution.MINUTE)
-
-# TradeBar objects
-history_trade_bars = self.history[TradeBar](contract.symbol, 100, Resolution.MINUTE)
-
-# QuoteBar objects
-history_quote_bars = self.history[QuoteBar](contract.symbol, 100, Resolution.MINUTE)
-
-# OpenInterest objects
-history_oi = self.history[OpenInterest](contract.symbol, 100, Resolution.MINUTE)
    
-   
    // TradeBar objects 
-var historyTradeBars = History(contract.Symbol, 100, Resolution.Minute);
-
-// QuoteBar objects 
-var historyQuoteBars = History<QuoteBar>(contract.Symbol, 100, Resolution.Minute);
-
-// OpenInterest objects
-var historyOpenInterest = History<OpenInterest>(contract.Symbol, 100, Resolution.Minute);
    
-  
    
-  
    
-   For more information about historical data in algorithms, see
-   
-    History Requests
-   
-   .
-  
    
-  
    
-   Historical Data In Research
-  
    
-  
    
-   To get historical US Equity Options data in the Research Environment, call the
-   
-    History
-   
-   
-    history
-   
-   or
-   
-    OptionHistory
-   
-   
-    option_history
-   
-   method. The
-   
-    History
-   
-   
-    history
-   
-   method returns the price, volume, and open interest history for some given Option contract(s). The
-   
-    OptionHistory
-   
-   
-    option_history
-   
-   method returns the price and volume history for the contracts that pass your daily universe filter.
-  
    
-  
    
-   
    qb = QuantBook()
-option = qb.add_option("GOOG") 
-option.set_filter(-2, 2, 0, 90)
-history = qb.option_history(option.symbol.underlying, datetime(2020, 6, 1), datetime(2020, 6, 5))
-history_df = history.data_frame
-expiries = history.get_expiry_dates() 
-strikes = history.get_strikes()
    
-   
    var qb = new QuantBook();
-var option = qb.AddOption("GOOG");
-option.SetFilter(-2, 2, 0, 90);
-var history = qb.OptionHistory(option.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 5));
-
-var contracts = history
-    .SelectMany(x => x.OptionChains.SelectMany(y => y.Value.Contracts.Keys))
-    .Distinct().ToList();
-var expiries = contracts.Select(x => x.ID.Date).Distinct().ToList();
-var strikes = contracts.Select(x => x.ID.StrikePrice).Distinct().ToList();
    
-  
    
-  
    
-   To get historical data for arbitrary US Equity Option contracts instead of just the that pass your universe filter, call the
-   
-    History
-   
-   
-    history
-   
-   method like you would in an algorithm, but on the
-   
-    QuantBook
-   
-   object. For more information about historical data in the Research Environment, see
-   
-    Key Concepts
-   
-   .
-  
    
-  
    
-   Historical Greeks and IV Data
-  
    
-  
    
-   To get historical data for the Greeks and implied volatility of Equity Options, see the
-   
-    US Equity Option Universe
-   
-   dataset.
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   To view the supported assets in the US Equity Options dataset, see the
-   
-    Data Explorer
-   
-   .
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Equity Options dataset enables you to accurately design Option strategies. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Buying put Options to hedge against downward price movement in positive Equity positions
-   
      • 
-   
    • 
-    Exploiting arbitrage opportunities that arise when the price of Option contracts deviates from their theoretical value
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm subscribes to Google Options that fall within two strikes of the underlying stock price and expire within seven days. Within this Option chain, the algorithm buys the call Option contract that has the furthest expiry and has its strike price closest to the underlying stock price. When the contract expires, the algorithm rolls over to the next contract that meets this criteria.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class USEquityOptionsDataAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        self.universe_settings.asynchronous = True
-        # Requesting data
-        self.underlying = self.add_equity("GOOG").symbol
-        option = self.add_option("GOOG")
-        self.option_symbol = option.symbol
-        # To speculate trade the underlying with a low cost, filter for the ATM calls that expiring in the current week
-        # -2/+2 strike buffer is given for small price change
-        option.set_filter(lambda u: u.calls_only().strikes(-2, +2).expiration(0, 6))
-        
-        self.contract = None
-
-    def on_data(self, slice: Slice) -> None:
-        # If the Option contract is exercised, close the underlying position.
-        if self.portfolio[self.underlying].invested:
-            self.liquidate(self.underlying)
-        # If the Option contract expires, rollover to the next contract.
-        if self.contract and not self.portfolio[self.contract.symbol].invested:
-            self.contract = None
-        # Select with the lastest option chain data only
-        chain = slice.option_chains.get(self.option_symbol)
-        if not self.contract and chain:
-            # Select the call contracts with the furthest expiration (week end)
-            furthest_expiry = max([c.expiry for c in chain])
-            furthest_expiry_calls = [contract for contract in chain if contract.expiry == furthest_expiry]
-            # Get the ATM call for speculate trade with low cost and limited loss
-            self.contract = sorted(furthest_expiry_calls, key = lambda x: abs(chain.underlying.price - x.strike))[0]
-            self.market_order(self.contract.symbol, 1)
-            
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            # Historical data
-            history = self.history(security.symbol, 10, Resolution.MINUTE)
-            self.debug(f"We got {len(history)} from our history request for {security.symbol}")
    
-   
    public class USEquityOptionsDataAlgorithm : QCAlgorithm
-{
-    private Symbol _underlying, _optionSymbol;
-    private OptionContract? _contract = null;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-        // Requesting data
-        _underlying = AddEquity("GOOG").Symbol;
-        var option = AddOption("GOOG");
-        _optionSymbol = option.Symbol;
-        // To speculate trade the underlying with a low cost, filter for the ATM calls that expiring in the current week
-        // -2/+2 strike buffer is given for small price change
-        option.SetFilter((u) => u.CallsOnly().Strikes(-2, +2).Expiration(0, 7));
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // If the Option contract is exercised, close the underlying position.
-        if (Portfolio[_underlying].Invested)
-        {
-            Liquidate(_underlying);
-        }
-        // If the Option contract expires, rollover to the next contract.
-        if (_contract != null && !Portfolio[_contract.Symbol].Invested)
-        {
-            _contract = null;
-        }
-        // Select with the lastest option chain data only
-        if (_contract == null && slice.OptionChains.TryGetValue(_optionSymbol, out var chain))
-        {
-            // Select the call contracts with the furthest expiration (week end)
-            var furthestExpiry = chain.Max(c => c.Expiry);
-            var furthestExpiryCalls = chain.Where(c => c.Expiry == furthestExpiry);
-            // Get the ATM call for speculate trade with low cost and limited loss
-            _contract = furthestExpiryCalls.OrderByDescending(x => Math.Abs(chain.Underlying.Price - x.Strike)).Last();
-            MarketOrder(_contract.Symbol, 1);
-        }
-    }
-    
-    public override void OnSecuritiesChanged(SecurityChanges changes)
-    {
-        foreach (var security in changes.AddedSecurities)
-        {
-            // Historical data
-            var history = History(security.Symbol, 100, Resolution.Minute);
-            Debug($"We got {history.Count()} from our history request for {security.Symbol}");
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm buys a call Option contract for Google that falls within one strike of the underlying stock price and expires within seven days. When the contract expires, the algorithm rolls over to the next contract that meets this criteria.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class USEquityOptionsDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        self.universe_settings.asynchronous = True
-        # Requesting data
-        self.set_universe_selection(EarliestExpiringWeeklyAtTheMoneyCallOptionUniverseSelectionModel())
-        
-        self.set_alpha(ConstantOptionsAlphaModel())
-
-        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
-        
-        
-class EarliestExpiringWeeklyAtTheMoneyCallOptionUniverseSelectionModel(OptionUniverseSelectionModel):
-
-    def __init__(self) -> None:
-        # Daily update with the select_option_chain_symbols function
-        super().__init__(timedelta(1), self.select_option_chain_symbols)
-
-    def select_option_chain_symbols(self, utcTime: datetime) -> List[Symbol]:
-        # Always select only GOOG options as our focus
-        return [ Symbol.create("GOOG", SecurityType.OPTION, Market.USA) ]
-
-    def filter(self, filter: OptionFilterUniverse) -> OptionFilterUniverse:
-        # To speculate trade the underlying with a low cost, filter for the ATM calls that expiring in the current week
-        # -1/+1 strike buffer is given for small price change
-        return (filter.calls_only()
-                      .strikes(-1, -1)
-                      .expiration(0, 7))  
-        
-
-class ConstantOptionsAlphaModel(AlphaModel):
-
-    underlying = None
-    contract = None
-
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        
-        insights = []
-        
-        # Liquidate the underlying if the option is being exercised/assigned
-        if algorithm.portfolio[self.underlying].invested:
-            insights.append(Insight.price(self.underlying, timedelta(days=7), InsightDirection.FLAT))
-
-        if self.contract is not None and algorithm.portfolio[self.contract.symbol].invested:
-            return insights
-
-        # Get the ATM call for speculate trade with low cost and limited loss that expires at week end
-        for kvp in slice.option_chains:
-            chain = kvp.Value
-            expiry = max(x.expiry for x in chain)
-            self.contract = sorted([x for x in chain if x.expiry == expiry],
-                                               key=lambda x: abs(x.strike - x.underlying_last_price))[0]
-            insights.append(Insight.price(self.contract.symbol, self.contract.expiry + timedelta(days=1), InsightDirection.UP))
-        
-        return insights
-        
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            if security.type == SecurityType.EQUITY:
-                self.underlying = security.symbol
-            else:
-                # Historical data
-                history = algorithm.history(security.symbol, 10, Resolution.MINUTE)
-                algorithm.debug(f"We got {len(history)} from our history request for {security.symbol}")
-
-
-class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
-
-    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
-        targets = []
-        for insight in insights:
-            if algorithm.securities[insight.symbol].is_tradable:
-                targets.append(PortfolioTarget(insight.symbol, insight.direction))
-        return targets
    
-   
    public class USEquityOptionsDataAlgorithm : QCAlgorithm
-{        
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-        // Requesting data
-        SetUniverseSelection(new EarliestExpiringWeeklyAtTheMoneyCallOptionUniverseSelectionModel());
-        
-        SetAlpha(new ConstantOptionsAlphaModel());
-        
-        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
-    }
-}   
-    
-class EarliestExpiringWeeklyAtTheMoneyCallOptionUniverseSelectionModel : OptionUniverseSelectionModel
-{
-    // Daily update with the SelectOptionChainSymbols function
-    public EarliestExpiringWeeklyAtTheMoneyCallOptionUniverseSelectionModel()
-            : base(TimeSpan.FromDays(1), SelectOptionChainSymbols) {}
-    
-    private static IEnumerable<Symbol> SelectOptionChainSymbols(DateTime utcTime)
-    {
-        // Select only GOOG options as our focus
-        return new[] {QuantConnect.Symbol.Create("GOOG", SecurityType.Option, Market.USA)};
-    }
-
-    protected override OptionFilterUniverse Filter(OptionFilterUniverse filter)
-    {
-        // To speculate trade the underlying with a low cost, filter for the ATM calls that expiring in the current week
-        // -2/+2 strike buffer is given for small price change
-        return filter.CallsOnly()
-            .Strikes(-1, -1)
-            .Expiration(0, 7);
-    }
-}
-    
-class ConstantOptionsAlphaModel : AlphaModel
-{ 
-    private Symbol? _underlying = null;
-    private OptionContract? _contract = null;
-    
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        var insights = new List<Insight>();
-        
-        // Liquidate the underlying if the option is being exercised/assigned
-        if (algorithm.Portfolio[_underlying].Invested)
-        {
-            insights.Add(Insight.Price(_underlying, TimeSpan.FromDays(7), InsightDirection.Flat));
-        }
-            
-        if (_contract != null && algorithm.Portfolio[_contract.Symbol].Invested)
-        {
-            return insights;
-        }
-        
-        // Get the ATM call for speculate trade with low cost and limited loss that expires at week end
-        foreach (var kvp in slice.OptionChains)
-        {
-            var chain = kvp.Value;
-            var expiry = chain.Max(x => x.Expiry);
-            _contract = chain.Where(x => x.Expiry == expiry)
-                .OrderBy(x => Math.Abs(x.Strike - x.UnderlyingLastPrice))
-                .First();
-            insights.Add(Insight.Price(_contract.Symbol, _contract.Expiry + TimeSpan.FromDays(1), InsightDirection.Up));
-        }    
-        return insights;
-    }
-
-    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-    {
-        foreach (var security in changes.AddedSecurities)
-        {
-            if (security.Type == SecurityType.Equity)
-            {
-                _underlying = security.Symbol;
-            }
-            else {
-                // Historical data
-                var history = algorithm.History(security.Symbol, 100, Resolution.Minute);
-                algorithm.Debug($"We got {history.Count()} from our history request for {security.Symbol}");    
-            }
-        }
-    }
-}
-    
-    
-class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
-{
-    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
-    {
-        var targets = new List<PortfolioTarget>();
-        foreach (var insight in insights)
-        {
-            if (algorithm.Securities[insight.Symbol].IsTradable)
-            {
-                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
-            }
-        }
-        return targets;
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Equity Options dataset provides
-   
-    TradeBar
-   
-   ,
-   
-    QuoteBar
-   
-   , and
-   
-    OpenInterest
-   
-   objects.
-  
    
-  
    
-   TradeBar Attributes
-  
    
-  
    
-   
-    TradeBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   QuoteBar Attributes
-  
    
-  
    
-   
-    QuoteBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   OpenInterest Attributes
-  
    
-  
    
-   
-    OpenInterest
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > AlgoSeek > US Future Options
    
-
    
-    
    
-        
    AlgoSeek
    
-        
    US Future Options
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Future Options dataset by AlgoSeek provides Option data on US Future contracts, including prices, strikes, and expires. The data covers 15 Monthly Future contracts, starts in January 2012, and is delivered on a minute frequency. This dataset is created by monitoring the trading activity on the CME, CBOT, NYMEX, and COMEX markets.
-  
    
-  
    
-   This dataset depends on the following datasets:
-  
    
-  
-  
    
-   For more information about the US Future Options dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering equities, futures, options, cash forex, and cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning.  For more information about AlgoSeek, visit
-   
-    algoseek.com
-   
-   .
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the US Future Options dataset:
-  
    
-  
    
-   
    future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
-future.set_filter(0, 90)
-self.add_future_option(future.symbol, lambda universe: universe.strikes(-5, +5))
    
-   
    var future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
-future.SetFilter(0, 90);
-AddFutureOption(future.Symbol, universe => universe.Strikes(-5, +5));
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 2012
-     
    
-      Asset Coverage
-     
-      15 Monthly Future Contracts. Standard expires only*.
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Minute, Hourly, & Daily
-     
    
-      Timezone
-     
-      New York
-     
    
-      Market Hours
-     
-      
-       Regular and Extended
-      
-     
    
-  * No weeklies or 0DTE contracts.
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add US Future Options data to your algorithm, call the
-   
-    AddFutureOption
-   
-   
-    add_future_option
-   
-   method.
-  
    
-  
    
-   
    class FutureOptionDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2020, 1, 28)
-        self.set_end_date(2020, 6, 1)
-        self.set_cash(100000)
-        self.universe_settings.asynchronous = True
-        future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
-        future.set_filter(0, 90)
-        self.add_future_option(future.symbol, lambda universe: universe.strikes(-5, +5))
    
-   
    public class FutureOptionDataAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2020, 1, 28);
-        SetEndDate(2020, 6, 1);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-        var future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
-        future.SetFilter(0, 90);
-        AddFutureOption(future.Symbol, universe => universe.Strikes(-5, +5));
-    }
-}
    
-  
    
-  
    
-   The Future resolution must be less than or equal to the Future Option resolution. For example, if you set the Future resolution to minute, then the Future Option resolution must be minute, hour, or daily.
-  
    
-  
    
-   For more information about creating Future Options subscriptions, see
-   
-    Requesting Data
-   
-   or
-   
-    Future Options Universes
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To get the current Future Options data, iterate through the
-   
-    OptionChains
-   
-   
-    option_chains
-   
-   property of the current
-   
-    
-     Slice
-    
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your Future Options at every time step.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    for canonical_fop_symbol, chain in slice.option_chains.items():
-        for contract in chain:
-            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
    
-   
    public override void OnData(Slice slice)
-{
-    foreach (var kvp in slice.OptionChains)
-    {
-        var canonicalFOPSymbol = kvp.Key;
-        var chain = kvp.Value;
-        foreach (var contract in chain)
-        {
-            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
-        }
-    }
-}
    
-  
    
-  
    
-   You can also iterate through the
-   
-    FuturesChains
-   
-   
-    futures_chains
-   
-   in the current
-   
-    Slice
-   
-   first.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    for continuous_future_symbol, futures_chain in slice.futures_chains.items():
-        # Select a Future Contract and create its canonical FOP Symbol
-        futures_contract = [contract for contract in futures_chain][0]
-        canonical_fop_symbol = Symbol.create_canonical_option(futures_contract.symbol)
-        option_chain = slice.option_chains.get(canonical_fop_symbol)
-        if option_chain:
-            for fop_contract in option_chain:
-                 self.log(f"{fop_contract.symbol} price at {slice.time}: {fop_contract.last_price}")
    
-   
    public override void OnData(Slice slice)
-{
-    foreach (var kvp in slice.FuturesChains)
-    {
-        var continuousContractSymbol = kvp.Key;
-        var futuresChain = kvp.Value;
-        
-        // Select a Future Contract and create its canonical FOP Symbol
-        var futuresContract = futuresChain.First();
-        var canonicalFOPSymbol = QuantConnect.Symbol.CreateCanonicalOption(futuresContract.Symbol);
-        if (slice.OptionChains.TryGetValue(canonicalFOPSymbol, out var optionChain))
-        {
-            foreach (var fopContract in optionChain)
-            {
-                Log($"{fopContract.Symbol} price at {slice.Time}: {fopContract.LastPrice}");
-            }
-        }
-    }
-}
    
-  
    
-  
    
-   For more information about accessing Future Options data, see
-   
-    Handling Data
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical US Future Options data in an algorithm and the Research Environment.
-  
    
-  
    
-   Historical Data In Algorithms
-  
    
-  
    
-   To get historical US Future Options data in an algorithm, call the
-   
-    History
-   
-   
-    history
-   
-   method with the Future Option contract
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame of trade and quote data
-history_df = self.history(contract.symbol, 100, Resolution.MINUTE)
-
-# DataFrame of open interest data
-history_oi_df = self.history(OpenInterest, contract.symbol, 100, Resolution.MINUTE)
-
-# TradeBar objects
-history_trade_bars = self.history[TradeBar](contract.symbol, 100, Resolution.MINUTE)
-
-# QuoteBar objects
-history_quote_bars = self.history[QuoteBar](contract.symbol, 100, Resolution.MINUTE)
-
-# OpenInterest objects
-history_oi = self.history[OpenInterest](contract.symbol, 100, Resolution.MINUTE)
-
    
-   
    // TradeBar objects 
-var historyTradeBars = History(contract.Symbol, 100, Resolution.Minute);
-
-// QuoteBar objects 
-var historyQuoteBars = History<QuoteBar>(contract.Symbol, 100, Resolution.Minute);
-
-// OpenInterest objects 
-var historyOpenInterest = History<OpenInterest >(contract.Symbol, 100, Resolution.Minute);
    
-  
    
-  
    
-   For more information about historical data in algorithms, see
-   
-    History Requests
-   
-   .
-  
    
-  
    
-   Historical Data In Research
-  
    
-  
    
-   To get historical US Future Options data in the Research Environment, call the
-   
-    History
-   
-   
-    history
-   
-   or
-   
-    OptionHistory
-   
-   
-    option_history
-   
-   method. The
-   
-    History
-   
-   
-    history
-   
-   method returns the price, volume, and open interest history for some given Future Option contract(s). The
-   
-    OptionHistory
-   
-   
-    option_history
-   
-   method returns the price and volume history for the contracts that pass your daily universe filter.
-  
    
-  
    
-   
    qb = QuantBook()
-future = qb.add_future(Futures.Indices.SP_500_E_MINI)
-start_date = datetime(2024, 1, 2)
-future_contract_symbol = sorted(
-    qb.future_chain_provider.get_future_contract_list(future.symbol, start_date), 
-    key=lambda s: s.id.date
-)[0]
-history = qb.option_history(
-    future_contract_symbol, start_date, future_contract_symbol.id.date, Resolution.HOUR
-)
-history_df = history.data_frame
-expiries = history.get_expiry_dates()
-strikes = history.get_strikes()
    
-   
    var qb = new QuantBook();
-var future = qb.AddFuture(Futures.Indices.SP500EMini);
-var startDate = new DateTime(2024, 1, 2);
-var futureContractSymbol = qb.FutureChainProvider.GetFutureContractList(future.Symbol, startDate)
-    .OrderBy(x => x.ID.Date)
-    .First();
-var history = qb.OptionHistory(
-    futureContractSymbol, startDate, futureContractSymbol.ID.Date, Resolution.Hour
-);
-
-var contracts = history
-    .SelectMany(x => x.OptionChains.SelectMany(y => y.Value.Contracts.Keys))
-    .Distinct().ToList();
-var expiries = contracts.Select(x => x.ID.Date).Distinct().ToList();
-var strikes = contracts.Select(x => x.ID.StrikePrice).Distinct().ToList();
    
-  
    
-  
    
-   To get historical data for arbitrary US Equity Option contracts instead of just the that pass your universe filter, call the
-   
-    History
-   
-   
-    history
-   
-   method like you would in an algorithm, but on the
-   
-    QuantBook
-   
-   object. For more information about historical data in the Research Environment, see
-   
-    Key Concepts
-   
-   .
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   The following list shows the available (15) Futures Options:
-  
    
-  
      
-   
    • 
-    
-     Futures.Energy.CrudeOilWTI
-    
-    
-     Futures.Energy.CRUDE_OIL_WTI
-    
-    : Crude Oil WTI Futures (NYMEX: LO | Underlying: CL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.Gasoline
-    
-    
-     Futures.Energy.GASOLINE
-    
-    : Gasoline RBOB Futures (NYMEX: OB | Underlying: RB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.HeatingOil
-    
-    
-     Futures.Energy.HEATING_OIL
-    
-    : Heating Oil Futures (NYMEX: OH | Underlying: HO)
-   
      • 
-   
    • 
-    
-     Futures.Energy.NaturalGas
-    
-    
-     Futures.Energy.NATURAL_GAS
-    
-    : Natural Gas Futures (NYMEX: ON | Underlying: NG)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y10TreasuryNote
-    
-    
-     Futures.Financials.Y_10_TREASURY_NOTE
-    
-    : 10Y U.S. Treasury Note Futures (CBOT: OZN | Underlying: ZN)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y2TreasuryNote
-    
-    
-     Futures.Financials.Y_2_TREASURY_NOTE
-    
-    : 2Y U.S. Treasury Note Futures (CBOT: OZT | Underlying: ZT)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y30TreasuryBond
-    
-    
-     Futures.Financials.Y_30_TREASURY_BOND
-    
-    : 30Y U.S. Treasury Bond Futures (CBOT: OZB | Underlying: ZB)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Corn
-    
-    
-     Futures.Grains.CORN
-    
-    : Corn Futures (CBOT: OZC | Underlying: ZC)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Soybeans
-    
-    
-     Futures.Grains.SOYBEANS
-    
-    : Soybeans Futures (CBOT: OZS | Underlying: ZS)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Wheat
-    
-    
-     Futures.Grains.WHEAT
-    
-    : Default wheat contract is SRWWheat (CBOT: OZW | Underlying: ZW)
-   
      • 
-   
    • 
-    
-     Futures.Indices.NASDAQ100EMini
-    
-    
-     Futures.Indices.NASDAQ_100_E_MINI
-    
-    : E-mini NASDAQ 100 Futures (CME: NQ)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SP500EMini
-    
-    
-     Futures.Indices.SP_500_E_MINI
-    
-    : E-mini S&P 500 Futures (CME: ES)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Copper
-    
-    
-     Futures.Metals.COPPER
-    
-    : Copper Futures (COMEX: HXE | Underlying: HG)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Gold
-    
-    
-     Futures.Metals.GOLD
-    
-    : Gold Futures (COMEX: OG | Underlying: GC)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Silver
-    
-    
-     Futures.Metals.SILVER
-    
-    : Silver Futures (COMEX: SO | Underlying: SI)
-   
      • 
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Future Options dataset enables you to accurately design Future Option strategies. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Selling out of the money Future Option contracts to collect the premium that the Option buyer pays
-   
      • 
-   
    • 
-    Buying put Options to hedge against downward price movement in Future contracts you bought
-   
      • 
-   
    • 
-    Exploiting arbitrage opportunities that arise when the price of Option contracts deviates from their theoretical value
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example demonstrates a weekly-renewing
-   
-    covered call
-   
-   strategy to collect credit of selling the option. It filters the ATM call contract that expires within the current week at week start using
-   
-    SetFilter
-   
-   filtering function.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class FutureOptionExampleAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(5_000_000)
-        # Subscribe the underlying since the updated price is needed for filtering
-        self.underlying = self.add_future(Futures.Indices.SP_500_E_MINI,
-            extended_market_hours=True,
-            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
-            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
-            contract_depth_offset=0)
-        # Filter the underlying continuous Futures to narrow the FOP spectrum
-        self.underlying.set_filter(0, 182)
-        # Filter for the current-week-expiring calls to formulate a covered call that expires at the end of week
-        self.add_future_option(self.underlying.symbol, lambda u: u.calls_only().expiration(0, 5))
-
-    def on_data(self, slice: Slice) -> None:
-        # Create canonical symbol for the mapped future contract, since option chains are mapped by canonical symbol
-        symbol = Symbol.create_canonical_option(self.underlying.mapped)
-
-        # Get option chain data for the mapped future, as both the underlying and FOP have the highest liquidity among all other contracts
-        chain = slice.option_chains.get(symbol)
-        if not self.portfolio.invested and chain:
-            # Obtain the ATM call that expires at the end of week, such that both underlying and the FOP expires the same time
-            expiry = max(x.expiry for x in chain)
-            atm_call = sorted([x for x in chain if x.expiry == expiry],
-                key=lambda x: abs(x.strike - x.underlying_last_price))[0]
-
-            # Use abstraction method to order a covered call to avoid manual error
-            option_strategy = OptionStrategies.covered_call(symbol, atm_call.strike,expiry)
-            self.buy(option_strategy, 1)
-        
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            if security.type == SecurityType.FUTURE_OPTION:
-                # Historical data
-                history = self.history(security.symbol, 10, Resolution.MINUTE)
    
-   
    public class FutureOptionExampleAlgorithm : QCAlgorithm
-{
-    private Future _underlying;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(5000000);
-        // Subscribe the underlying since the updated price is needed for filtering
-        _underlying = AddFuture(Futures.Indices.SP500EMini,
-            extendedMarketHours: true,
-            dataMappingMode: DataMappingMode.OpenInterest,
-            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
-            contractDepthOffset: 0);
-        // Filter the underlying continuous Futures to narrow the FOP spectrum
-        _underlying.SetFilter(0, 182);
-        // Filter for the current-week-expiring calls to formulate a covered call that expires at the end of week
-        AddFutureOption(_underlying.Symbol, (u) => u.CallsOnly().Expiration(0, 5));
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // Create canonical symbol for the mapped future contract, since option chains are mapped by canonical symbol
-        var symbol = QuantConnect.Symbol.CreateCanonicalOption(_underlying.Mapped);
-
-        // Get option chain data for the mapped future, as both the underlying and FOP have the highest liquidity among all other contracts
-        if (!Portfolio.Invested && 
-            slice.OptionChains.TryGetValue(symbol, out var chain))
-        {
-            // Obtain the ATM call that expires at the end of week, such that both underlying and the FOP expires the same time
-            var expiry = chain.Max(x => x.Expiry);
-            var atmCall = chain.Where(x => x.Expiry == expiry)
-                .OrderBy(x => Math.Abs(x.Strike - x.UnderlyingLastPrice))
-                .First();
-
-            // Use abstraction method to order a covered call to avoid manual error
-            var optionStrategy = OptionStrategies.CoveredCall(symbol, atmCall.Strike, expiry);
-            Buy(optionStrategy, 1);
-        }
-    }
-
-    public override void OnSecuritiesChanged(SecurityChanges changes)
-    {
-        foreach (var security in changes.AddedSecurities)
-        {
-            if (security.Type == SecurityType.FutureOption)
-            {
-                // Historical data
-                var history = History(security.Symbol, 10, Resolution.Minute);
-            }
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example demonstrates a daily renewing speculation trade on upward movement of Gold with FOP using algorithm framework. Using FOP, traders can achieve lower cost to get similar absolute profit, given the FOP is selected to be deep ITM with high Delta value.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class USFuturesDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        # Override an option universe selection model to select the FOPs
-        self.set_universe_selection(GoldFOPUniverseSelectionModel(self))
-        # Handle the FOP trading logic in an alpha model
-        self.add_alpha(ConstantFutureOptionsAlphaModel())
-        # To order a single contract per insight, use a custom portfolio construction model
-        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
-        
-
-class GoldFOPUniverseSelectionModel(OptionUniverseSelectionModel):
-
-    # Daily renewed universe since option contract list is updated on a daily basis
-    def __init__(self, algorithm) -> None:
-        self._algorithm = algorithm
-        super().__init__(timedelta(1), self.select_fop_symbols)
-
-    def select_fop_symbols(self, utc_time: datetime) -> List[Symbol]:
-        #  Create the underlying symbol to get the FOP contracts later
-        future_symbol = Symbol.create(Futures.Metals.GOLD, SecurityType.FUTURE, Market.COMEX)
-        # Get all gold FOP contracts of the front month gold contract expiring within 3 months, since their liquidity is the highest
-        future_contract_list = self._algorithm.future_chain_provider.get_future_contract_list(future_symbol, self._algorithm.time)
-        return [Symbol.create_canonical_option(x) for x in future_contract_list 
-                    if x.id.date <= self._algorithm.time + timedelta(90)]
-
-    def filter(self, option_filter_universe: OptionFilterUniverse) -> OptionFilterUniverse:
-        # Filter for the ATM calls that expires latest (same expiry as the underlying)
-        # +/-5 strike range buffer for price movement
-        return option_filter_universe.back_month().strikes(-5, +5).calls_only()
-
-
-class ConstantFutureOptionsAlphaModel(AlphaModel):
-
-    # A dictionary to cache the mapped FOP of the underlying Future for filtering wanted option chain data and check if invested
-    option_contract_by_future_underlying_contract = {}
-
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        insights = []
-
-        for kvp in slice.option_chains:
-            # Liquidate underlying Future contract after Option assignment
-            underlying_future_contract = kvp.key.underlying
-            if algorithm.portfolio[underlying_future_contract].invested:
-                algorithm.insights.cancel([underlying_future_contract])
-                del self.option_contract_by_future_underlying_contract[underlying_future_contract]
-
-            # Do not repeatly invest in the same underlying's FOP
-            chain = [contract for contract in kvp.value if algorithm.securities[contract.symbol].is_tradable]
-            if not chain or underlying_future_contract in self.option_contract_by_future_underlying_contract:
-                continue
-            
-            # Select the Option contract with the lowest strike price to speculate trade the underlying with lowest cost and highest delta
-            contract = sorted(chain, key=lambda x: x.strike)[0]
-            insights.append(Insight.price(contract.symbol, contract.expiry + timedelta(1), InsightDirection.UP))
-            self.option_contract_by_future_underlying_contract[underlying_future_contract] = contract
-        
-        return insights
-
-
-class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
-
-    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
-        targets = []
-        for insight in insights:
-            if algorithm.securities[insight.symbol].is_tradable:
-                # Use integer target to create a portfolio target to trade a single contract
-                targets.append(PortfolioTarget(insight.symbol, insight.direction))
-        return targets
    
-   
    public class FutureOptionDataAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        // Override an option universe selection model to select the FOPs
-        AddUniverseSelection(new GoldFOPUniverseSelectionModel(this));
-        // Handle the FOP trading logic in an alpha model
-        SetAlpha(new ConstantFutureOptionsAlphaModel());
-        // To order a single contract per insight, use a custom portfolio construction model
-        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
-    }
-}
-
-public class GoldFOPUniverseSelectionModel : OptionUniverseSelectionModel
-{
-    // Daily renewed universe since option contract list is updated on a daily basis
-    public GoldFOPUniverseSelectionModel(QCAlgorithm algorithm)
-        : base(TimeSpan.FromDays(1), _ => OptionChainSymbolSelector(algorithm, _))
-    {
-    }
-
-    private static IEnumerable<Symbol> OptionChainSymbolSelector(QCAlgorithm algorithm, DateTime utcTime)
-    {
-        // Create the underlying symbol to get the FOP contracts later
-        var futureSymbol = QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX);
-        // Get all gold FOP contracts of the front month gold contract expiring within 3 months, since their liquidity is the highest
-        return algorithm.FutureChainProvider.GetFutureContractList(futureSymbol, algorithm.Time)
-            .Where(futureContractSymbol => futureContractSymbol.ID.Date <= algorithm.Time + TimeSpan.FromDays(90))
-            .Select(futureContractSymbol => QuantConnect.Symbol.CreateCanonicalOption(futureContractSymbol));
-    }
-
-    protected override OptionFilterUniverse Filter(OptionFilterUniverse optionFilterUniverse)
-    {
-        // Filter for the ATM calls that expires latest (same expiry as the underlying)
-        // +/-5 strike range buffer for price movement
-        return optionFilterUniverse.BackMonth().Strikes(-5, +5).CallsOnly();
-    }
-}
-
-
-class ConstantFutureOptionsAlphaModel : AlphaModel
-{
-    // A dictionary to cache the mapped FOP of the underlying Future for filtering wanted option chain data and check if invested
-    private Dictionary<Symbol, OptionContract> optionContractByUnderlyingFutureContract = new Dictionary<Symbol, OptionContract>();
-    
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        var insights = new List<Insight>();
-        
-        foreach (var kvp in slice.OptionChains)
-        {
-            // Liquidate underlying Future contract after Option assignment
-            var underlyingFutureContract = kvp.Key.Underlying;
-            if (algorithm.Portfolio[underlyingFutureContract].Invested)
-            {
-                algorithm.Insights.Cancel(new[] { underlyingFutureContract });
-                optionContractByUnderlyingFutureContract.Remove(underlyingFutureContract);
-            }
-            
-            // Do not repeatly invest in the same underlying's FOP
-            var chain = kvp.Value.Where(contract => algorithm.Securities[contract.Symbol].IsTradable);
-            if (chain.Count() == 0 || optionContractByUnderlyingFutureContract.ContainsKey(underlyingFutureContract))
-            {
-                continue;
-            }
-
-            // Select the Option contract with the lowest strike price to speculate trade the underlying with lowest cost and highest delta
-            var contract = chain.MinBy(contract => contract.Strike);
-            insights.Add(Insight.Price(contract.Symbol, contract.Expiry.AddDays(1), InsightDirection.Up));
-            optionContractByUnderlyingFutureContract.Add(kvp.Key.Underlying, contract);
-        }            
-        return insights;
-    }
-}
-
-
-class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
-{
-    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
-    {
-        var targets = new List<PortfolioTarget>();
-        foreach (var insight in insights)
-        {
-            if (algorithm.Securities[insight.Symbol].IsTradable)
-            {
-                // Use integer target to create a portfolio target to trade a single contract
-                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
-            }
-        }
-        return targets;
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Future Options dataset provides
-   
-    TradeBar
-   
-   ,
-   
-    QuoteBar
-   
-   , and
-   
-    OpenInterest
-   
-   objects.
-  
    
-  
    
-   TradeBar Attributes
-  
    
-  
    
-   
-    TradeBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   QuoteBar Attributes
-  
    
-  
    
-   
-    QuoteBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   OpenInterest Attributes
-  
    
-  
    
-   
-    OpenInterest
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > AlgoSeek > US Futures
    
-
    
-    
    
-        
    AlgoSeek
    
-        
    US Futures
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Futures dataset by AlgoSeek provides Futures data, including price, volume, open interest, and expiry. The data covers the 157 most liquid contracts, starts in May 2009, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on the CFE
-   
-    *
-   
-   , CBOT, CME, COMEX, NYMEX, and ICE
-   
-    *
-   
-   .
-  
    
-  
    
-   This dataset also depends on the
-   
-    US Futures Security Master
-   
-   because the US Futures Security Master dataset contains information to construct continuous Futures.
-  
    
-  
    
-   For more information about the US Futures dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering equities, futures, options, cash forex, and cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning.  For more information about AlgoSeek, visit
-   
-    algoseek.com
-   
-   .
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the US Futures dataset:
-  
    
-  
    
-   
    future = self.add_future(Futures.Metals.GOLD)
-future.set_filter(0, 90)
    
-   
    var future = AddFuture(Futures.Metals.Gold);
-future.SetFilter(0, 90);
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      May 2009
-     
    
-      Asset Coverage
-     
-      157 Futures
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Tick, Second, Minute, Hour, & Daily
-     
    
-      Timezone
-     
-      
      
-       
    • 
-        Chicago (CFE, CME or CBOT)
-       
      • 
-       
    • 
-        New York (ICE, NYMEX or COMEX)
-       
      • 
-      
    
-     
    
-      Market Hours
-     
-      
-       Regular and Extended
-      
-     
    
-  
    
-   This dataset only includes Sugar (SB) from the ICE exchange.
-  
    
-  
    
-   This data provider doesn't include a live data feed for the CFE market (VIX).
-  
    
-  
    
-   LBS (Random Length Lumber) was deprecated for LBR (Lumber).
-  
    
- 
-
-
-
    Volume Discrepancies
    
-
- 
-  
    
-   The volume of the daily trade bars from this dataset is different from the daily volume that CME and other platforms like Yahoo Finance report because the CME includes pit trades in their daily volume calculation. In contrast, the volume of the daily trade bars in this dataset (from AlgoSeek) doesn't include pit trades because pit trades are over-the-counter, which algorithms can't trade.
-  
    
-  
    
-   Another discrepancy occurs from the start and end times of the daily bars. Daily bars from the CME and Yahoo Finance span from 5 PM Central Time (CT) to 4 PM CT on the following day. In contrast, QuantConnect consolidates daily bars from from 12 AM Eastern Time (ET) to 12 AM ET the following day. Therefore, to calculate the daily volume in the same way that CME does (excluding the pit trades), sum the volume of intraday bars that span 5 PM CT to 4 PM CT on the following day. For an example implementation, see
-   
-    Examples
-   
-   .
-  
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add US Futures data to your algorithm, call the
-   
-    AddFuture
-   
-   
-    add_future
-   
-   method. Save a reference to the Future so you can access the data later in your algorithm.
-  
    
-  
    
-   
    class USFuturesDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2013, 12, 20) 
-        self.set_end_date(2014, 2, 20) 
-        self.set_cash(1000000) 
-        self.universe_settings.asynchronous = True
-        # Request Gold Future data
-        future = self.add_future(Futures.Metals.GOLD) 
-        # Set filter to obtain the contracts expire within 90 days
-        future.set_filter(0, 90)
-        self.future_symbol = future.symbol
    
-   
    public class USFuturesDataAlgorithm : QCAlgorithm
-{
-    private Symbol _futureSymbol;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2013, 12, 20);
-        SetEndDate(2014, 2, 20);
-        SetCash(1000000);
-        UniverseSettings.Asynchronous = true;
-        // Request Gold Future data
-        var future = AddFuture(Futures.Metals.Gold);
-        // Set filter to obtain the contracts expire within 90 days
-        future.SetFilter(0, 90);
-        _futureSymbol = future.Symbol;
-    }
-}
-
    
-  
    
-  
    
-   For more information about creating Future subscriptions, see
-   
-    Requesting Data
-   
-   or
-   
-    Futures Universes
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To get the current US Futures data, index the
-   
-    Bars
-   
-   
-    bars
-   
-   ,
-   
-    QuoteBars
-   
-   
-    quote_bars
-   
-   , or
-   
-    Ticks
-   
-   
-    ticks
-   
-   properties of the current
-   
-    
-     Slice
-    
-   
-   with the Future
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your security at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Access data: TradeBar data
-    if self.future_symbol in slice.bars:
-        trade_bar = slice.bars[self.future_symbol]
-        self.log(f"{self.future_symbol} close at {slice.time}: {trade_bar.close}")
-
-    # Access data: QuoteBar data
-    if self.future_symbol in slice.quote_bars:
-        quote_bar = slice.quote_bars[self.future_symbol]
-        self.log(f"{self.future_symbol} bid at {slice.time}: {quote_bar.bid.close}")
-
-    # Access data: Ticks data
-    if self.future_symbol in slice.ticks:
-        ticks = slice.ticks[self.future_symbol]
-        for tick in ticks:
-            self.log(f"{self.future_symbol} price at {slice.time}: {tick.price}")
    
-   
    public override void OnData(Slice slice)
-{
-    // Access data: TradeBar data
-    if (slice.Bars.TryGetValue(_futureSymbol, out var tradeBar))
-    {
-        Log($"{_futureSymbol} price at {slice.Time}: {tradeBar.Close}");
-    }
-
-    // Access data: QuoteBar data
-    if (slice.QuoteBars.TryGetValue(_futureSymbol, out var quoteBar))
-    {
-        Log($"{_futureSymbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
-    }
-
-    // Access data: Ticks data
-    if (slice.Ticks.TryGetValue(_futureSymbol, out var ticks))
-    {
-        foreach (var tick in ticks)
-        {
-            Log($"{_futureSymbol} price at {slice.Time}: {tick.Price}");
-        }
-    }
-}
-
    
-  
    
-  
    
-   You can also iterate through all of the data objects in the current
-   
-    Slice
-   
-   .
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Iterate all TradeBar received
-    for symbol, trade_bar in slice.bars.items():
-        self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")
-
-    # Iterate all QuoteBar received
-    for symbol, quote_bar in slice.quote_bars.items():
-        self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
-
-    # Iterate all Ticks received
-    for symbol, ticks in slice.ticks.items():
-        for tick in ticks:
-            self.log(f"{symbol} price at {slice.time}: {tick.price}")
    
-   
    public override void OnData(Slice slice)
-{
-    // Iterate all TradeBar received
-    foreach (var kvp in slice.Bars)
-    {
-        var symbol = kvp.Key;
-        var tradeBar = kvp.Value;
-        Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
-    }
-
-    // Iterate all QuoteBar received
-    foreach (var kvp in slice.QuoteBars)
-    {
-        var symbol = kvp.Key;
-        var quoteBar = kvp.Value;
-        Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
-    }
-
-    // Iterate all Ticks received
-    foreach (var kvp in slice.Ticks)
-    {
-        var symbol = kvp.Key;
-        var ticks = kvp.Value;
-        foreach (var tick in ticks)
-        {
-            Log($"{symbol} price at {slice.Time}: {tick.Price}");
-        }
-    }
-}
-
    
-  
    
-  
    
-   For more information about accessing US Futures data, see
-   
-    Handling Data
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical US Futures data in an algorithm and the Research Environment.
-  
    
-  
    
-   Historical Data In Algorithms
-  
    
-  
    
-   To get historical US Futures data in an algorithm, call the
-   
-    History
-   
-   
-    history
-   
-   method with the canonical Futures
-   
-    Symbol
-   
-   or a Futures contract
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame objects
-contract_history_df = self.history(contract.symbol, 100, Resolution.MINUTE)
-continuous_history_df = self.history(self.future_symbol,
-    start=self.time - timedelta(days=15), 
-    end=self.time, 
-    resolution=Resolution.MINUTE, 
-    fill_forward=False, 
-    extended_market_hours=False,
-    data_mapping_mode=DataMappingMode.OPEN_INTEREST, 
-    data_normalization_mode=DataNormalizationMode.RAW, 
-    contract_depth_offset=0)
-
-# TradeBar objects
-contract_history_trade_bars = self.history[TradeBar](contract.symbol, 100, Resolution.MINUTE)
-continous_history_trade_bars = self.history[TradeBar](self.future_symbol, 100, Resolution.MINUTE)
-
-# QuoteBar objects
-contract_history_quote_bars = self.history[QuoteBar](contract.symbol, 100, Resolution.MINUTE)
-continous_history_quote_bars = self.history[QuoteBar](self.future_symbol, 100, Resolution.MINUTE)
-
-# Tick objects
-contract_history_ticks = self.history[Tick](contract.symbol, timedelta(seconds=10), Resolution.TICK)
-continous_history_ticks = self.history[Tick](self.future_symbol, timedelta(seconds=10), Resolution.TICK)
    
-   
    // TradeBar objects
-var contractHistoryTradeBars = History(contract.Symbol, 100, Resolution.Minute);
-var continuousHistoryTradeBars = History(
-    symbols: new[] {_futureSymbol}, 
-    start: Time - TimeSpan.FromDays(15),
-    end: Time,
-    resolution: Resolution.Minute,
-    fillForward: false,
-    extendedMarketHours: false,
-    dataMappingMode: DataMappingMode.OpenInterest,
-    dataNormalizationMode: DataNormalizationMode.Raw,
-    contractDepthOffset: 0);
-
-// QuoteBar objects
-var contractHistoryQuoteBars = History<QuoteBar>(contract.Symbol, 100, Resolution.Minute);
-var continuousHistoryQuoteBars = History<QuoteBar>(_futureSymbol, 100, Resolution.Minute);
-
-// Tick objects
-var contractHistoryTicks = History<Tick>(contract.Symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
-var continuousHistoryTicks = History<Tick>(_futureSymbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
-  
    
-  
    
-   For more information about historical data in algorithms, see
-   
-    History Requests
-   
-   . For more information about the price adjustments for continuous contracts, see
-   
-    Continous Contracts
-   
-   .
-  
    
-  
    
-   Historical Data In Research
-  
    
-  
    
-   To get historical US Futures data in the Research Environment for an entire Futures chain, call the
-   
-    FutureHistory
-   
-   
-    future_history
-   
-   method with the canonical Future
-   
-    Symbol
-   
-   .
-  
    
-  
    
-   
    qb = QuantBook()
-future = qb.add_future(Futures.Metals.GOLD) 
-future.set_filter(0, 90)
-history = qb.future_history(future.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5))
-history_df = history.data_frame
-all_history = history.get_all_data()
-expiries = history.get_expiry_dates()
    
-   
    var qb = new QuantBook();
-var future = qb.AddFuture(Futures.Metals.Gold);
-future.SetFilter(0, 90);
-var history = qb.FutureHistory(future.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 5));
-    
-var contracts = history.SelectMany(x => x.OptionChains.SelectMany(y => y.Value.Contracts.Keys)).Distinct().ToList();
-var expiries = contracts.Select(x => x.ID.Date).Distinct().ToList();
    
-  
    
-  
    
-   To get historical data for a single US Futures contract or the continuous Futures contract, call the
-   
-    History
-   
-   
-    history
-   
-   method like you would in an algorithm but on the
-   
-    QuantBook
-   
-   object. For more information about historical data in the Research Environment, see
-   
-    Futures
-   
-   .
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   The following list shows the available (157) Futures:
-  
    
-  
      
-   
    • 
-    
-     Futures.Currencies.AUD
-    
-    : Australian Dollar Futures (CME: 6A)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.AUDCAD
-    
-    : Australian Dollar/Canadian Dollar Futures (CME: ACD)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.AUDJPY
-    
-    : Australian Dollar/Japanese Yen Futures (CME: AJY)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.AUDNZD
-    
-    : Australian Dollar/New Zealand Dollar Futures (CME: ANE)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.BRL
-    
-    : Brazillian Real Futures (CME: 6L)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.BTC
-    
-    : Bitcoin Futures (CME: BTC)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.BTICMicroBTC
-    
-    
-     Futures.Currencies.BTIC_MICRO_BTC
-    
-    : BTIC on Micro Bitcoin Futures (CME: MIB)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.BTICMicroEther
-    
-    
-     Futures.Currencies.BTIC_MICRO_ETHER
-    
-    : BTIC on Micro Ether Futures (CME: MRB)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.CAD
-    
-    : Canadian Dollar Futures (CME: 6C)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.CADJPY
-    
-    : Canadian Dollar/Japanese Yen Futures (CME: CJY)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.CHF
-    
-    : Swiss Franc Futures (CME: 6S)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.ETH
-    
-    : Ether Futures (CME: ETH)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EUR
-    
-    : Euro FX Futures (CME: 6E)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EURAUD
-    
-    : Euro/Australian Dollar Futures (CME: EAD)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EURCAD
-    
-    : Euro/Canadian Dollar Futures (CME: ECD)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EuroFXEmini
-    
-    
-     Futures.Currencies.EURO_FX_EMINI
-    
-    : E-mini Euro FX Futures (CME: E7)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.EURSEK
-    
-    : Euro/Swedish Krona Futures (CME: ESK)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.GBP
-    
-    : British Pound Futures (CME: 6B)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.JapaneseYenEmini
-    
-    
-     Futures.Currencies.JAPANESE_YEN_EMINI
-    
-    : E-mini Japanese Yen Futures (CME: J7)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.JPY
-    
-    : Japanese Yen Futures (CME: 6J)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroAUD
-    
-    
-     Futures.Currencies.MICRO_AUD
-    
-    : Micro AUD/USD Futures (CME: M6A)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroBTC
-    
-    
-     Futures.Currencies.MICRO_BTC
-    
-    : Micro Bitcoin Futures (CME: MBT)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroCAD
-    
-    
-     Futures.Currencies.MICRO_CAD
-    
-    : Micro USD/CAD Futures (CME: M6C)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroCADUSD
-    
-    
-     Futures.Currencies.MICRO_CADUSD
-    
-    : Micro CAD/USD Futures (CME: MCD)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroCHF
-    
-    
-     Futures.Currencies.MICRO_CHF
-    
-    : Micro CHF/USD Futures (CME: MSF)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroEther
-    
-    
-     Futures.Currencies.MICRO_ETHER
-    
-    : Micro Ether Futures (CME: MET)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroEUR
-    
-    
-     Futures.Currencies.MICRO_EUR
-    
-    : Micro EUR/USD Futures (CME: M6E)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroGBP
-    
-    
-     Futures.Currencies.MICRO_GBP
-    
-    : Micro GBP/USD Futures (CME: M6B)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroINRUSD
-    
-    
-     Futures.Currencies.MICRO_INRUSD
-    
-    : Micro INR/USD Futures (CME: MIR)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroJPY
-    
-    
-     Futures.Currencies.MICRO_JPY
-    
-    : Micro JPY/USD Futures (CME: MJY)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroUSDCHF
-    
-    
-     Futures.Currencies.MICRO_USDCHF
-    
-    : Micro USD/CHF Futures (CME: M6S)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroUSDCNH
-    
-    
-     Futures.Currencies.MICRO_USDCNH
-    
-    : Micro USD/CNH Futures (CME: MNH)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MicroUSDJPY
-    
-    
-     Futures.Currencies.MICRO_USDJPY
-    
-    : Micro USD/JPY Futures (CME: M6J)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.MXN
-    
-    : Mexican Peso Futures (CME: 6M)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.NZD
-    
-    : New Zealand Dollar Futures (CME: 6N)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.RUB
-    
-    : Russian Ruble Futures (CME: 6R)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.StandardSizeUSDOffshoreRMBCNH
-    
-    
-     Futures.Currencies.STANDARD_SIZE_USD_OFFSHORE_RMBCNH
-    
-    : Standard-Size USD/Offshore RMB (CNH) Futures (CME: CNH)
-   
      • 
-   
    • 
-    
-     Futures.Currencies.ZAR
-    
-    : South African Rand Futures (CME: 6Z)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ArgusLLSvsWTIArgusTradeMonth
-    
-    
-     Futures.Energy.ARGUS_LL_SVS_WTI_ARGUS_TRADE_MONTH
-    
-    : Argus LLS vs. WTI (Argus) Trade Month Futures (NYMEX: AE5)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ArgusPropaneFarEastIndex
-    
-    
-     Futures.Energy.ARGUS_PROPANE_FAR_EAST_INDEX
-    
-    : Argus Propane Far East Index Futures (NYMEX: A7E)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ArgusPropaneSaudiAramco
-    
-    
-     Futures.Energy.ARGUS_PROPANE_SAUDI_ARAMCO
-    
-    : Argus Propane (Saudi Aramco) Futures (NYMEX: A9N)
-   
      • 
-   
    • 
-    
-     Futures.Energy.BrentCrudeOilVsDubaiCrudeOilPlatts
-    
-    
-     Futures.Energy.BRENT_CRUDE_OIL_VS_DUBAI_CRUDE_OIL_PLATTS
-    
-    : Brent Crude Oil vs. Dubai Crude Oil (Platts) Futures (NYMEX: ADB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.BrentLastDayFinancial
-    
-    
-     Futures.Energy.BRENT_LAST_DAY_FINANCIAL
-    
-    : Brent Last Day Financial Futures (NYMEX: BZ)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ChicagoEthanolPlatts
-    
-    
-     Futures.Energy.CHICAGO_ETHANOL_PLATTS
-    
-    : Chicago Ethaanol (Platts) Futures (NYMEX: CU)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ConwayPropaneOPIS
-    
-    
-     Futures.Energy.CONWAY_PROPANE_OPIS
-    
-    : Conway Propane (OPIS) Futures (NYMEX: A8K)
-   
      • 
-   
    • 
-    
-     Futures.Energy.CrudeOilWTI
-    
-    
-     Futures.Energy.CRUDE_OIL_WTI
-    
-    : Crude Oil WTI Futures (NYMEX: CL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.DubaiCrudeOilPlattsFinancial
-    
-    
-     Futures.Energy.DUBAI_CRUDE_OIL_PLATTS_FINANCIAL
-    
-    : Dubai Crude Oil (Platts) Financial Futures (NYMEX: DCB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EastWestGasolineSpreadPlattsArgus
-    
-    
-     Futures.Energy.EAST_WEST_GASOLINE_SPREAD_PLATTS_ARGUS
-    
-    : East-West Gasoline Spread (Platts-Argus) Futures (NYMEX: EWG)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EastWestNaphthaJapanCFvsCargoesCIFNWESpreadPlatts
-    
-    
-     Futures.Energy.EAST_WEST_NAPHTHA_JAPAN_C_FVS_CARGOES_CIFNWE_SPREAD_PLATTS
-    
-    : East-West Naphtha: Japan C&F vs. Cargoes CIF NWE Spread (Platts) Futures (NYMEX: EWN)
-   
      • 
-   
    • 
-    
-     Futures.Energy.Ethanol
-    
-    
-     Futures.Energy.ETHANOL
-    
-    : Ethanol Futures (CBOT: EH)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EthanolT2FOBRdamIncludingDutyPlatts
-    
-    
-     Futures.Energy.ETHANOL_T_2_FOB_RDAM_INCLUDING_DUTY_PLATTS
-    
-    : Ethanol T2 FOB Rdam Including Duty (Platts) Futures (NYMEX: AZ1)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EuropeanNaphthaPlattsCrackSpread
-    
-    
-     Futures.Energy.EUROPEAN_NAPHTHA_PLATTS_CRACK_SPREAD
-    
-    : European Naphtha (Platts) Crack Spread Futures (NYMEX: EN)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EuropeanPropaneCIFARAArgus
-    
-    
-     Futures.Energy.EUROPEAN_PROPANE_CIFARA_ARGUS
-    
-    : European Propane CIF ARA (Argus) Futures (NYMEX: APS)
-   
      • 
-   
    • 
-    
-     Futures.Energy.EuropeanPropaneCIFARAArgusVsNaphthaCargoesCIFNWEPlatts
-    
-    
-     Futures.Energy.EUROPEAN_PROPANE_CIFARA_ARGUS_VS_NAPHTHA_CARGOES_CIFNWE_PLATTS
-    
-    : European Propane CIF ARA (Argus) vs. Naphtha Cargoes CIF NWE (Platts) Futures (NYMEX: EPN)
-   
      • 
-   
    • 
-    
-     Futures.Energy.FreightRouteTC14Baltic
-    
-    
-     Futures.Energy.FREIGHT_ROUTE_TC_14_BALTIC
-    
-    : Freight Route TC14 (Baltic) Futures (NYMEX: FRC)
-   
      • 
-   
    • 
-    
-     Futures.Energy.Gasoline
-    
-    
-     Futures.Energy.GASOLINE
-    
-    : Gasoline RBOB Futures (NYMEX: RB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GasolineEurobobOxyNWEBargesArgus
-    
-    
-     Futures.Energy.GASOLINE_EUROBOB_OXY_NWE_BARGES_ARGUS
-    
-    : Gasoline Euro-bob Oxy NWE Barges (Argus) Futures (NYMEX: B7H)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GroupThreeSuboctaneGasolinePlattsVsRBOB
-    
-    
-     Futures.Energy.GROUP_THREE_SUBOCTANE_GASOLINE_PLATTS_VS_RBOB
-    
-    : Group Three Sub-octane Gasoliine (Platts) vs. RBOB Futures (NYMEX: AA8)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GroupThreeULSDPlattsVsNYHarborULSD
-    
-    
-     Futures.Energy.GROUP_THREE_ULSD_PLATTS_VS_NY_HARBOR_ULSD
-    
-    : Group Three ULSD (Platts) vs. NY Harbor ULSD Futures (NYMEX: AA6)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GulfCoastCBOBGasolineA2PlattsVsRBOBGasoline
-    
-    
-     Futures.Energy.GULF_COAST_CBOB_GASOLINE_A_2_PLATTS_VS_RBOB_GASOLINE
-    
-    : Gulf Coast CBOB Gasoline A2 (Platts) vs. RBOB Gasoline Futures (NYMEX: CRB)
-   
      • 
-   
    • 
-    
-     Futures.Energy.GulfCoastHSFOPlattsVsEuropeanThreePointFivePercentFuelOilBargesFOBRdamPlatts
-    
-    
-     Futures.Energy.GULF_COAST_HSFO_PLATTS_VS_EUROPEAN_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
-    
-    : Gulf Coast HSFO (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: GCU)
-   
      • 
-   
    • 
-    
-     Futures.Energy.HeatingOil
-    
-    
-     Futures.Energy.HEATING_OIL
-    
-    : Heating Oil Futures (NYMEX: HO)
-   
      • 
-   
    • 
-    
-     Futures.Energy.LosAngelesCARBOBGasolineOPISvsRBOBGasoline
-    
-    
-     Futures.Energy.LOS_ANGELES_CARBOB_GASOLINE_OPI_SVS_RBOB_GASOLINE
-    
-    : Los Angeles CARBOB Gasoline (OPIS) vs. RBOB Gasoline Futures (NYMEX: AJL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.LosAngelesCARBDieselOPISvsNYHarborULSD
-    
-    
-     Futures.Energy.LOS_ANGELES_CARB_DIESEL_OPI_SVS_NY_HARBOR_ULSD
-    
-    : Los Angeles CARB Diesel (OPIS) vs. NY Harbor ULSD Futures (NYMEX: AKL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.LosAngelesJetOPISvsNYHarborULSD
-    
-    
-     Futures.Energy.LOS_ANGELES_JET_OPI_SVS_NY_HARBOR_ULSD
-    
-    : Los Angeles Jet (OPIS) vs. NY Harbor ULSD Futures (NYMEX: AJS)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MarsArgusVsWTIFinancial
-    
-    
-     Futures.Energy.MARS_ARGUS_VS_WTI_FINANCIAL
-    
-    : Mars (Argus) vs. WTI Financial Futures (NYMEX: AYX)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MarsArgusVsWTITradeMonth
-    
-    
-     Futures.Energy.MARS_ARGUS_VS_WTI_TRADE_MONTH
-    
-    : Mars (Argus) vs. WTI Trade Month Futures (NYMEX: AYV)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroCrudeOilWTI
-    
-    
-     Futures.Energy.MICRO_CRUDE_OIL_WTI
-    
-    : Micro WTI Crude Oil Futures (NYMEX: MCL)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroEuropeanFOBRdamMarineFuelZeroPointFivePercentBargesPlatts
-    
-    
-     Futures.Energy.MICRO_EUROPEAN_FOB_RDAM_MARINE_FUEL_ZERO_POINT_FIVE_PERCENT_BARGES_PLATTS
-    
-    : Micro European FOB Rdam Marine Fuel 0.5% Barges (Platts) Futures (NYMEX: R5O)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroEuropeanThreePointFivePercentOilBargesFOBRdamPlatts
-    
-    
-     Futures.Energy.MICRO_EUROPEAN_THREE_POINT_FIVE_PERCENT_OIL_BARGES_FOB_RDAM_PLATTS
-    
-    : Micro European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: MEF)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroGasoilZeroPointOnePercentBargesFOBARAPlatts
-    
-    
-     Futures.Energy.MICRO_GASOIL_ZERO_POINT_ONE_PERCENT_BARGES_FOBARA_PLATTS
-    
-    : Micro Gasoil 0.1% Barges FOB ARA (Platts) Futures (NYMEX: M1B)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroSingaporeFOBMarineFuelZeroPointFivePercetPlatts
-    
-    
-     Futures.Energy.MICRO_SINGAPORE_FOB_MARINE_FUEL_ZERO_POINT_FIVE_PERCET_PLATTS
-    
-    : Micro Singapore FOB Marine Fuel 0.5% (Platts) Futures (NYMEX: S5O)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MicroSingaporeFuelOil380CSTPlatts
-    
-    
-     Futures.Energy.MICRO_SINGAPORE_FUEL_OIL_380_CST_PLATTS
-    
-    : Micro Singapore Fuel Oil 380CST (Platts) Futures (NYMEX: MAF)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MiniEuropeanThreePointPercentFiveFuelOilBargesPlatts
-    
-    
-     Futures.Energy.MINI_EUROPEAN_THREE_POINT_PERCENT_FIVE_FUEL_OIL_BARGES_PLATTS
-    
-    : Mini European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: A0D)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MiniSingaporeFuelOil180CstPlatts
-    
-    
-     Futures.Energy.MINI_SINGAPORE_FUEL_OIL_180_CST_PLATTS
-    
-    : Mini Singapore Fuel Oil 180 cst (Platts) Futures (NYMEX: A0F)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MontBelvieuEthaneOPIS
-    
-    
-     Futures.Energy.MONT_BELVIEU_ETHANE_OPIS
-    
-    : Mont Belvieu Ethane (OPIS) Futures (NYMEX: AC0)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MontBelvieuLDHPropaneOPIS
-    
-    
-     Futures.Energy.MONT_BELVIEU_LDH_PROPANE_OPIS
-    
-    : Mont Belvieu LDH Propane (OPIS) Futures (NYMEX: B0)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MontBelvieuNaturalGasolineOPIS
-    
-    
-     Futures.Energy.MONT_BELVIEU_NATURAL_GASOLINE_OPIS
-    
-    : Mont Belvieu Natural Gasoline (OPIS) Futures (NYMEX: A7Q)
-   
      • 
-   
    • 
-    
-     Futures.Energy.MontBelvieuNormalButaneOPIS
-    
-    
-     Futures.Energy.MONT_BELVIEU_NORMAL_BUTANE_OPIS
-    
-    : Mont Belvieu Normal Butane (OPIS) Futures (NYMEX: AD0)
-   
      • 
-   
    • 
-    
-     Futures.Energy.NaturalGas
-    
-    
-     Futures.Energy.NATURAL_GAS
-    
-    : Natural Gas Futures (NYMEX: NG)
-   
      • 
-   
    • 
-    
-     Futures.Energy.NaturalGasHenryHubLastDayFinancial
-    
-    
-     Futures.Energy.NATURAL_GAS_HENRY_HUB_LAST_DAY_FINANCIAL
-    
-    : Natural Gas (Henry Hub) Last-day Financial Futures (NYMEX: HH)
-   
      • 
-   
    • 
-    
-     Futures.Energy.NaturalGasHenryHubPenultimateFinancial
-    
-    
-     Futures.Energy.NATURAL_GAS_HENRY_HUB_PENULTIMATE_FINANCIAL
-    
-    : Natural Gas (Henry Hub) Penultimate Financial Futures (NYMEX: HP)
-   
      • 
-   
    • 
-    
-     Futures.Energy.OnePercentFuelOilCargoesFOBNWEPlattsVsThreePointFivePercentFuelOilBargesFOBRdamPlatts
-    
-    
-     Futures.Energy.ONE_PERCENT_FUEL_OIL_CARGOES_FOBNWE_PLATTS_VS_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
-    
-    : 1% Fuel Oil Cargoes FOB NWE (Platts) vs. 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: FSS)
-   
      • 
-   
    • 
-    
-     Futures.Energy.PremiumUnleadedGasoline10ppmFOBMEDPlatts
-    
-    
-     Futures.Energy.PREMIUM_UNLEADED_GASOLINE_10_PPM_FOBMED_PLATTS
-    
-    : Premium Unleaded Gasoline 10 ppm FOB MED (Platts) Futures (NYMEX: A3G)
-   
      • 
-   
    • 
-    
-     Futures.Energy.PropaneNonLDHMontBelvieuOPIS
-    
-    
-     Futures.Energy.PROPANE_NON_LDH_MONT_BELVIEU_OPIS
-    
-    : Propane Non-LDH Mont Belvieu (OPIS) Futures (NYMEX: A1R)
-   
      • 
-   
    • 
-    
-     Futures.Energy.RBOBGasolineCrackSpread
-    
-    
-     Futures.Energy.RBOB_GASOLINE_CRACK_SPREAD
-    
-    : RBOB Gasoline Crack Spread Futures (NYMEX: ARE)
-   
      • 
-   
    • 
-    
-     Futures.Energy.RBOBGasolineVsEurobobOxyNWEBargesArgusThreeHundredFiftyThousandGallons
-    
-    
-     Futures.Energy.RBOB_GASOLINE_VS_EUROBOB_OXY_NWE_BARGES_ARGUS_THREE_HUNDRED_FIFTY_THOUSAND_GALLONS
-    
-    : RBOB Gasoline vs. Euro-bob Oxy NWE Barges (Argus) (350,000 gallons) Futures (NYMEX: EXR)
-   
      • 
-   
    • 
-    
-     Futures.Energy.SingaporeFuelOil380cstPlattsVsEuropeanThreePointFivePercentFuelOilBargesFOBRdamPlatts
-    
-    
-     Futures.Energy.SINGAPORE_FUEL_OIL_380_CST_PLATTS_VS_EUROPEAN_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
-    
-    : Singapore Fuel Oil 380 cst (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: EVC)
-   
      • 
-   
    • 
-    
-     Futures.Energy.SingaporeGasoilPlattsVsLowSulphurGasoilFutures
-    
-    
-     Futures.Energy.SINGAPORE_GASOIL_PLATTS_VS_LOW_SULPHUR_GASOIL_FUTURES
-    
-    : Singapore Gasoil (Platts) vs. Low Sulphur Gasoil Futures (NYMEX: AGA)
-   
      • 
-   
    • 
-    
-     Futures.Energy.SingaporeMogas92UnleadedPlattsBrentCrackSpread
-    
-    
-     Futures.Energy.SINGAPORE_MOGAS_92_UNLEADED_PLATTS_BRENT_CRACK_SPREAD
-    
-    : Singapore Mogas 92 Unleaded (Platts) Brent Crack Spread Futures (NYMEX: D1N)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ThreePointFivePercentFuelOilBargesFOBRdamPlattsCrackSpread
-    
-    
-     Futures.Energy.THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS_CRACK_SPREAD
-    
-    : 3.5% Fuel Oil Barges FOB Rdam (Platts) Crack Spread Futures (NYMEX: FO)
-   
      • 
-   
    • 
-    
-     Futures.Energy.ThreePointFivePercentFuelOilBargesFOBRdamPlattsCrackSpread1000mt
-    
-    
-     Futures.Energy.THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS_CRACK_SPREAD_1000_MT
-    
-    : 3.5% Fuel Oil Barges FOB Rdam (Platts) Crack Spread (1000mt) Futures (NYMEX: BOO)
-   
      • 
-   
    • 
-    
-     Futures.Energy.WTIBrentFinancial
-    
-    
-     Futures.Energy.WTI_BRENT_FINANCIAL
-    
-    : WTI-Brent Financial Futures (NYMEX: BK)
-   
      • 
-   
    • 
-    
-     Futures.Energy.WTIFinancial
-    
-    
-     Futures.Energy.WTI_FINANCIAL
-    
-    : WTI Financial Futures (NYMEX: CSX)
-   
      • 
-   
    • 
-    
-     Futures.Energy.WTIHoustonArgusVsWTITradeMonth
-    
-    
-     Futures.Energy.WTI_HOUSTON_ARGUS_VS_WTI_TRADE_MONTH
-    
-    : WTI Houston (Argus) vs. WTI Trade Month Futures (NYMEX: HTT)
-   
      • 
-   
    • 
-    
-     Futures.Energy.WTIHoustonCrudeOil
-    
-    
-     Futures.Energy.WTI_HOUSTON_CRUDE_OIL
-    
-    : WTI Houston Crude Oil Futures (NYMEX: HCL)
-   
      • 
-   
    • 
-    
-     Futures.Financials.EuroDollar
-    
-    
-     Futures.Financials.EURO_DOLLAR
-    
-    : EuroDollar Futures (CME: GE)
-   
      • 
-   
    • 
-    
-     Futures.Financials.FiveYearUSDMACSwap
-    
-    
-     Futures.Financials.FIVE_YEAR_USDMAC_SWAP
-    
-    : 5-Year USD MAC Swap Futures (CBOT: F1U)
-   
      • 
-   
    • 
-    
-     Futures.Financials.MicroY10TreasuryNote
-    
-    
-     Futures.Financials.MICRO_Y_10_TREASURY_NOTE
-    
-    : Micro 10-Year Yield Futures (CBOT: 10Y)
-   
      • 
-   
    • 
-    
-     Futures.Financials.MicroY2TreasuryBond
-    
-    
-     Futures.Financials.MICRO_Y_2_TREASURY_BOND
-    
-    : Micro 2-Year Yield Futures (CBOT: 2YY)
-   
      • 
-   
    • 
-    
-     Futures.Financials.MicroY30TreasuryBond
-    
-    
-     Futures.Financials.MICRO_Y_30_TREASURY_BOND
-    
-    : Micro 30-Year Yield Futures (CBOT: 30Y)
-   
      • 
-   
    • 
-    
-     Futures.Financials.MicroY5TreasuryBond
-    
-    
-     Futures.Financials.MICRO_Y_5_TREASURY_BOND
-    
-    : Micro 5-Year Yield Futures (CBOT: 5YY)
-   
      • 
-   
    • 
-    
-     Futures.Financials.UltraTenYearUSTreasuryNote
-    
-    
-     Futures.Financials.ULTRA_TEN_YEAR_US_TREASURY_NOTE
-    
-    : Ultra 10-Year U.S. Treasury Note Futures (CBOT: TN)
-   
      • 
-   
    • 
-    
-     Futures.Financials.UltraUSTreasuryBond
-    
-    
-     Futures.Financials.ULTRA_US_TREASURY_BOND
-    
-    : Ultra U.S. Treasury Bond Futures (CBOT: UB)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y10TreasuryNote
-    
-    
-     Futures.Financials.Y_10_TREASURY_NOTE
-    
-    : 10Y U.S. Treasury Note Futures (CBOT: ZN)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y2TreasuryNote
-    
-    
-     Futures.Financials.Y_2_TREASURY_NOTE
-    
-    : 2Y U.S. Treasury Note Futures (CBOT: ZT)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y30TreasuryBond
-    
-    
-     Futures.Financials.Y_30_TREASURY_BOND
-    
-    : 30Y U.S. Treasury Bond Futures (CBOT: ZB)
-   
      • 
-   
    • 
-    
-     Futures.Financials.Y5TreasuryNote
-    
-    
-     Futures.Financials.Y_5_TREASURY_NOTE
-    
-    : 5Y U.S. Treasury Note Futures (CBOT: ZF)
-   
      • 
-   
    • 
-    
-     Futures.Forestry.Lumber
-    
-    
-     Futures.Forestry.LUMBER
-    
-    : Lumber Futures (CME: LBR)
-   
      • 
-   
    • 
-    
-     Futures.Forestry.RandomLengthLumber
-    
-    
-     Futures.Forestry.RANDOM_LENGTH_LUMBER
-    
-    : Random Length Lumber Futures (CME: LBS)
-   
      • 
-   
    • 
-    
-     Futures.Grains.BlackSeaCornFinanciallySettledPlatts
-    
-    
-     Futures.Grains.BLACK_SEA_CORN_FINANCIALLY_SETTLED_PLATTS
-    
-    : Black Sea Corn Financially Settled (Platts) Futures (CBOT: BCF)
-   
      • 
-   
    • 
-    
-     Futures.Grains.BlackSeaWheatFinanciallySettledPlatts
-    
-    
-     Futures.Grains.BLACK_SEA_WHEAT_FINANCIALLY_SETTLED_PLATTS
-    
-    : Black Sea Wheat Financially Settled (Platts) Futures (CBOT: BWF)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Corn
-    
-    
-     Futures.Grains.CORN
-    
-    : Corn Futures (CBOT: ZC)
-   
      • 
-   
    • 
-    
-     Futures.Grains.HRWWheat
-    
-    
-     Futures.Grains.HRW_WHEAT
-    
-    : KC HRW Wheat Futures (CBOT: KE)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Oats
-    
-    
-     Futures.Grains.OATS
-    
-    : Oats Futures (CBOT: ZO)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Soybeans
-    
-    
-     Futures.Grains.SOYBEANS
-    
-    : Soybeans Futures (CBOT: ZS)
-   
      • 
-   
    • 
-    
-     Futures.Grains.SoybeanMeal
-    
-    
-     Futures.Grains.SOYBEAN_MEAL
-    
-    : Soybean Meal Futures (CBOT: ZM)
-   
      • 
-   
    • 
-    
-     Futures.Grains.SoybeanOil
-    
-    
-     Futures.Grains.SOYBEAN_OIL
-    
-    : Soybean Oil Futures (CBOT: ZL)
-   
      • 
-   
    • 
-    
-     Futures.Grains.Wheat
-    
-    
-     Futures.Grains.WHEAT
-    
-    : Default wheat contract is SRWWheat (CBOT: ZW)
-   
      • 
-   
    • 
-    
-     Futures.Indices.BloombergCommodityIndex
-    
-    
-     Futures.Indices.BLOOMBERG_COMMODITY_INDEX
-    
-    : Bloomberg Commodity Index Futures (CBOT: AW)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Dow30EMini
-    
-    
-     Futures.Indices.DOW_30_E_MINI
-    
-    : E-mini Dow Indu 30 Futures (CBOT: YM)
-   
      • 
-   
    • 
-    
-     Futures.Indices.DowJonesRealEstate
-    
-    
-     Futures.Indices.DOW_JONES_REAL_ESTATE
-    
-    : Dow Jones Real Estate futures on CME (CME: RX)
-   
      • 
-   
    • 
-    
-     Futures.Indices.FTSEEmergingEmini
-    
-    
-     Futures.Indices.FTSE_EMERGING_EMINI
-    
-    : E-mini FTSE Emerging Index Futures (CME: EI)
-   
      • 
-   
    • 
-    
-     Futures.Indices.MicroDow30EMini
-    
-    
-     Futures.Indices.MICRO_DOW_30_E_MINI
-    
-    : Micro E-mini Dow Jones Industrial Average Index Futures (CBOT: MYM)
-   
      • 
-   
    • 
-    
-     Futures.Indices.MicroNASDAQ100EMini
-    
-    
-     Futures.Indices.MICRO_NASDAQ_100_E_MINI
-    
-    : Micro E-mini Nasdaq-100 Index Futures (CME: MNQ)
-   
      • 
-   
    • 
-    
-     Futures.Indices.MicroRussell2000EMini
-    
-    
-     Futures.Indices.MICRO_RUSSELL_2000_E_MINI
-    
-    : Micro E-mini Russell 2000 Index Futures (CME: M2K)
-   
      • 
-   
    • 
-    
-     Futures.Indices.MicroSP500EMini
-    
-    
-     Futures.Indices.MICRO_SP_500_E_MINI
-    
-    : Micro E-mini S&P 500 Index Futures (CME: MES)
-   
      • 
-   
    • 
-    
-     Futures.Indices.NASDAQ100BiotechnologyEMini
-    
-    
-     Futures.Indices.NASDAQ_100_BIOTECHNOLOGY_E_MINI
-    
-    : E-mini Nasdaq-100 Biotechnology Index Futures (CME: BIO)
-   
      • 
-   
    • 
-    
-     Futures.Indices.NASDAQ100EMini
-    
-    
-     Futures.Indices.NASDAQ_100_E_MINI
-    
-    : E-mini NASDAQ 100 Futures (CME: NQ)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Nikkei225Dollar
-    
-    
-     Futures.Indices.NIKKEI_225_DOLLAR
-    
-    : Nikkei-225 Dollar Futures (CME: NKD)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Nikkei225YenCME
-    
-    
-     Futures.Indices.NIKKEI_225_YEN_CME
-    
-    : Nikkei-225 Yen denominated Futures on CME (CME: NIY)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Russell1000EMini
-    
-    
-     Futures.Indices.RUSSELL_1000_E_MINI
-    
-    : E-mini Russell 1000 futures on CME (CME: RS1)
-   
      • 
-   
    • 
-    
-     Futures.Indices.Russell2000EMini
-    
-    
-     Futures.Indices.RUSSELL_2000_E_MINI
-    
-    : E-mini Russell 2000 Futures (CME: RTY)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SPGSCICommodity
-    
-    
-     Futures.Indices.SPGSCI_COMMODITY
-    
-    : S&P-GSCI Commodity Index Futures (CME: GD)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SP400MidCapEmini
-    
-    
-     Futures.Indices.SP_400_MID_CAP_EMINI
-    
-    : E-mini S&P MidCap 400 Futures (CME: EMD)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SP500AnnualDividendIndex
-    
-    
-     Futures.Indices.SP_500_ANNUAL_DIVIDEND_INDEX
-    
-    :  (CME: SDA)
-   
      • 
-   
    • 
-    
-     Futures.Indices.SP500EMini
-    
-    
-     Futures.Indices.SP_500_E_MINI
-    
-    : E-mini S&P 500 Futures (CME: ES)
-   
      • 
-   
    • 
-    
-     Futures.Indices.TOPIXYEN
-    
-    : YEN Denominated Topix Index Futures on CME (CME: TPY)
-   
      • 
-   
    • 
-    
-     Futures.Indices.USDDenominatedIbovespa
-    
-    
-     Futures.Indices.USD_DENOMINATED_IBOVESPA
-    
-    : USD-Denominated Ibovespa Index Futures (CME: IBV)
-   
      • 
-   
    • 
-    
-     Futures.Indices.VIX
-    
-    : CBOE Volatility Index Futures (CFE: VX)
-   
      • 
-   
    • 
-    
-     Futures.Meats.FeederCattle
-    
-    
-     Futures.Meats.FEEDER_CATTLE
-    
-    : Feeder Cattle Futures (CME: GF)
-   
      • 
-   
    • 
-    
-     Futures.Meats.LeanHogs
-    
-    
-     Futures.Meats.LEAN_HOGS
-    
-    : Lean Hogs Futures (CME: HE)
-   
      • 
-   
    • 
-    
-     Futures.Meats.LiveCattle
-    
-    
-     Futures.Meats.LIVE_CATTLE
-    
-    : Live Cattle Futures (CME: LE)
-   
      • 
-   
    • 
-    
-     Futures.Metals.AluminiumEuropeanPremiumDutyPaidMetalBulletin
-    
-    
-     Futures.Metals.ALUMINIUM_EUROPEAN_PREMIUM_DUTY_PAID_METAL_BULLETIN
-    
-    : Aluminium European Premium Duty-Paid (Metal Bulletin) Futures (COMEX: EDP)
-   
      • 
-   
    • 
-    
-     Futures.Metals.AluminumMWUSTransactionPremiumPlatts25MT
-    
-    
-     Futures.Metals.ALUMINUM_MWUS_TRANSACTION_PREMIUM_PLATTS_25_MT
-    
-    : Aluminum MW U.S. Transaction Premium Platts (25MT) Futures (COMEX: AUP)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Copper
-    
-    
-     Futures.Metals.COPPER
-    
-    : Copper Futures (COMEX: HG)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Gold
-    
-    
-     Futures.Metals.GOLD
-    
-    : Gold Futures (COMEX: GC)
-   
      • 
-   
    • 
-    
-     Futures.Metals.MicroGold
-    
-    
-     Futures.Metals.MICRO_GOLD
-    
-    : Micro Gold Futures (COMEX: MGC)
-   
      • 
-   
    • 
-    
-     Futures.Metals.MicroGoldTAS
-    
-    
-     Futures.Metals.MICRO_GOLD_TAS
-    
-    : Micro Gold TAS Futures (COMEX: MGT)
-   
      • 
-   
    • 
-    
-     Futures.Metals.MicroPalladium
-    
-    
-     Futures.Metals.MICRO_PALLADIUM
-    
-    : Micro Palladium Futures (NYMEX: PAM)
-   
      • 
-   
    • 
-    
-     Futures.Metals.MicroSilver
-    
-    
-     Futures.Metals.MICRO_SILVER
-    
-    : Micro Silver Futures (COMEX: SIL)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Palladium
-    
-    
-     Futures.Metals.PALLADIUM
-    
-    : Palladium Futures (NYMEX: PA)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Platinum
-    
-    
-     Futures.Metals.PLATINUM
-    
-    : Platinum Futures (NYMEX: PL)
-   
      • 
-   
    • 
-    
-     Futures.Metals.Silver
-    
-    
-     Futures.Metals.SILVER
-    
-    : Silver Futures (COMEX: SI)
-   
      • 
-   
    • 
-    
-     Futures.Metals.USMidwestDomesticHotRolledCoilSteelCRUIndex
-    
-    
-     Futures.Metals.US_MIDWEST_DOMESTIC_HOT_ROLLED_COIL_STEEL_CRU_INDEX
-    
-    : U.S. Midwest Domestic Hot-Rolled Coil Steel (CRU) Index Futures (NYMEX: HRC)
-   
      • 
-   
    • 
-    
-     Futures.Softs.Sugar11
-    
-    
-     Futures.Softs.SUGAR_11
-    
-    : Sugar #11 Futures ICE (ICE: SB)
-   
      • 
-   
    • 
-    
-     Futures.Softs.Sugar11CME
-    
-    
-     Futures.Softs.SUGAR_11_CME
-    
-    : Sugar #11 Futures CME (NYMEX: YO)
-   
      • 
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Futures dataset enables you to accurately design Futures strategies. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Buying the Futures contract with the most open interest to reduce slippage and market impact
-   
      • 
-   
    • 
-    Trading bull calendar spreads to reduce volatility and margin requirements
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm buys the Mini Gold Futures contract with the greatest open interest and sells the Micro Gold Futures contract with the greatest open interest. When the open interest of a different contract exceeds the open interest of a contract in the portfolio, the algorithm rebalances the portfolio.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class USFuturesDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(1000000)
-        # Setting the continuous contract mapping criteria for both Gold and Micro Gold contracts, since we want to order the highest liquidity contracts
-        self.settings.seed_initial_prices = True
-        self.gold = self.add_future(Futures.Metals.GOLD,
-            extended_market_hours=True,
-            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
-            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
-            contract_depth_offset=0)
-        self.micro_gold = self.add_future(Futures.Metals.MICRO_GOLD,
-            extended_market_hours=True,
-            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
-            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
-            contract_depth_offset=0)
-        # The contract multiplier is cached in the Security symbol_properties property from the symbol properties database
-        self.gold_multiplier = self.gold.symbol_properties.contract_multiplier
-        self.micro_gold_multiplier = self.micro_gold.symbol_properties.contract_multiplier
-
-    def on_data(self, slice: Slice) -> None:
-        # Make sure to calculate the order size by the most updated price data of both contracts
-        if not self.portfolio.invested and self.gold.symbol in slice.bars and self.micro_gold.symbol in slice.bars:
-            # Calculate the order size for $500k
-            # Get the quotient after dividing the contract multiplier since the order size must be whole number
-            gold_quantity = 500000 / slice.bars[self.gold.symbol].close // self.gold_multiplier
-            micro_gold_quantity = 500000 / slice.bars[self.micro_gold.symbol].close // self.micro_gold_multiplier
-
-            self.market_order(self.gold.mapped, gold_quantity)
-            self.market_order(self.micro_gold.mapped, micro_gold_quantity)
-                
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            # Historical data
-            history = self.history(security.symbol, 10, Resolution.MINUTE)
-            self.debug(f"We got {len(history)} from our history request for {security.symbol}")
    
-   
    public class USFuturesDataAlgorithm : QCAlgorithm
-{
-    private Future _gold, _microGold;
-    private decimal _goldMulitplier, _microGoldMulitplier;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(1000000);
-        // Setting the continuous contract mapping criteria for both Gold and Micro Gold contracts, since we want to order the highest liquidity contracts
-        Settings.SeedInitialPrices = true;
-        _gold = AddFuture(Futures.Metals.Gold,
-            extendedMarketHours: true,
-            dataMappingMode: DataMappingMode.OpenInterest,
-            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
-            contractDepthOffset: 0);
-        _microGold = AddFuture(Futures.Metals.MicroGold,
-            extendedMarketHours: true,
-            dataMappingMode: DataMappingMode.OpenInterest,
-            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
-            contractDepthOffset: 0);
-        // The contract multiplier is cached in the Security SymbolProperties property from the symbol properties database
-        _goldMulitplier = _gold.SymbolProperties.ContractMultiplier;
-        _microGoldMulitplier = _microGold.SymbolProperties.ContractMultiplier;
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // Make sure to calculate the order size by the most updated price data of both contracts
-        if (!Portfolio.Invested && slice.Bars.ContainsKey(_gold.Symbol) && slice.Bars.ContainsKey(_microGold.Symbol))
-        {
-            // Calculate the order size for $500k
-            // Get the quotient after dividing the contract multiplier since the order size must be whole number
-            var goldQuantity = Math.Floor(500000m / slice.Bars[_gold.Symbol].Close / _goldMulitplier);
-            var microGoldQuantity = Math.Floor(500000m / slice.Bars[_microGold.Symbol].Close / _microGoldMulitplier);
-
-            MarketOrder(_gold.Mapped, goldQuantity);
-            MarketOrder(_microGold.Mapped, microGoldQuantity);
-        }
-    }
-    
-    public override void OnSecuritiesChanged(SecurityChanges changes)
-    {
-        foreach (var security in changes.AddedSecurities)
-        {
-            // Historical data
-            var history = History(security.Symbol, 100, Resolution.Minute);
-            Debug($"We got {history.Count()} from our history request for {security.Symbol}");
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm buys the front-month Mini Gold Futures contract and sells the front-month Micro Gold Futures contract. When the front-month contract changes, the algorithm rebalances the portfolio.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class USFuturesDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        self.universe_settings.asynchronous = True
-        # Set up an universe selection model that selects the front month contract
-        self.set_universe_selection(FrontMonthFutureUniverseSelectionModel())
-        self.add_alpha(ConstantFuturesAlphaModel())
-        # A portfolio construction model that only order a single share per insight signal
-        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
-        
-class FrontMonthFutureUniverseSelectionModel(FutureUniverseSelectionModel):
-    def __init__(self,) -> None:
-        # Daily updating with select_future_chain_symbols function
-        super().__init__(timedelta(1), self.select_future_chain_symbols)
-
-    def select_future_chain_symbols(self, utcTime: datetime) -> List[Symbol]:
-        # Select gold and micro gold contracts for the strategy need
-        future_pairs = [
-            (Futures.Metals.GOLD, Market.COMEX),
-            (Futures.Metals.MICRO_GOLD, Market.COMEX)
-        ]
-        return [Symbol.create(pair[0], SecurityType.FUTURE, pair[1]) for pair in future_pairs]
-
-    def filter(self, filter: FutureFilterUniverse) -> FutureFilterUniverse:
-        # Filter only front month contract for liquidity and most informed information
-        return filter.front_month().only_apply_filter_at_market_open()
-
-class ConstantFuturesAlphaModel(AlphaModel):
-    # Long gold and short micro gold in this strategy
-    long_symbol = Symbol.create(Futures.Metals.GOLD, SecurityType.FUTURE, Market.COMEX)
-    short_symbol = Symbol.create(Futures.Metals.MICRO_GOLD, SecurityType.FUTURE, Market.COMEX)
-        
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        if algorithm.portfolio.invested:
-            return []
-        
-        insights = []
-        # For both gold and micro gold, select the front month contract (only contract) in the chain
-        for symbol, contracts in slice.future_chains.items():
-            chain = [contract for contract in contracts]
-            contract = chain[0]
-            
-            # Long gold and short micro gold as planned
-            if symbol == self.long_symbol:
-                insights.append(Insight.price(contract.symbol, contract.expiry + timedelta(days=1), InsightDirection.UP))
-            elif symbol == self.short_symbol:
-                insights.append(Insight.price(contract.symbol, contract.expiry + timedelta(days=1), InsightDirection.DOWN))
-        
-        return insights
-        
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            # Historical data
-            history = algorithm.history(security.symbol, 10, Resolution.MINUTE)
-            algorithm.debug(f"We got {len(history)} from our history request for {security.symbol}")
-
-class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
-    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
-        targets = []
-        for insight in insights:
-            if algorithm.securities[insight.symbol].is_tradable:
-                # Single share only using integer portfolio target
-                targets.append(PortfolioTarget(insight.symbol, insight.direction))
-        return targets
    
-   
    public class USFuturesDataAlgorithm : QCAlgorithm
-{        
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(1000000);
-        UniverseSettings.Asynchronous = true;
-        // Set up an universe selection model that selects the front month contract
-        SetUniverseSelection(new FrontMonthFutureUniverseSelectionModel());
-        SetAlpha(new ConstantFuturesAlphaModel());
-        // A portfolio construction model that only order a single share per insight signal
-        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
-    }
-}
-    
-class FrontMonthFutureUniverseSelectionModel : FutureUniverseSelectionModel
-{
-    // Daily updating with select_future_chain_symbols function
-    public FrontMonthFutureUniverseSelectionModel()
-        : base(TimeSpan.FromDays(1), SelectFutureChainSymbols) {}
-
-    private static IEnumerable<Symbol> SelectFutureChainSymbols(DateTime utcTime)
-    {
-        //Select gold and micro gold contracts for the strategy need
-        return new List<Symbol> {
-            QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX),
-            QuantConnect.Symbol.Create(Futures.Metals.MicroGold, SecurityType.Future, Market.COMEX)
-        };
-    }
-
-    protected override FutureFilterUniverse Filter(FutureFilterUniverse filter)
-    {
-        // Filter only front month contract for liquidity and most informed information
-        return filter.FrontMonth().OnlyApplyFilterAtMarketOpen();
-    }
-}
-
-
-class ConstantFuturesAlphaModel : AlphaModel
-{
-    // Long gold and short micro gold in this strategy
-    private Symbol 
-        _longSymbol = QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX), 
-        _shortSymbol = QuantConnect.Symbol.Create(Futures.Metals.MicroGold, SecurityType.Future, Market.COMEX);
-    
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        var insights = new List<Insight>();
-
-        if (algorithm.Portfolio.Invested)
-        {
-            return insights;
-        }
-        
-        // For both gold and micro gold, select the front month contract (only contract) in the chain
-        foreach (var kvp in slice.FutureChains)
-        {
-            var symbol = kvp.Key;
-            var chain = kvp.Value;
-            var contract = chain.First();
-            
-            // Long gold and short micro gold as planned
-            if (symbol == _longSymbol)
-            {
-                insights.Add(Insight.Price(contract.Symbol, contract.Expiry + TimeSpan.FromDays(1), InsightDirection.Up));
-            }
-            else if (symbol == _shortSymbol)
-            {
-                insights.Add(Insight.Price(contract.Symbol, contract.Expiry + TimeSpan.FromDays(1), InsightDirection.Down));
-            }
-        }
-
-        return insights;
-    }
-    
-    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-    {
-        foreach (var security in changes.AddedSecurities)
-        {
-            // Historical data
-            var history = algorithm.History(security.Symbol, 100, Resolution.Minute);
-            algorithm.Debug($"We got {history.Count()} from our history request for {security.Symbol}");
-        }
-    }
-}
-    
-    
-class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
-{
-    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
-    {
-        var targets = new List<PortfolioTarget>();
-        foreach (var insight in insights)
-        {
-            if (algorithm.Securities[insight.Symbol].IsTradable)
-            {
-                // Single share only using integer portfolio target
-                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
-            }
-        }
-        return targets;
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Futures dataset provides
-   
-    FuturesChain
-   
-   ,
-   
-    Future
-   
-   , and
-   
-    OpenInterest
-   
-   objects. To configure the continuous Future settings, use the
-   
-    DataNormalizationMode
-   
-   and
-   
-    DataMappingMode
-   
-   enumerations.
-  
    
-  
    
-   DataNormalizationMode Values
-  
    
-  
    
-   The
-   
-    DataNormalizationMode
-   
-   enumeration has the following values:
-  
    
-  
    
-  
    
-  
    
-   DataMappingMode Values
-  
    
-  
    
-   The
-   
-    DataMappingMode
-   
-   enumeration has the following values:
-  
    
-  
    
-  
    
-  
    
-   Future Attributes
-  
    
-  
    
-   
-    Future
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   FuturesChain Attributes
-  
    
-  
    
-   
-    FuturesChain
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   OpenInterest Attributes
-  
    
-  
    
-   
-    OpenInterest
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > AlgoSeek > US Index Options
    
-
    
-    
    
-        
    AlgoSeek
    
-        
    US Index Options
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Index Options dataset by AlgoSeek provides Option data, including prices, strikes, expires, and open interest. The data covers European Option contracts for 3 US Indices: SPX, VIX, and NDX. It starts from January 2012 and is delivered on minute resolution. This dataset is created by monitoring the Options Price Reporting Authority (OPRA) data feed, which consolidates last sale and quotation information originating from the national securities exchanges that have been approved by the Securities and Exchange Commission.
-  
    
-  
    
-   The US Index Options dataset depends on the
-   
-    US Index Option Universe
-   
-   dataset because the US Index Options Universe dataset contains information on the available contracts, including their daily Greeks and implied volatility values.
-  
    
-  
    
-   For more information about the US Index Options dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering Equities, Futures, Options, cash FOREX, and Cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning.  For more information about AlgoSeek, visit
-   
-    algoseek.com
-   
-   .
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the US Index Options dataset:
-  
    
-  
    
-   
    self.index_symbol = self.add_index('VIX').symbol
-option = self.add_index_option(self.index_symbol)
-option.set_filter(-2, 2, 0, 90)
-self.option_symbol = option.symbol
    
-   
    _indexSymbol = AddIndex("VIX").Symbol;
-var option = AddIndexOption(_indexSymbol);
-option.SetFilter(-2, 2, 0, 90);
-_optionSymbol = option.Symbol;
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 2012
-     
    
-      Asset Coverage
-     
-      7 Index Options
-     
    
-      Data Density
-     
-      Regular
-     
    
-      Resolution
-     
-      Minute, Hourly, & Daily
-     
    
-      Timezone
-     
-      New York
-     
    
-      Market Hours
-     
-      
-       Regular Only
-      
-     
    
- 
-
-
-
    Specification Over Time
    
-
- 
-  
    
-   According to the SPX Options
-   
-    contract specification
-   
-   , some SPX contracts expire every month and SPXW contracts expires every day. Before 2021, you could only trade SPX contracts with the following expiration dates:
-  
    
-  
      
-   
    • 
-    Expires within the next 4 months
-   
      • 
-   
    • 
-    Expires in September within the next 14 months
-   
      • 
-   
    • 
-    Expires in January, March, or June within the next 2 years
-   
      • 
-   
    • 
-    Expires in December within the next 3 years
-   
      • 
-  
    
-  
    
-   During this time, SPXW didn't have 0DTE every day.
-  
    
-  
    
-   
-    Sources
-   
-   :
-   
    
-   -
-   
-    Cboe Options Exchange to List Three Long-Dated SPX Options Expirations, Beginning November 1, 2021
-   
-   
    
-   -
-   
-    S&P 500 Weekly Options Now Expire Five Days a Week
-   
-  
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add US Index Options data to your algorithm, call the
-   
-    AddIndexOption
-   
-   
-    add_index_option
-   
-   method. Save a reference to the Index Option
-   
-    Symbol
-   
-   so you can access the data later in your algorithm.
-  
    
-  
    
-   
    class IndexOptionsDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2021, 1, 1);
-        self.set_end_date(2021, 6, 1);
-        self.set_cash(1000000);
-        self.universe_settings.asynchronous = True
-        self.index_symbol = self.add_index('VIX').symbol
-        
-        standard_option = self.add_index_option(self.index_symbol)
-        standard_option.set_filter(-2, 2, 0, 90)
-        self.standard_option_symbol = standard_option.symbol
-
-        weekly_option = self.add_index_option(self.index_symbol, "VIXW")
-        weekly_option.set_filter(-2, 2, 0, 90)
-        self.weekly_option_symbol = weekly_option.symbol
    
-   
    public class IndexOptionsDataAlgorithm : QCAlgorithm
-{
-    private Symbol _indexSymbol, _standardOptionSymbol, _weeklyOptionSymbol;
-
-    public override void Initialize()
-    {
-        SetStartDate(2021, 1, 1);
-        SetEndDate(2021, 6, 1);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-        _indexSymbol = AddIndex("VIX").Symbol;
-
-        var standardOption = AddIndexOption(_indexSymbol);
-        standardOption.SetFilter(-2, 2, 0, 90);
-        _standardOptionSymbol = standardOption.Symbol;
-
-        var weeklyOption = AddIndexOption(_indexSymbol, "VIXW");
-        weeklyOption.SetFilter(-2, 2, 0, 90);
-        _weeklyOptionSymbol = weeklyOption.Symbol;
-    }
-}
    
-  
    
-  
    
-   The Index resolution must be less than or equal to the Index Option resolution. For example, if you set the Index resolution to minute, then you must set the Index Option resolution to minute, hour, or daily.
-  
    
-  
    
-   For more information about creating US Index Option subscriptions, see
-   
-    Requesting Data
-   
-   or
-   
-    Index Options Universes
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To get the current US Index Options data, index the
-   
-    OptionChains
-   
-   
-    option_chains
-   
-   property of the current
-   
-    
-     Slice
-    
-   
-   with the canonical Index Option
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your Index Option at every time step.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    standard_chain = slice.option_chains.get(self.standard_option_symbol)
-    if standard_chain:
-        for contract in standard_chain:
-            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
-
-    weekly_chain = slice.option_chains.get(self.weekly_option_symbol)
-    if weekly_chain:
-        for contract in weekly_chain:
-            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
    
-   
    public override void OnData(Slice slice)
-{
-    if (slice.OptionChains.ContainsKey(_standardOptionSymbol))
-    {
-        var standardChain = slice.OptionChains[_standardOptionSymbol];
-        foreach (var contract in standardChain)
-        {
-            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
-        }
-    }
-
-    if (slice.OptionChains.ContainsKey(_weeklyOptionSymbol))
-    {
-        var weeklyChain = slice.OptionChains[_weeklyOptionSymbol];
-        foreach (var contract in weeklyChain)
-        {
-            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
-        }
-    }
-}
    
-  
    
-  
    
-   You can also iterate through all of the
-   
-    OptionChain
-   
-   objects in the current
-   
-    Slice
-   
-   .
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    for canonical_symbol, chain in slice.option_chains.items():
-        for contract in chain:
-            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
-
    
-   
    public override void OnData(Slice slice)
-{
-    foreach (var kvp in slice.OptionChains)
-    {
-        var canonicalSymbol = kvp.Key;
-        var chain = kvp.Value;
-        foreach (var contract in chain)
-        {
-            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
-        }
-    }
-}
    
-  
    
-  
    
-   For more information about accessing US Index Options data, see
-   
-    Handling Data
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical US Index Options data in an algorithm and the Research Environment.
-  
    
-  
    
-   Historical Data In Algorithms
-  
    
-  
    
-   To get historical US Index Options data in an algorithm, call the
-   
-    History
-   
-   
-    history
-   
-   method with the Index Option contract
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame of trade and quote data
-history_df = self.history(contract.symbol, 100, Resolution.MINUTE)
-
-# DataFrame of open interest data
-history_oi_df = self.history(OpenInterest, contract.symbol, 100, Resolution.MINUTE)
-
-# TradeBar objects
-history_trade_bars = self.history[TradeBar](contract.symbol, 100, Resolution.MINUTE)
-
-# QuoteBar objects
-history_quote_bars = self.history[QuoteBar](contract.symbol, 100, Resolution.MINUTE)
-
-# OpenInterest objects
-history_oi = self.history[OpenInterest](contract.symbol, 100, Resolution.MINUTE)
    
-   
    // TradeBar objects 
-var historyTradeBars = History(contract.Symbol, 100, Resolution.Minute);
-
-// QuoteBar objects 
-var historyQuoteBars = History<QuoteBar>(contract.Symbol, 100, Resolution.Minute);
-
-// OpenInterest objects
-var historyOpenInterest = History<OpenInterest>(contract.Symbol, 100, Resolution.Minute);
    
-  
    
-  
    
-   For more information about historical data in algorithms, see
-   
-    History Requests
-   
-   .
-  
    
-  
    
-   Historical Data In Research
-  
    
-  
    
-   To get historical US Index Options data in the Research Environment, call the
-   
-    History
-   
-   
-    history
-   
-   or
-   
-    OptionHistory
-   
-   
-    option_history
-   
-   method. The
-   
-    History
-   
-   
-    history
-   
-   method returns the price, volume, and open interest history for some given Option contract(s). The
-   
-    OptionHistory
-   
-   
-    option_history
-   
-   method returns the price and volume history for the contracts that pass your daily universe filter.
-  
    
-  
    
-   
    qb = QuantBook()
-index_symbol = qb.add_index('VIX').symbol
-option = qb.add_index_option(index_symbol) # or qb.add_index_option(index_symbol, "VIXW")
-option.set_filter(-2, 2, 0, 90)
-history = qb.option_history(option.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5))
-history_df = history.data_frame
-expiries = history.get_expiry_dates() 
-strikes = history.get_strikes()
    
-   
    var qb = new QuantBook();
-var indexSymbol = qb.AddIndex("VIX").Symbol;
-var option = qb.AddIndexOption(indexSymbol); // or qb.AddIndexOption(indexSymbol, "VIXW");
-option.SetFilter(-2, 2, 0, 90);
-var history = qb.OptionHistory(option.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 5));
-
-var contracts = history
-    .SelectMany(x => x.OptionChains.SelectMany(y => y.Value.Contracts.Keys))
-    .Distinct().ToList();
-var expiries = contracts.Select(x => x.ID.Date).Distinct().ToList();
-var strikes = contracts.Select(x => x.ID.StrikePrice).Distinct().ToList();
    
-  
    
-  
    
-   To get historical data for arbitrary US Index Option contracts instead of just the that pass your universe filter, call the
-   
-    History
-   
-   
-    history
-   
-   method like you would in an algorithm, but on the
-   
-    QuantBook
-   
-   object. For more information about historical data in the Research Environment, see
-   
-    Key Concepts
-   
-   .
-  
    
-  
    
-   Historical Greeks and IV Data
-  
    
-  
    
-   To get historical data for the Greeks and implied volatility of Index Options, see the
-   
-    US Index Option Universe
-   
-   dataset.
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   The following table shows the available Index Options:
-  
    
-  
-   
-    
-     
-     
-     
-     
-     
-     
-    
-   
-   
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-     
-    
-   
-  
    
-      Underlying Index
-     
-      Underlying Ticker
-     
-      Target Ticker
-     
-      Standard Contracts
-     
-      Weekly Contracts
-     
-      Tradable on Expiry Day
-     
    
-      NASDAQ-100
-     
-      NDX
-     
-     
-      green check
-     
-     
-     
    
-      NASDAQ-100
-     
-      NDX
-     
-      NDXP
-     
-      green check
-     
-     
-      green check
-     
    
-      NASDAQ-100
-     
-      NDX
-     
-      NQX
-     
-      green check
-     
-      green check
-     
-      green check
-     
    
-      Russell 2000
-     
-      RUT
-     
-     
-      green check
-     
-     
-     
    
-      Russell 2000
-     
-      RUT
-     
-      RUTW
-     
-     
-      green check
-     
-      green check
-     
    
-      S&P500
-     
-      SPX
-     
-     
-      green check
-     
-     
-     
    
-      S&P500
-     
-      SPX
-     
-      SPXW
-     
-     
-      green check
-     
-      green check
-     
    
-      S&P500
-     
-      VIX
-     
-     
-      green check
-     
-     
-     
    
-      S&P500
-     
-      VIX
-     
-      VIXW
-     
-     
-      green check
-     
-     
    
-  
    
-   For more information about each underlying Index, see
-   
-    Supported Indices
-   
-   .
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Index Options dataset enables you to accurately design strategies for Index Options. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Buying VIX call Options to hedge against upcoming volatility
-   
      • 
-   
    • 
-    Buying VIX put Options to capture the natural downward price movement in the VIX index
-   
      • 
-   
    • 
-    Buying SPX put Options to protect against downward price movement in the S&P 500
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm buys the VIX Index Option Bull Call Spread with the furthest expiration:
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class IndexOptionsDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(200000)
-        # Asynchronous can use computational resources efficiently
-        self.universe_settings.asynchronous = True
-        # Filter to get ATM calls expiring in 180 days to form the Bull Call Spread
-        option = self.add_index_option("SPX")
-        option.set_filter(lambda u: u.calls_only().strikes(-2, +2).expiration(0, 180))
-        self.option_symbol = option.symbol
-
-    def on_data(self, slice: Slice) -> None:
-        if not self.portfolio.invested and self.is_market_open(self.option_symbol):
-            # Make sure getting the updated VIX option chain
-            chain = slice.option_chains.get(self.option_symbol)
-            if chain:
-                expiry = max([c.expiry for c in chain])
-                call_contracts = sorted([c for c in chain if c.expiry == expiry],
-                    key=lambda c: c.strike)
-                
-                # Need 2 contracts to form a call spread
-                if len(call_contracts) < 2:
-                    return
-                
-                # Obtain 2 call contracts with different strike price to form the call spread
-                longCall, shortCall = call_contracts[0:2]
-                
-                # Use all the buying power, but need to ensure the order size of the long and short call are the same
-                quantity = min([
-                    abs(self.calculate_order_quantity(shortCall.symbol, -1)),
-                    abs(self.calculate_order_quantity(longCall.symbol, 1))])
-                if not quantity:
-                    return
-
-                # Bull call spread consists of long a lower-strike call and short a higher-strike call
-                self.market_order(shortCall.symbol, -quantity)
-                self.market_order(longCall.symbol, quantity)
-                
-                expected_margin_usage = max((longCall.strike - shortCall.strike) * self.securities[longCall.symbol].symbol_properties.contract_multiplier * quantity, 0)
-                if expected_margin_usage != self.portfolio.total_margin_used:
-                    raise Exception("Unexpect margin used!")
-
-
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            if security.type == SecurityType.INDEX_OPTION:
-                # Historical data
-                history = self.history(security.symbol, 10, Resolution.MINUTE)
    
-   
    public class IndexOptionsDataAlgorithm : QCAlgorithm
-{
-    private Symbol _optionSymbol;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(200000);
-        // Asynchronous can use computational resources efficiently
-        UniverseSettings.Asynchronous = true;
-        // Filter to get ATM calls expiring in 180 days to form the Bull Call Spread
-        var option = AddIndexOption("SPX");
-        option.SetFilter((u) => u.CallsOnly().Strikes(-2, +2).Expiration(0, 180));
-        _optionSymbol = option.Symbol;
-    }
-    
-    public override void OnData(Slice slice)
-    {
-        if (!Portfolio.Invested && IsMarketOpen(_optionSymbol))
-        {
-            // Make sure getting the updated VIX option chain
-            if (slice.OptionChains.TryGetValue(_optionSymbol, out var chain))
-            {
-                var expiry = chain.Max(x => x.Expiry);
-                var callContracts = chain
-                    .Where(x => x.Expiry == expiry)
-                    .OrderBy(x => x.Strike)
-                    .ToList();
-
-                // Need 2 contracts to form a call spread
-                if (callContracts.Count < 2)
-                {
-                    return;
-                }
-
-                // Obtain 2 call contracts with different strike price to form the call spread
-                var longCall = callContracts.First();
-                var shortCall = callContracts.First(contract => contract.Strike > longCall.Strike);
-
-                // Use all the buying power, but need to ensure the order size of the long and short call are the same
-                var quantity = new[] {
-                    CalculateOrderQuantity(shortCall.Symbol, -1m),
-                    CalculateOrderQuantity(longCall.Symbol, 1m) }
-                    .Min(x=> Math.Abs(x));
-                if (quantity == 0) return;
-
-                // Bull call spread consists of long a lower-strike call and short a higher-strike call
-                MarketOrder(shortCall.Symbol, -quantity);
-                MarketOrder(longCall.Symbol, quantity);
-
-                var expectedMarginUsage = Math.Max((longCall.Strike - shortCall.Strike) * Securities[longCall.Symbol].SymbolProperties.ContractMultiplier * quantity, 0);
-                if (expectedMarginUsage != Portfolio.TotalMarginUsed)
-                {
-                    throw new Exception("Unexpect margin used!");
-                }
-            }
-        }
-    }
-
-    public override void OnSecuritiesChanged(SecurityChanges changes)
-    {
-        foreach (var security in changes.AddedSecurities)
-        {
-            if (security.Type == SecurityType.IndexOption)
-            {
-                // Historical data
-                var history = History(security.Symbol, 10, Resolution.Minute);
-            }
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm buys the VIX Index Option Bull Call Spread with the furthest expiration:
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class IndexOptionsDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(200000)
-        self.universe_settings.asynchronous = True
-        self.set_universe_selection(VIXIndexOptionUniverseSelectionModel())
-
-        self.set_alpha(BullCallSpreadAlphaModel())
-
-        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
-
-
-class VIXIndexOptionUniverseSelectionModel(OptionUniverseSelectionModel):
-
-    def __init__(self):
-        super().__init__(timedelta(1), self.option_chain_symbol_selector)
-    
-    def option_chain_symbol_selector(self, utc_time: datetime) -> List[Symbol]:
-        # Only interest in VIX options
-        return [Symbol.create("VIX", SecurityType.INDEX_OPTION, Market.USA)]
-
-    def filter(self, option_filter_universe: OptionFilterUniverse) -> OptionFilterUniverse:
-        # Filter to get ATM calls expiring in 180 days to form the Bull Call Spread
-        return option_filter_universe.calls_only().strikes(-2, +2).expiration(0, 180)
-
-
-class BullCallSpreadAlphaModel(AlphaModel):
-
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        if algorithm.portfolio.invested:
-            return []
-            
-        for canonical_symbol, chain in slice.option_chains.items():
-            if not chain:
-                return []
-            # Bull call spread legs are having the same expiry                  
-            expiry = max([c.expiry for c in chain])
-            call_contracts = sorted([c for c in chain if c.expiry == expiry],
-                key=lambda c: c.strike)
-
-            # Need 2 contracts to form a call spread
-            if len(call_contracts) < 2:
-                return []
-
-            # Obtain 2 call contracts with different strike price to form the call spread
-            long_call, short_call = call_contracts[0:2]
-            
-            # Bull call spread consists of long a lower-strike call and short a higher-strike call
-            return Insight.group(
-                [
-                    Insight.price(long_call.symbol, expiry + timedelta(1), InsightDirection.UP),
-                    Insight.price(short_call.symbol, expiry + timedelta(1), InsightDirection.DOWN)
-                ])
-        return []
-        
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            if security.type == SecurityType.INDEX_OPTION:
-                # Historical data
-                history = algorithm.history(security.symbol, 10, Resolution.MINUTE)
-
-
-class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
-
-    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
-        if not insights:
-            return []
-        # Only a single contract toi ensure both legs size is equal
-        quantity = min([abs(algorithm.calculate_order_quantity(i.symbol, int(i.direction)))
-            for i in insights])
-    
-        return [PortfolioTarget(i.symbol, quantity * int(i.direction))
-            for i in insights]
    
-   
    public class IndexOptionsDataAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(200000);
-        UniverseSettings.Asynchronous = true;
-        SetUniverseSelection(new VIXIndexOptionUniverseSelectionModel());
-
-        SetAlpha(new BullCallSpreadAlphaModel());
-
-        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
-    }
-}
-
-public class VIXIndexOptionUniverseSelectionModel : OptionUniverseSelectionModel
-{
-    // Only interest in VIX options
-    public VIXIndexOptionUniverseSelectionModel()
-        : base(TimeSpan.FromDays(1), 
-            _ => new[] {QuantConnect.Symbol.Create("VIX", SecurityType.IndexOption, Market.USA)})
-    {
-    }
-
-    protected override OptionFilterUniverse Filter(OptionFilterUniverse optionFilterUniverse)
-    {
-        // Filter to get ATM calls expiring in 180 days to form the Bull Call Spread
-        return optionFilterUniverse.CallsOnly().Strikes(-2, +2).Expiration(0, 180);
-    }
-}
-
-public class BullCallSpreadAlphaModel : AlphaModel
-{
-    public BullCallSpreadAlphaModel()
-    {
-    }
-
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        var insights = Enumerable.Empty<Insight>();
-        if (algorithm.Portfolio.Invested)
-        {
-            return insights;
-        }
-            
-        foreach (var kvp in slice.OptionChains)
-        {
-            var canoncialSymbol = kvp.Key;
-            var chain = kvp.Value;
-            if (chain.IsNullOrEmpty())
-            {
-                return insights;
-            }
-            // Bull call spread legs are having the same expiry
-            var expiry = chain.Max(contract => contract.Expiry);
-            var callContracts = chain
-                .Where(contract => contract.Expiry == expiry)
-                .OrderBy(x => x.Strike)
-                .ToList();
-
-            // Need 2 contracts to form a call spread
-            if (callContracts.Count < 2)
-            {
-                return insights;
-            }
-
-            // Obtain 2 call contracts with different strike price to form the call spread
-            var longCall = callContracts.First();
-            var shortCall = callContracts.First(contract => contract.Strike > longCall.Strike);
-
-            // Bull call spread consists of long a lower-strike call and short a higher-strike call
-            return Insight.Group(
-                Insight.Price(longCall.Symbol, expiry.AddDays(1), InsightDirection.Up),
-                Insight.Price(shortCall.Symbol, expiry.AddDays(1), InsightDirection.Down));
-        }
-        return insights;
-    }
-}
-    
-public class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
-{
-    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
-    {
-        if (insights.Count() == 0)
-        {
-            return Enumerable.Empty<PortfolioTarget>();
-        }
-        // Only a single contract toi ensure both legs size is equal
-        var quantity = insights
-            .Select(i => Math.Abs(algorithm.CalculateOrderQuantity(i.Symbol, (decimal)i.Direction)))
-            .Min();
-
-        return insights.Select(i => new PortfolioTarget(i.Symbol, quantity * (decimal)i.Direction));
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Index Options dataset provides
-   
-    TradeBar
-   
-   ,
-   
-    QuoteBar
-   
-   , and
-   
-    OpenInterest
-   
-   objects.
-  
    
-  
    
-   TradeBar Attributes
-  
    
-  
    
-   
-    TradeBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   QuoteBar Attributes
-  
    
-  
    
-   
-    QuoteBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   OpenInterest Attributes
-  
    
-  
    
-   
-    OpenInterest
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > Morningstar
    
-
    
-    
    
-        
    Datasets
    
-        
    Morningstar
    
-    
    
-
    
-
- 
-  
    
-   
    
-    
    
-     Morningstar was founded by Joe Mansueto in 1984 with the goal of empowering investors by connecting people to the investing information and tools they need. Morningstar provides access extensive line of products and services for individual investors, financial advisors, asset managers, and retirement plan providers. Morningstar provides data on approximately 525,000 investment offerings including stocks, mutual funds, and similar vehicles, along with real-time global market data on nearly 18 million equities, indexes, futures, options, commodities, and precious metals, in addition to foreign exchange and Treasury markets. Morningstar also offers investment management services through its investment advisory subsidiaries, with $244 billion in assets under advisement or management as of 2021.
-    
    
-   
    
-   
    
-    
    
-     
-      
    
-       
    
-        
    
-         US Fundamental Data
-        
    
-       
    
-      
    
-         
-    
    
-   
    
-  
    
- 
-
-
-
     
    
-
    Datasets > Morningstar > US Fundamental Data
    
-
    
-    
    
-        
    Morningstar
    
-        
    US Fundamental Data
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The US Fundamental Data by Morningstar tracks US Equity fundamentals. The data covers 8,000 US Equities, starts in January 1998, and is delivered on a daily frequency. The data does not include ETFs, ADRs, or OTC equities. This dataset is created by using a combination of string matching, Regular Expressions, and Machine Learning to gather the fundamental data published by companies.
-  
    
-  
    
-   For older symbols, the file date is approximated 45 days after the as of date. When a filing date is present on the Morningstar data, it is used. As we are a quant platform, all the data is loaded using "As Original Reported" figures. If there was a mistake reporting the figure, this will not be fixed later. The market typically responds quickly to these initially reported figures. Data is available for multiple periods depending on the property. Periods available include: 1 mo, 2 mo, 3 mo, 6 mo, 9 mo, 12 mo, 1 Yr, 2 Yr, 3 Yr, and 5 Yr. Morningstar symbols cover the NYSE, NASDAQ, AMEX, and BATS exchanges.
-  
    
-  
    
-   In live trading, Morningstar data is delivered to your algorithm at approximately 6 am each day. The majority of the fundamental data update occurs once per month. This includes updates to all of the key information for each security Morningstar supports. On top of this monthly update, there are daily updates of the financial ratios.
-  
    
-  
    
-   As Morningstar data arrives, it updates the master copy and is passed into your algorithm, similar to how TradeBars are fill-forwarded in your data feed. If there have been no updates for a day, you'll receive the same fundamental data.
-  
    
-  
    
-   This dataset depends on the
-   
-    US Equity Security Master
-   
-   dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
-  
    
-  
    
-   QuantConnect/LEAN combines the data of this dataset with
-   
-    US Coarse Universe
-   
-   data in runtime.
-  
    
-  
    
-   For more information about the US Fundamental Data dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   Morningstar was founded by Joe Mansueto in 1984 with the goal of empowering investors by connecting people to the investing information and tools they need. Morningstar provides access extensive line of products and services for individual investors, financial advisors, asset managers, and retirement plan providers. Morningstar provides data on approximately 525,000 investment offerings including stocks, mutual funds, and similar vehicles, along with real-time global market data on nearly 18 million equities, indexes, futures, options, commodities, and precious metals, in addition to foreign exchange and Treasury markets. Morningstar also offers investment management services through its investment advisory subsidiaries, with $244 billion in assets under advisement or management as of 2021.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippets demonstrate how to request data from the US Fundamental dataset:
-  
    
-  
      
-   
    • 
-    Direct Access using the Security object
-   
      • 
-  
    
-  
    
-   
    equity = self.add_equity("IBM")
-ibm_fundamental = equity.fundamentals
    
-   
    var equity = AddEquity("IBM");
-var ibmFundamental = equity.Fundamentals;
    
-  
    
-  
      
-   
    • 
-    Direct Access using the Symbol object
-   
      • 
-  
    
-  
    
-   
    ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
-ibm_fundamental = self.fundamentals(ibm)
    
-   
    var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
-var ibmFundamental = Fundamentals(ibm);
    
-  
    
-  
      
-   
    • 
-    Universe Selection
-   
      • 
-  
    
-  
    
-   
    def initialize(self) -> None:
-    self._universe = self.add_universe(self.fundamental_filter_function)
-
-def fundamental_filter_function(self, fundamental: List[Fundamental]):
-    filtered = [f for f in fundamental if f.has_fundamental_data and f.price > 10 and not np.isnan(f.valuation_ratios.pe_ratio)]
-    sorted_by_pe_ratio = sorted(filtered, key=lambda f: f.valuation_ratios.pe_ratio)
-    return [f.symbol for f in sorted_by_pe_ratio[:10]]
    
-   
    public override void Initialize()
-{
-    _universe = AddUniverseSelection(new FundamentalUniverseSelectionModel(FundamentalFilterFunction));
-}
-
-public override List<Symbol> FundamentalFilterFunction(List<Fundamental> fundamental)
-{
-    return (from f in fundamental
-            where f.HasFundamentalData && f.Price > 10 && !Double.IsNaN(f.ValuationRatios.PERatio)
-            orderby f.ValuationRatios.PERatio
-            select f.Symbol).Take(10);
-}
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 1998
-     
    
-      Asset Coverage
-     
-      8,000 US Equities
-      
-       *
-      
-     
    
-      Corporate Indicators / Tracked Fields
-     
-      900 Fields
-     
    
-      Data Density
-     
-      Sparse
-     
    
-      Resolution
-     
-      Daily
-     
    
-      Timezone
-     
-      New York
-     
    
-  
-   *
-  
-  The data does not include ETFs, ADRs, or OTC equities.
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   You don't need any special code to request US Fundamental Data. You can access the current and historical fundamental data for any of the US Equities that this dataset includes.
-  
    
-  
    
-   
    class MorningStarDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2021, 1, 1)
-        self.set_end_date(2021, 7, 1)
-        self.set_cash(100000) 
-        self.universe_settings.asynchronous = True
-
-        # Option 1: Subscribe to individual US Equity assets
-        self.add_equity("IBM")        
-
-        # Option 2A: Create a fundamental universe (classic version)
-        self._universe = self.add_universe(self.fundamental_function)
-
-        # Option 2B: Create a fundamental universe (framework version)
-        self.add_universe_selection(FundamentalUniverseSelectionModel(self.fundamental_function))
    
-   
    public class MorningStarDataAlgorithm : QCAlgorithm
-{
-    private Universe _universe;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2021, 1, 1);
-        SetEndDate(2021, 7, 1);
-        SetCash(100000);
-        UniverseSettings.Asynchronous = true;
-        
-        // Option 1: Subscribe to individual US Equity assets
-        AddEquity("IBM");
-
-        // Option 2A: Create a fundamental universe (classic version)
-        _universe = AddUniverse(FundamentalFilterFunction);
-
-        // Option 2B: Create a fundamental universe (framework version)
-        AddUniverseSelection(new FundamentalUniverseSelectionModel(FundamentalFilterFunction));
-    }
-}
    
-  
    
-  
    
-   For more information about universe settings, see
-   
-    Settings
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   If you add a
-   
-    fundamental universe
-   
-   to your algorithm, you can access fundamental data in the universe selection function.
-  
    
-  
    
-   
    def fundamental_function(self, fundamental: List[Fundamental]) -> List[Symbol]:
-    return [f.symbol for f in fundamental if f.price > 10 and f.valuation_ratios.pe_ratio > 30]
    
-   
    public IEnumerable<Symbol> FundamentalFunction(IEnumerable<Fundamental> fundamental)
-{
-    return fundamental.Where(f => f.Price > 10 && f.ValuationRatios.PERatios > 30).Select(f => f.Symbol);
-}
    
-  
    
-  
    
-   To get fundamental data for Equities in your algorithm, use the
-   
-    Fundamentals
-   
-   
-    fundamentals
-   
-   property of the
-   
-    Equity
-   
-   objects. The fundamental data represent the corporate fundamentals for the current algorithm time.
-  
    
-  
    
-   
    fundamentals = self.securities[symbol].fundamentals
    
-   
    var fundamentals = Securities[symbol].Fundamentals;
    
-  
    
-  
    
-   To get fundamental data for Equities, regardless of whether or not you have subscribed to them in your algorithm, call the
-   
-    Fundamentals
-   
-   
-    fundamentals
-   
-   method. If you pass one
-   
-    Symbol
-   
-   , the method returns a
-   
-    Fundamental
-   
-   object. If you pass a list of
-   
-    Symbol
-   
-   objects, the method returns a list of
-   
-    Fundamental
-   
-   objects.
-  
    
-  
    
-   
    // Single asset 
-var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
-var ibmFundamental = Fundamentals(ibm);
-
-// Multiple assets
-var nb = QuantConnect.Symbol.Create("NB", SecurityType.Equity, Market.USA);
-var fundamentals = Fundamentals(new List<Symbol>{ nb, ibm }).ToList();
    
-   
    # Single asset
-ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
-ibm_fundamental = self.fundamentals(ibm)
-
-# Multiple assets
-nb = Symbol.create("NB", SecurityType.EQUITY, Market.USA)
-fundamentals = self.fundamentals([ nb, ibm ])
    
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical fundamental data in an algorithm and in the Research Environment.
-  
    
-  
    
-   Historical Data in Algorithms
-  
    
-  
    
-   To get historical fundamental data in an algorithm, call the
-   
-    History
-   
-   
-    history
-   
-   method with
-   
-    Fundamental
-   
-   type and the Equity
-   
-    Symbol
-   
-   or the
-   
-    Universe
-   
-   object. If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
-
-// Fundamental objects
-var fundamentalHistory = History<Fundamental>(ibm, TimeSpan.FromDays(30));
-
-// Fundamentals objects for all US Equities (including delisted companies)
-var fundamentalsHistory = History<Fundamentals>(TimeSpan.FromDays(30));
-
-// Collection of Fundamental objects for all US Equities (including delisted companies)
-var collectionHistory = History(_universe, 30, Resolution.Daily);
-foreach (var fundamental in collectionHistory)
-{
-    // Cast to Fundamental is required
-    var highestMarketCap = fundamental.OfType<Fundamental>().OrderByDescending(x => x.MarketCap).Take(5);
-}
    
-   
    ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
-
-# DataFrame object where the columns are the Fundamental attributes
-asset_df_history = self.history(Fundamental, ibm, timedelta(30), flatten=True)
-
-# Fundamental objects
-fundamental_history= self.history[Fundamental](ibm, timedelta(30))
-
-# Fundamentals objects for all US Equities (including delisted companies)
-fundamentals_history = self.history[Fundamentals](timedelta(30))
-
-# Multi-index Series objects where the values are lists of Fundamental objects
-series_history = self.history(self.universe, 30, Resolution.DAILY)
-for (universe_symbol, time), fundamentals in series_history.items():
-    highest_market_cap = sorted(fundamentals, key=lambda x: x.market_cap)[-5:]
    
-  
    
-  
    
-   Historical Data in the Research Environment
-  
    
-  
    
-   To get historical data in the Research Environment, call any of the preceding methods or call the
-   
-    UniverseHistory
-   
-   
-    universe_history
-   
-   method with the
-   
-    Universe
-   
-   object, a start date, and an end date. This method returns the filtered universe. Alternatively, call the
-   
-    GetFundamental
-   
-   
-    get_fundamental
-   
-   method with the Equity
-   
-    Symbol
-   
-   , a
-   
-    Fundamental
-   
-   property, a start date, and an end date. If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame object where the columns are the Fundamental attributes
-universe_history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
-
-# Multi-index Series objects where the values are lists of Fundamental objects
-universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (universe_symbol, time), fundamentals in universe_history.items():
-    for fundamental in fundamentals:
-        print(f"{fundamental.symbol} market cap at {fundamental.end_time}: {fundamental.market_cap}")
-
-# DataFrame of a single Fundamental attribute
-history = qb.get_fundamental(symbol, "ValuationRatios.pe_ratios", datetime(2021, 1, 1), datetime(2021, 7, 1))
    
-   
    var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-foreach (var fundamentals in universeHistory)
-{
-    foreach (Fundamental fundamental in fundamentals)
-    {
-        Console.WriteLine($"{fundamental.Symbol} market cap at {fundamental.EndTime}: {fundamental.MarketCap}");
-    }
-}
-
-var history = qb.GetFundamental(symbol, "ValuationRatios.PERatios", new DateTime(2021, 1, 1), new DateTime(2021, 7, 1));
    
-  
    
-  
    
-   For more information about historical US Equity fundamental data, see
-   
-    Equity Fundamental Data
-   
-   .
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The US Fundamentals dataset enables you to design strategies harnessing fundamental data points. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Ranking a universe of securities by a value factor like the
-    
-     Piotroski F-score
-    
-    and buying a subset of the universe with the best factor ranking
-   
      • 
-   
    • 
-    Using the Morningstar asset classification to target specific industries or to ensure your strategy is diversified across several sectors
-   
      • 
-   
    • 
-    Trading securities that recently performed an IPO
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm creates a dynamic universe of US Equities. The first stage filter selects the 100 most liquid US Equities and the second stage filter selects the 10 assets with the greatest PE ratio. The algorithm then forms an equal-weighted portfolio with the 10 assets in the universe.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class MorningStarDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000) 
-        # Seed the price of each asset with its last known price to avoid trading errors.
-        self.settings.seed_initial_prices = True
-        self.universe_settings.resolution = Resolution.DAILY
-        self.universe_size = 10
-        # Add universe selection to filter with fundamental data
-        self.add_universe(self.fundamental_selection_function)
-        
-    def fundamental_selection_function(self, fundamental: List[Fundamental]) -> List[Symbol]:
-        # Make sure fundamental data and PE ratio available 
-        # To avoid penny stocks with high volatility, set price above $1
-        selected = [f for f in fundamental if f.has_fundamental_data and f.price > 1 and not np.isnan(f.valuation_ratios.pe_ratio)]
-        # Filter the top 100 high dollar volume equities to ensure liquidity
-        sorted_by_dollar_volume = sorted(selected, key=lambda f: f.dollar_volume, reverse=True)[:100]
-        # Get the top 10 PE Ratio stocks to follow speculations of a large number of capital
-        sorted_by_pe_ratio = sorted(sorted_by_dollar_volume, key=lambda f: f.valuation_ratios.pe_ratio, reverse=True)[:self.universe_size]
-        return [ f.symbol for f in sorted_by_pe_ratio ]
-        
-
-    def on_data(self, slice: Slice) -> None:
-        # if we have no changes, do nothing
-        if self._changes is None or not slice.bars: return
-
-        # liquidate removed securities, as not being the most popular stocks anymore
-        for security in self._changes.removed_securities:
-            if security.invested:
-                self.liquidate(security.symbol)
-
-        # we want 1/N allocation in each security in our universe to evenly dissipate risk
-        for security in self._changes.added_securities:
-            self.set_holdings(security.symbol, 1 / self.universe_size)
-
-        self._changes = None
-           
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
-        self._changes = changes
-
    
-   
    public class MorningStarDataAlgorithm : QCAlgorithm
-{
-    private int _universeSize = 10;
-    private SecurityChanges _changes = SecurityChanges.None;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        // Seed the price of each asset with its last known price to avoid trading errors.
-        Settings.SeedInitialPrices = true;
-        // Requesting data
-        UniverseSettings.Resolution = Resolution.Daily;
-        // Add universe selection to filter with fundamental data
-        AddUniverse(FundamentalSelectionFunction);
-    }
-
-    public IEnumerable<Symbol> FundamentalSelectionFunction(IEnumerable<Fundamental> fundamental)
-    {
-        // Make sure fundamental data and PE ratio available 
-        // To avoid penny stocks with high volatility, set price above $1
-        // Filter the top 100 high dollar volume equities to ensure liquidity
-        // Get the top 10 PE Ratio stocks to follow speculations of a large number of capital
-        return fundamental
-            .Where(x => x.HasFundamentalData && x.Price > 1 && !Double.IsNaN(x.ValuationRatios.PERatio))
-            .OrderByDescending(x => x.DollarVolume)
-            .Take(100)
-            .OrderByDescending(x => x.ValuationRatios.PERatio)
-            .Take(_universeSize)
-            .Select(x => x.Symbol);
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // if we have no changes, do nothing
-        if (_changes == SecurityChanges.None || slice.Bars.Count == 0) return;
-
-        // liquidate removed securities, as not being the most popular stocks anymore
-        foreach (var security in _changes.RemovedSecurities)
-        {
-            if (security.Invested)
-            {
-                Liquidate(security.Symbol);
-            }
-        }
-
-        // we want 1/N allocation in each security in our universe to evenly dissipate risk
-        foreach (var security in _changes.AddedSecurities)
-        {
-            SetHoldings(security.Symbol, 1m / _universeSize);
-        }
-
-        _changes = SecurityChanges.None;
-    }
-    
-    public override void OnSecuritiesChanged(SecurityChanges changes)
-    {
-        _changes = changes;
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm creates a dynamic universe of US Equities. The first stage filter selects the 100 most liquid US Equities and the second stage filter selects the 10 assets with the greatest PE ratio. The algorithm then forms an equal-weighted portfolio with the 10 assets in the universe.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-class MorningStarDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000) 
-
-        # Add universe selection to filter with fundamental data
-        self.universe_settings.resolution = Resolution.DAILY
-        self.set_universe_selection(CustomFundamentalUniverseSelectionModel())
-
-        self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(1)))
-
-        # we want 1/N allocation in each security in our universe to evenly dissipate risk
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
-
-        self.add_risk_management(NullRiskManagementModel())
-
-        self.set_execution(ImmediateExecutionModel())
-
-
-class CustomFundamentalUniverseSelectionModel(FundamentalUniverseSelectionModel):
-
-    def select(self, algorithm: QCAlgorithm, fundamental: List[Fundamental]) -> List[Symbol]:
-        # Make sure fundamental data and PE ratio available 
-        # To avoid penny stocks with high volatility, set price above $1
-        # Filter the top 100 high dollar volume equities to ensure liquidity
-        sorted_by_dollar_volume = sorted([x for x in fundamental if x.price > 1 and not np.isnan(x.valuation_ratios.pe_ratio)],
-            key=lambda x: x.dollar_volume, reverse=True)[:100]
-        # Get the top 10 PE Ratio stocks to follow speculations of a large number of capital
-        sorted_by_pe_ratio = sorted(sorted_by_dollar_volume, key=lambda x: x.valuation_ratios.pe_ratio, reverse=True)[:10]
-
-        # Take the top entries from our sorted collection
-        return [ x.symbol for x in sorted_by_pe_ratio ]
    
-   
    public class MorningStarDataAlgorithm : QCAlgorithm
-{        
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        
-        // Add universe selection to filter with fundamental data
-        UniverseSettings.Resolution = Resolution.Daily;
-        AddUniverseSelection(new CustomFundamentalUniverseSelectionModel());
-        
-        SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
-
-        // we want 1/N allocation in each security in our universe to evenly dissipate risk
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
-        
-        AddRiskManagement(new NullRiskManagementModel());
-        
-        SetExecution(new ImmediateExecutionModel());
-    }
-}
-
-public class CustomFundamentalUniverseSelectionModel : FundamentalUniverseSelectionModel
-{
-    public override IEnumerable<Symbol> Select(QCAlgorithm algorithm, IEnumerable<Fundamental> fundamental)
-    {
-        return fundamental
-            // Make sure fundamental data and PE ratio available 
-            // To avoid penny stocks with high volatility, set price above $1
-            .Where(x => x.HasFundamentalData && x.Price > 1 && !Double.IsNaN(x.ValuationRatios.PERatio))
-            // Filter the top 100 high dollar volume equities to ensure liquidity
-            .OrderByDescending(x => x.DollarVolume)
-            .Take(100)
-            // Get the top 10 PE Ratio stocks to follow speculations of a large number of capital
-            .OrderByDescending(x => x.ValuationRatios.PERatio)
-            .Take(10)
-            .Select(x => x.Symbol);
-    }
-}
    
-  
    
-  
    
-   Research Example
-  
    
-  
    
-   The following example selects the 100 most liquid US Equities and lists the 10 assets with the greatest PE ratio.:
-  
    
-  
    
-   
    var qb = new QuantBook();
-
-// Add Fundamental Universe Selection
-IEnumerable<Symbol> FundamentalSelectionFunction(IEnumerable<Fundamental> fundamentals)
-{
-    return fundamentals
-        .Where(x => !Double.IsNaN(x.ValuationRatios.PERatio))
-        .OrderByDescending(x => x.DollarVolume).Take(100)
-        .OrderByDescending(x => x.ValuationRatios.PERatio).Take(10)
-        .Select(x => x.Symbol);
-}
-
-var universe = qb.AddUniverse(FundamentalSelectionFunction); 
-
-// Historical Universe data
-var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-foreach (var fundamentals in universeHistory)
-{
-    foreach (Fundamental fundamental in fundamentals)
-    {
-        Console.WriteLine($"{fundamental.Symbol} PE ratio at {fundamental.EndTime}: {fundamental.ValuationRatios.PERatio}");
-    }
-}
    
-   
    qb = QuantBook()
-
-# Add Fundamental Universe Selection
-def fundamental_selection_function(fundamentals):
-    selected = [f for f in fundamentals if not np.isnan(f.valuation_ratios.pe_ratio)]
-    sorted_by_dollar_volume = sorted(selected, key=lambda f: f.dollar_volume, reverse=True)[:100]
-
-    sorted_by_pe_ratio = sorted(sorted_by_dollar_volume,
-        key=lambda f: f.valuation_ratios.pe_ratio, reverse=True)[:10]
-    return [ f.symbol for f in sorted_by_pe_ratio ]
-
-universe = qb.add_universe(fundamental_selection_function)
-
-# Historical Universe data
-universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (universe_symbol, time), fundamentals in universe_history.items():
-    for fundamental in fundamentals:
-        print(f"{fundamental.symbol} PE ratio volume at {fundamental.end_time}: {fundamental.valuation_ratios.pe_ratio}")
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The US Fundamentals dataset provides
-   
-    Fundamental
-   
-   objects. To filter
-   
-    Fundamental
-   
-   objects, you can use the
-   
-    MorningstarSectorCode
-   
-   ,
-   
-    MorningstarIndustryGroupCode
-   
-   , and
-   
-    MorningstarIndustryCode
-   
-   enumeration values.
-  
    
-  
    
-   Fundamental Attributes
-  
    
-  
    
-   
-    Fundamental
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   MorningstarSectorCode Enumeration
-  
    
-  
    
-   Sectors are large super categories of data. To access the sector of an Equity, use the
-   
-    MorningstarSectorCode
-   
-   property.
-  
    
-  
    
-   
    filteredFundamentals = fundamental.Where(x =>
-    x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology);
    
-   
    filtered_fundamentals = [x for x in fundamental
-    if x.asset_classification.morningstar_sector_code == MorningstarSectorCode.TECHNOLOGY]
    
-  
    
-  
    
-   The
-   
-    MorningstarSectorCode
-   
-   enumeration has the following members:
-  
    
-  
    
-  
    
-  
    
-   MorningstarIndustryGroupCode Enumeration
-  
    
-  
    
-   Industry groups are clusters of related industries that tie together.  To access the industry group of an Equity, use the
-   
-    MorningstarIndustryGroupCode
-   
-   property.
-  
    
-  
    
-   
    filteredFundamentals = fundamental.Where(x =>
-    x.AssetClassification.MorningstarIndustryGroupCode == MorningstarIndustryGroupCode.Software);
    
-   
    filtered_fundamentals = [x for x in fundamental
-    if x.asset_classification.morningstar_industry_group_code == MorningstarIndustryGroupCode.SOFTWARE]
    
-  
    
-  
    
-   The
-   
-    MorningstarIndustryGroupCode
-   
-   enumeration has the following members:
-  
    
-  
    
-  
    
-  
    
-   MorningstarIndustryCode Enumeration
-  
    
-  
    
-   Industries are the finest classification level available and are the individual industries according to the Morningstar classification system.  To access the industry group of an Equity, use the
-   
-    MorningstarIndustryCode
-   
-   property.
-  
    
-  
    
-   
    filteredFundamentals = fundamental.Where(x =>
-    x.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.SoftwareApplication);
    
-   
    filtered_fundamentals = [x for x in fundamental
-    if x.asset_classification.morningstar_industry_code == MorningstarIndustryCode.SOFTWARE_APPLICATION]
    
-  
    
-  
    
-   The
-   
-    MorningstarIndustryCode
-   
-   enumeration has the following members:
-  
    
-  
    
-  
    
-  
    
-   Exchange ID Values
-  
    
-  
    
-   The exchange ID represents the exchange that lists the Equity. To access the exchange ID of an Equity, use the
-   
-    PrimaryExchangeID
-   
-   property.
-  
    
-  
    
-   
    filteredFundamentals = fundamental.Where(x =>
-    x.CompanyReference.PrimaryExchangeID == "NAS");
    
-   
    filtered_fundamentals = [x for x in fundamental
-    if x.company_reference.primary_exchange_id == "NAS"]
    
-  
    
-  
    
-   The exchanges are represented by the following string values:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
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-    
-    
-     
-     
-    
-   
-  
    
-      String Representation
-     
-      Exchange
-     
    
-      NYS
-     
-      New York Stock Exchange (NYSE)
-     
    
-      NAS
-     
-      NASDAQ
-     
    
-      ASE
-     
-      American Stock Exchange (AMEX)
-     
    
-      TSE
-     
-      Tokyo Stock Exchange
-     
    
-      AMS
-     
-      Amsterdam Internet Exchange
-     
    
-      SGO
-     
-      Santiago Stock Exchange
-     
    
-      XMAD
-     
-      Madrid Stock Exchange
-     
    
-      ASX
-     
-      Australian Securities Exchange
-     
    
-      BVMF
-     
-      B3 (stock exchange)
-     
    
-      LON
-     
-      London Stock Exchange
-     
    
-      TKS
-     
-      Istanbul Stock Exchange Settlement and Custody Bank
-     
    
-      SHG
-     
-      Shanghai Exchange
-     
    
-      LIM
-     
-      Lima Stock Exchange
-     
    
-      FRA
-     
-      Frankfurt Stock Exchange
-     
    
-      JSE
-     
-      Johannesburg Stock Exchange
-     
    
-      MIL
-     
-      Milan Stock Exchange
-     
    
-      TAE
-     
-      Tel Aviv Stock Exchange
-     
    
-      STO
-     
-      Stockholm Stock Exchange
-     
    
-      ETR
-     
-      Deutsche Boerse Xetra Core
-     
    
-      PAR
-     
-      Paris Stock Exchange
-     
    
-      BUE
-     
-      Buenos Aires Stock Exchange
-     
    
-      KRX
-     
-      Korea Exchange
-     
    
-      SWX
-     
-      SIX Swiss Exchange
-     
    
-      PINX
-     
-      Pink Sheets (OTC)
-     
    
-      CSE
-     
-      Canadian Securities Exchange
-     
    
-      PHS
-     
-      Philippine Stock Exchange
-     
    
-      MEX
-     
-      Mexican Stock Exchange
-     
    
-      TAI
-     
-      Taiwan Stock Exchange
-     
    
-      IDX
-     
-      Indonesia Stock Exchange
-     
    
-      OSL
-     
-      Oslo Stock Exchange
-     
    
-      BOG
-     
-      Colombia Stock Exchange
-     
    
-      NSE
-     
-      National Stock Exchange of India
-     
    
-      HEL
-     
-      Nasdaq Helsinki
-     
    
-      MISX
-     
-      Moscow Exchange
-     
    
-      HKG
-     
-      Hong Kong Stock Exchange
-     
    
-      IST
-     
-      Istanbul Stock Exchange
-     
    
-      BOM
-     
-      Bombay Stock Exchange
-     
    
-      TSX
-     
-      Toronto Stock Exchange
-     
    
-      BRU
-     
-      Brussels Stock Exchange
-     
    
-      BATS
-     
-      BATS Global Markets
-     
    
-      ARCX
-     
-      NYSE Arca
-     
    
-      GREY
-     
-      Grey Market (OTC)
-     
    
-      DUS
-     
-      Dusseldorf Stock Exchange
-     
    
-      BER
-     
-      Berlin Stock Exchange
-     
    
-      ROCO
-     
-      Taipei Exchange
-     
    
-      CNQ
-     
-      Canadian Trading and Quotation System Inc.
-     
    
-      BSP
-     
-      Bangko Sentral ng Pilipinas
-     
    
-      NEOE
-     
-      NEO Exchange
-     
    
- 
-
-
-
     
    
-
    Datasets > TickData
    
-
    
-    
    
-        
    Datasets
    
-        
    TickData
    
-    
    
-
    
-
- 
-  
    
-   
    
-    
    
-     
-      TickData
-     
-     was founded by a futures broker and a programmer in 1984 as the first company in the world to offer historical tick-by-tick prices on the futures and index markets. TickData provides access to comprehensive and detailed market data and analytics products in the financial industry covering Equities, Futures, Options, cash FOREX, and Cash Indices.
-    
    
-   
    
-   
    
-    
    
-     
-      
    
-       
    
-        
    
-         International Futures
-        
    
-       
    
-      
    
-         
-    
    
-   
    
-  
    
- 
-
-
-
     
    
-
    Datasets > TickData > International Futures
    
-
    
-    
    
-        
    TickData
    
-        
    International Futures
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The International Futures dataset by TickData provides Futures data, including price, volume, open interest, and expiry. The data covers the 4 contracts, DAX, FESX, HSI, and NKD, starting in July 1998 and is delivered on any frequency from tick to daily. This dataset is created by TickData, which negotiates directly with exchanges for their official archive and partners with real-time data providers with direct exchange connections and multiple redundant ticker plants.
-  
    
-  
    
-   This dataset also depends on the
-   
-    US Futures Security Master
-   
-   because the US Futures Security Master dataset contains information to construct continuous Futures.
-  
    
-  
    
-   For more information about the International Futures dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   
-    TickData
-   
-   was founded by a futures broker and a programmer in 1984 as the first company in the world to offer historical tick-by-tick prices on the futures and index markets. TickData provides access to comprehensive and detailed market data and analytics products in the financial industry covering Equities, Futures, Options, cash FOREX, and Cash Indices.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the International Futures dataset:
-  
    
-  
    
-   
    hsi = self.add_future(Futures.Indices.HANG_SENG, Resolution.MINUTE).symbol           # "HSI"
-dax = self.add_future(Futures.Indices.DAX, Resolution.MINUTE).symbol                 # "DAX"
-fesx = self.add_future(Futures.Indices.EURO_STOXX_50, Resolution.MINUTE).symbol      # "FESX"
-nkd = self.add_future(Futures.Indices.NIKKEI_225_DOLLAR, Resolution.MINUTE).symbol   # "NKD"
    
-   
    var hsi= AddFuture(Futures.Indices.HangSeng, Resolution.Minute).Symbol           // "HSI";
-var dax = AddFuture(Futures.Indices.Dax, Resolution.Minute).Symbol               // "DAX";
-var fesx = AddFuture(Futures.Indices.EuroStoxx50, Resolution.Minute).Symbol      // "FESX";
-var nkd = AddFuture(Futures.Indices.Nikkei225Dollar, Resolution.Minute).Symbol   // "NKD";
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      July 1998, see
-      
-       Supported Assets
-      
-      below for details
-     
    
-      Coverage
-     
-      4 Contracts
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Minute, Hour, & Daily
-     
    
-      Timezone
-     
-      Various, refer to
-      
-       Supported Assets
-      
-      below
-     
    
-      Market Hours
-     
-      
-       Regular and Extended
-      
-     
    
- 
-
-
-
    Volume Discrepancies
    
-
- 
-  For Futures contracts trading in the US, for example, NKD, see
-  
-   US Futures
-  
-  .
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add International Futures data to your algorithm, call the
-   
-    AddFuture
-   
-   
-    add_future
-   
-   method. Save a reference to the Future so you can access the data later in your algorithm.
-  
    
-  
    
-   
    class InternationalFuturesDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2013, 12, 20) 
-        self.set_end_date(2014, 2, 20) 
-        self.set_cash(1000000) 
-        self.universe_settings.asynchronous = True
-        # Request Hang Seng Index Futures data
-        future = self.add_future(Futures.Indices.HANG_SENG) 
-        # Set filter to obtain the contracts expire within 90 days
-        future.set_filter(0, 90)
-        self.future_symbol = future.symbol
    
-   
    public class InternationalFuturesDataAlgorithm : QCAlgorithm
-{
-    private Symbol _futureSymbol;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2013, 12, 20);
-        SetEndDate(2014, 2, 20);
-        SetCash(1000000);
-        UniverseSettings.Asynchronous = true;
-        // Request Hang Seng Index Futures data
-        var future = AddFuture(Futures.Indices.HangSeng);
-        // Set filter to obtain the contracts expire within 90 days
-        future.SetFilter(0, 90);
-        _futureSymbol = future.Symbol;
-    }
-}
    
-  
    
-  
    
-   For more information about creating Future subscriptions, see
-   
-    Requesting Data
-   
-   or
-   
-    Futures Universes
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To get the current International Futures data, index the
-   
-    FuturesChains
-   
-   
-    futures_chains
-   
-   property of the current
-   
-    
-     Slice
-    
-   
-   with the canonical Futures
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your Future at every time step.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Get future chain of the canonical symbol
-    chain = slice.futures_chains.get(self.future_symbol)
-    if chain:
-        # Iterate each contract in the future chain
-        for contract in chain:
-            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
-
    
-   
    public override void OnData(Slice slice)
-{
-    // Get future chain of the canonical symbol
-    if (slice.FuturesChains.TryGetValue(_futureSymbol, out var chain))
-    {
-        // Iterate each contract in the future chain
-        foreach (var contract in chain)
-        {
-            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
-        }
-    }
-}
    
-  
    
-  
    
-   You can also iterate through all of the
-   
-    FuturesChain
-   
-   objects in the current
-   
-    Slice
-   
-   .
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    # Iterate all future chains
-    for canonical_symbol, chain in slice.futures_chains.items():
-        # Iterate each contract in the future chain
-        for contract in chain:
-            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
    
-   
    public override void OnData(Slice slice)
-{
-    // Iterate all future chains
-    foreach (var kvp in slice.FuturesChains)
-    {
-        var canonicalSymbol = kvp.Key;
-        var chain = kvp.Value;
-        // Iterate each contract in the future chain
-        foreach (var contract in chain)
-        {
-            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
-        }
-    }
-}
    
-  
    
-  
    
-   For more information about accessing Futures data, see
-   
-    Handling Data
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   You can get historical International Futures data in an algorithm and the Research Environment.
-  
    
-  
    
-   Historical Data In Algorithms
-  
    
-  
    
-   To get historical International Futures data in an algorithm, call the
-   
-    History
-   
-   
-    history
-   
-   method with the canonical Futures
-   
-    Symbol
-   
-   or a Futures contract
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame objects
-contract_history_df = self.history(contract.symbol, 100, Resolution.MINUTE)
-continuous_history_df = self.history(self.future_symbol,
-    start=self.time - timedelta(days=15), 
-    end=self.time, 
-    resolution=Resolution.MINUTE, 
-    fill_forward=False, 
-    extended_market_hours=False,
-    data_mapping_mode=DataMappingMode.OPEN_INTEREST, 
-    data_normalization_mode=DataNormalizationMode.RAW, 
-    contract_depth_offset=0)
-
-# TradeBar objects
-contract_history_trade_bars = self.history[TradeBar](contract.symbol, 100, Resolution.MINUTE)
-continous_history_trade_bars = self.history[TradeBar](self.future_symbol, 100, Resolution.MINUTE)
-
-# QuoteBar objects
-contract_history_quote_bars = self.history[QuoteBar](contract.symbol, 100, Resolution.MINUTE)
-continous_history_quote_bars = self.history[QuoteBar](self.future_symbol, 100, Resolution.MINUTE)
-
-# Tick objects
-contract_history_ticks = self.history[Tick](contract.symbol, timedelta(seconds=10), Resolution.TICK)
-continous_history_ticks = self.history[Tick](self.future_symbol, timedelta(seconds=10), Resolution.TICK)
    
-   
    // TradeBar objects
-var contractHistoryTradeBars = History(contract.Symbol, 100, Resolution.Minute);
-var continuousHistoryTradeBars = History(
-    symbols: new[] {_futureSymbol}, 
-    start: Time - TimeSpan.FromDays(15),
-    end: Time,
-    resolution: Resolution.Minute,
-    fillForward: false,
-    extendedMarketHours: false,
-    dataMappingMode: DataMappingMode.OpenInterest,
-    dataNormalizationMode: DataNormalizationMode.Raw,
-    contractDepthOffset: 0);
-
-// QuoteBar objects
-var contractHistoryQuoteBars = History<QuoteBar>(contract.Symbol, 100, Resolution.Minute);
-var continuousHistoryQuoteBars = History<QuoteBar>(_futureSymbol, 100, Resolution.Minute);
-
-// Tick objects
-var contractHistoryTicks = History<Tick>(contract.Symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
-var continuousHistoryTicks = History<Tick>(_futureSymbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
-  
    
-  
    
-   For more information about historical data in algorithms, see
-   
-    History Requests
-   
-   . For more information about the price adjustments for continuous contracts, see
-   
-    Continous Contracts
-   
-   .
-  
    
-  
    
-   Historical Data In Research
-  
    
-  
    
-   To get historical International Futures data in the Research Environment for an entire Futures chain, call the
-   
-    FutureHistory
-   
-   
-    future_history
-   
-   method with the canonical Future
-   
-    Symbol
-   
-   .
-  
    
-  
    
-   
    qb = QuantBook()
-future = qb.add_future(Futures.Indices.HANG_SENG) 
-future.set_filter(0, 90)
-history = qb.future_history(future.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5))
-history_df = history.data_frame
-all_history = history.get_all_data()
-expiries = history.get_expiry_dates()
    
-   
    var qb = new QuantBook();
-var future = qb.AddFuture(Futures.Indices.HangSeng);
-future.SetFilter(0, 90);
-var history = qb.FutureHistory(future.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 5));
-    
-var contracts = history.SelectMany(x => x.OptionChains.SelectMany(y => y.Value.Contracts.Keys)).Distinct().ToList();
-var expiries = contracts.Select(x => x.ID.Date).Distinct().ToList();
    
-  
    
-  
    
-   To get historical data for a single International Futures contract or the continuous Futures contract, call the
-   
-    History
-   
-   
-    history
-   
-   method like you would in an algorithm but on the
-   
-    QuantBook
-   
-   object. For more information about historical data in the Research Environment, see
-   
-    Futures
-   
-   .
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   The following table shows the available Futures:
-  
    
-  
-   
-    
-     
-     
-     
-     
-     
-    
-   
-   
-    
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-    
-    
-     
-     
-     
-     
-     
-    
-   
-  
    
-      Ticker
-     
-      Future
-     
-      Start Date
-     
-      Time Zone
-     
-      Currency
-     
    
-      HSI
-     
-      Hang Seng Index Futures
-     
-      Jan 2010
-     
-      Asia/Hong Kong
-     
-      HKD
-     
    
-      DAX
-     
-      German Index Futures
-     
-      Jan 2010
-     
-      Europe/Berlin
-     
-      EUR
-     
    
-      FESX
-     
-      EURO STOXX 50 Index Futures
-     
-      Jul 1998
-     
-      Europe/Berlin
-     
-      EUR
-     
    
-      NKD
-     
-      Nikkei 225 Index Futures
-     
-      Jan 2007
-     
-      America/Chicago
-     
-      USD
-     
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The International Futures dataset enables you to design Futures strategies accurately. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Buying the Futures contract with the most open interest to reduce slippage and market impact
-   
      • 
-   
    • 
-    Trade speculation on an International Index
-   
      • 
-   
    • 
-    Trading bull calendar spreads to reduce volatility and margin requirements
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example -- HSI Futures
-  
    
-  
    
-   The following example algorithm uses
-   
-    ZigZag
-   
-   to determine the trend of Hang Seng Index. To trade the Indices, we make use of the HSI Futures.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class InternationalFuturesDataAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        # Set the time zone to HKT to make it more comparable with the exchange.
-        self.set_time_zone(TimeZones.HONG_KONG)
-        # Set the account currency as HKD to trade HSI Futures.
-        self.set_account_currency("HKD", 1000000)
-        # Seed the last price of the contracts for filling.
-        self.settings.seed_initial_prices = True
-
-        # Request HSI Futures to trade. 
-        # Note that we will trade the contract with the highest open interest for liquidity.
-        self.hsi_future = self.add_future(
-            Futures.Indices.HANG_SENG,
-            extended_market_hours=True,
-            data_mapping_mode=DataMappingMode.LAST_TRADING_DAY,
-            contract_depth_offset=0
-        )
-        # Request the corresponding underlying Index for feeding indicator for trade signal generation.
-        hsi_index = self.add_index("HSI").symbol
-
-        # Create a ZigZag indicator to trade Hang Seng Index price pivot points.
-        self._zz = self.zz(hsi_index, 0.15, 5, Resolution.DAILY)
-        # Warm up indicator for immediate readiness to trade.
-        self.warm_up_indicator(hsi_index, self._zz, Resolution.DAILY)
-
-    def on_data(self, slice: Slice) -> None:
-        # Only place trade if the Future contracts is in market opening hours to avoid stale fills.
-        if self.is_market_open(self.hsi_future.symbol) and self._zz.is_ready:
-            pivot = self._zz.pivot_type
-            # If the last pivot point is a low point, the current trend is increasing after this low point.
-            if pivot == PivotPointType.LOW and not self.portfolio[self.hsi_future.symbol].is_long:
-                self.set_holdings(self.hsi_future.mapped, 0.2)
-            # If the last pivot point is a high point, the current trend is decreasing after this high point.
-            elif pivot == PivotPointType.HIGH and not self.portfolio[self.hsi_future.symbol].is_short:
-                self.set_holdings(self.hsi_future.mapped, -0.2)
-
-        # Handle rollover in case the current mapped contract changes.
-        for _, changed_event in slice.symbol_changed_events.items():
-            old_symbol = changed_event.old_symbol
-            new_symbol = self.add_future_contract(changed_event.new_symbol).symbol
-            quantity = self.portfolio[old_symbol].quantity
-            # Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
-            self.liquidate(old_symbol)
-            if quantity != 0:
-                self.market_order(new_symbol, quantity)
    
-   
    public class InternationalFuturesDataAlgorithm : QCAlgorithm
-{
-    private Future _hsiFuture;
-    private ZigZag _zz;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        // Set the time zone to HKT to make it more comparable with the exchange.
-        SetTimeZone(TimeZones.HongKong);
-        // Set the account currency as HKD to trade HSI Futures.
-        SetAccountCurrency("HKD", 1000000);
-
-        // Seed the last price of the contracts for filling.
-        Settings.SeedInitialPrices = true;
-
-        // Request HSI Futures to trade. 
-        // Note that we will trade the contract with the highest open interest for liquidity.
-        _hsiFuture = AddFuture(
-            Futures.Indices.HangSeng,
-            extendedMarketHours: true,
-            dataMappingMode: DataMappingMode.LastTradingDay,
-            contractDepthOffset: 0
-        );
-        // Request the corresponding underlying index for feeding indicators for trade signal generation.
-        var hsiIndex = AddIndex("HSI").Symbol;
-
-        // Create a ZigZag indicator to trade Hang Seng Index price pivot points.
-        _zz = ZZ(hsiIndex, 0.15m, 5, Resolution.Daily);
-        // Warm up indicator for immediate readiness to trade.
-        WarmUpIndicator(hsiIndex, _zz, Resolution.Daily);
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // Only place trade if the Future contracts is in market opening hours to avoid stale fills.
-        if (IsMarketOpen(_hsiFuture.Symbol) && _zz.IsReady)
-        {
-            var pivot = _zz.PivotType;
-            // If the last pivot point is low, the current trend is increasing after this low point.
-            if (pivot == PivotPointType.Low && !Portfolio[_hsiFuture.Symbol].IsLong)
-            {
-                SetHoldings(_hsiFuture.Mapped, 0.2m);
-            }
-            // If the last pivot point is high, the current trend decreases after this high point.
-            else if (pivot == PivotPointType.High && !Portfolio[_hsiFuture.Symbol].IsShort)
-            {
-                SetHoldings(_hsiFuture.Mapped, -0.2m);
-            }
-        }
-
-        // Handle rollover in case the current mapped contract changes.
-        foreach (var (_, changedEvent) in slice.SymbolChangedEvents)
-        {
-            var oldSymbol = changedEvent.OldSymbol;
-            var newSymbol = AddFutureContract(changedEvent.NewSymbol).Symbol;
-            var quantity = Portfolio[oldSymbol].Quantity;
-            // Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
-            Liquidate(oldSymbol);
-            if (quantity != 0)
-            {
-                MarketOrder(newSymbol, quantity);
-            }
-        }
-    }
-}
    
-  
    
-  
    
-   Classic Algorithm Example -- FESX Futures
-  
    
-  
    
-   The following example algorithm uses the
-   
-    MACD
-   
-   indicator to determine the trend of the EuroStoxx50 Index.  To trade the Index, we make use of the FESX Futures.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class InternationalFuturesDataAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        # Set the time zone to Berlin to make it more comparable with the exchange.
-        self.set_time_zone(TimeZones.BERLIN)
-        # Set the account currency as EUR to trade FSX Futures.
-        self.set_account_currency("EUR", 1000000)
-        # Seed the last price of the contracts for filling.
-        self.settings.seed_initial_prices = True
-
-        # Request FESX Futures to trade. 
-        # Note that we will trade the contract with the highest open interest for liquidity.
-        self.fesx_future = self.add_future(
-            Futures.Indices.EURO_STOXX_50,
-            extended_market_hours=True,
-            data_mapping_mode=DataMappingMode.LAST_TRADING_DAY,
-            contract_depth_offset=0
-        )
-        # Request the corresponding underlying Index for feeding indicator for trade signal generation.
-        fesx_index = self.add_index("SX5E").symbol
-
-        # Create a MACD indicator to trade EuroStoxx trend changes on the fast-slow term trend convergence.
-        self._macd = self.macd(fesx_index, 12, 26, 9, resolution=Resolution.DAILY)
-        # Warm up indicator for immediate readiness to trade.
-        self.warm_up_indicator(fesx_index, self._macd, Resolution.DAILY)
-
-    def on_data(self, slice: Slice) -> None:
-        # Only place trade if the Future contracts is in market opening hours to avoid stale fills.
-        if self.is_market_open(self.fesx_future.symbol) and self._macd.is_ready:
-            # To identify a sensitive change in trend, use the change in the histogram value of the MACD.
-            histogram = self._macd.histogram
-            histogram_chg = histogram.current.value - histogram.previous.value
-            # Positive histogram value increasing means the fast trend is rising faster than the slow trend, indicating a strong uptrend.
-            if histogram_chg > 0 and histogram.current.value > 0 and not self.portfolio[self.fesx_future.symbol].is_long:
-                self.set_holdings(self.fesx_future.mapped, 0.2)
-            # Negative histogram value decreasing means the slow trend is dropping faster than the fast trend, indicating a strong downtrend.
-            elif histogram_chg < 0 and histogram.current.value < 0 and not self.portfolio[self.fesx_future.symbol].is_short:
-                self.set_holdings(self.fesx_future.mapped, -0.2)
-
-        # Handle rollover in case the current mapped contract changes.
-        for _, changed_event in slice.symbol_changed_events.items():
-            old_symbol = changed_event.old_symbol
-            new_symbol = self.add_future_contract(changed_event.new_symbol).symbol
-            quantity = self.portfolio[old_symbol].quantity
-            # Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
-            self.liquidate(old_symbol)
-            if quantity != 0:
-                self.market_order(new_symbol, quantity)
    
-   
    public class InternationalFuturesDataAlgorithm : QCAlgorithm
-{
-    private Future _fesxFuture;
-    private MovingAverageConvergenceDivergence _macd;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        // Set the time zone to Berlin to make it more comparable with the exchange.
-        SetTimeZone(TimeZones.Berlin);
-        // Set the account currency as EUR to trade FSX Futures.
-        SetAccountCurrency("EUR", 1000000);
-        // Seed the last price of the contracts for filling.
-        Settings.SeedInitialPrices = true;
-
-        // Request FESX Futures to trade. 
-        // Note that we will trade the contract with the highest open interest for liquidity.
-        _fesxFuture = AddFuture(
-            Futures.Indices.EuroStoxx50,
-            extendedMarketHours: true,
-            dataMappingMode: DataMappingMode.LastTradingDay,
-            contractDepthOffset: 0
-        );
-        // Request the corresponding underlying Index for feeding indicator for trade signal generation.
-        var fesxIndex = AddIndex("SX5E").Symbol;
-
-        // Create a MACD indicator to trade EuroStoxx trend changes on the fast-slow term trend convergence.
-        _macd = MACD(fesxIndex, 12, 26, 9, resolution: Resolution.Daily);
-        // Warm up indicator for immediate readiness to trade.
-        WarmUpIndicator(fesxIndex, _macd, Resolution.Daily);
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // Only place trade if the Future contracts is in market opening hours to avoid stale fills.
-        if (IsMarketOpen(_fesxFuture.Symbol) && _macd.IsReady)
-        {
-            // To identify a sensitive change in trend, use the change in the histogram value of the MACD.
-            var histogram = _macd.Histogram;
-            var histogramChg = histogram - histogram.Previous;
-            // Positive histogram value increasing means the fast trend is rising faster than the slow trend, indicating a strong uptrend.
-            if (histogramChg > 0 && histogram > 0 && !Portfolio[_fesxFuture.Symbol].IsLong)
-            {
-                SetHoldings(_fesxFuture.Mapped, 0.2m);
-            }
-            // Negative histogram value decreasing means the slow trend is dropping faster than the fast trend, indicating a strong downtrend.
-            else if (histogramChg < 0 && histogram < 0 && !Portfolio[_fesxFuture.Symbol].IsShort)
-            {
-                SetHoldings(_fesxFuture.Mapped, -0.2m);
-            }
-        }
-
-        // Handle rollover in case the current mapped contract changes.
-        foreach (var (_, changedEvent) in slice.SymbolChangedEvents)
-        {
-            var oldSymbol = changedEvent.OldSymbol;
-            var newSymbol = AddFutureContract(changedEvent.NewSymbol).Symbol;
-            var quantity = Portfolio[oldSymbol].Quantity;
-            // Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
-            Liquidate(oldSymbol);
-            if (quantity != 0)
-            {
-                MarketOrder(newSymbol, quantity);
-            }
-        }
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The International Futures dataset provides
-   
-    Future
-   
-   ,
-   
-    FuturesChain
-   
-   , and
-   
-    OpenInterest
-   
-   objects. To configure the continuous Future settings, use the
-   
-    DataNormalizationMode
-   
-   and
-   
-    DataMappingMode
-   
-   enumerations.
-  
    
-  
    
-   Future Attributes
-  
    
-  
    
-   
-    Future
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   FuturesChain Attributes
-  
    
-  
    
-   
-    FuturesChain
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   OpenInterest Attributes
-  
    
-  
    
-   
-    OpenInterest
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   DataNormalizationMode Values
-  
    
-  
    
-   The
-   
-    DataNormalizationMode
-   
-   enumeration has the following values:
-  
    
-  
    
-  
    
-  
    
-   DataMappingMode Values
-  
    
-  
    
-   The
-   
-    DataMappingMode
-   
-   enumeration has the following values:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > CoinAPI
    
-
    
-    
    
-        
    Datasets
    
-        
    CoinAPI
    
-    
    
-
    
-
- 
-  
    
-   
    
-    
    
-     
-      CoinAPI
-     
-     was founded by Artur Pietrzyk in 2016 with the goal of providing real-time and historical cryptocurrency market data, collected from hundreds of exchanges. CoinAPI provides access to Cryptocurrencies for traders, market makers, and developers building third-party applications.
-    
    
-   
    
-   
    
-    
    
-     
-      
    
-       
    
-        
    
-         Binance Crypto Future Price Data
-        
    
-       
    
-      
    
-         
-    
    
-    
    
-     
-      
    
-       
    
-        
    
-         Binance Crypto Price Data
-        
    
-       
    
-      
    
-         
-    
    
-    
    
-     
-      
    
-       
    
-        
    
-         Binance US Crypto Price Data
-        
    
-       
    
-      
    
-         
-    
    
-    
    
-     
-      
    
-       
    
-        
    
-         Bitfinex Crypto Price Data
-        
    
-       
    
-      
    
-         
-    
    
-    
    
-     
-      
    
-       
    
-        
    
-         Bybit Crypto Future Price Data
-        
    
-       
    
-      
    
-         
-    
    
-    
    
-     
-      
    
-       
    
-        
    
-         Bybit Crypto Price Data
-        
    
-       
    
-      
    
-         
-    
    
-    
    
-     
-      
    
-       
    
-        
    
-         Coinbase Crypto Price Data
-        
    
-       
    
-      
    
-         
-    
    
-    
    
-     
-      
    
-       
    
-        
    
-         Kraken Crypto Price Data
-        
    
-       
    
-      
    
-         
-    
    
-   
    
-  
    
- 
-
-
-
     
    
-
    Datasets > CoinAPI > Binance Crypto Future Price Data
    
-
    
-    
    
-        
    CoinAPI
    
-        
    Binance Crypto Future Price Data
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The Binance Crypto Future Price Data by CoinAPI is for Crypto-currency futures price and volume data points. The data covers 718 Cryptocurrency pairs, starts in August 2020, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Binance.
-  
    
-  
    
-   The
-   
-    Binance Crypto Future Margin Rate Data
-   
-   dataset provides margin interest data to model margin costs.
-  
    
-  
    
-   For more information about the Binance Crypto Future Price Data dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   
-    CoinAPI
-   
-   was founded by Artur Pietrzyk in 2016 with the goal of providing real-time and historical cryptocurrency market data, collected from hundreds of exchanges. CoinAPI provides access to Cryptocurrencies for traders, market makers, and developers building third-party applications.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the Binance Crypto Future Price dataset:
-  
    
-  
    
-   
    def initialize(self) -> None:
-    self.set_brokerage_model(BrokerageName.BINANCE_FUTURES, AccountType.MARGIN)
-    #self.set_brokerage_model(BrokerageName.BINANCE_COIN_FUTURES, AccountType.MARGIN)
-
-    self.crypto_future_symbol = self.add_crypto_future("BTCBUSD", Resolution.MINUTE).symbol
-
    
-   
    private Symbol _cryptoFutureSymbol;
-
-public override void Initialize
-{
-    SetBrokerageModel(BrokerageName.BinanceFutures, AccountType.Margin);
-    //SetBrokerageModel(BrokerageName.BinanceCoinFutures, AccountType.Margin);
-
-    // perpetual futures does not have a filter function
-    _cryptoFutureSymbol = AddCryptoFuture("BTCBUSD", Resolution.Minute).Symbol;
-}
-
    
-  
    
- 
-
-
-
    Data Summary
    
-
- 
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      August 2020
-     
    
-      Asset Coverage
-     
-      718 Crypto Futures Pairs
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Tick, Second, Minute, Hourly, & Daily
-     
    
-      Timezone
-     
-      UTC
-     
    
-      Market Hours
-     
-      
-       Always Open
-      
-     
    
- 
-
-
-
    Requesting Data
    
-
- 
-  
    
-   To add Binance Crypto Future Price data to your algorithm, call the
-   
-    AddCryptoFuture
-   
-   
-    add_crypto_future
-   
-   method. Save a reference to the Crypto Future
-   
-    Symbol
-   
-   so you can access the data later in your algorithm.
-  
    
-  
    
-   
    class CoinAPIDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2020, 6, 1)
-        self.set_end_date(2021, 6, 1)
-
-        # Set Account Currency to Binance Stable Coin for USD
-        self.set_account_currency("BUSD")
-        self.set_cash(100000)
-
-        self.set_brokerage_model(BrokerageName.BINANCE_FUTURES, AccountType.MARGIN)
-        self.set_brokerage_model(BrokerageName.BINANCE_COIN_FUTURES, AccountType.MARGIN)
-        
-        crypto_future = self.add_crypto_future("BTCBUSD", Resolution.MINUTE)
-        # perpetual futures does not have a filter function
-        self.btcbusd = crypto_future.symbol
    
-   
    public class CoinAPIDataAlgorithm : QCAlgorithm
-{
-    private Symbol _symbol ;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2020, 6, 1);
-        SetEndDate(2021, 6, 1);
-
-        // Set Account Currency to Binance Stable Coin for USD
-        SetAccountCurrency("BUSD");
-        SetCash(100000);
-
-        SetBrokerageModel(BrokerageName.BinanceFutures, AccountType.Margin);
-        SetBrokerageModel(BrokerageName.BinanceCoinFutures, AccountType.Margin);
-        
-        var cryptoFuture = AddCryptoFuture("BTCBUSD", Resolution.Minute);
-        // perpetual futures does not have a filter function
-        _symbol = cryptoFuture.Symbol;
-    }
-}
    
-  
    
-  
    
-   For more information about creating Crypto Future subscriptions, see
-   
-    Requesting Data
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To get the current Binance Crypto Future Price data, index the
-   
-    Bars
-   
-   
-    bars
-   
-   ,
-   
-    QuoteBars
-   
-   
-    quote_bars
-   
-   , or
-   
-    Ticks
-   
-   
-    ticks
-   
-   properties of the current
-   
-    
-     Slice
-    
-   
-   with the Crypto Future
-   
-    Symbol
-   
-   .
-   
-    Slice
-   
-   objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your security at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    if self.btcbusd in slice.bars:
-        trade_bar = slice.bars[self.btcbusd]
-        self.log(f"{self.btcbusd} close at {slice.time}: {trade_bar.close}")
-
-    if self.btcbusd in slice.quote_bars:
-        quote_bar = slice.quote_bars[self.btcbusd]
-        self.log(f"{self.btcbusd} bid at {slice.time}: {quote_bar.bid.close}")
-
-    if self.btcbusd in slice.ticks:
-        ticks = slice.ticks[self.btcbusd]
-        for tick in ticks:
-            self.log(f"{self.btcbusd} price at {slice.time}: {tick.price}")
    
-   
    public override void OnData(Slice slice)
-{
-    if (slice.Bars.ContainsKey(_symbol))
-    {
-        var tradeBar = slice.Bars[_symbol];
-        Log($"{_symbol} price at {slice.Time}: {tradeBar.Close}");
-    }
-
-    if (slice.QuoteBars.ContainsKey(_symbol))
-    {
-        var quoteBar = slice.QuoteBars[_symbol];
-        Log($"{_symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
-    }
-
-    if (slice.Ticks.ContainsKey(_symbol))
-    {
-        var ticks = slice.Ticks[_symbol];
-        foreach (var tick in ticks)
-        {
-            Log($"{_symbol} price at {slice.Time}: {tick.Price}");
-        }
-    }
-}
-
    
-  
    
-  
    
-   You can also iterate through all of the data objects in the current
-   
-    Slice
-   
-   .
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    for symbol, trade_bar in slice.bars.items():
-        self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")
-
-    for symbol, quote_bar in slice.quote_bars.items():
-        self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
-
-    for symbol, ticks in slice.ticks.items():
-        for tick in ticks:
-            self.log(f"{symbol} price at {slice.time}: {tick.price}")
    
-   
    public override void OnData(Slice slice)
-{
-    foreach (var kvp in slice.Bars)
-    {
-        var symbol = kvp.Key;
-        var tradeBar = kvp.Value;
-        Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
-    }
-
-    foreach (var kvp in slice.QuoteBars)
-    {
-        var symbol = kvp.Key;
-        var quoteBar = kvp.Value;
-        Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
-    }
-
-    foreach (var kvp in slice.Ticks)
-    {
-        var symbol = kvp.Key;
-        var ticks = kvp.Value;
-        foreach (var tick in ticks)
-        {
-            Log($"{symbol} price at {slice.Time}: {tick.Price}");
-        }
-    }
-}
-
    
-  
    
-  
    
-   For more information about accessing Crypto Future data, see
-   
-    Handling Data
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   To get historical Binance Crypto Future Price data, call the
-   
-    History
-   
-   
-    history
-   
-   method with the Crypto Future
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame
-history_df = self.history(self.btcbusd, 100, Resolution.DAILY)
-
-# TradeBar objects
-history_trade_bars = self.history[TradeBar](self.btcbusd, 100, Resolution.MINUTE)
-
-# QuoteBar objects
-history_quote_bars = self.history[QuoteBar](self.btcbusd, 100, Resolution.MINUTE)
-
-# Tick objects
-history_ticks = self.history[Tick](self.btcbusd, timedelta(seconds=10), Resolution.TICK)
    
-   
    // TradeBar objects 
-var historyTradeBars = History(_symbol, 100, Resolution.Daily);
-
-// QuoteBar objects 
-var historyQuoteBars = History<QuoteBar>(_symbol, 100, Resolution.Minute);
-
-// Tick objects 
-var historyTicks = History<Tick>(_symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
-  
    
-  
    
-   For more information about historical data, see
-   
-    History Requests
-   
-   .
-  
    
- 
-
-
-
    Remove Subscriptions
    
-
- 
-  
    
-   To unsubscribe from a Crypto Future contract that you added with the
-   
-    AddCryptoFuture
-   
-   
-    add_crypto_future
-   
-   method, call the
-   
-    RemoveSecurity
-   
-   
-    remove_security
-   
-   method.
-  
    
-  
    
-   
    self.remove_security(self.btcbusd)
    
-   
    RemoveSecurity(_symbol);
    
-  
    
-  
    
-   The
-   
-    RemoveSecurity
-   
-   
-    remove_security
-   
-   method cancels your open orders for the security and liquidates your Crypto Future holdings.
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  
    
-   The following table shows the available Crypto Future pairs:
-  
    
-  
    
-   
-    
-     
-      
-     
-    
-    
-     
-      
-      
-      
-      
-      
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-     
-     
-      
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-      
-      
-      
-      
-     
-     
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-      
-      
-      
-      
-      
-     
-     
-      
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-      
-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
-      
-      
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-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
-      
-      
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-      
-      
-     
-     
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-      
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-     
-     
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-     
-     
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-     
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-     
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-     
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-     
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-     
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-     
-     
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-      
-     
-     
-      
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-     
-     
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-     
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-     
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-     
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-     
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-     
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-     
-     
-      
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-     
-     
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-     
-     
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-      
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-     
-     
-      
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-      
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-     
-     
-      
-      
-      
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-      
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-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
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-      
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-      
-     
-     
-      
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-      
-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
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-     
-     
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-     
-     
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-     
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-     
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-     
-     
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-     
-     
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-     
-     
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-      
-     
-     
-      
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-      
-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
-      
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-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
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-      
-      
-     
-     
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-      
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-      
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-     
-     
-      
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-      
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-     
-     
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-      
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-      
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-     
-     
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-     
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-     
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-     
-     
-      
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-      
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-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
+      
-     
-    
-   
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The Binance Crypto Future Price dataset enables you to accurately design strategies for Cryptocurrencies with term structure. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Horizontal/Diagonal arbitrage with the underlying cryptocurrencies
-   
      • 
-   
    • 
-    Trade Contango/Backwardation predictions
-   
      • 
-   
    • 
-    Hedge for illiquid cryptocurrencies
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm buys BTCUSDT perpetual future contract if the last day's close price was close to ask close price than bid close price, sells short of that in opposite, through the Binance exchange:
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class BinanceCryptoFutureDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        # Set the account currency to USDT, since you can't trade with 
-        # USD and the algorithm doesn't automatically convert USD & USDT
-        # holdings.
-        self.set_account_currency("USDT", 100000)
-        # Binance Futures Exchange accepts both Cash and Margin account 
-        # types. Select the one you need for the best reality modeling.
-        self.set_brokerage_model(BrokerageName.BINANCE_FUTURES, AccountType.MARGIN)
-        # Add BTCUSDT data from the Binance Future exchange.
-        # Perpetual futures does not have a filter function.
-        self._btc = self.add_crypto_future("BTCUSDT", Resolution.DAILY)
-        # Historical data
-        history = self.history(self._btc, 10, Resolution.DAILY)
-
-    def on_data(self, slice: Slice) -> None:
-        # Trade only based on updated price data.
-        if self._btc.symbol not in slice.bars or self._btc.symbol not in slice.quote_bars:
-            return
-        # Scalp-trade the bid-ask spread based on the supply-demand strength
-        if self._btc.price - self._btc.bid_price > self._btc.ask_price - self._btc.price:
-            self.set_holdings(self._btc, -0.5)
-        else:
-            self.set_holdings(self._btc, 1)
    
-   
    public class BinanceCryptoFutureDataAlgorithm : QCAlgorithm
-{
-    public CryptoFuture _btc;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        // Set the account currency to USDT, since you can't trade with 
-        // USD and the algorithm doesn't automatically convert USD & USDT
-        // holdings.
-        SetAccountCurrency("USDT", 100000);
-        // Binance Futures Exchange accepts both Cash and Margin account 
-        // types. Select the one you need for the best reality modeling.
-        SetBrokerageModel(BrokerageName.BinanceFutures, AccountType.Margin);
-        // Add BTCUSDT data from the Binance Future exchange.
-        // Perpetual futures does not have a filter function.
-        _btc = AddCryptoFuture("BTCUSDT", Resolution.Daily);
-        // Historical data
-        var history = History(_btc, 10, Resolution.Daily);
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // Trade only based on updated price data.
-        if (!slice.Bars.ContainsKey(_btc) || !slice.QuoteBars.ContainsKey(_btc))
-        {
-            return;
-        }
-        // Scalp-trade the bid-ask spread based on the supply-demand strength
-        if (_btc.Price - _btc.BidPrice > _btc.AskPrice - _btc.Price)
-        {
-            SetHoldings(_btc, -0.5m);
-        }
-        else
-        {
-            SetHoldings(_btc, 1m);
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm hold a long BTCUSDT Future portfolio if the last day's close price was close to ask close price than bid close price, while hold short of that in opposite, through the Binance exchange using the
-   
-    algorithm framework
-   
-   implementation:
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class BinanceCryptoFutureDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        # Set the account currency to USDT, since you can't trade with 
-        # USD and the algorithm doesn't automatically convert USD & USDT
-        # holdings.
-        self.set_account_currency("USDT", 100000)
-        # Binance Futures Exchange accepts both Cash and Margin account types, select the one you need for the best reality modeling.
-        self.set_brokerage_model(BrokerageName.BINANCE_FUTURES, AccountType.MARGIN)
-        self.universe_settings.resolution = Resolution.DAILY
-        self.universe_settings.leverage = 2
-        # We only trade on BTCUSDT Future in Binance Future exchange
-        symbols = Symbol.create("BTCUSDT", SecurityType.CRYPTO_FUTURE, Market.BINANCE)
-        self.add_universe_selection(ManualUniverseSelectionModel(symbols))
-        # Custom alpha model to emit insights based on the Crypto Future price data
-        self.add_alpha(CryptoFutureAlphaModel())
-        # Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
-        self.set_execution(ImmediateExecutionModel())
-
-class CryptoFutureAlphaModel(AlphaModel):
-
-    def __init__(self) -> None:
-        self.symbols = []
-
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        insights = []
-
-        for symbol in self.symbols:
-            # Trade only based on updated price data
-            if not slice.bars.contains_key(symbol) or not slice.quote_bars.contains_key(symbol):
-                continue
-
-            quote = slice.quote_bars[symbol]
-            price = slice.bars[symbol].price
-            
-            # Scalp-trade the bid-ask spread based on the supply-demand strength
-            if price - quote.bid.close > quote.ask.close - price:
-                direction = InsightDirection.DOWN
-            else:
-                direction = InsightDirection.UP
-            insights.append(Insight.price(symbol, timedelta(1), direction))
-            
-        return insights
-
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            symbol = security.symbol
-            self.symbols.append(symbol)
-
-            # Historical data
-            history = algorithm.history(symbol, 10, Resolution.DAILY)
-
-        for security in changes.removed_securities:
-            symbol = security.symbol
-            if symbol in self.symbols:
-                self.symbols.remove(symbol)
-
    
-   
    public class BinanceCryptoFutureDataAlgorithm : QCAlgorithm
-{
-    public Symbol _symbol;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        // Set the account currency to USDT, since you can't trade with 
-        // USD and the algorithm doesn't automatically convert USD & USDT
-        // holdings.
-        SetAccountCurrency("USDT", 100000);
-        // Binance Futures Exchange accepts both Cash and Margin account types, select the one you need for the best reality modeling.
-        SetBrokerageModel(BrokerageName.BinanceFutures, AccountType.Margin);
-        UniverseSettings.Resolution = Resolution.Daily;
-        UniverseSettings.Leverage = 2;
-        // We only trade on BTCUSDT Future in Binance Future exchange
-        var symbols = new List<Symbol> {
-            QuantConnect.Symbol.Create("BTCUSDT", SecurityType.CryptoFuture, Market.Binance)
-        };
-        AddUniverseSelection(new ManualUniverseSelectionModel(symbols));
-        // Custom alpha model to emit insights based on the Crypto Future price data
-        AddAlpha(new CryptoFutureAlphaModel());
-        // Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
-        SetExecution(new ImmediateExecutionModel());
-    }
-}
-
-public class CryptoFutureAlphaModel : AlphaModel
-{
-    private List<Symbol> _symbols = new();
-
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        var insights = new List<Insight>();
-
-        foreach (var symbol in _symbols)
-        {
-            // Trade only based on updated price data
-            if (!slice.Bars.ContainsKey(symbol) || !slice.QuoteBars.TryGetValue(symbol, out var quote))
-            {
-                continue;
-            }
-            var price = slice.Bars[symbol].Price;
-            
-            // Scalp-trade the bid-ask spread based on the supply-demand strength
-            InsightDirection direction;
-            if (price - quote.Bid.Close > quote.Ask.Close - price)
-            {
-                direction = InsightDirection.Down;
-            }
-            else
-            {
-                direction = InsightDirection.Up;
-            }
-            insights.Add(Insight.Price(symbol, TimeSpan.FromDays(1), direction));
-        }
-        return insights;
-    }
-
-    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-    {
-        foreach (var security in changes.AddedSecurities)
-        {
-            var symbol = security.Symbol;
-            _symbols.Add(symbol);
-
-            // Historical data
-            var history = algorithm.History(symbol, 10, Resolution.Daily);
-        }
-
-        foreach (var security in changes.RemovedSecurities)
-        {
-            _symbols.Remove(security.Symbol);
-        }
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The Binance Crypto Future Price dataset provides
-   
-    TradeBar
-   
-   ,
-   
-    QuoteBar
-   
-   , and
-   
-    Tick
-   
-   objects.
-  
    
-  
    
-   TradeBar Attributes
-  
    
-  
    
-   
-    TradeBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   QuoteBar Attributes
-  
    
-  
    
-   
-    QuoteBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   Tick Attributes
-  
    
-  
    
-   
-    Tick
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > CoinAPI > Binance Crypto Price Data
    
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    CoinAPI
    
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    Binance Crypto Price Data
    
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-
    Introduction
    
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- 
-  
    
-   The Binance Crypto Price Data by CoinAPI is for Cryptocurrency price and volume data points. The data covers 3,320 Cryptocurrency pairs, starts in July 2017, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Binance.
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-  
    
-   For more information about the Binance Crypto Price Data dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
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-
-
    About the Provider
    
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- 
-  
    
-   
-    CoinAPI
-   
-   was founded by Artur Pietrzyk in 2016 with the goal of providing real-time and historical cryptocurrency market data, collected from hundreds of exchanges. CoinAPI provides access to Cryptocurrencies for traders, market makers, and developers building third-party applications.
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-
    Getting Started
    
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- 
-  
    
-   The following snippet demonstrates how to request data from the Binance Crypto Price dataset:
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-   
    # Binance accepts both Cash and Margin account types only.
-self.set_brokerage_model(BrokerageName.BINANCE, AccountType.CASH)
-#self.set_brokerage_model(BrokerageName.BINANCE, AccountType.MARGIN)
-
-self.btcbusd = self.add_crypto("BTCBUSD", Resolution.MINUTE, Market.BINANCE).symbol
-
-self._universe = self.add_universe(CryptoUniverse.binance(self.universe_selection_filter))
-
    
-   
    // Binance accepts both Cash and Margin account types only.
-SetBrokerageModel(BrokerageName.Binance, AccountType.Cash);
-//SetBrokerageModel(BrokerageName.Binance, AccountType.Margin);
-
-_symbol = AddCrypto("BTCBUSD", Resolution.Minute, Market.Binance).Symbol;
-
-_universe = AddUniverse(CryptoUniverse.Binance(UniverseSelectionFilter));
    
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-
-
    Data Summary
    
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- 
-  
    
-   The following table describes the dataset properties:
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-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
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-     
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-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      July 2017
-     
    
-      Asset Coverage
-     
-      3,320 Currency Pairs
-     
    
-      Data Density
-     
-      Dense
-     
    
-      Resolution
-     
-      Tick, Second, Minute, Hourly, & Daily
-     
    
-      Timezone
-     
-      UTC
-     
    
-      Market Hours
-     
-      
-       Always Open
-      
-     
    
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    Requesting Data
    
-
- 
-  
    
-   To add Binance Crypto Price data to your algorithm, call the
-   
-    AddCrypto
-   
-   
-    add_crypto
-   
-   method. Save a reference to the Crypto
-   
-    Symbol
-   
-   so you can access the data later in your algorithm.
-  
    
-  
    
-   
    class CoinAPIDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2020, 6, 1)
-        self.set_end_date(2021, 6, 1)
-
-        # Set Account Currency to Binance Stable Coin for USD
-        self.set_account_currency("BUSD")
-        self.set_cash(100000)
-
-        # Binance accepts both Cash and Margin account types.
-        self.set_brokerage_model(BrokerageName.BINANCE, AccountType.MARGIN)
-        
-        self.btcbusd = self.add_crypto("BTCBUSD", Resolution.MINUTE, Market.BINANCE).symbol
    
-   
    public class CoinAPIDataAlgorithm : QCAlgorithm
-{
-    private Symbol _symbol;
-    
-    public override void Initialize()
-    {
-        SetStartDate(2020, 6, 1);
-        SetEndDate(2021, 6, 1);
-
-        // Set Account Currency to Binance Stable Coin for USD
-        SetAccountCurrency("BUSD");
-        SetCash(100000);
-
-        // Binance accepts both Cash and Margin account types.
-        SetBrokerageModel(BrokerageName.Binance, AccountType.Margin);
-        
-        _symbol = AddCrypto("BTCBUSD", Resolution.Minute, Market.Binance).Symbol;
-    }
-}
    
-  
    
-  
    
-   For more information about creating Crypto subscriptions, see
-   
-    Requesting Data
-   
-   .
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To get the current Binance Crypto Price data, index the
-   
-    Bars
-   
-   
-    bars
-   
-   ,
-   
-    QuoteBars
-   
-   
-    quote_bars
-   
-   , or
-   
-    Ticks
-   
-   
-    ticks
-   
-   properties of the current
-   
-    
-     Slice
-    
-   
-   with the Crypto
-   
-    Symbol
-   
-   .
-   
-    Slice
-   
-   objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your security at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    if self.btcbusd in slice.bars:
-        trade_bar = slice.bars[self.btcbusd]
-        self.log(f"{self.btcbusd} close at {slice.time}: {trade_bar.close}")
-
-    if self.btcbusd in slice.quote_bars:
-        quote_bar = slice.quote_bars[self.btcbusd]
-        self.log(f"{self.btcbusd} bid at {slice.time}: {quote_bar.bid.close}")
-
-    if self.btcbusd in slice.ticks:
-        ticks = slice.ticks[self.btcbusd]
-        for tick in ticks:
-            self.log(f"{self.btcbusd} price at {slice.time}: {tick.price}")
    
-   
    public override void OnData(Slice slice)
-{
-    if (slice.Bars.ContainsKey(_symbol))
-    {
-        var tradeBar = slice.Bars[_symbol];
-        Log($"{_symbol} price at {slice.Time}: {tradeBar.Close}");
-    }
-
-    if (slice.QuoteBars.ContainsKey(_symbol))
-    {
-        var quoteBar = slice.QuoteBars[_symbol];
-        Log($"{_symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
-    }
-
-    if (slice.Ticks.ContainsKey(_symbol))
-    {
-        var ticks = slice.Ticks[_symbol];
-        foreach (var tick in ticks)
-        {
-            Log($"{_symbol} price at {slice.Time}: {tick.Price}");
-        }
-    }
-}
-
    
-  
    
-  
    
-   You can also iterate through all of the data objects in the current
-   
-    Slice
-   
-   .
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    for symbol, trade_bar in slice.bars.items():
-        self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")
-
-    for symbol, quote_bar in slice.quote_bars.items():
-        self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
-
-    for symbol, ticks in slice.ticks.items():
-        for tick in ticks:
-            self.log(f"{symbol} price at {slice.time}: {tick.price}")
    
-   
    public override void OnData(Slice slice)
-{
-    foreach (var kvp in slice.Bars)
-    {
-        var symbol = kvp.Key;
-        var tradeBar = kvp.Value;
-        Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
-    }
-
-    foreach (var kvp in slice.QuoteBars)
-    {
-        var symbol = kvp.Key;
-        var quoteBar = kvp.Value;
-        Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
-    }
-
-    foreach (var kvp in slice.Ticks)
-    {
-        var symbol = kvp.Key;
-        var ticks = kvp.Value;
-        foreach (var tick in ticks)
-        {
-            Log($"{symbol} price at {slice.Time}: {tick.Price}");
-        }
-    }
-}
-
    
-  
    
-  
    
-   For more information about accessing Crypto data, see
-   
-    Handling Data
-   
-   .
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   To get historical Binance Crypto Price data, call the
-   
-    History
-   
-   
-    history
-   
-   method with the Crypto
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    # DataFrame
-history_df = self.history(self.btcbusd, 100, Resolution.DAILY)
-
-# TradeBar objects
-history_trade_bars = self.history[TradeBar](self.btcbusd, 100, Resolution.MINUTE)
-
-# QuoteBar objects
-history_quote_bars = self.history[QuoteBar](self.btcbusd, 100, Resolution.MINUTE)
-
-# Tick objects
-history_ticks = self.history[Tick](self.btcbusd, timedelta(seconds=10), Resolution.TICK)
    
-   
    // TradeBar objects 
-var historyTradeBars = History(_symbol, 100, Resolution.Daily);
-
-// QuoteBar objects 
-var historyQuoteBars = History<QuoteBar>(_symbol, 100, Resolution.Minute);
-
-// Tick objects 
-var historyTicks = History<Tick>(_symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
-  
    
-  
    
-   For more information about historical data, see
-   
-    History Requests
-   
-   .
-  
    
- 
-
-
-
    Universe Selection
    
-
- 
-  
    
-   To select a dynamic universe of Binance Crypto pairs, call the
-   
-    AddUniverse
-   
-   
-    add_universe
-   
-   method with a
-   
-    CryptoUniverse
-   
-   object. A
-   
-    Crypto universe
-   
-   uses a selection function to select Crypto pairs based on their OHLCV and dollar volume of the previous day as of midnight Coordinated Universal Time (UTC).
-  
    
-  
    
-   
    def initialize(self) -> None:
-    self.universe_settings.asynchronous = True
-    self.set_brokerage_model(BrokerageName.BINANCE, AccountType.MARGIN)
-    self._universe = self.add_universe(CryptoUniverse.binance(self.universe_selection_filter))
-
-def universe_selection_filter(self, universe_day):
-    return [c.symbol for c in universe_day if c.volume >= 100 and c.volume_in_usd > 10000]
    
-   
    using QuantConnect.Data.UniverseSelection;
-
-public override void Initialize()
-{
-    UniverseSettings.Asynchronous = true;
-    SetBrokerageModel(BrokerageName.Binance, AccountType.Margin);
-    _universe = AddUniverse(CryptoUniverse.Binance(UniverseSelectionFilter));
-}
-
-private IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
-{
-    return from c in universeDay
-           where c.Volume >= 100m && c.VolumeInUsd > 10000m
-           select c.Symbol;
-}
    
-  
    
-  
    
-   For more information about universe settings, see
-   
-    Settings
-   
-   .
-  
    
- 
-
-
-
    Universe History
    
-
- 
-  
    
-   You can get historical universe data in an algorithm and in the Research Environment.
-  
    
-  
    
-   Historical Universe Data in Algorithms
-  
    
-  
    
-   To get historical universe data in an algorithm, call the
-   
-    History
-   
-   
-    history
-   
-   method with the
-   
-    Universe
-   
-   object, and the lookback period. If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    var history = History(_universe, 30, Resolution.Daily);
-foreach (var universeDay in history)
-{
-    foreach (CryptoUniverse universeItem in universeDay)
-    {
-        Log($"{universeItem.Symbol} price at {universeItem.EndTime}: {universeItem.Close}");
-    }
-}
    
-   
    # DataFrame example where the columns are the CryptoUniverse attributes: 
-history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)
-
-# Series example where the values are lists of CryptoUniverse objects: 
-history = self.history(self._universe, 30, Resolution.DAILY)
-for (univere_symbol, time), universe_day in history.items():
-    for universe_item in universe_day:
-        self.log(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")
    
-  
    
-  
    
-   Historical Universe Data in Research
-  
    
-  
    
-   To get historical universe data in research, call the
-   
-    UniverseHistory
-   
-   
-    universe_history
-   
-   method with the
-   
-    Universe
-   
-   object, and the lookback period. The
-   
-    UniverseHistory
-   
-   
-    universe_history
-   
-   returns the filtered universe. If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-foreach (var universeDay in universeHistory)
-{
-    foreach (CryptoUniverse universeItem in universeDay)
-    {
-        Console.WriteLine($"{universeItem.Symbol} price at {universeItem.EndTime}: {universeItem.Close}");
-    }
-}
    
-   
    # DataFrame example where the columns are the CryptoUniverse attributes: 
-history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
-
-# Series example where the values are lists of CryptoUniverse objects: 
-universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (univere_symbol, time), universe_day in universe_history.items():
-    for universe_item in universe_day:
-        print(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")
    
-  
    
-  
    
-   You can call the
-   
-    History
-   
-   
-    history
-   
-   method in Research.
-  
    
- 
-
-
-
    Remove Subcriptions
    
-
- 
-  
    
-   To unsubscribe from a Crypto pair that you added with the
-   
-    AddCrypto
-   
-   
-    add_crypto
-   
-   method, call the
-   
-    RemoveSecurity
-   
-   
-    remove_security
-   
-   method.
-  
    
-  
    
-   
    self.remove_security(self.btcbusd)
    
-   
    RemoveSecurity(_symbol);
    
-  
    
-  
    
-   The
-   
-    RemoveSecurity
-   
-   
-    remove_security
-   
-   method cancels your open orders for the security and liquidates your holdings in the
-   
-    virtual pair
-   
-   .
-  
    
- 
-
-
-
    Supported Assets
    
-
- 
-  The following table shows the available Cryptocurrency pairs:
-  
    
-   
-    
+      
+      
+     
-      
+      
+      
+      
+      
+      
+      
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+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
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+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
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+      
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+     
+     
+      
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+      
+     
+     
+      
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+     
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+      
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+      
+      
+     
+     
+      
+      
+    
+   
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The Binance Crypto Price dataset enables you to accurately design strategies for Cryptocurrencies. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Buy and hold
+   
      • 
+   
    • 
+    Trading Cryptocurrency volatility and price action
+   
      • 
+   
    • 
+    Allocating a small portion of your portfolio to Cryptocurrencies to hedge against inflation
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm buys and holds Bitcoin through the Binance exchange:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class CoinAPIDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        # Set the account currency to USDT, since you can't trade with 
+        # USD and the algorithm doesn't automatically convert USD & USDT
+        # holdings.
+        self.set_account_currency("USDT", 100000)
+        self.universe_settings.asynchronous = True
+        # Binance accepts both Cash and Margin account types. Select the
+        # one you need for the best reality modeling.
+        self.set_brokerage_model(BrokerageName.BINANCE, AccountType.MARGIN)
+        # Add BTCUSDT data.
+        self._btc = self.add_crypto("BTCUSDT", Resolution.MINUTE, Market.BINANCE)
+        # Historical data
+        history = self.history(self._btc.symbol, 30, Resolution.DAILY)
+        # Add a Crypto universe that selects trading pairs on Binance.
+        universe = self.add_universe(CryptoUniverse.binance(self._select_assets))
+        # Historical Universe data
+        universe_history = self.history(universe, 30, Resolution.DAILY)
+        for (universe_symbol, time), universe_day in universe_history.items():
+            for universe_item in universe_day:
+                symbol = universe_item.symbol
+                price = universe_item.close
+
+    def _select_assets(self, universe_day):
+        # Filter for materially traded Crypto pairs with significant 
+        # size and dollar volume, assuming higher capital flow in for 
+        # higher return.
+        return [universe_item.symbol for universe_item in universe_day
+                if universe_item.volume >= 100 
+                and universe_item.volume_in_usd > 10_000_000]
+
+    def on_data(self, slice: Slice) -> None:
+        # Speculate-invest all available free cash on BTCUSDT, repecting 
+        # the order quantity restrictions to avoid invalid orders.
+        if self.portfolio.cash_book['BTC'].amount != 0:
+            return
+        # Define a buffer so that we can scale down the order size,
+        # avoiding trading errors.
+        buffer = max(
+            BinanceFeeModel.TAKER_TIER_1_FEE, 
+            self.settings.free_portfolio_value_percentage
+        )
+        quantity = (
+            self.portfolio.cash_book['USDT'].amount
+            / self._btc.ask_price
+            * (1-buffer)
+        )
+        # Check if the quantity exceeds the minimum order size. On 
+        # Binance, the minimum order size is measured in the quote
+        # currency (USDT in this case).
+        minimum_order_size = (
+            self._btc.symbol_properties.minimum_order_size 
+            / self._btc.ask_price
+        )
+        if abs(quantity) < minimum_order_size:
+            return
+        self.market_order(self._btc, quantity)
    
+   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+{
+    private Crypto _btc;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        // Set the account currency to USDT, since you can't trade with 
+        // USD and the algorithm doesn't automatically convert USD & USDT
+        // holdings.
+        SetAccountCurrency("USDT", 100000);
+        UniverseSettings.Asynchronous = true;
+        // Binance accepts both Cash and Margin account types. Select the
+        // one you need for the best reality modeling.
+        SetBrokerageModel(BrokerageName.Binance, AccountType.Margin);
+        // Add BTCUSDT data.
+        _btc = AddCrypto("BTCUSDT", Resolution.Minute, Market.Binance);   
+        // Historical data
+        var history = History(_btc.Symbol, 30, Resolution.Daily);
+        // Add a Crypto universe that selects trading pairs on Binance.
+        var universe = AddUniverse(CryptoUniverse.Binance(SelectAssets));
+        // Historical Universe data
+        var universeHistory = History(universe, 30, Resolution.Daily);
+        foreach (var universeDay in universeHistory)
+        {
+            foreach (CryptoUniverse universeItem in universeDay)
+            {
+                var symbol = universeItem.Symbol;
+                var price = universeItem.Close;
+            }
+        }
+    }
+
+    private IEnumerable<Symbol> SelectAssets(IEnumerable<CryptoUniverse> universeDay)
+    {
+        // Filter for materially traded Crypto pairs with significant 
+        // size and dollar volume, assuming higher capital flow in for 
+        // higher return.
+        return from universeItem in universeDay
+                where universeItem.Volume >= 100m 
+                && universeItem.VolumeInUsd > 10000000m
+                select universeItem.Symbol;
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // Speculate-invest all available free cash on BTCUSDT, repecting 
+        // the order quantity restrictions to avoid invalid orders.
+        if (Portfolio.CashBook["BTC"].Amount != 0)
+        {
+            return;
+        }
+        // Define a buffer so that we can scale down the order size,
+        // avoiding trading errors.
+        var buffer = Math.Max(BinanceFeeModel.TakerTier1Fee, Settings.FreePortfolioValuePercentage);
+        var quantity = Portfolio.CashBook["USDT"].Amount / _btc.AskPrice * (1-buffer);
+        // Check if the quantity exceeds the minimum order size. On 
+        // Binance, the minimum order size is measured in the quote 
+        // currency (USDT in this case).
+        var minimumOrderSize = _btc.SymbolProperties.MinimumOrderSize / _btc.AskPrice;
+        if (Math.Abs(quantity) < minimumOrderSize)
+        {
+            return;
+        }
+        MarketOrder(_btc, quantity);
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm creates a dynamic universe of Crypto pairs on the Binance exchange and then forms a equal-weighted portfolio of all the pairs in the universe:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class CoinAPIDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        # Set Account Currency to Tether, since USD and USDT will not auto-convert and USD cannot be used to trade
+        self.set_account_currency("USDT", 100000)
+        self.settings.free_portfolio_value_percentage = 0.05
+
+        # Binance accepts both Cash and Margin account types, select the one you need for the best reality modeling.
+        self.set_brokerage_model(BrokerageName.BINANCE, AccountType.MARGIN)
+
+        # Warm up the security with the last known price to avoid conversion error
+        self.settings.seed_initial_prices = True
+        self.universe_settings.asynchronous = True
+        self.universe_settings.resolution = Resolution.MINUTE
+        # Add Crypto Universe Selection that select crypto pairs in Binance exchange
+        self.add_universe(CryptoUniverse.binance(self.universe_selection_filter))
+        # Get the pairs on Binance that have USDT as the quote currency.
+        self._market_pairs = [
+            x.key.symbol 
+            for x in self.symbol_properties_database.get_symbol_properties_list(Market.BINANCE) 
+            if x.value.quote_currency == self.account_currency
+        ]
+        
+        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(minutes = 20), 0.025, None))
+        # Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+
+    def universe_selection_filter(self, universe_day: List[CryptoUniverse]) -> List[Symbol]:
+        # Select pairs where the quote currency is USDT.
+        pairs = [c for c in universe_day if c.symbol.value in self._market_pairs]
+        # Filter for materially traded crypto pairs with significant dollar volume, assuming higher capital flow in for higher return.
+        pairs = sorted(pairs, key=lambda c: c.volume_in_usd)[-3:]      
+        return [c.symbol for c in pairs]
+
+    def on_securities_changed(self, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            # Historical data
+            history = self.history(security.symbol, 30, Resolution.DAILY)
    
+   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+{
+    private List<string> _marketPairs;
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        Settings.FreePortfolioValuePercentage = 0.05m;
+
+        // Set Account Currency to Tether, since USD and USDT will not auto-convert and USD cannot be used to trade
+        SetAccountCurrency("USDT", 100000);
+        // Binance accepts both Cash and Margin account types, select the one you need for the best reality modeling.
+        SetBrokerageModel(BrokerageName.Binance, AccountType.Margin);
+
+        // Warm up the security with the last known price to avoid conversion error
+        Settings.SeedInitialPrices = true;
+        UniverseSettings.Asynchronous = true;
+        UniverseSettings.Resolution = Resolution.Minute;
+        // Add Crypto Universe Selection that select crypto pairs in Binance exchange
+        AddUniverse(CryptoUniverse.Binance(UniverseSelectionFilter));
+        // Get the pairs on Binance that have USDT as the quote currency.
+        _marketPairs = SymbolPropertiesDatabase.GetSymbolPropertiesList(Market.Binance)
+            .Where(x => x.Value.QuoteCurrency == AccountCurrency)
+            .Select(x => x.Key.Symbol)
+            .ToList();
+
+        AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, null));
+        // Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+    }
+
+    private IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
+    {
+        // Select pairs where the quote currency is USDT.
+        return universeDay.Where(c => _marketPairs.Contains(c.Symbol.Value))
+            // Filter for materially traded crypto pairs with significant size and dollar volume, assuming higher capital flow in for higher return
+            .OrderBy(c => c.VolumeInUsd)
+            .TakeLast(3)
+            .Select(c => c.Symbol);
+    }
+
+    public override void OnSecuritiesChanged(SecurityChanges changes)
+    {
+        foreach(var security in changes.AddedSecurities)
+        {
+            // Historical data
+            var history = History(security.Symbol, 30, Resolution.Daily);
+        }
+    }
+}
    
+  
    
+  
    
+   Research Example
+  
    
+  
    
+   The following example lists crypto-currency pairs with the greatest dollar volume in the Binance exchange:
+  
    
+  
    
+   
    var qb = new QuantBook();
+
+// Add Cryptocurrency pair
+var symbol = qb.AddCrypto("BTCUSDT", market:Market.Binance).Symbol;
+
+// Historical data
+var history = qb.History(symbol, 30, Resolution.Daily);
+foreach (var bar in history)
+{
+    Console.WriteLine($"{bar.EndTime} {bar}");
+}
+
+// Add Crypto Universe Selection
+IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
+{
+    return universeDay
+        .Where(x => x.VolumeInUsd != null && x.VolumeInUsd > 10000m)
+        .OrderByDescending(x => x.VolumeInUsd)
+        .Take(5)
+        .Select(x => x.Symbol);
+}
+var universe = qb.AddUniverse(CryptoUniverse.Binance(UniverseSelectionFilter));
+
+// Historical Universe data
+var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var universeDay in universeHistory)
+{
+    Console.WriteLine($"=== {universeDay.First().EndTime} ===");
+    foreach (CryptoUniverse universeItem in universeDay.OrderByDescending(x => x.VolumeInUsd))
+    {
+        Console.WriteLine($"{universeItem.Symbol}: {universeItem.VolumeInUsd}");
+    }
+}
    
+   
    qb = QuantBook()
+
+# Add Cryptocurrency pair
+symbol = qb.add_crypto("BTCUSDT", market=Market.BINANCE).symbol
+
+# Historical data
+history = qb.history(symbol, 30, Resolution.DAILY)
+for (symbol, time), row in history.iterrows():
+    print(f'{time} {symbol} {row.close}')
+
+# Add Crypto Universe Selection
+def universe_selection_filter(universe_day):
+    selected = sorted([x for x in universe_day
+        if x.volume_in_usd and x.volume_in_usd > 10000],
+        key=lambda x: x.volume_in_usd, reverse=True)[:5]
+    return [x.symbol for x in selected]
+
+universe = qb.add_universe(CryptoUniverse.binance(universe_selection_filter))
+
+# Historical Universe data
+history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+for (univere_symbol, time), universe_day in history.items():
+    print(f'=== {time} ===')
+    for universe_item in sorted(universe_day, key=lambda x: x.volume_in_usd, reverse=True):
+        print(f"{universe_item.symbol}: {universe_item.volume_in_usd}")
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The Binance Crypto Price dataset provides
+   
+    TradeBar
+   
+   ,
+   
+    QuoteBar
+   
+   ,
+   
+    Tick
+   
+   , and
+   
+    CryptoUniverse
+   
+   objects.
+  
    
+  
    
+   TradeBar Attributes
+  
    
+  
    
+   
+    TradeBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   QuoteBar Attributes
+  
    
+  
    
+   
+    QuoteBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   Tick Attributes
+  
    
+  
    
+   
+    Tick
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   CryptoUniverse Attributes
+  
    
+  
    
+   
+    CryptoUniverse
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > Binance US Crypto Price Data
    
+
    
+    
    
+        
    QuantConnect
    
+        
    Binance US Crypto Price Data
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The Binance US Crypto Price Data is for Cryptocurrency price and volume data points. The data covers 604 Cryptocurrency pairs, starts in October 2019, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Binance US.
+  
    
+  
    
+   For more information about the Binance US Crypto Price Data dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   Binance was founded by Changpeng Zhao in 2017 with the goal to "increase the freedom of money globally". Binance provides access to trading Crypto through spot markets and perpetual Futures. They serve clients with no minimum deposit when depositing Crypto. Binance also provides an NFT marketplace, a mining pool, and services to deposit Crypto coins in liquidity pools to earn rewards.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippets demonstrates how to set the brokerage model, request data, and perform universe selection with the Binance US dataset:
+  
    
+  
    
+   
    # Binance US only accepts cash accounts
+self.set_brokerage_model(BrokerageName.BINANCE_US, AccountType.CASH)
+
+self.btcbusd = self.add_crypto("BTCUSD", Resolution.MINUTE, Market.BINANCE_US).symbol
+
+self._universe = AddUniverse(CryptoUniverse.binance_us(self.universe_selection_filter));
    
+   
    // Binance US only accepts cash accounts
+SetBrokerageModel(BrokerageName.BinanceUS, AccountType.Cash);
+
+_symbol = AddCrypto("BTCUSD", Resolution.Minute, Market.BinanceUS).Symbol;
+
+_universe = AddUniverse(CryptoUniverse.BinanceUS(UniverseSelectionFilter));
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      October 2019
+     
    
+      Asset Coverage
+     
+      604 Cryptocurrency Pairs
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Tick, Second, Minute, Hourly, & Daily
+     
    
+      Timezone
+     
+      UTC
+     
    
+      Market Hours
+     
+      
+       Always Open
+      
+     
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add Binance US Crypto Price data to your algorithm, call the
+   
+    AddCrypto
+   
+   
+    add_crypto
+   
+   method. Save a reference to the Crypto
+   
+    Symbol
+   
+   so you can access the data later in your algorithm.
+  
    
+  
    
+   
    class CoinAPIDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2020, 6, 1)
+        self.set_end_date(2021, 6, 1)
+        self.set_cash(100000)
+
+        # BinanceUS accepts Cash account type only, AccountType.MARGIN will result in an exception.
+        self.set_brokerage_model(BrokerageName.BINANCE_US, AccountType.CASH)
+        
+        self.btcbusd = self.add_crypto("BTCUSD", Resolution.MINUTE, Market.BINANCE_US).symbol
    
+   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+{
+    private Symbol _symbol;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2020, 6, 1);
+        SetEndDate(2021, 6, 1);
+        SetCash(100000);
+    
+        // BinanceUS accepts Cash account type only, AccountType.Margin will result in an exception.
+        SetBrokerageModel(BrokerageName.BinanceUS, AccountType.Cash);
+        
+        _symbol = AddCrypto("BTCUSD", Resolution.Minute, Market.BinanceUS).Symbol;
+    }
+}
    
+  
    
+  
    
+   For more information about creating Crypto subscriptions, see
+   
+    Requesting Data
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current Binance US Crypto Price data, index the
+   
+    Bars
+   
+   
+    bars
+   
+   ,
+   
+    QuoteBars
+   
+   
+    quote_bars
+   
+   , or
+   
+    Ticks
+   
+   
+    ticks
+   
+   properties of the current
+   
+    
+     Slice
+    
+   
+   with the Crypto
+   
+    Symbol
+   
+   .
+   
+    Slice
+   
+   objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your security at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    if self.btcbusd in slice.bars:
+        trade_bar = slice.bars[self.btcbusd]
+        self.log(f"{self.btcbusd} close at {slice.time}: {trade_bar.close}")
+
+    if self.btcbusd in slice.quote_bars:
+        quote_bar = slice.quote_bars[self.btcbusd]
+        self.log(f"{self.btcbusd} bid at {slice.time}: {quote_bar.bid.close}")
+
+    if self.btcbusd in slice.ticks:
+        ticks = slice.ticks[self.btcbusd]
+        for tick in ticks:
+            self.log(f"{self.btcbusd} price at {slice.time}: {tick.price}")
    
+   
    public override void OnData(Slice slice)
+{
+    if (slice.Bars.ContainsKey(_symbol))
+    {
+        var tradeBar = slice.Bars[_symbol];
+        Log($"{_symbol} price at {slice.Time}: {tradeBar.Close}");
+    }
+
+    if (slice.QuoteBars.ContainsKey(_symbol))
+    {
+        var quoteBar = slice.QuoteBars[_symbol];
+        Log($"{_symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
+    }
+
+    if (slice.Ticks.ContainsKey(_symbol))
+    {
+        var ticks = slice.Ticks[_symbol];
+        foreach (var tick in ticks)
+        {
+            Log($"{_symbol} price at {slice.Time}: {tick.Price}");
+        }
+    }
+}
+
    
+  
    
+  
    
+   You can also iterate through all of the data objects in the current
+   
+    Slice
+   
+   .
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    for symbol, trade_bar in slice.bars.items():
+        self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")
+
+    for symbol, quote_bar in slice.quote_bars.items():
+        self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
+
+    for symbol, ticks in slice.ticks.items():
+        for tick in ticks:
+            self.log(f"{symbol} price at {slice.time}: {tick.price}")
    
+   
    public override void OnData(Slice slice)
+{
+    foreach (var kvp in slice.Bars)
+    {
+        var symbol = kvp.Key;
+        var tradeBar = kvp.Value;
+        Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
+    }
+
+    foreach (var kvp in slice.QuoteBars)
+    {
+        var symbol = kvp.Key;
+        var quoteBar = kvp.Value;
+        Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
+    }
+
+    foreach (var kvp in slice.Ticks)
+    {
+        var symbol = kvp.Key;
+        var ticks = kvp.Value;
+        foreach (var tick in ticks)
+        {
+            Log($"{symbol} price at {slice.Time}: {tick.Price}");
+        }
+    }
+}
+
    
+  
    
+  
    
+   For more information about accessing Crypto data, see
+   
+    Handling Data
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   To get historical Binance US Crypto Price data, call the
+   
+    History
+   
+   
+    history
+   
+   method with the Crypto
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame
+history_df = self.history(self.btcbusd, 100, Resolution.DAILY)
+
+# TradeBar objects
+history_trade_bars = self.history[TradeBar](self.btcbusd, 100, Resolution.MINUTE)
+
+# QuoteBar objects
+history_quote_bars = self.history[QuoteBar](self.btcbusd, 100, Resolution.MINUTE)
+
+# Tick objects
+history_ticks = self.history[Tick](self.btcbusd, timedelta(seconds=10), Resolution.TICK)
    
+   
    // TradeBar objects 
+var historyTradeBars = History(_symbol, 100, Resolution.Daily);
+
+// QuoteBar objects 
+var historyQuoteBars = History<QuoteBar>(_symbol, 100, Resolution.Minute);
+
+// Tick objects 
+var historyTicks = History<Tick>(_symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
+  
    
+  
    
+   For more information about historical data, see
+   
+    History Requests
+   
+   .
+  
    
+ 
+
+
+
    Universe Selection
    
+
+ 
+  
    
+   To select a dynamic universe of Binance US Crypto pairs, call the
+   
+    AddUniverse
+   
+   
+    add_universe
+   
+   method with a
+   
+    CryptoUniverse
+   
+   object. A
+   
+    Crypto universe
+   
+   uses a selection function to select Crypto pairs based on their OHLCV and dollar volume of the previous day as of midnight Coordinated Universal Time (UTC).
+  
    
+  
    
+   
    def initialize(self) -> None:
+    self.universe_settings.asynchronous = True
+    self.set_brokerage_model(BrokerageName.BINANCE_US, AccountType.CASH)
+    self._universe = self.add_universe(CryptoUniverse.binance_us(self.universe_selection_filter))
+
+def universe_selection_filter(self, universe_day):
+    return [c.symbol for c in universe_day if c.volume >= 100 and c.volume_in_usd > 10000]
    
+   
    public override void Initialize()
+{
+    UniverseSettings.Asynchronous = true;
+    SetBrokerageModel(BrokerageName.BinanceUS, AccountType.Cash);
+    _universe = AddUniverse(CryptoUniverse.BinanceUS(UniverseSelectionFilter));
+}
+
+private IEnumerable<Symol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
+{
+    return from c in universeDay
+           where c.Volume >= 100m && c.VolumeInUsd > 10000m
+           select c.Symbol;
+}
    
+  
    
+  
    
+   For more information about universe settings, see
+   
+    Settings
+   
+   .
+  
    
+ 
+
+
+
    Universe History
    
+
+ 
+  
    
+   You can get historical universe data in an algorithm and in the Research Environment.
+  
    
+  
    
+   Historical Universe Data in Algorithms
+  
    
+  
    
+   To get historical universe data in an algorithm, call the
+   
+    History
+   
+   
+    history
+   
+   method with the
+   
+    Universe
+   
+   object, and the lookback period. If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    var history = History(_universe, 30, Resolution.Daily);
+foreach (var universeDay in history)
+{
+    foreach (CryptoUniverse universeItem in universeDay)
+    {
+        Log($"{universeItem.Symbol} price at {universeItem.EndTime}: {universeItem.Close}");
+    }
+}
    
+   
    # DataFrame example where the columns are the CryptoUniverse attributes: 
+history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)
+
+# Series example where the values are lists of CryptoUniverse objects: 
+history = self.history(self._universe, 30, Resolution.DAILY)
+for (univere_symbol, time), universe_day in history.items():
+    for universe_item in universe_day:
+        self.log(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")
    
+  
    
+  
    
+   Historical Universe Data in Research
+  
    
+  
    
+   To get historical universe data in research, call the
+   
+    UniverseHistory
+   
+   
+    universe_history
+   
+   method with the
+   
+    Universe
+   
+   object, and the lookback period. The
+   
+    UniverseHistory
+   
+   
+    universe_history
+   
+   returns the filtered universe. If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var universeDay in universeHistory)
+{
+    foreach (CryptoUniverse universeItem in universeDay)
+    {
+        Console.WriteLine($"{universeItem.Symbol} price at {universeItem.EndTime}: {universeItem.Close}");
+    }
+}
    
+   
    # DataFrame example where the columns are the CryptoUniverse attributes: 
+history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
+
+# Series example where the values are lists of CryptoUniverse objects: 
+universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+for (univere_symbol, time), universe_day in universe_history.items():
+    for universe_item in universe_day:
+        print(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")
    
+  
    
+  
    
+   You can call the
+   
+    History
+   
+   
+    history
+   
+   method in Research.
+  
    
+ 
+
+
+
    Remove Subscriptions
    
+
+ 
+  
    
+   To unsubscribe from a Crypto pair that you added with the
+   
+    AddCrypto
+   
+   
+    add_crypto
+   
+   method, call the
+   
+    RemoveSecurity
+   
+   
+    remove_security
+   
+   method.
+  
    
+  
    
+   
    self.remove_security(self.btcbusd)
    
+   
    RemoveSecurity(_symbol);
    
+  
    
+  
    
+   The
+   
+    RemoveSecurity
+   
+   
+    remove_security
+   
+   method cancels your open orders for the security and liquidates your holdings in the
+   
+    virtual pair
+   
+   .
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   The following table shows the available Cryptocurrency pairs:
+  
    
+  
    
+   
+    
-      
-      
-      
-      
-      
-      
+      
+    
+    
+      
+      
@@ -215133,7 +185425,7 @@ 
    Data Summary
    
       Asset Coverage
      
@@ -215179,7 +185471,7 @@ 
    Requesting Data
    
-   To add Binance US Crypto Price data to your algorithm, call the
+   To add Bitfinex Crypto Price data to your algorithm, call the
    
     AddCrypto
    
@@ -215200,10 +185492,10 @@ 
    Requesting Data
    
         self.set_end_date(2021, 6, 1)
         self.set_cash(100000)
 
-        # BinanceUS accepts Cash account type only, AccountType.MARGIN will result in an exception.
-        self.set_brokerage_model(BrokerageName.BINANCE_US, AccountType.CASH)
+        # Bitfinex accepts both Cash and Margin type account.
+        self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.MARGIN)
         
-        self.btcbusd = self.add_crypto("BTCUSD", Resolution.MINUTE, Market.BINANCE_US).symbol
+        self.btcusd = self.add_crypto("BTCUSD", Resolution.MINUTE, Market.BITFINEX).symbol
    public class CoinAPIDataAlgorithm : QCAlgorithm
 {
     private Symbol _symbol;
@@ -215213,11 +185505,11 @@ 
    Requesting Data
    
         SetStartDate(2020, 6, 1);
         SetEndDate(2021, 6, 1);
         SetCash(100000);
-    
-        // BinanceUS accepts Cash account type only, AccountType.Margin will result in an exception.
-        SetBrokerageModel(BrokerageName.BinanceUS, AccountType.Cash);
+
+        // Bitfinex accepts both Cash and Margin type account.
+        SetBrokerageModel(BrokerageName.Bitfinex, AccountType.Margin);
         
-        _symbol = AddCrypto("BTCUSD", Resolution.Minute, Market.BinanceUS).Symbol;
+        _symbol = AddCrypto("BTCUSD", Resolution.Minute, Market.Bitfinex).Symbol;
     }
 }
    
@@ -215235,7 +185527,7 @@ 
    Accessing Data
    
-   To get the current Binance US Crypto Price data, index the
+   To get the current Bitfinex Crypto Price data, index the
    
     Bars
    
@@ -215282,18 +185574,18 @@ 
    Accessing Data
    
    
    def on_data(self, slice: Slice) -> None:
-    if self.btcbusd in slice.bars:
-        trade_bar = slice.bars[self.btcbusd]
-        self.log(f"{self.btcbusd} close at {slice.time}: {trade_bar.close}")
+    if self.btcusd in slice.bars:
+        trade_bar = slice.bars[self.btcusd]
+        self.log(f"{self.btcusd} close at {slice.time}: {trade_bar.close}")
 
-    if self.btcbusd in slice.quote_bars:
-        quote_bar = slice.quote_bars[self.btcbusd]
-        self.log(f"{self.btcbusd} bid at {slice.time}: {quote_bar.bid.close}")
+    if self.btcusd in slice.quote_bars:
+        quote_bar = slice.quote_bars[self.btcusd]
+        self.log(f"{self.btcusd} bid at {slice.time}: {quote_bar.bid.close}")
 
-    if self.btcbusd in slice.ticks:
-        ticks = slice.ticks[self.btcbusd]
+    if self.btcusd in slice.ticks:
+        ticks = slice.ticks[self.btcusd]
         for tick in ticks:
-            self.log(f"{self.btcbusd} price at {slice.time}: {tick.price}")
    
+            self.log(f"{self.btcusd} price at {slice.time}: {tick.price}")
    
    public override void OnData(Slice slice)
 {
     if (slice.Bars.ContainsKey(_symbol))
@@ -215379,7 +185671,7 @@ 
    Historical Data
    
 
  
   
    
-   To get historical Binance US Crypto Price data, call the
+   To get historical Bitfinex Crypto Price data, call the
    
     History
    
@@ -215394,16 +185686,16 @@ 
    Historical Data
    
   
    
   
    
    
    # DataFrame
-history_df = self.history(self.btcbusd, 100, Resolution.DAILY)
+history_df = self.history(self.btcusd, 100, Resolution.DAILY)
 
 # TradeBar objects
-history_trade_bars = self.history[TradeBar](self.btcbusd, 100, Resolution.MINUTE)
+history_trade_bars = self.history[TradeBar](self.btcusd, 100, Resolution.MINUTE)
 
 # QuoteBar objects
-history_quote_bars = self.history[QuoteBar](self.btcbusd, 100, Resolution.MINUTE)
+history_quote_bars = self.history[QuoteBar](self.btcusd, 100, Resolution.MINUTE)
 
 # Tick objects
-history_ticks = self.history[Tick](self.btcbusd, timedelta(seconds=10), Resolution.TICK)
    
+history_ticks = self.history[Tick](self.btcusd, timedelta(seconds=10), Resolution.TICK)
    
    
    // TradeBar objects 
 var historyTradeBars = History(_symbol, 100, Resolution.Daily);
 
@@ -215427,7 +185719,7 @@ 
    Universe Selection
    
 
  
   
    
-   To select a dynamic universe of Binance US Crypto pairs, call the
+   To select a dynamic universe of Bitfinex Crypto pairs, call the
    
     AddUniverse
    
@@ -215447,19 +185739,19 @@ 
    Universe Selection
    
   
    
    
    def initialize(self) -> None:
     self.universe_settings.asynchronous = True
-    self.set_brokerage_model(BrokerageName.BINANCE_US, AccountType.CASH)
-    self._universe = self.add_universe(CryptoUniverse.binance_us(self.universe_selection_filter))
+    self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.MARGIN)
+    self._universe = self.add_universe(CryptoUniverse.bitfinex(self.universe_selection_filter))
 
 def universe_selection_filter(self, universe_day):
     return [c.symbol for c in universe_day if c.volume >= 100 and c.volume_in_usd > 10000]
    
    
    public override void Initialize()
 {
     UniverseSettings.Asynchronous = true;
-    SetBrokerageModel(BrokerageName.BinanceUS, AccountType.Cash);
-    _universe = AddUniverse(CryptoUniverse.BinanceUS(UniverseSelectionFilter));
+    SetBrokerageModel(BrokerageName.Bitfinex, AccountType.Margin);
+    _universe = AddUniverse(CryptoUniverse.Bitfinex(UniverseSelectionFilter));
 }
 
-private IEnumerable<Symol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
+private IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
 {
     return from c in universeDay
            where c.Volume >= 100m && c.VolumeInUsd > 10000m
@@ -215593,7 +185885,7 @@ 
    Remove Subscriptions
    
    method.
   
    
   
    
-   
    self.remove_security(self.btcbusd)
    
+   
    self.remove_security(self.btcusd)
    
    
    RemoveSecurity(_symbol);
    
   
    
   
    
@@ -215624,172 +185916,135 @@ 
    Supported Assets
    
     
    
-      
-      
-      
-     
-     
-      
-      
-      
-      
-     
-     
-      
-      
-      
-      
+     
+     
-     
-     
@@ -215797,173 +186052,130 @@ 
    Supported Assets
    
        ARBUSDT
       
-      
-      
-      
-     
-     
-      
+     
+     
-      
-     
-     
-      
-      
-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
-     
-     
-      
-      
-      
-     
-     
-      
-      
-      
-      
-      
-      
@@ -217574,73 +187746,79 @@ 
    Supported Assets
    
        XRPUSDT
       
+      
+      
@@ -217653,7 +187831,7 @@ 
    Example Applications
    
-   The Binance US Crypto Price dataset enables you to accurately design strategies for Cryptocurrencies. Examples include the following strategies:
+   The Bitfinex Crypto Price dataset enables you to accurately design strategies for Cryptocurrencies. Examples include the following strategies:
   
      
    
    • 
@@ -217670,7 +187848,7 @@ 
      
    Classic Algorithm Example
   
      
   
      
-   The following example algorithm buys and holds Bitcoin through the Binance US exchange:
+   The following example algorithm buys and holds Bitcoin through the Bitfinex exchange:
   
      
   
      
    
      from AlgorithmImports import *
@@ -217682,17 +187860,16 @@ 
      
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
         self.universe_settings.asynchronous = True
-        # Set USDT as the account currency so we can trade BTCUSDT.
-        self.set_account_currency('USDT', 100_000)
-        # Binance US accepts Cash account type only. AccountType.MARGIN
-        # results in an exception.
-        self.set_brokerage_model(BrokerageName.BINANCE_US, AccountType.CASH)
-        # Add BTCUSDT data.
-        self._btc = self.add_crypto("BTCUSDT", Resolution.MINUTE, Market.BINANCE_US)
+        self.set_cash(100_000)
+        # Bitfinex accepts both Cash and Margin account types. Select the
+        # one you need for the best reality modeling.
+        self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.MARGIN)
+        # Add BTCUSD data.
+        self._btc = self.add_crypto("BTCUSD", Resolution.MINUTE, Market.BITFINEX)
         # Historical data
         history = self.history(self._btc.symbol, 30, Resolution.DAILY)
-        # Add a Crypto universe that selects Crypto pairs on Binance US.
-        universe = self.add_universe(CryptoUniverse.binance_us(self._select_assets))
+        # Add a Crypto universe that selects Crypto pairs on Bitfinex.
+        universe = self.add_universe(CryptoUniverse.bitfinex(self._select_assets))
         # Historical Universe data
         universe_history = self.history(universe, 30, Resolution.DAILY)
         for (universe_symbol, time), universe_day in universe_history.items():
@@ -217709,29 +187886,25 @@ 
      
                 and universe_item.volume_in_usd > 10_000_000]
 
     def on_data(self, slice: Slice) -> None:
-        # Speculate-invest all available free cash on BTCUSDT, repecting 
+        # Speculate-invest all available free cash on BTCUSD, repecting 
         # the order quantity restrictions to avoid invalid orders.
         if self.portfolio.cash_book['BTC'].amount != 0:
             return
         # Define a buffer so that we can scale down the order size,
         # avoiding trading errors.
         buffer = max(
-            BinanceFeeModel.TAKER_TIER_1_FEE,
+            BitfinexFeeModel.TAKER_FEE, 
             self.settings.free_portfolio_value_percentage
         )
         quantity = (
-            self.portfolio.cash_book['USDT'].amount
+            self.portfolio.cash_book['USD'].amount
             / self._btc.ask_price
             * (1-buffer)
         )
         # Check if the quantity exceeds the minimum order size. On 
-        # Binance US, the minimum order size is measured in the quote 
-        # currency (USDT in this case).
-        minimum_order_size = (
-            self._btc.symbol_properties.minimum_order_size 
-            / self._btc.ask_price
-        )
-        if abs(quantity) < minimum_order_size:
+        # Bitfinex, the minimum order size is measured in the base
+        # currency (BTC in this case).
+        if abs(quantity) < self._btc.symbol_properties.minimum_order_size:
             return
         self.market_order(self._btc, quantity)
      
    
      public class CoinAPIDataAlgorithm : QCAlgorithm
@@ -217742,18 +187915,17 @@ 
      
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        // Set USDT as the account currency so we can trade BTCUSDT.
-        SetAccountCurrency("USDT", 100000);
         UniverseSettings.Asynchronous = true;
-        // Binance US accepts Cash account type only. AccountType.Margin
-        // results in an exception.
-        SetBrokerageModel(BrokerageName.BinanceUS, AccountType.Cash);
-        // Add BTCUSDT data.
-        _btc = AddCrypto("BTCUSDT", Resolution.Minute, Market.BinanceUS);   
+        SetCash(100000);
+        // Bitfinex accepts both Cash and Margin account types. Select the
+        // one you need for the best reality modeling.
+        SetBrokerageModel(BrokerageName.Bitfinex, AccountType.Margin);
+        // Add BTCUSD data.
+        _btc = AddCrypto("BTCUSD", Resolution.Minute, Market.Bitfinex);   
         // Historical data
         var history = History(_btc.Symbol, 30, Resolution.Daily);
-        // Add a Crypto universe that selects trading pairs on Binance US.
-        var universe = AddUniverse(CryptoUniverse.BinanceUS(SelectAssets));
+        // Add a Crypto universe that selects trading pairs on Bitfinex.
+        var universe = AddUniverse(CryptoUniverse.Bitfinex(SelectAssets));
         // Historical Universe data
         var universeHistory = History(universe, 30, Resolution.Daily);
         foreach (var universeDay in universeHistory)
@@ -217779,7 +187951,7 @@ 
      
 
     public override void OnData(Slice slice)
     {
-        // Speculate-invest all available free cash on BTCUSDT, repecting 
+        // Speculate-invest all available free cash on BTCUSD, repecting 
         // the order quantity restrictions to avoid invalid orders.
         if (Portfolio.CashBook["BTC"].Amount != 0)
         {
@@ -217787,13 +187959,12 @@ 
      
         }
         // Define a buffer so that we can scale down the order size,
         // avoiding trading errors.
-        var buffer = Math.Max(BinanceFeeModel.TakerTier1Fee, Settings.FreePortfolioValuePercentage);
-        var quantity = Portfolio.CashBook["USDT"].Amount / _btc.AskPrice * (1-buffer);
+        var buffer = Math.Max(BitfinexFeeModel.TakerFee, Settings.FreePortfolioValuePercentage);
+        var quantity = Portfolio.CashBook["USD"].Amount / _btc.AskPrice * (1-buffer);
         // Check if the quantity exceeds the minimum order size. On 
-        // Binance US, the minimum order size is measured in the quote 
-        // currency (USDT in this case).
-        var minimumOrderSize = _btc.SymbolProperties.MinimumOrderSize / _btc.AskPrice;
-        if (Math.Abs(quantity) < minimumOrderSize)
+        // Bitfinex, the minimum order size is measured in the base
+        // currency (BTC in this case).
+        if (Math.Abs(quantity) < _btc.SymbolProperties.MinimumOrderSize)
         {
             return;
         }
@@ -217805,7 +187976,7 @@ 
      
    Framework Algorithm Example
   
      
   
      
-   The following example algorithm creates a dynamic universe of Crypto pairs on the Binance US exchange and then forms a equal-weighted portfolio of all the pairs in the universe:
+   The following example algorithm creates a dynamic universe of Crypto pairs on the Bitfinex exchange and then forms a equal-weighted portfolio of all the pairs in the universe:
   
      
   
      
    
      from AlgorithmImports import *
@@ -217816,20 +187987,24 @@ 
      
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        self.set_cash(1_000_000)
+        self.set_cash(100000)
         self.settings.free_portfolio_value_percentage = 0.05
+        # Bitfinex accepts both Cash and Margin account types, select the one you need for the best reality modeling.
+        self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.MARGIN)
+
         # Warm up the security with the last known price to avoid conversion error
         self.settings.seed_initial_prices = True
         self.universe_settings.asynchronous = True
-        # Add Crypto Universe Selection that select crypto pairs in BinanceUS exchange
-        self.add_universe(CryptoUniverse.binance_us(self._select_assets))
-        # Get the pairs on Binance US that have USD as the quote currency.
+        self.universe_settings.resolution = Resolution.MINUTE
+        # Add Crypto Universe Selection that select crypto pairs in Bitfinex exchange
+        self.add_universe(CryptoUniverse.bitfinex(self._select_assets))
+        # Get the pairs on Bitfinex that have USD as the quote currency.
         self._market_pairs = [
             x.key.symbol 
-            for x in self.symbol_properties_database.get_symbol_properties_list(Market.BINANCE_US) 
+            for x in self.symbol_properties_database.get_symbol_properties_list(Market.BITFINEX) 
             if x.value.quote_currency == self.account_currency
         ]
-        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(minutes=20), 0.025, None))
+        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(hours=4), 0.025, None))
         # Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
         self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
 
@@ -217839,7 +188014,7 @@ 
      
         # Filter for materially traded crypto pairs with significant dollar volume, assuming higher capital flow in for higher return.
         pairs = sorted(pairs, key=lambda c: c.volume_in_usd)[-3:]      
         return [c.symbol for c in pairs]
-        
+
     def on_securities_changed(self, changes: SecurityChanges) -> None:
         for security in changes.added_securities:
             # Historical data
@@ -217847,32 +188022,34 @@ 
      
             self.debug(f"We got {len(history)} items from our history request")
      
    
      public class CoinAPIDataAlgorithm : QCAlgorithm
 {
-    private Symbol _btcusd;
     private List<string> _marketPairs;
     
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        SetCash(1000000);
+        SetCash(100000);
         Settings.FreePortfolioValuePercentage = 0.05m;
+        // Bitfinex accepts both Cash and Margin account types, select the one you need for the best reality modeling.
+        SetBrokerageModel(BrokerageName.Bitfinex, AccountType.Margin);
+
         // Warm up the security with the last known price to avoid conversion error
         Settings.SeedInitialPrices = true;
         UniverseSettings.Asynchronous = true;
         UniverseSettings.Resolution = Resolution.Minute;
-        // Add Crypto Universe Selection that select crypto pairs in BinanceUS exchange
-        AddUniverse(CryptoUniverse.BinanceUS(UniverseSelectionFilter));
-        // Get the pairs on Binance US that have USD as the quote currency.
-        _marketPairs = SymbolPropertiesDatabase.GetSymbolPropertiesList(Market.BinanceUS)
+        // Add Crypto Universe Selection that select crypto pairs in Bitfinex exchange            
+        AddUniverse(CryptoUniverse.Bitfinex(SelectAssets));
+        // Get the pairs on Bitfinex that have USD as the quote currency.
+        _marketPairs = SymbolPropertiesDatabase.GetSymbolPropertiesList(Market.Bitfinex)
             .Where(x => x.Value.QuoteCurrency == AccountCurrency)
             .Select(x => x.Key.Symbol)
             .ToList();
-        AddAlpha( new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, null) );
+        AddAlpha( new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromHours(4), 0.025, null) );
         // Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
         SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
     }
 
-    private IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
+    private IEnumerable<Symbol> SelectAssets(IEnumerable<CryptoUniverse> universeDay)
     {
         // Select pairs where the quote currency is USD.
         return universeDay.Where(c => _marketPairs.Contains(c.Symbol.Value))
@@ -217894,16 +188071,16 @@ 
      
 }
      
   
      
   
      
-   Research Example
+   Research  Example
   
      
   
      
-   The following example lists crypto-currency pairs with the greatest dollar volume in the BinanceUS exchange:
+   The following example lists crypto-currency pairs with the greatest dollar volume in the Bitfinex exchange:
   
      
   
      
    
      var qb = new QuantBook();
 
 // Add Cryptocurrency pair
-var symbol = qb.AddCrypto("BTCUSDT", market:Market.BinanceUS).Symbol;
+var symbol = qb.AddCrypto("BTCUSDT", market:Market.Bitfinex).Symbol;
 
 // Historical data
 var history = qb.History(symbol, 30, Resolution.Daily);
@@ -217921,7 +188098,7 @@ 
      
         .Take(5)
         .Select(x => x.Symbol);
 }
-var universe = qb.AddUniverse(CryptoUniverse.BinanceUS(UniverseSelectionFilter));
+var universe = qb.AddUniverse(CryptoUniverse.Bitfinex(UniverseSelectionFilter));
 
 // Historical Universe data
 var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
@@ -217936,7 +188113,7 @@ 
      
    
      qb = QuantBook()
 
 # Add Cryptocurrency pair
-symbol = qb.add_crypto("BTCUSDT", market=Market.BINANCE_US).symbol
+symbol = qb.add_crypto("BTCUSDT", market=Market.BITFINEX).symbol
 
 # Historical data
 history = qb.history(symbol, 30, Resolution.DAILY)
@@ -217950,7 +188127,7 @@ 
      
         key=lambda x: x.volume_in_usd, reverse=True)[:5]
     return [x.symbol for x in selected]
 
-universe = qb.add_universe(CryptoUniverse.binance_us(universe_selection_filter))
+universe = qb.add_universe(CryptoUniverse.bitfinex(universe_selection_filter))
 
 # Historical Universe data
 history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
@@ -217966,7 +188143,7 @@ 
      Data Point Attributes
      
 
  
   
      
-   The Binance US Crypto Price dataset provides
+   The Bitfinex Crypto Price dataset provides
    
     TradeBar
    
@@ -218032,22 +188209,29 @@ 
      
 
 
 
       
      
-
      Datasets > CoinAPI > Bitfinex Crypto Price Data
      
+
      Datasets > QuantConnect > Bybit Crypto Future Margin Rate Data
      
 
      
-    
      
-        
      CoinAPI
      
-        
      Bitfinex Crypto Price Data
      
+    
      
+        
      QuantConnect
      
+        
      Bybit Crypto Future Margin Rate Data
      
     
      
 
      
 
      Introduction
      
 
  
   
      
-   The Bitfinex Crypto Price Data by CoinAPI is for Cryptocurrency price and volume data points. The data covers 570 Cryptocurrency pairs, starts in January 2013, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Bitfinex.
+   The Bybit Crypto Future Margin Rate Data by QuantConnect is for Cryptocurrency Futures margin interest data points. The data covers 631 Cryptocurrency pairs, starts in August 2020, and is delivered on a daily update frequency. This dataset is created by downloading data using Bybit API.
   
      
   
      
-   For more information about the Bitfinex Crypto Price Data dataset, including CLI commands and pricing, see the
-   
+   This dataset is an important companion to the
+   
+    Bybit Crypto Future Price Data
+   
+   dataset because it contains information on margin interest data to model margin costs.
+  
      
+  
      
+   For more information about the Bybit Crypto Future Margin Rate Data dataset, including CLI commands and pricing, see the
+   
     dataset listing
    
    .
@@ -218061,10 +188245,7 @@ 
      About the Provider
      
 
  
   
      
-   
-    CoinAPI
-   
-   was founded by Artur Pietrzyk in 2016 with the goal of providing real-time and historical cryptocurrency market data, collected from hundreds of exchanges. CoinAPI provides access to Cryptocurrencies for traders, market makers, and developers building third-party applications.
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading.  Through the QuantConnect web platform, more than 50,000 quants are served every month.
   
      
  
 
@@ -218073,24 +188254,28 @@ 
      Getting Started
      
 
  
   
      
-   The following snippet demonstrates how to request data from the Bitfinex Crypto Price dataset:
+   The following snippet demonstrates how to request data from the Bybit Crypto Future Margin Rate dataset:
   
      
   
      
-   
      # Bitfinex accepts both Cash and Margin type account.
-self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.CASH)
-#self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.MARGIN)
-
-self.btcusd = self.add_crypto("BTCUSD", Resolution.MINUTE, Market.BITFINEX).symbol
+   
      def initialize(self) -> None:
+    self.set_brokerage_model(BrokerageName.BYBIT, AccountType.MARGIN)
 
-self._universe = self.add_universe(CryptoUniverse.bitfinex(self.universe_selection_filter))
      
-   
      // Bitfinex accepts both Cash and Margin type account.
-SetBrokerageModel(BrokerageName.Bitfinex, AccountType.Cash);
-//SetBrokerageModel(BrokerageName.Bitfinex, AccountType.Margin);
+    self.crypto_future_symbol = self.add_crypto_future("BTCBUSD", Resolution.MINUTE).symbol
+
      
+   
      private Symbol _cryptoFutureSymbol;
 
-_symbol = AddCrypto("BTCUSD", Resolution.Minute, Market.Bitfinex).Symbol;
+public override void Initialize
+{
+    SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin);
 
-_universe = AddUniverse(CryptoUniverse.Bitfinex(UniverseSelectionFilter));
      
+    // perpetual futures does not have a filter function
+    _cryptoFutureSymbol = AddCryptoFuture("BTCBUSD", Resolution.Minute).Symbol;
+}
+
      
   
      
+  
      
+   The Bybit Crypto Future Margin Rate data is added to your algorithm along with the market data when you add a crypto future subscription.
+  
      
  
 
 
@@ -218117,7 +188302,7 @@ 
      Data Summary
      
       Start Date
      
      
    
@@ -218125,7 +188310,7 @@ 
    Data Summary
    
       Asset Coverage
      
@@ -218133,7 +188318,7 @@ 
    Data Summary
    
       Data Density
      
@@ -218141,7 +188326,7 @@ 
    Data Summary
    
       Resolution
      
@@ -218171,51 +188356,60 @@ 
    Requesting Data
    
-   To add Bitfinex Crypto Price data to your algorithm, call the
+   To add Bybit Crypto Future Margin Rate data to your algorithm, call the
    
-    AddCrypto
+    AddCryptoFuture
    
    
-    add_crypto
+    add_crypto_future
    
-   method. Save a reference to the Crypto
+   method. Save a reference to the Crypto Future
    
     Symbol
    
    so you can access the data later in your algorithm.
   
    
-   
    class CoinAPIDataAlgorithm(QCAlgorithm):
+   
    from AlgorithmImports import *
+from QuantConnect.DataSource import *
+
+class CoinAPIDataAlgorithm(QCAlgorithm):
 
     def initialize(self) -> None:
         self.set_start_date(2020, 6, 1)
         self.set_end_date(2021, 6, 1)
-        self.set_cash(100000)
 
-        # Bitfinex accepts both Cash and Margin type account.
-        self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.MARGIN)
+        # Set Account Currency to Tether
+        self.set_account_currency("USDT", 100000)
+
+        self.set_brokerage_model(BrokerageName.BYBIT, AccountType.MARGIN)
         
-        self.btcusd = self.add_crypto("BTCUSD", Resolution.MINUTE, Market.BITFINEX).symbol
    
+        crypto_future = self.add_crypto_future("BTCUSDT", Resolution.MINUTE)
+        # perpetual futures does not have a filter function
+        self.btcusdt = crypto_future.symbol
    
    
    public class CoinAPIDataAlgorithm : QCAlgorithm
 {
-    private Symbol _symbol;
+    private Symbol _symbol ;
     
     public override void Initialize()
     {
         SetStartDate(2020, 6, 1);
         SetEndDate(2021, 6, 1);
-        SetCash(100000);
 
-        // Bitfinex accepts both Cash and Margin type account.
-        SetBrokerageModel(BrokerageName.Bitfinex, AccountType.Margin);
+        // Set Account Currency to Tether
+        SetAccountCurrency("USDT", 100000);
+
+        SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin);
         
-        _symbol = AddCrypto("BTCUSD", Resolution.Minute, Market.Bitfinex).Symbol;
+        var cryptoFuture = AddCryptoFuture("BTCUSDT", Resolution.Minute);
+        // perpetual futures does not have a filter function
+        _symbol = cryptoFuture.Symbol;
     }
 }
    
   
    
-   For more information about creating Crypto subscriptions, see
-   
+   For more information about creating Crypto Future subscriptions, see
+   
     Requesting Data
    
    .
@@ -218227,34 +188421,20 @@ 
    Accessing Data
    
-   To get the current Bitfinex Crypto Price data, index the
-   
-    Bars
-   
-   
-    bars
-   
-   ,
-   
-    QuoteBars
-   
-   
-    quote_bars
-   
-   , or
+   To get the current Bybit Crypto Margin Rate data, index the
    
-    Ticks
+    MarginInterestRates
    
    
-    ticks
+    margin_interest_rates
    
-   properties of the current
+   property of the current
    
     
      Slice
     
    
-   with the Crypto
+   with the Crypto Future
    
     Symbol
    
@@ -218274,42 +188454,17 @@ 
    Accessing Data
    
    
    def on_data(self, slice: Slice) -> None:
-    if self.btcusd in slice.bars:
-        trade_bar = slice.bars[self.btcusd]
-        self.log(f"{self.btcusd} close at {slice.time}: {trade_bar.close}")
-
-    if self.btcusd in slice.quote_bars:
-        quote_bar = slice.quote_bars[self.btcusd]
-        self.log(f"{self.btcusd} bid at {slice.time}: {quote_bar.bid.close}")
-
-    if self.btcusd in slice.ticks:
-        ticks = slice.ticks[self.btcusd]
-        for tick in ticks:
-            self.log(f"{self.btcusd} price at {slice.time}: {tick.price}")
    
+        if self.btcusdt in slice.margin_interest_rates:
+            interest_rate = slice.margin_interest_rates[self.btcusdt].interest_rate
+            self.log(f"{self.btcusdt} close at {slice.time}: {interest_rate}")
    
    public override void OnData(Slice slice)
 {
-    if (slice.Bars.ContainsKey(_symbol))
-    {
-        var tradeBar = slice.Bars[_symbol];
-        Log($"{_symbol} price at {slice.Time}: {tradeBar.Close}");
-    }
-
-    if (slice.QuoteBars.ContainsKey(_symbol))
-    {
-        var quoteBar = slice.QuoteBars[_symbol];
-        Log($"{_symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
-    }
-
-    if (slice.Ticks.ContainsKey(_symbol))
+    if (slice.MarginInterestRates.ContainsKey(_symbol))
     {
-        var ticks = slice.Ticks[_symbol];
-        foreach (var tick in ticks)
-        {
-            Log($"{_symbol} price at {slice.Time}: {tick.Price}");
-        }
+        var interestRate = slice.MarginInterestRates[_symbol].InterestRate;
+        Log($"{_symbol} price at {slice.Time}: {interestRate}");
     }
-}
-
    
+}
   
    
    You can also iterate through all of the data objects in the current
@@ -218320,46 +188475,23 @@ 
    Accessing Data
    
    
    def on_data(self, slice: Slice) -> None:
-    for symbol, trade_bar in slice.bars.items():
-        self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")
-
-    for symbol, quote_bar in slice.quote_bars.items():
-        self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
-
-    for symbol, ticks in slice.ticks.items():
-        for tick in ticks:
-            self.log(f"{symbol} price at {slice.time}: {tick.price}")
    
+    for symbol, margin_interest_rate in slice.margin_interest_rates.items():
+        interest_rate = margin_interest_rate.interest_rate
+        self.log(f"{symbol} close at {slice.time}: {interest_rate}")
    
    public override void OnData(Slice slice)
 {
-    foreach (var kvp in slice.Bars)
-    {
-        var symbol = kvp.Key;
-        var tradeBar = kvp.Value;
-        Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
-    }
-
-    foreach (var kvp in slice.QuoteBars)
-    {
-        var symbol = kvp.Key;
-        var quoteBar = kvp.Value;
-        Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
-    }
-
-    foreach (var kvp in slice.Ticks)
+    foreach (var kvp in slice.MarginInterestRates)
     {
         var symbol = kvp.Key;
-        var ticks = kvp.Value;
-        foreach (var tick in ticks)
-        {
-            Log($"{symbol} price at {slice.Time}: {tick.Price}");
-        }
+        var marginInterestRate = kvp.Value;
+        var interestRate = marginInterestRate.InterestRate;
+        Log($"{symbol} price at {slice.Time}: {interestRate}");
     }
-}
-
    
+}
   
    
-   For more information about accessing Crypto data, see
-   
+   For more information about accessing Crypto Future data, see
+   
     Handling Data
    
    .
@@ -218371,14 +188503,21 @@ 
    Historical Data
    
-   To get historical Bitfinex Crypto Price data, call the
+   To get historical Bybit Crypto Future Margin Rate data, call the
    
-    History
+    History<MarginInterestRate>
    
    
     history
    
-   method with the Crypto
+   method with
+   
+    
+     MarginInterestRate
+    
+    data type and
+   
+   the Crypto Future
    
     Symbol
    
@@ -218386,24 +188525,11 @@ 
    Historical Data
    
    
    # DataFrame
-history_df = self.history(self.btcusd, 100, Resolution.DAILY)
-
-# TradeBar objects
-history_trade_bars = self.history[TradeBar](self.btcusd, 100, Resolution.MINUTE)
-
-# QuoteBar objects
-history_quote_bars = self.history[QuoteBar](self.btcusd, 100, Resolution.MINUTE)
-
-# Tick objects
-history_ticks = self.history[Tick](self.btcusd, timedelta(seconds=10), Resolution.TICK)
    
-   
    // TradeBar objects 
-var historyTradeBars = History(_symbol, 100, Resolution.Daily);
-
-// QuoteBar objects 
-var historyQuoteBars = History<QuoteBar>(_symbol, 100, Resolution.Minute);
+history_df = self.history(MarginInterestRate, self.btcusdt, 100, Resolution.DAILY)
 
-// Tick objects 
-var historyTicks = History<Tick>(_symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
+# MarginInterestRate objects
+history = self.history[MarginInterestRate](self.btcusdt, 100, Resolution.DAILY)
+   
    var history = History<MarginInterestRate>(_symbol, 100, Resolution.Daily);
    
   
    
    For more information about historical data, see
@@ -218415,155 +188541,6 @@ 
    Historical Data
    
 
-
    Universe Selection
    
-
- 
-  
    
-   To select a dynamic universe of Bitfinex Crypto pairs, call the
-   
-    AddUniverse
-   
-   
-    add_universe
-   
-   method with a
-   
-    CryptoUniverse
-   
-   object. A
-   
-    Crypto universe
-   
-   uses a selection function to select Crypto pairs based on their OHLCV and dollar volume of the previous day as of midnight Coordinated Universal Time (UTC).
-  
    
-  
    
-   
    def initialize(self) -> None:
-    self.universe_settings.asynchronous = True
-    self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.MARGIN)
-    self._universe = self.add_universe(CryptoUniverse.bitfinex(self.universe_selection_filter))
-
-def universe_selection_filter(self, universe_day):
-    return [c.symbol for c in universe_day if c.volume >= 100 and c.volume_in_usd > 10000]
    
-   
    public override void Initialize()
-{
-    UniverseSettings.Asynchronous = true;
-    SetBrokerageModel(BrokerageName.Bitfinex, AccountType.Margin);
-    _universe = AddUniverse(CryptoUniverse.Bitfinex(UniverseSelectionFilter));
-}
-
-private IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
-{
-    return from c in universeDay
-           where c.Volume >= 100m && c.VolumeInUsd > 10000m
-           select c.Symbol;
-}
    
-  
    
-  
    
-   For more information about universe settings, see
-   
-    Settings
-   
-   .
-  
    
- 
-
-
-
    Universe History
    
-
- 
-  
    
-   You can get historical universe data in an algorithm and in the Research Environment.
-  
    
-  
    
-   Historical Universe Data in Algorithms
-  
    
-  
    
-   To get historical universe data in an algorithm, call the
-   
-    History
-   
-   
-    history
-   
-   method with the
-   
-    Universe
-   
-   object, and the lookback period. If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    var history = History(_universe, 30, Resolution.Daily);
-foreach (var universeDay in history)
-{
-    foreach (CryptoUniverse universeItem in universeDay)
-    {
-        Log($"{universeItem.Symbol} price at {universeItem.EndTime}: {universeItem.Close}");
-    }
-}
    
-   
    # DataFrame example where the columns are the CryptoUniverse attributes: 
-history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)
-
-# Series example where the values are lists of CryptoUniverse objects: 
-history = self.history(self._universe, 30, Resolution.DAILY)
-for (univere_symbol, time), universe_day in history.items():
-    for universe_item in universe_day:
-        self.log(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")
    
-  
    
-  
    
-   Historical Universe Data in Research
-  
    
-  
    
-   To get historical universe data in research, call the
-   
-    UniverseHistory
-   
-   
-    universe_history
-   
-   method with the
-   
-    Universe
-   
-   object, and the lookback period. The
-   
-    UniverseHistory
-   
-   
-    universe_history
-   
-   returns the filtered universe. If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-foreach (var universeDay in universeHistory)
-{
-    foreach (CryptoUniverse universeItem in universeDay)
-    {
-        Console.WriteLine($"{universeItem.Symbol} price at {universeItem.EndTime}: {universeItem.Close}");
-    }
-}
    
-   
    # DataFrame example where the columns are the CryptoUniverse attributes: 
-history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
-
-# Series example where the values are lists of CryptoUniverse objects: 
-universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (univere_symbol, time), universe_day in universe_history.items():
-    for universe_item in universe_day:
-        print(f"{universe_item.symbol} price at {universe_item.end_time}: {universe_item.close}")
    
-  
    
-  
    
-   You can call the
-   
-    History
-   
-   
-    history
-   
-   method in Research.
-  
    
- 
-
-
 
    Remove Subscriptions
    
@@ -218585,7 +188562,7 @@ 
    Remove Subscriptions
    
    method.
   
    
-   
    self.remove_security(self.btcusd)
    
+   
    self.remove_security(self.btcusdt)
    
    
    RemoveSecurity(_symbol);
    
   
    
@@ -218609,27 +188586,27 @@ 
    Supported Assets
    
-   The following table shows the available Cryptocurrency pairs:
+   The following table shows the available Crypto Future pairs:
   
    
    
    
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
@@ -218789,376 +188795,407 @@ 
    Supported Assets
    
        AVAXUSDT
       
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
@@ -219169,10 +189206,10 @@ 
    Supported Assets
    
        EOSUSDT
       
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+      
+      
+      
+     
+     
+      
+      
+      
+     
+     
+      
@@ -220531,144 +190713,117 @@ 
    Example Applications
    
-   The Bitfinex Crypto Price dataset enables you to accurately design strategies for Cryptocurrencies. Examples include the following strategies:
+   The Binance Crypto Future Margin Rate dataset enables correct margin cost so you can accurately design strategies for Cryptocurrencies with term structure. Examples include the following strategies:
   
      
    
    • 
-    Buy and hold
+    Horizontal/Diagonal arbitrage with the underlying Cryptocurrencies
    
      • 
    
    • 
-    Trading Cryptocurrency volatility and price action
+    Trade Contango/Backwardation predictions
    
      • 
    
    • 
-    Allocating a small portion of your portfolio to Cryptocurrencies to hedge against inflation
+    Hedge for illiquid Cryptocurrencies
    
      • 
   
    
    Classic Algorithm Example
   
    
-   The following example algorithm buys and holds Bitcoin through the Bitfinex exchange:
+   The following example algorithm holds a long position in the BTCUSDT perpetual Future contract on the Bybit exchange. When the margin interest rate is relatively low, it flips to a short position.
   
    
    
    from AlgorithmImports import *
 
 
-class CoinAPIDataAlgorithm(QCAlgorithm):
+class BybitCryptoFutureDataAlgorithm(QCAlgorithm):
+
+    # Add an indicator to track the low of trailing margin interest rates.
+    _low_interest_rate = Minimum(10)
 
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        self.universe_settings.asynchronous = True
-        self.set_cash(100_000)
-        # Bitfinex accepts both Cash and Margin account types. Select the
-        # one you need for the best reality modeling.
-        self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.MARGIN)
-        # Add BTCUSD data.
-        self._btc = self.add_crypto("BTCUSD", Resolution.MINUTE, Market.BITFINEX)
+        # Set Account Currency to USDT, since USD cannot be used to trade.
+        self.set_cash("USDT", 100_000)
+        # Bybit  accepts both Cash and Margin account types, select the one you need for the best reality modeling.
+        self.set_brokerage_model(BrokerageName.BYBIT, AccountType.MARGIN)
+        # Seed the price of each asset with its last known price to avoid trading errors.
+        self.settings.seed_initial_prices = True
+        # Requesting data, we only trade on BTCUSDT Future in Bybit  exchange
+        # Perpetual futures does not have a filter function.
+        self._future = self.add_crypto_future("BTCUSDT", Resolution.DAILY)
         # Historical data
-        history = self.history(self._btc.symbol, 30, Resolution.DAILY)
-        # Add a Crypto universe that selects Crypto pairs on Bitfinex.
-        universe = self.add_universe(CryptoUniverse.bitfinex(self._select_assets))
-        # Historical Universe data
-        universe_history = self.history(universe, 30, Resolution.DAILY)
-        for (universe_symbol, time), universe_day in universe_history.items():
-            for universe_item in universe_day:
-                symbol = universe_item.symbol
-                price = universe_item.close
-
-    def _select_assets(self, universe_day):
-        # Filter for materially traded Crypto pairs with significant 
-        # size and dollar volume, assuming higher capital flow in for 
-        # higher return.
-        return [universe_item.symbol for universe_item in universe_day
-                if universe_item.volume >= 100 
-                and universe_item.volume_in_usd > 10_000_000]
+        history = self.history[MarginInterestRate](self._future.symbol, 10, Resolution.DAILY)
+        for data_point in history:
+            self._low_interest_rate.update(data_point.end_time, data_point.interest_rate)
 
     def on_data(self, slice: Slice) -> None:
-        # Speculate-invest all available free cash on BTCUSD, repecting 
-        # the order quantity restrictions to avoid invalid orders.
-        if self.portfolio.cash_book['BTC'].amount != 0:
-            return
-        # Define a buffer so that we can scale down the order size,
-        # avoiding trading errors.
-        buffer = max(
-            BitfinexFeeModel.TAKER_FEE, 
-            self.settings.free_portfolio_value_percentage
-        )
-        quantity = (
-            self.portfolio.cash_book['USD'].amount
-            / self._btc.ask_price
-            * (1-buffer)
-        )
-        # Check if the quantity exceeds the minimum order size. On 
-        # Bitfinex, the minimum order size is measured in the base
-        # currency (BTC in this case).
-        if abs(quantity) < self._btc.symbol_properties.minimum_order_size:
+        # Note that you may want to access the margin interest of the crypto future to calculate if it would impact a trade's PnL
+        # Or you can calculate the trade size on keeping the quote currency constant
+        if self._future.symbol not in slice.margin_interest_rates:
             return
-        self.market_order(self._btc, quantity)
    
-   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+        interest_rate = slice.margin_interest_rates[self._future.symbol].interest_rate
+        self.plot('Margin Interest Rate', str(self._future.symbol), interest_rate)
+        # Short when margin interest rates are relatively low. Otherwise, long.
+        if interest_rate <= self._low_interest_rate.current.value:
+            if not self._future.holdings.is_short:
+                self.set_holdings(self._future.symbol, -0.5)
+        elif not self._future.holdings.is_long:
+            self.set_holdings(self._future.symbol, 1)
+        # Update the indicator.
+        self._low_interest_rate.update(self.time, interest_rate)
    
+   
    public class BybitCryptoFutureDataAlgorithm : QCAlgorithm
 {
-    private Crypto _btc;
-    
+    private CryptoFuture _future;
+    // Add an indicator to track the low of trailing margin interest rates.
+    private Minimum _lowInterestRate = new(10);
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        UniverseSettings.Asynchronous = true;
-        SetCash(100000);
-        // Bitfinex accepts both Cash and Margin account types. Select the
-        // one you need for the best reality modeling.
-        SetBrokerageModel(BrokerageName.Bitfinex, AccountType.Margin);
-        // Add BTCUSD data.
-        _btc = AddCrypto("BTCUSD", Resolution.Minute, Market.Bitfinex);   
+        // Set Account Currency to USDT, since USD cannot be used to trade.
+        SetCash("USDT", 100000);
+        // Bybit  accepts both Cash and Margin account types, select the one you need for the best reality modeling.
+        SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin);
+        // Seed the price of each asset with its last known price to avoid trading errors.
+        Settings.SeedInitialPrices = true;
+        // Requesting data, we only trade on BTCUSDT Future in Bybit  exchange
+        // Perpetual futures does not have a filter function.
+        _future = AddCryptoFuture("BTCUSDT", Resolution.Daily);
         // Historical data
-        var history = History(_btc.Symbol, 30, Resolution.Daily);
-        // Add a Crypto universe that selects trading pairs on Bitfinex.
-        var universe = AddUniverse(CryptoUniverse.Bitfinex(SelectAssets));
-        // Historical Universe data
-        var universeHistory = History(universe, 30, Resolution.Daily);
-        foreach (var universeDay in universeHistory)
+        var history = History<MarginInterestRate>(_future.Symbol, 10, Resolution.Daily);
+        foreach (var dataPoint in history)
         {
-            foreach (CryptoUniverse universeItem in universeDay)
-            {
-                var symbol = universeItem.Symbol;
-                var price = universeItem.Close;
-            }
+            _lowInterestRate.Update(dataPoint.EndTime, dataPoint.InterestRate);
         }
     }
 
-    private IEnumerable<Symbol> SelectAssets(IEnumerable<CryptoUniverse> universeDay)
-    {
-        // Filter for materially traded Crypto pairs with significant 
-        // size and dollar volume, assuming higher capital flow in for 
-        // higher return.
-        return from universeItem in universeDay
-                where universeItem.Volume >= 100m 
-                && universeItem.VolumeInUsd > 10000000m
-                select universeItem.Symbol;
-    }
-
     public override void OnData(Slice slice)
     {
-        // Speculate-invest all available free cash on BTCUSD, repecting 
-        // the order quantity restrictions to avoid invalid orders.
-        if (Portfolio.CashBook["BTC"].Amount != 0)
+        // Note that you may want to access the margin interest of the crypto future to calculate if it would impact a trade's PnL
+        // Or you can calculate the trade size on keeping the quote currency constant
+        if (!slice.MarginInterestRates.TryGetValue(_future.Symbol, out var dataPoint))
         {
             return;
         }
-        // Define a buffer so that we can scale down the order size,
-        // avoiding trading errors.
-        var buffer = Math.Max(BitfinexFeeModel.TakerFee, Settings.FreePortfolioValuePercentage);
-        var quantity = Portfolio.CashBook["USD"].Amount / _btc.AskPrice * (1-buffer);
-        // Check if the quantity exceeds the minimum order size. On 
-        // Bitfinex, the minimum order size is measured in the base
-        // currency (BTC in this case).
-        if (Math.Abs(quantity) < _btc.SymbolProperties.MinimumOrderSize)
+        var interestRate = dataPoint.InterestRate;
+        Plot("Margin Interest Rate", _future.Symbol.ToString(), interestRate);
+        // Short when margin interest rates are relatively low. Otherwise, long.
+        if (interestRate <= _lowInterestRate)
         {
-            return;
+            if (!_future.Holdings.IsShort)
+            {
+                SetHoldings(_future.Symbol, -0.5);
+            }
         }
-        MarketOrder(_btc, quantity);
+        else if (!_future.Holdings.IsLong)
+        {
+            SetHoldings(_future.Symbol, 1);
+        }
+        // Update the indicator.
+        _lowInterestRate.Update(Time, interestRate);
     }
 }
    
   
    
@@ -220676,165 +190831,147 @@ 
    
    Framework Algorithm Example
   
    
-   The following example algorithm creates a dynamic universe of Crypto pairs on the Bitfinex exchange and then forms a equal-weighted portfolio of all the pairs in the universe:
+   The following example algorithm holds a long position in the BTCUSDT perpetual Future contract on the Bybit exchange. When the margin interest rate is relatively low, it flips to a short position.
   
    
    
    from AlgorithmImports import *
 
 
-class CoinAPIDataAlgorithm(QCAlgorithm):
+class BybitCryptoFutureDataAlgorithm(QCAlgorithm):
 
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        self.settings.free_portfolio_value_percentage = 0.05
-        # Bitfinex accepts both Cash and Margin account types, select the one you need for the best reality modeling.
-        self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.MARGIN)
-
-        # Warm up the security with the last known price to avoid conversion error
+        self.settings.free_portfolio_value_percentage = 0.1
+        # Set Account Currency to USDT, since USD cannot be used to trade.
+        self.set_cash("USDT", 100000)
+        # Bybit accepts both Cash and Margin account types, select the one you need for the best reality modeling.
+        self.set_brokerage_model(BrokerageName.BYBIT, AccountType.MARGIN)
+        # Seed the price of each asset with its last known price to avoid trading errors.
         self.settings.seed_initial_prices = True
-        self.universe_settings.asynchronous = True
-        self.universe_settings.resolution = Resolution.MINUTE
-        # Add Crypto Universe Selection that select crypto pairs in Bitfinex exchange
-        self.add_universe(CryptoUniverse.bitfinex(self._select_assets))
-        # Get the pairs on Bitfinex that have USD as the quote currency.
-        self._market_pairs = [
-            x.key.symbol 
-            for x in self.symbol_properties_database.get_symbol_properties_list(Market.BITFINEX) 
-            if x.value.quote_currency == self.account_currency
-        ]
-        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(hours=4), 0.025, None))
+        self.universe_settings.resolution = Resolution.DAILY
+        self.universe_settings.leverage = 2
+        # We only trade on BTCUSDT Future in Bybit exchange
+        symbols = Symbol.create("BTCUSDT", SecurityType.CRYPTO_FUTURE, Market.BYBIT)
+        self.add_universe_selection(ManualUniverseSelectionModel(symbols))
+        # Custom alpha model to emit insights based on the Crypto Future price data
+        self.add_alpha(CryptoFutureAlphaModel())
         # Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
         self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+        self.set_execution(ImmediateExecutionModel())
 
-    def _select_assets(self, universe_day: List[CryptoUniverse]) -> List[Symbol]:
-        # Select pairs where the quote currency is USD.
-        pairs = [c for c in universe_day if c.symbol.value in self._market_pairs]
-        # Filter for materially traded crypto pairs with significant dollar volume, assuming higher capital flow in for higher return.
-        pairs = sorted(pairs, key=lambda c: c.volume_in_usd)[-3:]      
-        return [c.symbol for c in pairs]
 
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
+class CryptoFutureAlphaModel(AlphaModel):
+
+    _min_by_symbol = {}
+
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        insights = []
+        for symbol, low_interest_rate in self._min_by_symbol.items():
+            # Note that you may want to access the margin interest of the crypto future to calculate if it would impact a trade's PnL
+            # Or you can calculate the trade size on keeping the quote currency constant
+            if symbol not in slice.margin_interest_rates:
+                continue
+            interest_rate = slice.margin_interest_rates[symbol].interest_rate
+            algorithm.plot('Margin Interest Rate', str(symbol), interest_rate)
+            # Short when margin interest rates are relatively low. Otherwise, long.
+            if interest_rate <= low_interest_rate.current.value:
+                direction = InsightDirection.DOWN
+            else:
+                direction = InsightDirection.UP
+            insights.append(Insight.price(symbol, timedelta(1), direction))
+            # Update the indicator.
+            low_interest_rate.update(slice.time, interest_rate)
+        return insights
+
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
         for security in changes.added_securities:
+            symbol = security.symbol
+            # Add an indicator to track the low of trailing margin interest rates.
+            self._min_by_symbol[symbol] = Minimum(10)
             # Historical data
-            history = self.history(security.symbol, 30, Resolution.DAILY)
-            self.debug(f"We got {len(history)} items from our history request")
    
-   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+            history = algorithm.history[MarginInterestRate](symbol, 10, Resolution.DAILY)
+            for data_point in history:
+                self._min_by_symbol[symbol].update(data_point.end_time, data_point.interest_rate)
+        for security in changes.removed_securities:
+            self._min_by_symbol.pop(security.symbol, None)
    
+   
    public class BybitCryptoFutureDataAlgorithm : QCAlgorithm
 {
-    private List<string> _marketPairs;
-    
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        Settings.FreePortfolioValuePercentage = 0.05m;
-        // Bitfinex accepts both Cash and Margin account types, select the one you need for the best reality modeling.
-        SetBrokerageModel(BrokerageName.Bitfinex, AccountType.Margin);
-
-        // Warm up the security with the last known price to avoid conversion error
+        Settings.FreePortfolioValuePercentage = 0.1m;
+        // Set Account Currency to USDT, since USD cannot be used to trade.
+        SetCash("USDT", 100000);
+        // Bybit accepts both Cash and Margin account types, select the one you need for the best reality modeling.
+        SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin);
+        // Seed the price of each asset with its last known price to avoid trading errors.
         Settings.SeedInitialPrices = true;
-        UniverseSettings.Asynchronous = true;
-        UniverseSettings.Resolution = Resolution.Minute;
-        // Add Crypto Universe Selection that select crypto pairs in Bitfinex exchange            
-        AddUniverse(CryptoUniverse.Bitfinex(SelectAssets));
-        // Get the pairs on Bitfinex that have USD as the quote currency.
-        _marketPairs = SymbolPropertiesDatabase.GetSymbolPropertiesList(Market.Bitfinex)
-            .Where(x => x.Value.QuoteCurrency == AccountCurrency)
-            .Select(x => x.Key.Symbol)
-            .ToList();
-        AddAlpha( new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromHours(4), 0.025, null) );
+        UniverseSettings.Resolution = Resolution.Daily;
+        UniverseSettings.Leverage = 2;
+        // We only trade on BTCUSDT Future in Bybit exchange
+        var symbols = new List<Symbol>{
+            QuantConnect.Symbol.Create("BTCUSDT", SecurityType.CryptoFuture, Market.Bybit)
+        };
+        AddUniverseSelection(new ManualUniverseSelectionModel(symbols));
+        // Custom alpha model to emit insights based on the Crypto Future price data
+        AddAlpha(new CryptoFutureAlphaModel());
         // Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
         SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+        SetExecution(new ImmediateExecutionModel());
     }
+}
 
-    private IEnumerable<Symbol> SelectAssets(IEnumerable<CryptoUniverse> universeDay)
-    {
-        // Select pairs where the quote currency is USD.
-        return universeDay.Where(c => _marketPairs.Contains(c.Symbol.Value))
-            // Filter for materially traded crypto pairs with significant size and dollar volume, assuming higher capital flow in for higher return
-            .OrderBy(c => c.VolumeInUsd)
-            .TakeLast(3)
-            .Select(c => c.Symbol);
-    }
+public class CryptoFutureAlphaModel : AlphaModel
+{
+    private Dictionary<Symbol, Minimum> _minBySymbol = new();
 
-    public override void OnSecuritiesChanged(SecurityChanges changes)
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
     {
-        foreach(var security in changes.AddedSecurities)
+        var insights = new List<Insight>();
+        foreach (var kvp in _minBySymbol)
         {
-            // Historical data
-            var history = History(security.Symbol, 30, Resolution.Daily);
-            Debug($"We got {history.Count()} items from our history request");
+            var symbol = kvp.Key;
+            var lowInterestRate = kvp.Value;
+            // Note that you may want to access the margin interest of the crypto future to calculate if it would impact a trade's PnL
+            // Or you can calculate the trade size on keeping the quote currency constant
+            if (!slice.MarginInterestRates.TryGetValue(symbol, out var dataPoint))
+                continue;
+            var interestRate = dataPoint.InterestRate;
+            algorithm.Plot("Margin Interest Rate", symbol.ToString(), interestRate);
+            // Short when margin interest rates are relatively low. Otherwise, long.
+            var direction = interestRate <= lowInterestRate.Current.Value
+                ? InsightDirection.Down
+                : InsightDirection.Up;
+            insights.Add(Insight.Price(symbol, TimeSpan.FromDays(1), direction));
+            // Update the indicator.
+            lowInterestRate.Update(slice.Time, interestRate);
         }
+        return insights;
     }
-}
    
-  
    
-  
    
-   Research  Example
-  
    
-  
    
-   The following example lists crypto-currency pairs with the greatest dollar volume in the Bitfinex exchange:
-  
    
-  
    
-   
    var qb = new QuantBook();
-
-// Add Cryptocurrency pair
-var symbol = qb.AddCrypto("BTCUSDT", market:Market.Bitfinex).Symbol;
-
-// Historical data
-var history = qb.History(symbol, 30, Resolution.Daily);
-foreach (var bar in history)
-{
-    Console.WriteLine($"{bar.EndTime} {bar}");
-}
-
-// Add Crypto Universe Selection
-IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
-{
-    return universeDay
-        .Where(x => x.VolumeInUsd != null && x.VolumeInUsd > 10000m)
-        .OrderByDescending(x => x.VolumeInUsd)
-        .Take(5)
-        .Select(x => x.Symbol);
-}
-var universe = qb.AddUniverse(CryptoUniverse.Bitfinex(UniverseSelectionFilter));
 
-// Historical Universe data
-var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-foreach (var universeDay in universeHistory)
-{
-    Console.WriteLine($"=== {universeDay.First().EndTime} ===");
-    foreach (CryptoUniverse universeItem in universeDay.OrderByDescending(x => x.VolumeInUsd))
+    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
     {
-        Console.WriteLine($"{universeItem.Symbol}: {universeItem.VolumeInUsd}");
+        foreach (var security in changes.AddedSecurities)
+        {
+            var symbol = security.Symbol;
+            // Add an indicator to track the low of trailing margin interest rates.
+            var min = new Minimum(10);
+            _minBySymbol[symbol] = min;
+            // Historical data
+            var history = algorithm.History<MarginInterestRate>(symbol, 10, Resolution.Daily);
+            foreach (var dataPoint in history)
+            {
+                min.Update(dataPoint.EndTime, dataPoint.InterestRate);
+            }
+        }
+        foreach (var security in changes.RemovedSecurities)
+        {
+            _minBySymbol.Remove(security.Symbol);
+        }
     }
 }
    
-   
    qb = QuantBook()
-
-# Add Cryptocurrency pair
-symbol = qb.add_crypto("BTCUSDT", market=Market.BITFINEX).symbol
-
-# Historical data
-history = qb.history(symbol, 30, Resolution.DAILY)
-for (symbol, time), row in history.iterrows():
-    print(f'{time} {symbol} {row.close}')
-
-# Add Crypto Universe Selection
-def universe_selection_filter(universe_day):
-    selected = sorted([x for x in universe_day
-        if x.volume_in_usd and x.volume_in_usd > 10000],
-        key=lambda x: x.volume_in_usd, reverse=True)[:5]
-    return [x.symbol for x in selected]
-
-universe = qb.add_universe(CryptoUniverse.bitfinex(universe_selection_filter))
-
-# Historical Universe data
-history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (univere_symbol, time), universe_day in history.items():
-    print(f'=== {time} ===')
-    for universe_item in sorted(universe_day, key=lambda x: x.volume_in_usd, reverse=True):
-        print(f"{universe_item.symbol}: {universe_item.volume_in_usd}")
    
   
    
@@ -220843,76 +190980,22 @@ 
    Data Point Attributes
    
-   The Bitfinex Crypto Price dataset provides
-   
-    TradeBar
-   
-   ,
-   
-    QuoteBar
-   
-   ,
-   
-    Tick
-   
-   , and
-   
-    CryptoUniverse
-   
-   objects.
-  
    
-  
    
-   TradeBar Attributes
-  
    
-  
    
-   
-    TradeBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   QuoteBar Attributes
-  
    
-  
    
-   
-    QuoteBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   Tick Attributes
-  
    
-  
    
-   
-    Tick
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   CryptoUniverse Attributes
-  
    
-  
    
+   The Binance Crypto Future Margin Rate dataset provides
    
-    CryptoUniverse
+    MarginInterestRate
    
-   objects have the following attributes:
+   objects, which have the following attributes:
   
    
-  
    
+  
    
   
    
  
 
 
 
     
    
-
    Datasets > CoinAPI > Bybit Crypto Future Price Data
    
+
    Datasets > QuantConnect > Bybit Crypto Future Price Data
    
 
    
-    
    
-        
    CoinAPI
    
+    
    
+        
    QuantConnect
    
         
    Bybit Crypto Future Price Data
    
     
    
 
    
@@ -220920,7 +191003,7 @@ 
    Introduction
    
 
  
   
    
-   The Bybit Crypto Future Price Data by CoinAPI is for Cryptocurrency Futures price and volume data points. The data covers 631 Cryptocurrency pairs, starts in August 2020, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Bybit.
+   The Bybit Crypto Future Price Data is for Cryptocurrency Futures price and volume data points. The data covers 631 Cryptocurrency pairs, starts in August 2020, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Bybit.
   
    
   
    
    The
@@ -220949,10 +191032,19 @@ 
    About the Provider
    
 
  
   
    
-   
-    CoinAPI
+   Bybit was co-founded by Ben Zhou in March 2018 with the goal to offer a professional platform where Crypto traders can find an ultra-fast matching engine, excellent customer service, and multilingual community support. Bybit provides access to trading Crypto and Crypto Futures for clients outside of
+   
+    excluded jurisdictions
+   
+   with low minimum deposits to set up an account. For more information about
+   
+    Crypto
    
-   was founded by Artur Pietrzyk in 2016 with the goal of providing real-time and historical cryptocurrency market data, collected from hundreds of exchanges. CoinAPI provides access to Cryptocurrencies for traders, market makers, and developers building third-party applications.
+   and
+   
+    fiat deposits
+   
+   , see the Bybit documentation. Bybit also provides Crypto staking, initial DEX offerings, and community airdrops.
   
    
  
 
@@ -223781,10 +193873,10 @@ 
    
 
 
 
     
    
-
    Datasets > CoinAPI > Bybit Crypto Price Data
    
+
    Datasets > QuantConnect > Bybit Crypto Price Data
    
 
    
-    
    
-        
    CoinAPI
    
+    
    
+        
    QuantConnect
    
         
    Bybit Crypto Price Data
    
     
    
 
    
@@ -223792,7 +193884,7 @@ 
    Introduction
    
 
  
   
    
-   The Bybit Crypto Price Data by CoinAPI is for Cryptocurrency price and volume data points. The data covers 956 Cryptocurrency pairs, starts in July 2017, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Bybit.
+   The Bybit Crypto Price Data is for Cryptocurrency price and volume data points. The data covers 956 Cryptocurrency pairs, starts in July 2017, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Bybit.
   
    
   
    
    For more information about the Bybit Crypto Price Data dataset, including CLI commands and pricing, see the
@@ -223810,10 +193902,19 @@ 
    About the Provider
    
 
  
   
    
-   
-    CoinAPI
+   Bybit was co-founded by Ben Zhou in March 2018 with the goal to offer a professional platform where Crypto traders can find an ultra-fast matching engine, excellent customer service, and multilingual community support. Bybit provides access to trading Crypto and Crypto Futures for clients outside of
+   
+    excluded jurisdictions
+   
+   with low minimum deposits to set up an account. For more information about
+   
+    Crypto
+   
+   and
+   
+    fiat deposits
    
-   was founded by Artur Pietrzyk in 2016 with the goal of providing real-time and historical cryptocurrency market data, collected from hundreds of exchanges. CoinAPI provides access to Cryptocurrencies for traders, market makers, and developers building third-party applications.
+   , see the Bybit documentation. Bybit also provides Crypto staking, initial DEX offerings, and community airdrops.
   
    
  
 
@@ -225981,1592 +196082,3837 @@ 
    Supported Assets
    
        LTCBTC
       
       
    
+      
+      
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+     
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+     
+     
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+     
+     
+      
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+     
+     
+      
+      
+    
+   
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The Bybit Crypto Price dataset enables you to accurately design strategies for Cryptocurrencies. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Buy and hold
+   
      • 
+   
    • 
+    Trading Cryptocurrency volatility and price action
+   
      • 
+   
    • 
+    Allocating a small portion of your portfolio to Cryptocurrencies to hedge against inflation
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm buys and holds Bitcoin through the Bybit exchange:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class CoinAPIDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        # Set the account currency to USDT, since you can't trade with 
+        # USD and the algorithm doesn't automatically convert USD & USDT
+        # holdings.
+        self.set_account_currency("USDT", 100_000)
+        self.universe_settings.asynchronous = True
+        # Bybit accepts both Cash and Margin account types. Select the
+        # one you need for the best reality modeling.
+        self.set_brokerage_model(BrokerageName.BYBIT, AccountType.MARGIN)
+        # Add BTCUSDT data.
+        self._btc = self.add_crypto("BTCUSDT", Resolution.MINUTE, Market.BYBIT)
+        # Historical data
+        history = self.history(self._btc.symbol, 30, Resolution.DAILY)
+        # Add a Crypto universe that selects trading pairs on Bybit.
+        universe = self.add_universe(CryptoUniverse.bybit(self._select_assets))
+        # Historical Universe data
+        universe_history = self.history(universe, 30, Resolution.DAILY)
+        for (universe_symbol, time), universe_day in universe_history.items():
+            for universe_item in universe_day:
+                symbol = universe_item.symbol
+                price = universe_item.close
+
+    def _select_assets(self, universe_day):
+        # Filter for materially traded Crypto pairs with significant 
+        # size and dollar volume, assuming higher capital flow in for 
+        # higher return.
+        return [universe_item.symbol for universe_item in universe_day
+                if universe_item.volume >= 100 
+                and universe_item.volume_in_usd > 10_000_000]
+
+    def on_data(self, slice: Slice) -> None:
+        # Speculate-invest all available free cash on BTCUSDT, repecting 
+        # the order quantity restrictions to avoid invalid orders.
+        if self.portfolio.cash_book['BTC'].amount != 0:
+            return
+        # Define a buffer so that we can scale down the order size,
+        # avoiding trading errors.
+        buffer = max(
+            BybitFeeModel.TAKER_NON_VIP_FEE, 
+            self.settings.free_portfolio_value_percentage
+        )
+        quantity = (
+            self.portfolio.cash_book['USDT'].amount
+            / self._btc.ask_price
+            * (1-buffer)
+        )
+        # Check if the quantity exceeds the minimum order size. On 
+        # Bybit, the minimum order size is measured in the base
+        # currency (BTC in this case).
+        if abs(quantity) < self._btc.symbol_properties.minimum_order_size:
+            return
+        self.market_order(self._btc, quantity)
    
+   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+{
+    private Crypto _btc;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        // Set the account currency to USDT, since you can't trade with 
+        // USD and the algorithm doesn't automatically convert USD & USDT
+        // holdings.
+        SetAccountCurrency("USDT", 100000);
+        UniverseSettings.Asynchronous = true;
+        // Bybit accepts both Cash and Margin account types. Select the
+        // one you need for the best reality modeling.
+        SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin);
+        // Add BTCUSDT data.
+        _btc = AddCrypto("BTCUSDT", Resolution.Minute, Market.Bybit);   
+        // Historical data
+        var history = History(_btc.Symbol, 30, Resolution.Daily);
+        // Add a Crypto universe that selects trading pairs on Bybit.
+        var universe = AddUniverse(CryptoUniverse.Bybit(SelectAssets));
+        // Historical Universe data
+        var universeHistory = History(universe, 30, Resolution.Daily);
+        foreach (var universeDay in universeHistory)
+        {
+            foreach (CryptoUniverse universeItem in universeDay)
+            {
+                var symbol = universeItem.Symbol;
+                var price = universeItem.Close;
+            }
+        }
+    }
+
+    private IEnumerable<Symbol> SelectAssets(IEnumerable<CryptoUniverse> universeDay)
+    {
+        // Filter for materially traded Crypto pairs with significant 
+        // size and dollar volume, assuming higher capital flow in for 
+        // higher return.
+        return from universeItem in universeDay
+                where universeItem.Volume >= 100m 
+                && universeItem.VolumeInUsd > 10000000m
+                select universeItem.Symbol;
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // Speculate-invest all available free cash on BTCUSDT, repecting 
+        // the order quantity restrictions to avoid invalid orders.
+        if (Portfolio.CashBook["BTC"].Amount != 0)
+        {
+            return;
+        }
+        // Define a buffer so that we can scale down the order size,
+        // avoiding trading errors.
+        var buffer = Math.Max(BybitFeeModel.TakerNonVIPFee, Settings.FreePortfolioValuePercentage);
+        var quantity = Portfolio.CashBook["USDT"].Amount / _btc.AskPrice * (1-buffer);
+        // Check if the quantity exceeds the minimum order size. On 
+        // Bybit, the minimum order size is measured in the base 
+        // currency (BTC in this case).
+        if (Math.Abs(quantity) < _btc.SymbolProperties.MinimumOrderSize)
+        {
+            return;
+        }
+        MarketOrder(_btc, quantity);
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm creates a dynamic universe of Crypto pairs on the Bybit exchange and then forms a equal-weighted portfolio of all the pairs in the universe:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class CoinAPIDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        # Set Account Currency to Tether, since USD and USDT will not auto-convert and USD cannot be used to trade
+        self.set_account_currency("USDT", 100000)
+        self.settings.free_portfolio_value_percentage = 0.05
+
+        # Bybit accepts both Cash and Margin account types, select the one you need for the best reality modeling.
+        self.set_brokerage_model(BrokerageName.BYBIT, AccountType.MARGIN)
+
+        # Warm up the security with the last known price to avoid conversion error
+        self.settings.seed_initial_prices = True
+        self.universe_settings.asynchronous = True
+        self.universe_settings.resolution = Resolution.MINUTE
+        # Add Crypto Universe Selection that select crypto pairs in Bybit exchange
+        self.add_universe(CryptoUniverse.bybit(self.universe_selection_filter))
+        # Get the pairs on Bybit that have USDT as the quote currency.
+        self._market_pairs = [
+            x.key.symbol 
+            for x in self.symbol_properties_database.get_symbol_properties_list(Market.BYBIT) 
+            if x.value.quote_currency == self.account_currency
+        ]
+        
+        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(minutes = 20), 0.025, None))
+        # Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+
+    def universe_selection_filter(self, universe_day: List[CryptoUniverse]) -> List[Symbol]:
+        # Select pairs where the quote currency is USDT.
+        pairs = [c for c in universe_day if c.symbol.value in self._market_pairs]
+        # Filter for materially traded crypto pairs with significant dollar volume, assuming higher capital flow in for higher return.
+        pairs = sorted(pairs, key=lambda c: c.volume_in_usd)[-3:]      
+        return [c.symbol for c in pairs]
+
+    def on_securities_changed(self, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            # Historical data
+            history = self.history(security.symbol, 30, Resolution.DAILY)
+            self.debug(f"We got {len(history)} items from our history request")
    
+   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+{
+    private List<string> _marketPairs;
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        Settings.FreePortfolioValuePercentage = 0.05m;
+
+        // Set Account Currency to Tether, since USD and USDT will not auto-convert and USD cannot be used to trade
+        SetAccountCurrency("USDT", 100000);
+        // Bybit accepts both Cash and Margin account types, select the one you need for the best reality modeling.
+        SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin);
+
+        // Warm up the security with the last known price to avoid conversion error
+        Settings.SeedInitialPrices = true;
+        UniverseSettings.Asynchronous = true;
+        UniverseSettings.Resolution = Resolution.Minute;
+        // Add Crypto Universe Selection that select crypto pairs in Bybit exchange
+        AddUniverse(CryptoUniverse.Bybit(UniverseSelectionFilter));
+        // Get the pairs on Bybit that have USDT as the quote currency.
+        _marketPairs = SymbolPropertiesDatabase.GetSymbolPropertiesList(Market.Bybit)
+            .Where(x => x.Value.QuoteCurrency == AccountCurrency)
+            .Select(x => x.Key.Symbol)
+            .ToList();
+
+        AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, null));
+        // Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+    }
+
+    private IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
+    {
+        // Select pairs where the quote currency is USDT.
+        return universeDay.Where(c => _marketPairs.Contains(c.Symbol.Value))
+            // Filter for materially traded crypto pairs with significant size and dollar volume, assuming higher capital flow in for higher return
+            .OrderBy(c => c.VolumeInUsd)
+            .TakeLast(3)
+            .Select(c => c.Symbol);
+    }
+
+    public override void OnSecuritiesChanged(SecurityChanges changes)
+    {
+        foreach(var security in changes.AddedSecurities)
+        {
+            // Historical data
+            var history = History(security.Symbol, 30, Resolution.Daily);
+            Debug($"We got {history.Count()} items from our history request");
+        }
+    }
+}
    
+  
    
+  
    
+   Research Example
+  
    
+  
    
+   The following example lists crypto-currency pairs with the greatest dollar volume in the Bybit exchange:
+  
    
+  
    
+   
    var qb = new QuantBook();
+
+// Add Cryptocurrency pair
+var symbol = qb.AddCrypto("BTCUSDT", market:Market.Bybit).Symbol;
+
+// Historical data
+var history = qb.History(symbol, 30, Resolution.Daily);
+foreach (var bar in history)
+{
+    Console.WriteLine($"{bar.EndTime} {bar}");
+}
+
+// Add Crypto Universe Selection
+IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
+{
+    return universeDay
+        .Where(x => x.VolumeInUsd != null && x.VolumeInUsd > 10000m)
+        .OrderByDescending(x => x.VolumeInUsd)
+        .Take(5)
+        .Select(x => x.Symbol);
+}
+var universe = qb.AddUniverse(CryptoUniverse.Bybit(UniverseSelectionFilter));
+
+// Historical Universe data
+var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var universeDay in universeHistory)
+{
+    Console.WriteLine($"=== {universeDay.First().EndTime} ===");
+    foreach (CryptoUniverse universeItem in universeDay.OrderByDescending(x => x.VolumeInUsd))
+    {
+        Console.WriteLine($"{universeItem.Symbol}: {universeItem.VolumeInUsd}");
+    }
+}
    
+   
    qb = QuantBook()
+
+# Add Cryptocurrency pair
+symbol = qb.add_crypto("BTCUSDT", market=Market.BYBIT).symbol
+
+# Historical data
+history = qb.history(symbol, 30, Resolution.DAILY)
+for (symbol, time), row in history.iterrows():
+    print(f'{time} {symbol} {row.close}')
+
+# Add Crypto Universe Selection
+def universe_selection_filter(universe_day):
+    selected = sorted([x for x in universe_day
+        if x.volume_in_usd and x.volume_in_usd > 10000],
+        key=lambda x: x.volume_in_usd, reverse=True)[:5]
+    return [x.symbol for x in selected]
+
+universe = qb.add_universe(CryptoUniverse.bybit(universe_selection_filter))
+
+# Historical Universe data
+history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+for (univere_symbol, time), universe_day in history.items():
+    print(f'=== {time} ===')
+    for universe_item in sorted(universe_day, key=lambda x: x.volume_in_usd, reverse=True):
+        print(f"{universe_item.symbol}: {universe_item.volume_in_usd}")
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The Bybit Crypto Price dataset provides
+   
+    TradeBar
+   
+   ,
+   
+    QuoteBar
+   
+   ,
+   
+    Tick
+   
+   , and
+   
+    CryptoUniverse
+   
+   objects.
+  
    
+  
    
+   TradeBar Attributes
+  
    
+  
    
+   
+    TradeBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   QuoteBar Attributes
+  
    
+  
    
+   
+    QuoteBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   Tick Attributes
+  
    
+  
    
+   
+    Tick
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   CryptoUniverse Attributes
+  
    
+  
    
+   
+    CryptoUniverse
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > CFD Data
    
+
    
+    
    
+        
    QuantConnect
    
+        
    CFD Data
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The CFD Data by QuantConnect serves 51
+   
+    contracts for differences
+   
+   (CFD). The data starts as early as May 2002 and is delivered on any frequency from tick to daily. This dataset is created by QuantConnect processing raw tick data from OANDA.
+  
    
+  
    
+   CFD data does not include ask and bid sizes.
+  
    
+  
    
+   For more information about the CFD Data dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the CFD dataset:
+  
    
+  
    
+   
    self.xauusd = self.add_cfd("XAUUSD", Resolution.DAILY).symbol
    
+   
    _symbol = AddCfd("XAUUSD", Resolution.Daily).Symbol;
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      Mixed, earliest starts May 2002
+     
    
+      Asset Coverage
+     
+      51 Contracts
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Tick, Second, Minute, Hour, & Daily
+     
    
+      Timezone
+     
+      Mixed, in which the contract is listed*
+     
    
+      Market Hours
+     
+      
+       Always Open
+      
+      , except from Friday 5 PM EST to Sunday 5 PM EST.
+      
    
+      Index CFDs depends on the underlying market hour*
+     
    
+  
    
+   * E.g.:
+   
+    DE30EUR
+   
+   tracks DAX30 Index, which is listed in Europe/Berlin timezone.
+  
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add CFD data to your algorithm, call the
+   
+    AddCfd
+   
+   
+    add_cfd
+   
+   method. Save a reference to the CFD
+   
+    Symbol
+   
+   so you can access the data later in your algorithm.
+  
    
+  
    
+   
    class CfdAlgorithm (QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_account_currency('EUR');
+
+        self.set_start_date(2019, 2, 20)
+        self.set_end_date(2019, 2, 21)
+        self.set_cash('EUR', 100000)
+
+        self.de30eur = self.add_cfd('DE30EUR').symbol
+
+        self.set_benchmark(self.de30eur)
+
    
+   
    public class CfdAlgorithm : QCAlgorithm
+{
+    private Symbol _symbol;
+
+    public override void Initialize()
+    {
+        SetAccountCurrency("EUR");
+
+        SetStartDate(2019, 2, 20);
+        SetEndDate(2019, 2, 21);
+        SetCash("EUR", 100000);
+
+        _symbol = AddCfd("DE30EUR").Symbol;
+
+        SetBenchmark(_symbol);
+    }
+}
    
+  
    
+  
    
+   For more information about creating CFD subscriptions, see
+   
+    Requesting Data
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current CFD data, index the
+   
+    QuoteBars
+   
+   
+    quote_bars
+   
+   , or
+   
+    Ticks
+   
+   
+    ticks
+   
+   properties of the current
+   
+    
+     Slice
+    
+   
+   with the CFD
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your security at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    if self.de30eur in slice.quote_bars:
+        quote_bar = slice.quote_bars[self.de30eur]
+        self.log(f"{self.de30eur} bid at {slice.time}: {quote_bar.bid.close}")
+
+    if self.de30eur in slice.ticks:
+        ticks = slice.ticks[self.de30eur]
+        for tick in ticks:
+            self.log(f"{self.de30eur} price at {slice.time}: {tick.price}")
    
+   
    public override void OnData(Slice slice)
+{
+    if (slice.QuoteBars.ContainsKey(_symbol))
+    {
+        var quoteBar = slice.QuoteBars[_symbol];
+        Log($"{_symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
+    }
+
+    if (slice.Ticks.ContainsKey(_symbol))
+    {
+        var ticks = slice.Ticks[_symbol];
+        foreach (var tick in ticks)
+        {
+            Log($"{_symbol} price at {slice.Time}: {tick.Price}");
+        }
+    }
+}
+
    
+  
    
+  
    
+   You can also iterate through all of the data objects in the current
+   
+    Slice
+   
+   .
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    for symbol, quote_bar in slice.quote_bars.items():
+        self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
+
+    for symbol, ticks in slice.ticks.items():
+        for tick in ticks:
+            self.log(f"{symbol} price at {slice.time}: {tick.price}")
    
+   
    public override void OnData(Slice slice)
+{
+    foreach (var kvp in slice.QuoteBars)
+    {
+        var symbol = kvp.Key;
+        var quoteBar = kvp.Value;
+        Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
+    }
+
+    foreach (var kvp in slice.Ticks)
+    {
+        var symbol = kvp.Key;
+        var ticks = kvp.Value;
+        foreach (var tick in ticks)
+        {
+            Log($"{symbol} price at {slice.Time}: {tick.Price}");
+        }
+    }
+}
+
    
+  
    
+  
    
+   For more information about accessing CFD data, see
+   
+    Handling Data
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   To get historical CFD data, call the
+   
+    History
+   
+   
+    history
+   
+   method with the CFD
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame
+history_df = self.history(self.de30eur, 100, Resolution.MINUTE)
+
+# QuoteBar objects
+history_quote_bars = self.history[QuoteBar](self.de30eur, 100, Resolution.MINUTE)
+
+# Tick objects
+history_ticks = self.history[Tick](self.de30eur, timedelta(seconds=10), Resolution.TICK)
    
+   
    // QuoteBar objects 
+var historyQuoteBars = History<QuoteBar>(_symbol, 100, Resolution.Minute);
+
+// Tick objects 
+var historyTicks = History<Tick>(_symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
+  
    
+  
    
+   For more information about historical data, see
+   
+    History Requests
+   
+   .
+  
    
+ 
+
+
+
    Remove Subscriptions
    
+
+ 
+  
    
+   To remove a CFD subscription, call the
+   
+    RemoveSecurity
+   
+   
+    remove_security
+   
+   method.
+  
    
+  
    
+   
    self.remove_security(self.de30eur)
    
+   
    RemoveSecurity(_symbol);
    
+  
    
+  
    
+   The
+   
+    RemoveSecurity
+   
+   
+    remove_security
+   
+   method cancels your open orders for the security and liquidates your holdings.
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   The following table shows the available contracts:
+  
    
+  
    
+   
+    
-      
-      
-      
-      
-      
-      
+      
+    
+    
-      
-      
-      
+    
+   
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The CFD price data enables you to trade CFD assets in your algorithm. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Exploring the daily worldwide news cycles with CFDs that track international indices.
+   
      • 
+   
    • 
+    Trading price movements of commodities with no delivery of physical goods. For example, pairs trading between gold and silver, corn and wheat, brent and crude oil, etc.
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm implements a pairs trading strategy using Gold and Silver CFDs, XAUUSD and XAGUSD, respectively. When the spread is higher than one standard deviation above its mean, the algorithm buys the spread (buy XAUUSD and sell XAGUSD). When the spread is lower than one standard deviation below its mean, it sells the spread (buy XAGUSD and sell XAUUSD).
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class SMAPairsTrading(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+
+        # Request gold and sliver spot CFDs for trading their spread difference, assuming their spread series is cointegrated
+        self.add_cfd('XAUUSD', Resolution.HOUR)
+        self.add_cfd('XAGUSD', Resolution.HOUR)
+
+        # Use 500-step mean and SD indicator on determine the spread relative difference for trading signal generation
+        self.pair = [ ]
+        self.spread_mean = SimpleMovingAverage(500)
+        self.spread_std = StandardDeviation(500)
+        
+    def on_data(self, slice: Slice) -> None:
+        # Update the indicator with updated spread difference, such that the an updated cointegration threshold is calculated for trade inception
+        spread = self.pair[1].price - self.pair[0].price
+        self.spread_mean.update(self.time, spread)
+        self.spread_std.update(self.time, spread) 
+        
+        spread_mean = self.spread_mean.current.value
+        upperthreshold = spread_mean  + self.spread_std.current.value
+        lowerthreshold = spread_mean  - self.spread_std.current.value
+
+        # If the spread is higher than upper threshold, bet their spread series will revert to mean
+        if spread > upperthreshold:
+            self.set_holdings(self.pair[0].symbol, 1)
+            self.set_holdings(self.pair[1].symbol, -1)
+        elif spread < lowerthreshold:
+            self.set_holdings(self.pair[0].symbol, -1)
+            self.set_holdings(self.pair[1].symbol, 1)
+        # Close positions if mean reverted
+        elif (self.portfolio[self.pair[0].symbol].quantity > 0 and spread < spread_mean)\
+        or (self.portfolio[self.pair[0].symbol].quantity < 0 and spread > spread_mean):
+            self.liquidate()
+    
+    def on_securities_changed(self, changes: SecurityChanges) -> None:
+        self.pair = [x for x in changes.added_securities]
+        
+        #1. Call for 500 bars of history data for each symbol in the pair and save to the variable history
+        history = self.history([x.symbol for x in self.pair], 500)
+        #2. Unstack the Pandas data frame to reduce it to the history close price
+        history = history.close.unstack(level=0)
+        #3. Iterate through the history tuple and update the mean and standard deviation with historical data 
+        for tuple in history.itertuples():
+            self.spread_mean.update(tuple[0], tuple[2]-tuple[1])
+            self.spread_std.update(tuple[0], tuple[2]-tuple[1])
+
    
+   
    public class GoldSilverPairsTradingAlgorithm : QCAlgorithm
+{
+    // Use 500-step mean and SD indicator on determine the spread relative difference for trading signal generation
+    private SimpleMovingAverage _spreadMean = new SimpleMovingAverage(500);
+    private StandardDeviation _spreadStd = new StandardDeviation(500);
+    private Security[] _pair = new Security[2];
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);  
+
+        // Request gold and sliver spot CFDs for trading their spread difference, assuming their spread series is cointegrated
+        AddCfd("XAUUSD", Resolution.Hour);
+        AddCfd("XAGUSD", Resolution.Hour);
+    }
+
+    public override void OnData(Slice slice) 
+    {
+        // Update the indicator with updated spread difference, such that the an updated cointegration threshold is calculated for trade inception
+        var spread = _pair[1].Price - _pair[0].Price;
+        _spreadMean.Update(Time, spread);
+        _spreadStd.Update(Time, spread);
+        
+        var upperthreshold = _spreadMean + _spreadStd;
+        var lowerthreshold = _spreadMean - _spreadStd;
+        
+        // If the spread is higher than upper threshold, bet their spread series will revert to mean
+        if (spread > upperthreshold)
+        {
+            SetHoldings(_pair[0].Symbol, 1);
+            SetHoldings(_pair[1].Symbol, -1);
+        }
+        else if (spread < lowerthreshold)
+        {
+            SetHoldings(_pair[0].Symbol, -1);
+            SetHoldings(_pair[1].Symbol, 1);
+        }
+        // Close positions if mean reverted
+        else if ((Portfolio[_pair[0].Symbol].Quantity > 0m && spread < _spreadMean)
+        || (Portfolio[_pair[0].Symbol].Quantity < 0m && spread > _spreadMean))
+        {
+            Liquidate();
+        }
+    }
+    
+    public override void OnSecuritiesChanged(SecurityChanges changes)
+    {    
+        _pair = changes.AddedSecurities.ToArray();
+        
+        //1. Call for 500 days of history data for each symbol in the pair and save to the variable history
+        var history = History(_pair.Select(x => x.Symbol), 500);
+        
+        //2. Iterate through the history tuple and update the mean and standard deviation with historical data 
+        foreach(var slice in history)
+        {
+            var spread = slice[_pair[1].Symbol].Close - slice[_pair[0].Symbol].Close;
+            _spreadMean.Update(slice.Time, spread);
+            _spreadStd.Update(slice.Time, spread);
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm implements a pairs trading strategy using Gold and Silver CFDs, XAUUSD and XAGUSD, respectively. When the spread is higher than one standard deviation above its mean, the algorithm buys the spread (buy XAUUSD and sell XAGUSD). When the spread is lower than one standard deviation below its mean, it sells the spread (buy XAGUSD and sell XAUUSD).
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class GoldSilverPairsTradingAlgorithm (QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        
+        self.universe_settings.resolution = Resolution.HOUR
+        # Custom universe contains only gold and sliver spot CFDs for trading their spread difference, assuming their spread series is cointegrated
+        self.set_universe_selection(ManualUniverseSelectionModel
+        (
+            [ Symbol.create(x, SecurityType.CFD, Market.OANDA) for x in ["XAUUSD", "XAGUSD"] ]
+        ))
+        # Custom alpha model to emit trade insights based on the gold-sliver price spread
+        self.add_alpha(PairsTradingAlphaModel())
+        # Equal weighting trades assuming the spread is cointegrated by 1:1 ratio
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+
+class PairsTradingAlphaModel(AlphaModel):
+
+    def __init__(self) -> None:
+        # Use 500-step mean and SD indicator on determine the spread relative difference for trading signal generation
+        self.pair = [ ]
+        self.spread_mean = SimpleMovingAverage(500)
+        self.spread_std = StandardDeviation(500)
+        # Assume efficient mean reversal happens within 2 hours
+        self.period = timedelta(hours=2)
+        
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        # Update the indicator with updated spread difference, such that the an updated cointegration threshold is calculated for trade inception
+        spread = self.pair[1].price - self.pair[0].price
+        self.spread_mean.update(algorithm.time, spread)
+        self.spread_std.update(algorithm.time, spread) 
+        
+        upperthreshold = self.spread_mean.current.value + self.spread_std.current.value
+        lowerthreshold = self.spread_mean.current.value - self.spread_std.current.value
+
+        # If the spread is higher than upper threshold, bet their spread series will revert to mean
+        if spread > upperthreshold:
+            return Insight.group(
+                [
+                    Insight.price(self.pair[0].symbol, self.period, InsightDirection.UP),
+                    Insight.price(self.pair[1].symbol, self.period, InsightDirection.DOWN)
+                ])
+        elif spread < lowerthreshold:
+            return Insight.group(
+                [
+                    Insight.price(self.pair[0].symbol, self.period, InsightDirection.DOWN),
+                    Insight.price(self.pair[1].symbol, self.period, InsightDirection.UP)
+                ])
+
+        return []
+    
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
+        self.pair = [x for x in changes.added_securities]
+        
+        #1. Call for 500 bars of history data for each symbol in the pair and save to the variable history
+        history = algorithm.history([x.symbol for x in self.pair], 500)
+        #2. Unstack the Pandas data frame to reduce it to the history close price
+        history = history.close.unstack(level=0)
+        #3. Iterate through the history tuple and update the mean and standard deviation with historical data 
+        for tuple in history.itertuples():
+            self.spread_mean.update(tuple[0], tuple[2]-tuple[1])
+            self.spread_std.update(tuple[0], tuple[2]-tuple[1])
+
    
+   
    public class GoldSilverPairsTradingAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);  
+
+        UniverseSettings.Resolution = Resolution.Hour;
+        // Custom universe contains only gold and sliver spot CFDs for trading their spread difference, assuming their spread series is cointegrated
+        SetUniverseSelection(new ManualUniverseSelectionModel
+        (
+            new [] {"XAUUSD", "XAGUSD"}
+                .Select(x => QuantConnect.Symbol.Create(x, SecurityType.Cfd, Market.Oanda))
+        ));
+        // Custom alpha model to emit trade insights based on the gold-sliver price spread
+        AddAlpha(new PairsTradingAlphaModel());
+        // Equal weighting trades assuming the spread is cointegrated by 1:1 ratio
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel()); 
+    }
+}
+
+public partial class PairsTradingAlphaModel : AlphaModel
+{
+    // Use 500-step mean and SD indicator on determine the spread relative difference for trading signal generation
+    private SimpleMovingAverage _spreadMean = new SimpleMovingAverage(500);
+    private StandardDeviation _spreadStd = new StandardDeviation(500);
+    // Assume efficient mean reversal happens within 2 hours
+    private TimeSpan _period = TimeSpan.FromHours(2);
+    private Security[] _pair = new Security[2];
+
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice) 
+    {
+        // Update the indicator with updated spread difference, such that the an updated cointegration threshold is calculated for trade inception
+        var spread = _pair[1].Price - _pair[0].Price;
+        _spreadMean.Update(algorithm.Time, spread);
+        _spreadStd.Update(algorithm.Time, spread);
+        
+        var upperthreshold = _spreadMean + _spreadStd;
+        var lowerthreshold = _spreadMean - _spreadStd;
+
+        // If the spread is higher than upper threshold, bet their spread series will revert to mean
+        if (spread > upperthreshold)
+        {
+            return Insight.Group( 
+                Insight.Price(_pair[0].Symbol, _period, InsightDirection.Up),
+                Insight.Price(_pair[1].Symbol, _period, InsightDirection.Down)
+            );
+        }
+        else if (spread < lowerthreshold)
+        {
+            return Insight.Group( 
+                Insight.Price(_pair[0].Symbol, _period, InsightDirection.Down), 
+                Insight.Price(_pair[1].Symbol, _period, InsightDirection.Up) 
+            );
+        }
+
+        return Enumerable.Empty<Insight>();
+    }
+    
+    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
+    {    
+        _pair = changes.AddedSecurities.ToArray();
+        
+        //1. Call for 500 days of history data for each symbol in the pair and save to the variable history
+        var history = algorithm.History(_pair.Select(x => x.Symbol), 500);
+        
+        //2. Iterate through the history tuple and update the mean and standard deviation with historical data 
+        foreach (var slice in history)
+        {
+            var spread = slice[_pair[1].Symbol].Close - slice[_pair[0].Symbol].Close;
+            _spreadMean.Update(slice.Time, spread);
+            _spreadStd.Update(slice.Time, spread);
+        }
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The CFD dataset provides
+   
+    QuoteBar
+   
+   and
+   
+    Tick
+   
+   objects.
+  
    
+  
    
+   QuoteBar Attributes
+  
    
+  
    
+   
+    QuoteBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   Tick Attributes
+  
    
+  
    
+   
+    Tick
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > Cash Indices
    
+
    
+    
    
+        
    QuantConnect
    
+        
    Cash Indices
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The Cash Indices dataset by QuantConnect covers 125 US Indices and 3 International indices. The data starts on various dates from January 1998 and is delivered on any frequency from minute to daily.
+  
    
+  
    
+   For more information about the Cash Indices dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
+
+
    Live Data
    
+
+ 
+  
    
+   The Cash Indices dataset does not provide a live data feed. To receive Cash Indices' data in your live algorithm, you must add a brokerage or a third-party data provider (see
+   
+    Cloud Platform > Datasets
+   
+   ).
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the Cash Indices dataset:
+  
    
+  
    
+   
    self.vix = self.add_index("VIX", Resolution.DAILY).symbol
    
+   
    _symbol = AddIndex("VIX", Resolution.Daily).Symbol;
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 1998
+     
    
+      Coverage
+     
+      125 US Indices and 3 International indices (HSI, SX5E and N225)
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Minute, Hour, & Daily
+     
    
+      Timezone
+     
+      New York for US Indices, refer to
+      
+       Supported Assets
+      
+      below for International indices
+     
    
+      Market Hours
+     
+      
+       Regular Only
+      
+     
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add Cash Indices data to your algorithm, call the
+   
+    AddIndex
+   
+   
+    add_index
+   
+   method. Save a reference to the Index
+   
+    Symbol
+   
+   so you can access the data later in your algorithm.
+  
    
+  
    
+   
    class CashIndexAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2020, 6, 1)
+        self.set_end_date(2021, 6, 1)
+        self.set_cash(100000)
+
+        self.vix = self.add_index("VIX").symbol
+
    
+   
    public class CashIndexAlgorithm : QCAlgorithm
+{
+    private Symbol _symbol;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2020, 6, 1);
+        SetEndDate(2021, 6, 1);
+        SetCash(100000);
+        
+        _symbol = AddIndex("VIX").Symbol;
+    }
+}
    
+  
    
+  
    
+   For more information about creating Index subscriptions, see
+   
+    Requesting Data
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current Cash Indices data, index the
+   
+    Bars
+   
+   
+    bars
+   
+   property of the current
+   
+    
+     Slice
+    
+   
+   with the Index
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your security at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    if self.vix in slice.bars:
+        trade_bar = slice.bars[self.vix]
+        self.log(f"{self.vix} close at {slice.time}: {trade_bar.close}")
    
+   
    public override void OnData(Slice slice)
+{
+    if (slice.Bars.ContainsKey(_symbol))
+    {
+        var tradeBar = slice.Bars[_symbol];
+        Log($"{_symbol} price at {slice.Time}: {tradeBar.Close}");
+    }
+}
+
    
+  
    
+  
    
+   You can also iterate through all of the data objects in the current
+   
+    Slice
+   
+   .
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    for symbol, trade_bar in slice.bars.items():
+        self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")
    
+   
    public override void OnData(Slice slice)
+{
+    foreach (var kvp in slice.Bars)
+    {
+        var symbol = kvp.Key;
+        var tradeBar = kvp.Value;
+        Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
+    }
+}
+
    
+  
    
+  
    
+   For more information about accessing Index data, see
+   
+    Handling Data
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   To get historical Cash Indices data, call the
+   
+    History
+   
+   
+    history
+   
+   method with the Index
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame
+history_df = self.history(self.vix, 100, Resolution.DAILY)
+
+# TradeBar objects
+history_bars = self.history[TradeBar](self.vix, 100, Resolution.DAILY)
    
+   
    // TradeBar objects
+var historyBars = History(_symbol, 100, Resolution.Daily);
    
+  
    
+  
    
+   For more information about historical data, see
+   
+    History Requests
+   
+   .
+  
    
+ 
+
+
+
    Remove Subscriptions
    
+
+ 
+  
    
+   To remove an Index subscription, call the
+   
+    RemoveSecurity
+   
+   
+    remove_security
+   
+   method.
+  
    
+  
    
+   
    self.remove_security(self.vix)
    
+   
    RemoveSecurity(_symbol);
    
+  
    
+ 
+
+
+
    Supported Indices
    
+
+ 
+  
    
+   US Cash Indices
+  
    
+  
    
+   The following table shows the US Cash indices with Index Option support:
+  
    
+  
+   
+    
+     
+     
+     
+     
+    
+   
+   
+    
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+    
+   
+  
    
+      Ticker
+     
+      Index
+     
+      Expiry
+     
+      Start Date
+     
    
+      VIX
+     
+      S&P500
+     
+      30 Days
+     
+      Jul 2003
+     
    
+      SPX
+     
+      S&P500
+     
+      -
+     
+      Jan 1998
+     
    
+      NDX
+     
+      NASDAQ-100
+     
+      -
+     
+      Jan 1998
+     
    
+      RUT
+     
+      Russell 2000
+     
+      -
+     
+      Jan 2008
+     
    
+  
    
+   VIX - CBOE Volatility Index
+  
    
+  
    
+   The Cboe Volatility Index (VIX) is a real-time index that represents the market's expectations for the relative strength of near-term price changes of the S&P 500 Index (SPX). Because it's derived from the prices of SPX index Options with near-term expiration dates, it generates a 30-day forward projection of volatility. Volatility, or how fast prices change, is often seen as a way to gauge market sentiment, and in particular, the degree of fear among market participants.
+  
    
+  
    
+   SPX - S&P 500 Index
+  
    
+  
    
+   The S&P 500 Index, or the Standard & Poor's 500 Index, is a market-capitalization-weighted index of the 500 largest publicly-traded companies in the U.S. It is not an exact list of the top 500 U.S. companies by market capitalization because there are other criteria included in the index. The index is widely regarded as the best gauge of large-cap U.S. Equities.
+  
    
+  
    
+   NDX - Nasdaq 100 Index
+  
    
+  
    
+   The Nasdaq-100 Index is a modified market-capitalization-weighted index composed of securities issued by 100 of the largest non-financial companies listed on the Nasdaq Stock Market (Nasdaq). The index includes companies from various industries except for the financial industry, like commercial and investment banks. These non-financial sectors include retail, biotechnology, industrial, technology, health care, and others.
+  
    
+  
    
+   RUT - Russell 2000 Index
+  
    
+  
    
+   The Russell 2000 Index is a small-cap U.S. stock market index that makes up the smallest 2,000 stocks in the Russell Index. It was started by the Frank Russell Company in 1984. The index is maintained by FTSE Russell, a subsidiary of the London Stock Exchange Group (LSEG).
+  
    
+  
    
+   All US Cash Indices
+  
    
+  The following table shows all US Cash indices:
+  
    
+   
+    
-      
-      
-      
-      
-      
-      
+      
+    
+    
-      
-      
-      
-     
-     
   
    
+  
    
+   International Cash Indices
+  
    
+  
    
+   The following table shows the International Cash indices:
+  
    
+  
+   
+    
+     
+     
+     
+     
+     
+    
+   
+   
+    
+    
+    
+    
+    
+    
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+    
+   
+  
    
+      Ticker
+     
+      Index
+     
+      Start Date
+     
+      Time Zone
+     
+      Currency
+     
    
+     HSI
+    
+     Hang Seng Index
+    
+     Dec 2006
+    
+     Asia/Hong Kong
+    
+     HKD
+    
    
+      SX5E
+     
+      EUREX EU STOXX
+     
+      Jul 2003
+     
+      Europe/Berlin
+     
+      EUR
+     
    
+      N225
+     
+      Nikkei 225
+     
+      Jul 2003
+     
+      Asia/Tokyo
+     
+      JPY
+     
    
  
 
 
@@ -227574,321 +199920,235 @@ 
    Example Applications
    
 
  
   
    
-   The Bybit Crypto Price dataset enables you to accurately design strategies for Cryptocurrencies. Examples include the following strategies:
+   The Cash Indices enable you to incorporate popular indices into your trading algorithms. Examples include the following use cases:
   
    
   
      
    
    • 
-    Buy and hold
+    Exploring the difference between the Index and the ETF that tracks it
    
      • 
    
    • 
-    Trading Cryptocurrency volatility and price action
+    Using these indices as the underlying asset fo Index Options strategies
    
      • 
    
    • 
-    Allocating a small portion of your portfolio to Cryptocurrencies to hedge against inflation
+    Understanding the stock market's level of expected forward-looking volatility, also known as the "fear index". When the VIX starts moving higher, it tells you that traders are getting nervous. When the VIX starts moving lower, it tells you that traders are gaining confidence.
    
      • 
   
    
   
    
    Classic Algorithm Example
   
    
   
    
-   The following example algorithm buys and holds Bitcoin through the Bybit exchange:
+   The following example algorithm tracks the 80-day EMA and 200-day EMA of SPX. When the short EMA crosses above the long EMA, the algorithm buys SPY. Otherwise, it holds cash.
   
    
   
    
-   
    from AlgorithmImports import *
+   
    from AlgorithmImports import *
 
 
-class CoinAPIDataAlgorithm(QCAlgorithm):
+class IndexDataAlgorithm(QCAlgorithm):
 
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        # Set the account currency to USDT, since you can't trade with 
-        # USD and the algorithm doesn't automatically convert USD & USDT
-        # holdings.
-        self.set_account_currency("USDT", 100_000)
-        self.universe_settings.asynchronous = True
-        # Bybit accepts both Cash and Margin account types. Select the
-        # one you need for the best reality modeling.
-        self.set_brokerage_model(BrokerageName.BYBIT, AccountType.MARGIN)
-        # Add BTCUSDT data.
-        self._btc = self.add_crypto("BTCUSDT", Resolution.MINUTE, Market.BYBIT)
-        # Historical data
-        history = self.history(self._btc.symbol, 30, Resolution.DAILY)
-        # Add a Crypto universe that selects trading pairs on Bybit.
-        universe = self.add_universe(CryptoUniverse.bybit(self._select_assets))
-        # Historical Universe data
-        universe_history = self.history(universe, 30, Resolution.DAILY)
-        for (universe_symbol, time), universe_day in universe_history.items():
-            for universe_item in universe_day:
-                symbol = universe_item.symbol
-                price = universe_item.close
+        self.set_cash(100000)
 
-    def _select_assets(self, universe_day):
-        # Filter for materially traded Crypto pairs with significant 
-        # size and dollar volume, assuming higher capital flow in for 
-        # higher return.
-        return [universe_item.symbol for universe_item in universe_day
-                if universe_item.volume >= 100 
-                and universe_item.volume_in_usd > 10_000_000]
+        # Request SPY data as a trading vehicle for SPX
+        self.spy = self.add_equity("SPY").symbol
+
+        # Request SPX data for trade signal generation
+        spx = self.add_index("SPX").symbol
+
+        # Create short and long-term EMA indicators for trend estimation to trade
+        self.ema_fast = self.EMA(spx, 80, Resolution.DAILY)
+        self.ema_slow = self.EMA(spx, 200, Resolution.DAILY)
+        self.set_warm_up(200, Resolution.DAILY)
+
+        # Historical data
+        history = self.history(spx, 60, Resolution.DAILY)
+        self.debug(f'We got {len(history.index)} items from our history request')
 
     def on_data(self, slice: Slice) -> None:
-        # Speculate-invest all available free cash on BTCUSDT, repecting 
-        # the order quantity restrictions to avoid invalid orders.
-        if self.portfolio.cash_book['BTC'].amount != 0:
-            return
-        # Define a buffer so that we can scale down the order size,
-        # avoiding trading errors.
-        buffer = max(
-            BybitFeeModel.TAKER_NON_VIP_FEE, 
-            self.settings.free_portfolio_value_percentage
-        )
-        quantity = (
-            self.portfolio.cash_book['USDT'].amount
-            / self._btc.ask_price
-            * (1-buffer)
-        )
-        # Check if the quantity exceeds the minimum order size. On 
-        # Bybit, the minimum order size is measured in the base
-        # currency (BTC in this case).
-        if abs(quantity) < self._btc.symbol_properties.minimum_order_size:
+        # Trade signals required indicators to be ready
+        if self.is_warming_up or not self.ema_slow.is_ready:
             return
-        self.market_order(self._btc, quantity)
    
-   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+
+        # If short-term EMA is above long-term, it indicates an up trend, so we buy SPY
+        if not self.portfolio.invested and self.ema_fast > self.ema_slow:
+            self.set_holdings(self.spy, 1)
+        # If it is the reverse, it indicates a downtrend, and we liquidate any position
+        elif self.ema_fast < self.ema_slow:
+            self.liquidate()
+
    
+   
    public class IndexDataAlgorithm : QCAlgorithm
 {
-    private Crypto _btc;
-    
+    private Symbol _spy;
+    private ExponentialMovingAverage _emaSlow, _emaFast;
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        // Set the account currency to USDT, since you can't trade with 
-        // USD and the algorithm doesn't automatically convert USD & USDT
-        // holdings.
-        SetAccountCurrency("USDT", 100000);
-        UniverseSettings.Asynchronous = true;
-        // Bybit accepts both Cash and Margin account types. Select the
-        // one you need for the best reality modeling.
-        SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin);
-        // Add BTCUSDT data.
-        _btc = AddCrypto("BTCUSDT", Resolution.Minute, Market.Bybit);   
-        // Historical data
-        var history = History(_btc.Symbol, 30, Resolution.Daily);
-        // Add a Crypto universe that selects trading pairs on Bybit.
-        var universe = AddUniverse(CryptoUniverse.Bybit(SelectAssets));
-        // Historical Universe data
-        var universeHistory = History(universe, 30, Resolution.Daily);
-        foreach (var universeDay in universeHistory)
-        {
-            foreach (CryptoUniverse universeItem in universeDay)
-            {
-                var symbol = universeItem.Symbol;
-                var price = universeItem.Close;
-            }
-        }
-    }
+        SetCash(1000000);
 
-    private IEnumerable<Symbol> SelectAssets(IEnumerable<CryptoUniverse> universeDay)
-    {
-        // Filter for materially traded Crypto pairs with significant 
-        // size and dollar volume, assuming higher capital flow in for 
-        // higher return.
-        return from universeItem in universeDay
-                where universeItem.Volume >= 100m 
-                && universeItem.VolumeInUsd > 10000000m
-                select universeItem.Symbol;
+        // Request SPY data as trading vehicle for SPX
+        _spy = AddEquity("SPY").Symbol;
+
+        // Request SPX data for trade signal generation
+        var spx = AddIndex("SPX").Symbol;
+
+        // Create short and long-term EMA indicators for trend estimation to trade
+        _emaFast = EMA(spx, 80, Resolution.Daily);
+        _emaSlow = EMA(spx, 200, Resolution.Daily);
+        SetWarmUp(200, Resolution.Daily);
+
+        // Historical data
+        var history = History(spx, 60, Resolution.Daily);
+        Debug($"We got {history.Count()} items from our history request");
     }
 
     public override void OnData(Slice slice)
     {
-        // Speculate-invest all available free cash on BTCUSDT, repecting 
-        // the order quantity restrictions to avoid invalid orders.
-        if (Portfolio.CashBook["BTC"].Amount != 0)
+        // Trade signals required indicators to be ready
+        if (IsWarmingUp || !_emaSlow.IsReady)
         {
             return;
         }
-        // Define a buffer so that we can scale down the order size,
-        // avoiding trading errors.
-        var buffer = Math.Max(BybitFeeModel.TakerNonVIPFee, Settings.FreePortfolioValuePercentage);
-        var quantity = Portfolio.CashBook["USDT"].Amount / _btc.AskPrice * (1-buffer);
-        // Check if the quantity exceeds the minimum order size. On 
-        // Bybit, the minimum order size is measured in the base 
-        // currency (BTC in this case).
-        if (Math.Abs(quantity) < _btc.SymbolProperties.MinimumOrderSize)
+
+        // If short-term EMA is above long-term, it indicates an up trend, so we buy SPY
+        if (!Portfolio.Invested && _emaFast > _emaSlow)
         {
-            return;
+            SetHoldings(_spy, 1);
+        }
+        // If it is the reverse, it indicates a downtrend, and we liquidate any position
+        else if (_emaFast < _emaSlow)
+        {
+            Liquidate();
         }
-        MarketOrder(_btc, quantity);
     }
-}
    
+}
    
   
    
   
    
    Framework Algorithm Example
   
    
   
    
-   The following example algorithm creates a dynamic universe of Crypto pairs on the Bybit exchange and then forms a equal-weighted portfolio of all the pairs in the universe:
+   The following example algorithm tracks the 80-day EMA and 200-day EMA of SPX. When the short EMA crosses above the long EMA, the algorithm buys SPY. Otherwise, it holds cash.
   
    
   
    
    
    from AlgorithmImports import *
 
 
-class CoinAPIDataAlgorithm(QCAlgorithm):
+class IndexDataAlgorithm(QCAlgorithm):
 
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        # Set Account Currency to Tether, since USD and USDT will not auto-convert and USD cannot be used to trade
-        self.set_account_currency("USDT", 100000)
-        self.settings.free_portfolio_value_percentage = 0.05
+        self.set_cash(100000)
 
-        # Bybit accepts both Cash and Margin account types, select the one you need for the best reality modeling.
-        self.set_brokerage_model(BrokerageName.BYBIT, AccountType.MARGIN)
+        # Universe contains only SPY as a trading vehicle for SPX
+        self.set_universe_selection(ManualUniverseSelectionModel
+        (
+            Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
+        ))
+        # Custom alpha model that emits insights based on SPX index data
+        self.set_alpha(SpxEmaCrossAlphaModel(self))
+        # Equally investing can dissipate non-systematic risky event's capital concentration risk evenly
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel(Expiry.END_OF_MONTH))
+    
 
-        # Warm up the security with the last known price to avoid conversion error
-        self.settings.seed_initial_prices = True
-        self.universe_settings.asynchronous = True
-        self.universe_settings.resolution = Resolution.MINUTE
-        # Add Crypto Universe Selection that select crypto pairs in Bybit exchange
-        self.add_universe(CryptoUniverse.bybit(self.universe_selection_filter))
-        # Get the pairs on Bybit that have USDT as the quote currency.
-        self._market_pairs = [
-            x.key.symbol 
-            for x in self.symbol_properties_database.get_symbol_properties_list(Market.BYBIT) 
-            if x.value.quote_currency == self.account_currency
-        ]
-        
-        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(minutes = 20), 0.025, None))
-        # Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+class SpxEmaCrossAlphaModel(AlphaModel):
 
-    def universe_selection_filter(self, universe_day: List[CryptoUniverse]) -> List[Symbol]:
-        # Select pairs where the quote currency is USDT.
-        pairs = [c for c in universe_day if c.symbol.value in self._market_pairs]
-        # Filter for materially traded crypto pairs with significant dollar volume, assuming higher capital flow in for higher return.
-        pairs = sorted(pairs, key=lambda c: c.volume_in_usd)[-3:]      
-        return [c.symbol for c in pairs]
+    def __init__(self, algorithm: QCAlgorithm) -> None:
+        # Request SPX data for trade signal generation
+        spx = algorithm.add_index("SPX").symbol
 
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            # Historical data
-            history = self.history(security.symbol, 30, Resolution.DAILY)
-            self.debug(f"We got {len(history)} items from our history request")
    
-   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+        # Create short and long-term EMA indicators for trend estimation to trade
+        self.ema_fast = algorithm.EMA(spx, 80, Resolution.DAILY)
+        self.ema_slow = algorithm.EMA(spx, 200, Resolution.DAILY)
+        algorithm.set_warm_up(200, Resolution.DAILY)
+
+        # Historical data
+        history = algorithm.history(spx, 60, Resolution.DAILY)
+        algorithm.debug(f'We got {len(history.index)} items from our history request')
+    
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        # Trade signals required indicators to be ready
+        if algorithm.is_warming_up or not self.ema_slow.is_ready:
+            return []
+
+        # If short-term EMA is above long-term, it indicates an up trend, so we buy SPY
+        if not algorithm.portfolio.invested and self.ema_fast > self.ema_slow:
+            return [Insight.price(kvp.key, Expiry.END_OF_MONTH, InsightDirection.UP)
+                for kvp in algorithm.active_securities if kvp.value.is_tradable]
+        # If it is the reverse, it indicates downtrend, and we liquidate any position
+        elif self.ema_fast < self.ema_slow:
+            return [Insight.price(kvp.key, Expiry.END_OF_MONTH, InsightDirection.FLAT)
+                for kvp in algorithm.active_securities if kvp.value.is_tradable]
+            
+        return []
+
    
+   
    public class IndexDataAlgorithm : QCAlgorithm
 {
-    private List<string> _marketPairs;
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        Settings.FreePortfolioValuePercentage = 0.05m;
-
-        // Set Account Currency to Tether, since USD and USDT will not auto-convert and USD cannot be used to trade
-        SetAccountCurrency("USDT", 100000);
-        // Bybit accepts both Cash and Margin account types, select the one you need for the best reality modeling.
-        SetBrokerageModel(BrokerageName.Bybit, AccountType.Margin);
-
-        // Warm up the security with the last known price to avoid conversion error
-        Settings.SeedInitialPrices = true;
-        UniverseSettings.Asynchronous = true;
-        UniverseSettings.Resolution = Resolution.Minute;
-        // Add Crypto Universe Selection that select crypto pairs in Bybit exchange
-        AddUniverse(CryptoUniverse.Bybit(UniverseSelectionFilter));
-        // Get the pairs on Bybit that have USDT as the quote currency.
-        _marketPairs = SymbolPropertiesDatabase.GetSymbolPropertiesList(Market.Bybit)
-            .Where(x => x.Value.QuoteCurrency == AccountCurrency)
-            .Select(x => x.Key.Symbol)
-            .ToList();
+        SetCash(1000000);
 
-        AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, null));
-        // Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+        // Universe contains only SPY as a trading vehicle for SPX
+        SetUniverseSelection(new ManualUniverseSelectionModel
+        (
+            QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)
+        ));
+        // Custom alpha model that emits insights based on SPX index data
+        SetAlpha(new SpxEmaCrossAlphaModel(this));
+        // Equally invests can dissipate non-systematic risky event's capital concentration risk evenly
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(Expiry.EndOfMonth));
     }
+}
 
-    private IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
+public class SpxEmaCrossAlphaModel : AlphaModel
+{
+    private ExponentialMovingAverage _emaSlow, _emaFast;
+
+    public SpxEmaCrossAlphaModel(QCAlgorithm algorithm)
     {
-        // Select pairs where the quote currency is USDT.
-        return universeDay.Where(c => _marketPairs.Contains(c.Symbol.Value))
-            // Filter for materially traded crypto pairs with significant size and dollar volume, assuming higher capital flow in for higher return
-            .OrderBy(c => c.VolumeInUsd)
-            .TakeLast(3)
-            .Select(c => c.Symbol);
+        // Request SPX data for trade signal generation
+        var spx = algorithm.AddIndex("SPX").Symbol;
+
+        // Create short and long-term EMA indicators for trend estimation to trade
+        _emaFast = algorithm.EMA(spx, 80, Resolution.Daily);
+        _emaSlow = algorithm.EMA(spx, 200, Resolution.Daily);
+        algorithm.SetWarmUp(200, Resolution.Daily);
+
+        // Historical data
+        var history = algorithm.History(spx, 60, Resolution.Daily);
+        algorithm.Debug($"We got {history.Count()} items from our history request");
     }
 
-    public override void OnSecuritiesChanged(SecurityChanges changes)
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
     {
-        foreach(var security in changes.AddedSecurities)
+        // Trade signals required indicators to be ready
+        if (algorithm.IsWarmingUp || !_emaSlow.IsReady)
         {
-            // Historical data
-            var history = History(security.Symbol, 30, Resolution.Daily);
-            Debug($"We got {history.Count()} items from our history request");
+            return Enumerable.Empty<Insight>();
         }
-    }
-}
    
-  
    
-  
    
-   Research Example
-  
    
-  
    
-   The following example lists crypto-currency pairs with the greatest dollar volume in the Bybit exchange:
-  
    
-  
    
-   
    var qb = new QuantBook();
-
-// Add Cryptocurrency pair
-var symbol = qb.AddCrypto("BTCUSDT", market:Market.Bybit).Symbol;
-
-// Historical data
-var history = qb.History(symbol, 30, Resolution.Daily);
-foreach (var bar in history)
-{
-    Console.WriteLine($"{bar.EndTime} {bar}");
-}
 
-// Add Crypto Universe Selection
-IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
-{
-    return universeDay
-        .Where(x => x.VolumeInUsd != null && x.VolumeInUsd > 10000m)
-        .OrderByDescending(x => x.VolumeInUsd)
-        .Take(5)
-        .Select(x => x.Symbol);
-}
-var universe = qb.AddUniverse(CryptoUniverse.Bybit(UniverseSelectionFilter));
+        // If short-term EMA is above long-term, it indicates an up trend, so we buy SPY
+        if (!algorithm.Portfolio.Invested && _emaFast > _emaSlow)
+        {
+            return algorithm.ActiveSecurities
+                .Where(kvp => kvp.Value.IsTradable)
+                .Select(kvp => Insight.Price(kvp.Key, Expiry.EndOfMonth, InsightDirection.Up));
+        }
+        // If it is the reverse, it indicates downtrend, and we liquidate any position
+        else if (_emaFast < _emaSlow)
+        {
+            return algorithm.ActiveSecurities
+                .Where(kvp => kvp.Value.IsTradable)
+                .Select(kvp => Insight.Price(kvp.Key, Expiry.EndOfMonth, InsightDirection.Flat));
+        }
 
-// Historical Universe data
-var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-foreach (var universeDay in universeHistory)
-{
-    Console.WriteLine($"=== {universeDay.First().EndTime} ===");
-    foreach (CryptoUniverse universeItem in universeDay.OrderByDescending(x => x.VolumeInUsd))
-    {
-        Console.WriteLine($"{universeItem.Symbol}: {universeItem.VolumeInUsd}");
+        return Enumerable.Empty<Insight>();
     }
 }
    
-   
    qb = QuantBook()
-
-# Add Cryptocurrency pair
-symbol = qb.add_crypto("BTCUSDT", market=Market.BYBIT).symbol
-
-# Historical data
-history = qb.history(symbol, 30, Resolution.DAILY)
-for (symbol, time), row in history.iterrows():
-    print(f'{time} {symbol} {row.close}')
-
-# Add Crypto Universe Selection
-def universe_selection_filter(universe_day):
-    selected = sorted([x for x in universe_day
-        if x.volume_in_usd and x.volume_in_usd > 10000],
-        key=lambda x: x.volume_in_usd, reverse=True)[:5]
-    return [x.symbol for x in selected]
-
-universe = qb.add_universe(CryptoUniverse.bybit(universe_selection_filter))
-
-# Historical Universe data
-history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (univere_symbol, time), universe_day in history.items():
-    print(f'=== {time} ===')
-    for universe_item in sorted(universe_day, key=lambda x: x.volume_in_usd, reverse=True):
-        print(f"{universe_item.symbol}: {universe_item.volume_in_usd}")
    
   
    
  
 
@@ -227897,23 +200157,11 @@ 
    Data Point Attributes
    
 
  
   
    
-   The Bybit Crypto Price dataset provides
+   The Cash Indices dataset provides
    
     TradeBar
    
-   ,
-   
-    QuoteBar
-   
-   ,
-   
-    Tick
-   
-   , and
-   
-    CryptoUniverse
-   
-   objects.
+   object.
   
    
   
    
    TradeBar Attributes
@@ -227926,47 +200174,14 @@ 
    
   
    
   
    
   
    
-  
    
-   QuoteBar Attributes
-  
    
-  
    
-   
-    QuoteBar
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   Tick Attributes
-  
    
-  
    
-   
-    Tick
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   CryptoUniverse Attributes
-  
    
-  
    
-   
-    CryptoUniverse
-   
-   objects have the following attributes:
-  
    
-  
    
-  
    
  
 
 
 
     
    
-
    Datasets > CoinAPI > Coinbase Crypto Price Data
    
+
    Datasets > QuantConnect > Coinbase Crypto Price Data
    
 
    
-    
    
-        
    CoinAPI
    
+    
    
+        
    QuantConnect
    
         
    Coinbase Crypto Price Data
    
     
    
 
    
@@ -227974,7 +200189,7 @@ 
    Introduction
    
 
  
   
    
-   The Coinbase Crypto Price Data by CoinAPI provides Cryptocurrency price and volume data points. The data covers 970 Cryptocurrency pairs, starts in January 2015, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Coinbase.
+   The Coinbase Crypto Price Data provides Cryptocurrency price and volume data points. The data covers 970 Cryptocurrency pairs, starts in January 2015, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on Coinbase.
   
    
   
    
    For more information about the Coinbase Crypto Price Data dataset, including CLI commands and pricing, see the
@@ -227992,10 +200207,11 @@ 
    About the Provider
    
 
  
   
    
-   
-    CoinAPI
+   Coinbase was founded by Brian Armstrong and Fred Ehrsam in 2012 with the goal to "increase economic freedom in the world". Coinbase provides access to trading Crypto for clients in over
+   
+    100 countries
    
-   was founded by Artur Pietrzyk in 2016 with the goal of providing real-time and historical cryptocurrency market data, collected from hundreds of exchanges. CoinAPI provides access to Cryptocurrencies for traders, market makers, and developers building third-party applications.
+   with no minimum deposit. Coinbase also provides a self-hosted Crypto wallet, a Visa debit rewards card, and Bitcoin collateral-backed lines of credit.
   
    
  
 
@@ -231513,277 +203729,1280 @@ 
    Supported Assets
    
       
      
      
-      
-       VETUSDC
-      
-      
-       VGXEUR
-      
-      
-       VGXUSD
-      
-      
-       VGXUSDT
-      
-      
-       VOXELUSD
-      
-      
-       VOXELUSDC
-      
+      
+       VETUSDC
+      
+      
+       VGXEUR
+      
+      
+       VGXUSD
+      
+      
+       VGXUSDT
+      
+      
+       VOXELUSD
+      
+      
+       VOXELUSDC
+      
+     
+     
+      
+       VTHOUSD
+      
+      
+       VTHOUSDC
+      
+      
+       VVVUSD
+      
+      
+       VVVUSDC
+      
+      
+       WAMPLUSD
+      
+      
+       WAMPLUSDC
+      
+     
+     
+      
+       WAXLUSD
+      
+      
+       WAXLUSDC
+      
+      
+       WBTCBTC
+      
+      
+       WBTCUSD
+      
+      
+       WBTCUSDC
+      
+      
+       WCFGBTC
+      
+     
+     
+      
+       WCFGEUR
+      
+      
+       WCFGGBP
+      
+      
+       WCFGUSD
+      
+      
+       WCFGUSDC
+      
+      
+       WCFGUSDT
+      
+      
+       WELLUSD
+      
+     
+     
+      
+       WELLUSDC
+      
+      
+       WIFUSD
+      
+      
+       WIFUSDC
+      
+      
+       WLDUSD
+      
+      
+       WLDUSDC
+      
+      
+       WLUNABTC
+      
+     
+     
+      
+       WLUNAEUR
+      
+      
+       WLUNAGBP
+      
+      
+       WLUNAUSD
+      
+      
+       WLUNAUSDT
+      
+      
+       XCNUSD
+      
+      
+       XCNUSDC
+      
+     
+     
+      
+       XLMBTC
+      
+      
+       XLMEUR
+      
+      
+       XLMUSD
+      
+      
+       XLMUSDC
+      
+      
+       XLMUSDT
+      
+      
+       XRPBTC
+      
+     
+     
+      
+       XRPEUR
+      
+      
+       XRPGBP
+      
+      
+       XRPUSD
+      
+      
+       XRPUSDC
+      
+      
+       XRPUSDT
+      
+      
+       XTZBTC
+      
+     
+     
+      
+       XTZEUR
+      
+      
+       XTZGBP
+      
+      
+       XTZUSD
+      
+      
+       XTZUSDC
+      
+      
+       XYOBTC
+      
+      
+       XYOEUR
+      
+     
+     
+      
+       XYOUSD
+      
+      
+       XYOUSDC
+      
+      
+       XYOUSDT
+      
+      
+       YFIBTC
+      
+      
+       YFIIUSD
+      
+      
+       YFIUSD
+      
+     
+     
+      
+       YFIUSDC
+      
+      
+       ZECBTC
+      
+      
+       ZECUSD
+      
+      
+       ZECUSDC
+      
+      
+       ZENBTC
+      
+      
+       ZENUSD
+      
+     
+     
+      
+       ZENUSDC
+      
+      
+       ZENUSDT
+      
+      
+       ZETACHAINUSD
+      
+      
+       ZETACHAINUSDC
+      
+      
+       ZETAUSD
+      
+      
+       ZETAUSDC
+      
+     
+     
+      
+       ZKUSD
+      
+      
+       ZKUSDC
+      
+      
+       ZORAUSD
+      
+      
+       ZORAUSDC
+      
+      
+       ZROUSD
+      
+      
+       ZROUSDC
+      
+     
+     
+      
+       ZRXBTC
+      
+      
+       ZRXEUR
+      
+      
+       ZRXUSD
+      
+      
+       ZRXUSDC
+      
+     
+    
+   
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The Coinbase Crypto Price dataset enables you to accurately design strategies for Cryptocurrencies. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Buy and hold
+   
      • 
+   
    • 
+    Trading Cryptocurrency volatility and price action
+   
      • 
+   
    • 
+    Allocating a small portion of your portfolio to Cryptocurrencies to hedge against inflation
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm buys and holds Bitcoin through the Coinbase exchange:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class CoinAPIDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.universe_settings.asynchronous = True
+        self.set_cash(100_000)
+        # Coinbase accepts Cash account type only. AccountType.MARGIN
+        # results in an exception.
+        self.set_brokerage_model(BrokerageName.COINBASE, AccountType.CASH)
+        # Add BTCUSD data.
+        self._btc = self.add_crypto("BTCUSD", Resolution.MINUTE, Market.COINBASE)
+        # Historical data
+        history = self.history(self._btc.symbol, 30, Resolution.DAILY)
+        # Add a Crypto universe that selects Crypto pairs on Coinbase.
+        universe = self.add_universe(CryptoUniverse.coinbase(self._select_assets))
+        # Historical Universe data
+        universe_history = self.history(universe, 30, Resolution.DAILY)
+        for (universe_symbol, time), universe_day in universe_history.items():
+            for universe_item in universe_day:
+                symbol = universe_item.symbol
+                price = universe_item.close
+
+    def _select_assets(self, universe_day):
+        # Filter for materially traded Crypto pairs with significant 
+        # size and dollar volume, assuming higher capital flow in for 
+        # higher return.
+        return [universe_item.symbol for universe_item in universe_day
+                if universe_item.volume >= 100 
+                and universe_item.volume_in_usd > 10_000_000]
+
+    def on_data(self, slice: Slice) -> None:
+        # Speculate-invest all available free cash on BTCUSD, repecting 
+        # the order quantity restrictions to avoid invalid orders.
+        if self.portfolio.cash_book['BTC'].amount != 0:
+            return
+        # Define a buffer so that we can scale down the order size,
+        # avoiding trading errors.
+        buffer = max(
+            CoinbaseFeeModel.TAKER_ADVANCED_1, 
+            self.settings.free_portfolio_value_percentage
+        )
+        quantity = (
+            self.portfolio.cash_book['USD'].amount
+            / self._btc.ask_price
+            * (1-buffer)
+        )
+        # Check if the quantity exceeds the minimum order size. On 
+        # Coinbase, the minimum order size is measured in the base 
+        # currency (BTC in this case).
+        if abs(quantity) < self._btc.symbol_properties.minimum_order_size:
+            return
+        self.market_order(self._btc, quantity)
    
+   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+{
+    private Crypto _btc;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        UniverseSettings.Asynchronous = true;
+        SetCash(100000);
+        // Coinbase accepts Cash account type only. AccountType.Margin
+        // results in an exception.
+        SetBrokerageModel(BrokerageName.Coinbase, AccountType.Cash);
+        // Add BTCUSD data.
+        _btc = AddCrypto("BTCUSD", Resolution.Minute, Market.Coinbase);   
+        // Historical data
+        var history = History(_btc.Symbol, 30, Resolution.Daily);
+        // Add a Crypto universe that selects trading pairs on Coinbase.
+        var universe = AddUniverse(CryptoUniverse.Coinbase(SelectAssets));
+        // Historical Universe data
+        var universeHistory = History(universe, 30, Resolution.Daily);
+        foreach (var universeDay in universeHistory)
+        {
+            foreach (CryptoUniverse universeItem in universeDay)
+            {
+                var symbol = universeItem.Symbol;
+                var price = universeItem.Close;
+            }
+        }
+    }
+
+    private IEnumerable<Symbol> SelectAssets(IEnumerable<CryptoUniverse> universeDay)
+    {
+        // Filter for materially traded Crypto pairs with significant 
+        // size and dollar volume, assuming higher capital flow in for 
+        // higher return.
+        return from universeItem in universeDay
+                where universeItem.Volume >= 100m 
+                && universeItem.VolumeInUsd > 10000000m
+                select universeItem.Symbol;
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // Speculate-invest all available free cash on BTCUSD, repecting 
+        // the order quantity restrictions to avoid invalid orders.
+        if (Portfolio.CashBook["BTC"].Amount != 0)
+        {
+            return;
+        }
+        // Define a buffer so that we can scale down the order size,
+        // avoiding trading errors.
+        var buffer = Math.Max(CoinbaseFeeModel.TakerAdvanced1, Settings.FreePortfolioValuePercentage);
+        var quantity = Portfolio.CashBook["USD"].Amount / _btc.AskPrice * (1-buffer);
+        // Check if the quantity exceeds the minimum order size. On 
+        // Coinbase, the minimum order size is measured in the base 
+        // currency (BTC in this case).
+        if (Math.Abs(quantity) < _btc.SymbolProperties.MinimumOrderSize)
+        {
+            return;
+        }
+        MarketOrder(_btc, quantity);
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm creates a dynamic universe of Crypto pairs on the Coinbase exchange and then forms a equal-weighted portfolio of all the pairs in the universe:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class CoinAPIDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2020, 6, 1)
+        self.set_end_date(2021, 2, 1)
+        self.set_cash(100000)
+        self.settings.free_portfolio_value_percentage = 0.05
+        # Warm up the security with the last known price to avoid conversion error
+        self.settings.seed_initial_prices = True
+        self.universe_settings.asynchronous = True
+        self.universe_settings.resolution = Resolution.MINUTE
+        # Add Crypto Universe Selection that select crypto pairs in Coinbase exchange
+        self.add_universe(CryptoUniverse.coinbase(self._select_assets))
+        # Get the pairs on Coinbase that have USD as the quote currency.
+        self._market_pairs = [
+            x.key.symbol 
+            for x in self.symbol_properties_database.get_symbol_properties_list(Market.COINBASE) 
+            if x.value.quote_currency == self.account_currency
+        ]
+        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(hours=4), 0.025, None))
+        # Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+
+    def _select_assets(self, universe_day: List[CryptoUniverse]) -> List[Symbol]:
+        # Select pairs where the quote currency is USD.
+        pairs = [c for c in universe_day if c.symbol.value in self._market_pairs]
+        # Filter for materially traded crypto pairs with significant dollar volume, assuming higher capital flow in for higher return.
+        pairs = sorted(pairs, key=lambda c: c.volume_in_usd)[-3:]      
+        return [c.symbol for c in pairs]
+
+    def on_securities_changed(self, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            # Historical data
+            history = self.history(security.symbol, 30, Resolution.DAILY)
+            self.debug(f"We got {len(history)} items from our history request")
    
+   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+{
+    private List<string> _marketPairs;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2020, 6, 1);
+        SetEndDate(2021, 2, 1);
+        SetCash(100000);
+        Settings.FreePortfolioValuePercentage = 0.05m;
+        // Warm up the security with the last known price to avoid conversion error
+        Settings.SeedInitialPrices = true;
+        UniverseSettings.Asynchronous = true;
+        UniverseSettings.Resolution = Resolution.Minute;
+        // Add Crypto Universe Selection that select crypto pairs in Coinbase exchange
+        AddUniverse(CryptoUniverse.Coinbase(SelectAssets));
+        // Get the pairs on Coinbase that have USD as the quote currency.
+        _marketPairs = SymbolPropertiesDatabase.GetSymbolPropertiesList(Market.Coinbase)
+            .Where(x => x.Value.QuoteCurrency == AccountCurrency)
+            .Select(x => x.Key.Symbol)
+            .ToList();
+        AddAlpha( new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromHours(4), 0.025, null) );
+        // Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+    }
+
+    private IEnumerable<Symbol> SelectAssets(IEnumerable<CryptoUniverse> universeDay)
+    {
+        // Select pairs where the quote currency is USD.
+        return universeDay.Where(c => _marketPairs.Contains(c.Symbol.Value))
+            // Filter for materially traded crypto pairs with significant size and dollar volume, assuming higher capital flow in for higher return
+            .OrderBy(c => c.VolumeInUsd)
+            .TakeLast(3)
+            .Select(c => c.Symbol);
+    }
+
+    public override void OnSecuritiesChanged(SecurityChanges changes)
+    {
+        foreach(var security in changes.AddedSecurities)
+        {
+            // Historical data
+            var history = History(security.Symbol, 30, Resolution.Daily);
+            Debug($"We got {history.Count()} items from our history request");
+        }
+    }
+}
    
+  
    
+  
    
+   Research Example
+  
    
+  
    
+   The following example lists crypto-currency pairs with the greatest dollar volume in the Coinbase exchange:
+  
    
+  
    
+   
    var qb = new QuantBook();
+
+// Add Cryptocurrency pair
+var symbol = qb.AddCrypto("BTCUSDT", market:Market.Coinbase).Symbol;
+
+// Historical data
+var history = qb.History(symbol, 30, Resolution.Daily);
+foreach (var bar in history)
+{
+    Console.WriteLine($"{bar.EndTime} {bar}");
+}
+
+// Add Crypto Universe Selection
+IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
+{
+    return universeDay
+        .Where(x => x.VolumeInUsd != null && x.VolumeInUsd > 10000m)
+        .OrderByDescending(x => x.VolumeInUsd)
+        .Take(5)
+        .Select(x => x.Symbol);
+}
+var universe = qb.AddUniverse(CryptoUniverse.Coinbase(UniverseSelectionFilter));
+
+// Historical Universe data
+var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var universeDay in universeHistory)
+{
+    Console.WriteLine($"=== {universeDay.First().EndTime} ===");
+    foreach (CryptoUniverse universeItem in universeDay.OrderByDescending(x => x.VolumeInUsd))
+    {
+        Console.WriteLine($"{universeItem.Symbol}: {universeItem.VolumeInUsd}");
+    }
+}
    
+   
    qb = QuantBook()
+
+# Add Cryptocurrency pair
+symbol = qb.add_crypto("BTCUSDT", market=Market.COINBASE).symbol
+
+# Historical data
+history = qb.history(symbol, 30, Resolution.DAILY)
+for (symbol, time), row in history.iterrows():
+    print(f'{time} {symbol} {row.close}')
+
+# Add Crypto Universe Selection
+def universe_selection_filter(universe_day):
+    selected = sorted([x for x in universe_day
+        if x.volume_in_usd and x.volume_in_usd > 10000],
+        key=lambda x: x.volume_in_usd, reverse=True)[:5]
+    return [x.symbol for x in selected]
+
+universe = qb.add_universe(CryptoUniverse.coinbase(universe_selection_filter))
+
+# Historical Universe data
+history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+for (univere_symbol, time), universe_day in history.items():
+    print(f'=== {time} ===')
+    for universe_item in sorted(universe_day, key=lambda x: x.volume_in_usd, reverse=True):
+        print(f"{universe_item.symbol}: {universe_item.volume_in_usd}")
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The Coinbase Crypto Price dataset provides
+   
+    TradeBar
+   
+   ,
+   
+    QuoteBar
+   
+   ,
+   
+    Tick
+   
+   , and
+   
+    CryptoUniverse
+   
+   objects.
+  
    
+  
    
+   TradeBar Attributes
+  
    
+  
    
+   
+    TradeBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   QuoteBar Attributes
+  
    
+  
    
+   
+    QuoteBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   Tick Attributes
+  
    
+  
    
+   
+    Tick
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   CryptoUniverse Attributes
+  
    
+  
    
+   
+    CryptoUniverse
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > FOREX Data
    
+
    
+    
    
+        
    QuantConnect
    
+        
    FOREX Data
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The FOREX Data by QuantConnect serves 71
+   
+    foreign exchange
+   
+   (FOREX) pairs, starts on various dates from January 2007, and is delivered on any frequency from tick to daily. This dataset is created by QuantConnect processing raw tick data from OANDA.
+  
    
+  
    
+   FOREX data does not include ask and bid sizes.
+  
    
+  
    
+   For more information about the FOREX Data dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the FOREX dataset:
+  
    
+  
    
+   
    self.eurusd = self.add_forex("EURUSD", Resolution.DAILY).symbol
    
+   
    _symbol = AddForex("EURUSD", Resolution.Daily).Symbol;
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 2007
+     
    
+      Asset Coverage
+     
+      71 Currency pairs
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Tick, Second, Minute, Hour, & Daily
+     
    
+      Timezone
+     
+      UTC
+     
    
+      Market Hours
+     
+      
+       Always Open
+      
+      , except from Friday 5 PM EST to Sunday 5 PM EST
+     
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add FOREX data to your algorithm, call the
+   
+    AddForex
+   
+   
+    add_forex
+   
+   method. Save a reference to the Forex
+   
+    Symbol
+   
+   so you can access the data later in your algorithm.
+  
    
+  
    
+   
    +class ForexAlgorithm (QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2019, 2, 20)
+        self.set_end_date(2019, 2, 21)
+        self.set_cash(100000)
+
+        self.eurusd = self.add_forex('EURUSD', Resolution.MINUTE).symbol
+
+        self.set_benchmark(self.eurusd)
+
    
+   
    public class ForexAlgorithm : QCAlgorithm
+{
+    private Symbol _symbol;
+
+    public override void Initialize()
+    {
+        SetStartDate(2019, 2, 20);
+        SetEndDate(2019, 2, 21);
+        SetCash(100000);
+
+        _symbol = AddForex("EURUSD", Resolution.Minute).Symbol;
+
+        SetBenchmark(_symbol);
+    }
+}
    
+  
    
+  
    
+   For more information about creating Forex subscriptions, see
+   
+    Requesting Data
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current Forex data, index the
+   
+    QuoteBars
+   
+   
+    quote_bars
+   
+   , or
+   
+    Ticks
+   
+   
+    ticks
+   
+   properties of the current
+   
+    
+     Slice
+    
+   
+   with the Forex
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your security at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    if self.eurusd in slice.quote_bars:
+        quote_bar = slice.quote_bars[self.eurusd]
+        self.log(f"{self.eurusd} bid at {slice.time}: {quote_bar.bid.close}")
+
+    if self.eurusd in slice.ticks:
+        ticks = slice.ticks[self.eurusd]
+        for tick in ticks:
+            self.log(f"{self.eurusd} price at {slice.time}: {tick.price}")
    
+   
    public override void OnData(Slice slice)
+{
+    if (slice.QuoteBars.ContainsKey(_symbol))
+    {
+        var quoteBar = slice.QuoteBars[_symbol];
+        Log($"{_symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
+    }
+
+    if (slice.Ticks.ContainsKey(_symbol))
+    {
+        var ticks = slice.Ticks[_symbol];
+        foreach (var tick in ticks)
+        {
+            Log($"{_symbol} price at {slice.Time}: {tick.Price}");
+        }
+    }
+}
+
    
+  
    
+  
    
+   You can also iterate through all of the data objects in the current
+   
+    Slice
+   
+   .
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    for symbol, quote_bar in slice.quote_bars.items():
+        self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
+
+    for symbol, ticks in slice.ticks.items():
+        for tick in ticks:
+            self.log(f"{symbol} price at {slice.time}: {tick.price}")
    
+   
    public override void OnData(Slice slice)
+{
+    foreach (var kvp in slice.QuoteBars)
+    {
+        var symbol = kvp.Key;
+        var quoteBar = kvp.Value;
+        Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
+    }
+
+    foreach (var kvp in slice.Ticks)
+    {
+        var symbol = kvp.Key;
+        var ticks = kvp.Value;
+        foreach (var tick in ticks)
+        {
+            Log($"{symbol} price at {slice.Time}: {tick.Price}");
+        }
+    }
+}
+
    
+  
    
+  
    
+   For more information about accessing Forex data, see
+   
+    Handling Data
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   To get historical Forex data, call the
+   
+    History
+   
+   
+    history
+   
+   method with the Forex
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame
+history_df = self.history(self.eurusd, 100, Resolution.MINUTE)
+
+# QuoteBar objects
+history_quote_bars = self.history[QuoteBar](self.eurusd, 100, Resolution.MINUTE)
+
+# Tick objects
+history_ticks = self.history[Tick](self.eurusd, timedelta(seconds=10), Resolution.TICK)
    
+   
    // QuoteBar objects 
+var historyQuoteBars = History<QuoteBar>(_symbol, 100, Resolution.Minute);
+
+// Tick objects 
+var historyTicks = History<Tick>(_symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
+  
    
+  
    
+   For more information about historical data, see
+   
+    History Requests
+   
+   .
+  
    
+ 
+
+
+
    Remove Subscriptions
    
+
+ 
+  
    
+   To remove a Forex pair subscription, call the
+   
+    RemoveSecurity
+   
+   
+    remove_security
+   
+   method.
+  
    
+  
    
+   
    self.remove_security(self.eurusd)
    
+   
    RemoveSecurity(_symbol);
    
+  
    
+  
    
+   The
+   
+    RemoveSecurity
+   
+   
+    remove_security
+   
+   method cancels your open orders for the security and liquidates your holdings.
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   The following table shows the available Forex pairs:
+  
    
+  
    
+   
+    
+     
+      
+    
+    
-      
-      
-      
-      
-      
-     
-     
@@ -231796,382 +205015,1416 @@ 
    Example Applications
    
-   The Coinbase Crypto Price dataset enables you to accurately design strategies for Cryptocurrencies. Examples include the following strategies:
+   The FOREX price data enables you to trade currency pairs in the global market. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Exploring the impact that daily worldwide news cycles have on international currencies
+   
      • 
+   
    • 
+    Carry Trade: borrow from a lower interest currency pair to fund the purchase of a currency pair with a higher interest rate
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm implements a FOREX carry trade. It buys the FOREX pair of the country with the lowest interest rate and sells the FOREX pair of the country with the highest interest rate.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class ForexCarryTradeAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(25000)
+        
+        # We will use hard-coded interest rates for each base currency
+        self.rates = {
+            "USDAUD": 1.5,    # Australia
+            "USDCAD": 0.5,    # Canada
+            "USDCNY": 4.35,   # China
+            "USDEUR": 0.0,    # Euro Area
+            "USDINR": 6.5,    # India
+            "USDJPY": -0.1,   # Japan
+            "USDMXN": 4.25,   # Mexico
+            "USDTRY": 7.5,    # Turkey
+            "USDZAR": 7.0     # South Africa
+        }
+
+        # Subscribe to forex data for trading
+        for ticker in self.rates:
+            self.add_forex(ticker, Resolution.DAILY)
+
+        # Use a month counter as variable to control rebalancing
+        self.month = -1
+
+    def on_data(self, slice: Slice) -> None:
+        # Monthly rebalance checker
+        if self.month == self.time.month:
+            return
+        self.month = self.time.month
+        
+        # Long the pair with highest interest rate and sell the pair with the lowest to earn the max monetary inflation difference between the two
+        sorted_rates = sorted(self.rates.items(), key = lambda x: x[1])
+        self.set_holdings(sorted_rates[0][0], -0.5)
+        self.set_holdings(sorted_rates[-1][0], 0.5)
+
    
+   
    public class ForexCarryTradeAlgorithm : QCAlgorithm
+{
+    // Use a month counter as variable to control rebalancing
+    private int _month = -1;
+    private Dictionary<string, decimal> _rates;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(25000);
+        
+        // We will use hard-coded interest rates for each base currency
+        _rates = new Dictionary<string, decimal>()
+        {
+            {"USDAUD", 1.5m},    // Australia
+            {"USDCAD", 0.5m},    // Canada
+            {"USDCNY", 4.35m},   // China
+            {"USDEUR", 0.0m},    // Euro Area
+            {"USDINR", 6.5m},    // India
+            {"USDJPY", -0.1m},   // Japan
+            {"USDMXN", 4.25m},   // Mexico
+            {"USDTRY", 7.5m},    // Turkey
+            {"USDZAR", 7.0m}     // South Africa
+        };
+
+        // Subscribe to forex data for trading
+        foreach (var ticker in _rates.Keys)
+        {
+            AddForex(ticker, Resolution.Daily);
+        }
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // Monthly rebalance checker
+        if (_month == Time.Month) return;
+        _month = Time.Month;
+
+        // Long the pair with highest interest rate and sell the pair with the lowest to earn the max monetary inflation difference between the two
+        var sortedRates = (from kvp in _rates orderby kvp.Value ascending select kvp.Key).ToArray();
+        SetHoldings(sortedRates[0], -0.5);
+        SetHoldings(sortedRates[sortedRates.Length-1], 0.5);
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm implements a FOREX carry trade. It buys the FOREX pair of the country with the lowest interest rate and sells the FOREX pair of the country with the highest interest rate.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class ForexCarryTradeAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(25000)
+        
+        def to_symbol(ticker: str) -> Symbol:
+            return Symbol.create(ticker, SecurityType.FOREX, Market.OANDA)
+        
+        # We will use hard-coded interest rates for each base currency
+        rates = {
+            to_symbol("USDAUD"): 1.5,    # Australia
+            to_symbol("USDCAD"): 0.5,    # Canada
+            to_symbol("USDCNY"): 4.35,   # China
+            to_symbol("USDEUR"): 0.0,    # Euro Area
+            to_symbol("USDINR"): 6.5,    # India
+            to_symbol("USDJPY"): -0.1,   # Japan
+            to_symbol("USDMXN"): 4.25,   # Mexico
+            to_symbol("USDTRY"): 7.5,    # Turkey
+            to_symbol("USDZAR"): 7.0     # South Africa
+        }
+        
+        self.set_universe_selection(ManualUniverseSelectionModel(list(rates.keys())))
+        # A custom alpha model to emit insight according to interest rate
+        self.set_alpha(IntestRatesAlphaModel(rates))
+        # Equal size to capitalize the monetary value of quote currency only based on interest rate difference
+        # For dollar-neutral to save margin cost
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel(Expiry.END_OF_MONTH))
+
+class IntestRatesAlphaModel(AlphaModel):
+    def __init__(self, rates: float) -> None:
+        self.rates = rates
+        # Variable to control the rebalancing time
+        self.month = -1
+                
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        # Monthly rebalance
+        if self.month == algorithm.time.month:
+            return []
+        self.month = algorithm.time.month
+
+        # # Long the pair with highest interest rate and sell the pair with the lowest to earn the max monetary inflation difference between the two
+        sorted_rates = sorted(self.rates.items(), key = lambda x: x[1])
+        return Insight.group(
+            Insight.price(sorted_rates[0][0], Expiry.END_OF_MONTH, InsightDirection.UP),
+            Insight.price(sorted_rates[-1][0], Expiry.END_OF_MONTH, InsightDirection.DOWN)
+        )
+
    
+   
    public class ForexCarryTradeAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(25000);
+        
+        Symbol toSymbol(string ticker)
+        {
+            return QuantConnect.Symbol.Create(ticker, SecurityType.Forex, Market.Oanda);
+        }
+        
+        // We will use hard-coded interest rates for each base currency
+        var rates = new Dictionary<Symbol, decimal>()
+        {
+            {toSymbol("USDAUD"), 1.5m},    // Australia
+            {toSymbol("USDCAD"), 0.5m},    // Canada
+            {toSymbol("USDCNY"), 4.35m},   // China
+            {toSymbol("USDEUR"), 0.0m},    // Euro Area
+            {toSymbol("USDINR"), 6.5m},    // India
+            {toSymbol("USDJPY"), -0.1m},   // Japan
+            {toSymbol("USDMXN"), 4.25m},   // Mexico
+            {toSymbol("USDTRY"), 7.5m},    // Turkey
+            {toSymbol("USDZAR"), 7.0m}     // South Africa
+        };
+
+        SetUniverseSelection(new ManualUniverseSelectionModel(rates.Keys));
+        // A custom alpha model to emit insight according to interest rate
+        SetAlpha(new IntestRatesAlphaModel(rates));
+        // Equal size to capitalize the monetary value of quote currency only based on interest rate difference
+        // For dollar-neutral to save margin cost
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(Expiry.EndOfMonth));
+    }
+}
+
+public class IntestRatesAlphaModel : AlphaModel
+{
+    // Variable to control the rebalancing time
+    private int _month = -1;
+    private Dictionary<Symbol, decimal> _rates;
+
+    public IntestRatesAlphaModel(Dictionary<Symbol, decimal> rates)
+    {
+        _rates = rates;
+    }
+
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    {
+        // Monthly rebalance
+        if (_month == algorithm.Time.Month)
+        {
+            return Enumerable.Empty<Insight>();
+        }
+        _month = algorithm.Time.Month;
+
+        // Long the pair with highest interest rate and sell the pair with the lowest to earn the max monetary inflation difference between the two
+        var sortedRates = (from kvp in _rates orderby kvp.Value ascending select kvp.Key).ToArray();
+        
+        return Insight.Group(
+            Insight.Price(sortedRates[0], Expiry.EndOfMonth, InsightDirection.Up),
+            Insight.Price(sortedRates[sortedRates.Length-1], Expiry.EndOfMonth, InsightDirection.Down)
+        );
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The FOREX dataset provides
+   
+    QuoteBar
+   
+   and
+   
+    Tick
+   
+   objects.
+  
    
+  
    
+   QuoteBar Attributes
+  
    
+  
    
+   
+    QuoteBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   Tick Attributes
+  
    
+  
    
+   
+    Tick
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > Fear and Greed
    
+
    
+    
    
+        
    QuantConnect
    
+        
    Fear and Greed
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The Fear and Greed dataset by QuantConnect provides data points to quantify the degree of fear and greed in the US Equity market. The data starts in 2014 and is delivered on a daily frequency, delayed by two days. This dataset is created by aggregating seven indicators, including market volatility, stock market breadth, and put-call ratios.
+  
    
+  
    
+   For more information about the Fear and Greed dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the Fear and Greed dataset:
+  
    
+  
    
+   
    self._dataset_symbol = self.add_data(FearGreedIndex, 'FG').symbol
    
+   
    _datasetSymbol = AddData<FearGreedIndex>("FG").Symbol;
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      July 2014
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+  
    
+   The latest daily data points are about two days since the VIX and SPX prices are inputs into the index and their latest daily prices are also two days old. New data is available at 9 AM EST.
+  
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add Fear and Greed data to your algorithm, call the
+   
+    AddData<FearGreedIndex>
+   
+   
+    add_data
+   
+   method. Save a reference to the dataset
+   
+    Symbol
+   
+   so you can access the data later in your algorithm.
+  
    
+  
    
+   
    class FearAndGreedDataAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2019, 1, 1)
+        self.set_end_date(2020, 6, 1)
+        self.set_cash(100000)
+
+        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
+        self._dataset_symbol = self.add_data(FearGreedIndex, "FG").symbol
    
+   
    public class FearAndGreedDataAlgorithm : QCAlgorithm
+{
+    private Symbol _symbol, _datasetSymbol;
+
+    public override void Initialize()
+    {
+        SetStartDate(2019, 1, 1);
+        SetEndDate(2020, 6, 1);
+        SetCash(100000);
+        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
+        _datasetSymbol = AddData<FearGreedIndex>("FG").Symbol;
+    }
+}
    
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current Fear and Greed data, index the current
+   
+    
+     Slice
+    
+   
+   with the dataset
+   
+    Symbol
+   
+   .
+   
+    Slice
+   
+   objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your dataset at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    if slice.contains_key(self._dataset_symbol):
+        data_point = slice[self._dataset_symbol]
+        self.log(f"{self._dataset_symbol} value at {slice.time}: {data_point.value}")
    
+   
    public override void OnData(Slice slice)
+{
+    if (slice.ContainsKey(_datasetSymbol))
+    {
+        var dataPoint = slice[_datasetSymbol];
+        Log($"{_datasetSymbol} value at {slice.Time}: {dataPoint.Value}");
+    }
+}
+
    
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   To get historical Fear and Greed data, call the
+   
+    History
+   
+   
+    history
+   
+   method with the dataset
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame
+history_df = self.history(self._dataset_symbol, 100, Resolution.DAILY)
+
+# Dataset objects
+history = self.history[FearGreedIndex](self._dataset_symbol, 100, Resolution.DAILY)
    
+   
    var history = History<FearGreedIndex>(_datasetSymbol, 100, Resolution.Daily);
    
+  
    
+  
    
+   For more information about historical data, see
+   
+    History Requests
+   
+   .
+  
    
+ 
+
+
+
    Remove Subscription
    
+
+ 
+  
    
+   To remove a subscription to the Fear and Greed dataset, call the
+   
+    RemoveSecurity
+   
+   
+    remove_security
+   
+   method.
+  
    
+  
    
+   
    self.remove_security(self._dataset_symbol)
    
+   
    RemoveSecurity(_datasetSymbol);
    
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The Fear and Greed dataset quantifies the degree of fear and greed in the US Equity market. Example use cases include the following strategies:
   
    
   
      
    
    • 
-    Buy and hold
+    Accumulate long-term positions when the market is fearful (the index is low).
    
      • 
    
    • 
-    Trading Cryptocurrency volatility and price action
+    Short risky assets when the market is greedy.
    
      • 
    
    • 
-    Allocating a small portion of your portfolio to Cryptocurrencies to hedge against inflation
+    Trade
+    
+     butterfly Option strategies
+    
+    based on the market volatility indicator.
    
      • 
   
    
   
    
    Classic Algorithm Example
   
    
   
    
-   The following example algorithm buys and holds Bitcoin through the Coinbase exchange:
+   The following example algorithm buys SPY when the Fear and Greed index is less than 25 (extreme fear) and sells when it's above 75 (extreme greed).
   
    
   
    
    
    from AlgorithmImports import *
 
 
-class CoinAPIDataAlgorithm(QCAlgorithm):
+class FearAndGreedExampleAlgorithm(QCAlgorithm):
 
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        self.universe_settings.asynchronous = True
-        self.set_cash(100_000)
-        # Coinbase accepts Cash account type only. AccountType.MARGIN
-        # results in an exception.
-        self.set_brokerage_model(BrokerageName.COINBASE, AccountType.CASH)
-        # Add BTCUSD data.
-        self._btc = self.add_crypto("BTCUSD", Resolution.MINUTE, Market.COINBASE)
-        # Historical data
-        history = self.history(self._btc.symbol, 30, Resolution.DAILY)
-        # Add a Crypto universe that selects Crypto pairs on Coinbase.
-        universe = self.add_universe(CryptoUniverse.coinbase(self._select_assets))
-        # Historical Universe data
-        universe_history = self.history(universe, 30, Resolution.DAILY)
-        for (universe_symbol, time), universe_day in universe_history.items():
-            for universe_item in universe_day:
-                symbol = universe_item.symbol
-                price = universe_item.close
-
-    def _select_assets(self, universe_day):
-        # Filter for materially traded Crypto pairs with significant 
-        # size and dollar volume, assuming higher capital flow in for 
-        # higher return.
-        return [universe_item.symbol for universe_item in universe_day
-                if universe_item.volume >= 100 
-                and universe_item.volume_in_usd > 10_000_000]
+        self.set_cash(100000)
 
-    def on_data(self, slice: Slice) -> None:
-        # Speculate-invest all available free cash on BTCUSD, repecting 
-        # the order quantity restrictions to avoid invalid orders.
-        if self.portfolio.cash_book['BTC'].amount != 0:
-            return
-        # Define a buffer so that we can scale down the order size,
-        # avoiding trading errors.
-        buffer = max(
-            CoinbaseFeeModel.TAKER_ADVANCED_1, 
-            self.settings.free_portfolio_value_percentage
-        )
-        quantity = (
-            self.portfolio.cash_book['USD'].amount
-            / self._btc.ask_price
-            * (1-buffer)
-        )
-        # Check if the quantity exceeds the minimum order size. On 
-        # Coinbase, the minimum order size is measured in the base 
-        # currency (BTC in this case).
-        if abs(quantity) < self._btc.symbol_properties.minimum_order_size:
+        # Add the market index to trade.
+        self._equity = self.add_equity('SPY', Resolution.DAILY)
+        # Add the Fear and Greed dataset.
+        self._dataset_symbol = self.add_data(FearGreedIndex, 'FG').symbol
+    
+    def on_data(self, data: Slice) -> None:
+        # Wait until the Fear and Greed dataset has new data.
+        if self._dataset_symbol not in data:
             return
-        self.market_order(self._btc, quantity)
    
-   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+        value = data[self._dataset_symbol].value
+        # Buy when the market is fearful.
+        if not self._equity.invested and value < 25:
+            self.set_holdings(self._equity.symbol, 1)
+        # Sell when the market is greedy.
+        elif self._equity.invested and value > 75:
+            self.liquidate()
    
+   
    public class FearAndGreedExampleAlgorithm : QCAlgorithm
 {
-    private Crypto _btc;
-    
+    private Equity _equity;
+    private Symbol _datasetSymbol;
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        UniverseSettings.Asynchronous = true;
         SetCash(100000);
-        // Coinbase accepts Cash account type only. AccountType.Margin
-        // results in an exception.
-        SetBrokerageModel(BrokerageName.Coinbase, AccountType.Cash);
-        // Add BTCUSD data.
-        _btc = AddCrypto("BTCUSD", Resolution.Minute, Market.Coinbase);   
-        // Historical data
-        var history = History(_btc.Symbol, 30, Resolution.Daily);
-        // Add a Crypto universe that selects trading pairs on Coinbase.
-        var universe = AddUniverse(CryptoUniverse.Coinbase(SelectAssets));
-        // Historical Universe data
-        var universeHistory = History(universe, 30, Resolution.Daily);
-        foreach (var universeDay in universeHistory)
-        {
-            foreach (CryptoUniverse universeItem in universeDay)
-            {
-                var symbol = universeItem.Symbol;
-                var price = universeItem.Close;
-            }
-        }
-    }
 
-    private IEnumerable<Symbol> SelectAssets(IEnumerable<CryptoUniverse> universeDay)
-    {
-        // Filter for materially traded Crypto pairs with significant 
-        // size and dollar volume, assuming higher capital flow in for 
-        // higher return.
-        return from universeItem in universeDay
-                where universeItem.Volume >= 100m 
-                && universeItem.VolumeInUsd > 10000000m
-                select universeItem.Symbol;
+        // Add the market index to trade.
+        _equity = AddEquity("SPY", Resolution.Daily);
+        // Add the Fear and Greed dataset.
+        _datasetSymbol = AddData<FearGreedIndex>("FG").Symbol;
     }
 
-    public override void OnData(Slice slice)
+    public override void OnData(Slice data)
     {
-        // Speculate-invest all available free cash on BTCUSD, repecting 
-        // the order quantity restrictions to avoid invalid orders.
-        if (Portfolio.CashBook["BTC"].Amount != 0)
+        // Wait until the Fear and Greed dataset has new data
+        if (!data.ContainsKey(_datasetSymbol))
         {
             return;
         }
-        // Define a buffer so that we can scale down the order size,
-        // avoiding trading errors.
-        var buffer = Math.Max(CoinbaseFeeModel.TakerAdvanced1, Settings.FreePortfolioValuePercentage);
-        var quantity = Portfolio.CashBook["USD"].Amount / _btc.AskPrice * (1-buffer);
-        // Check if the quantity exceeds the minimum order size. On 
-        // Coinbase, the minimum order size is measured in the base 
-        // currency (BTC in this case).
-        if (Math.Abs(quantity) < _btc.SymbolProperties.MinimumOrderSize)
+        var value = data[_datasetSymbol].Value;
+        // Buy when the market is fearful.
+        if (!_equity.Invested && value < 25)
         {
-            return;
+            SetHoldings(_equity.Symbol, 1);
+        }
+        // Sell when the market is greedy.
+        else if (_equity.Invested && value > 75)
+        {
+            Liquidate();
         }
-        MarketOrder(_btc, quantity);
     }
 }
    
   
    
   
    
-   Framework Algorithm Example
+   Framework Example
   
    
   
    
-   The following example algorithm creates a dynamic universe of Crypto pairs on the Coinbase exchange and then forms a equal-weighted portfolio of all the pairs in the universe:
+   The following example algorithm buys SPY when the Fear and Greed index is less than 25 (extreme fear) and sells when it's above 75 (extreme greed).
   
    
   
    
    
    from AlgorithmImports import *
 
 
-class CoinAPIDataAlgorithm(QCAlgorithm):
+class FearAndGreedExampleAlgorithm(QCAlgorithm):
 
     def initialize(self) -> None:
-        self.set_start_date(2020, 6, 1)
-        self.set_end_date(2021, 2, 1)
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
         self.set_cash(100000)
-        self.settings.free_portfolio_value_percentage = 0.05
-        # Warm up the security with the last known price to avoid conversion error
-        self.settings.seed_initial_prices = True
-        self.universe_settings.asynchronous = True
-        self.universe_settings.resolution = Resolution.MINUTE
-        # Add Crypto Universe Selection that select crypto pairs in Coinbase exchange
-        self.add_universe(CryptoUniverse.coinbase(self._select_assets))
-        # Get the pairs on Coinbase that have USD as the quote currency.
-        self._market_pairs = [
-            x.key.symbol 
-            for x in self.symbol_properties_database.get_symbol_properties_list(Market.COINBASE) 
-            if x.value.quote_currency == self.account_currency
-        ]
-        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(hours=4), 0.025, None))
-        # Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
+
+        # Trade SPY on a daily basis.
+        self.universe_settings.resolution = Resolution.DAILY
+        symbols = [Symbol.create('SPY', SecurityType.EQUITY, Market.USA)]
+        self.add_universe_selection(ManualUniverseSelectionModel(symbols))
+        # Add a custom Alpha model that emits insights based on the Fear and Greed dataset.
+        self.add_alpha(FearAndGreedAlphaModel(self))
+        # Set the equal weighted PCM so the algorithm places trades.
         self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
 
-    def _select_assets(self, universe_day: List[CryptoUniverse]) -> List[Symbol]:
-        # Select pairs where the quote currency is USD.
-        pairs = [c for c in universe_day if c.symbol.value in self._market_pairs]
-        # Filter for materially traded crypto pairs with significant dollar volume, assuming higher capital flow in for higher return.
-        pairs = sorted(pairs, key=lambda c: c.volume_in_usd)[-3:]      
-        return [c.symbol for c in pairs]
 
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
+# Define the custom alpha model.
+class FearAndGreedAlphaModel(AlphaModel):
+
+    def __init__(self, algorithm):
+        # Create a list to track the current universe.
+        self._securities = []
+        # Add the Fear and Greed dataset. 
+        self._dataset_symbol = algorithm.add_data(FearGreedIndex, 'FG').symbol
+
+    def update(self, algorithm: QCAlgorithm, data: Slice) -> List[Insight]:
+        # Wait until the Fear and Greed dataset has new data.
+        if self._dataset_symbol not in data:
+            return []
+        # Wait until the Fear and Greed index is extreme.
+        value = data[self._dataset_symbol].value
+        if 25 <= value <= 75:
+            return []
+        insights = []
+        for security in self._securities:
+            # Buy when the market is fearful.
+            if not security.invested and value < 25:
+                direction = InsightDirection.UP
+            # Sell when the market is greedy.
+            elif security.invested and value > 75:
+                direction = InsightDirection.FLAT
+            else:
+                continue
+            insights.append(Insight.price(security.symbol, timedelta(2*365), direction))
+        return insights
+
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
+        # When assets enter the universe, save them.
         for security in changes.added_securities:
-            # Historical data
-            history = self.history(security.symbol, 30, Resolution.DAILY)
-            self.debug(f"We got {len(history)} items from our history request")
    
-   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+            self._securities.append(security)
+        # When assets leave the universe, remove them.
+        for security in changes.removed_securities:
+            if security in self._securities:
+                self._securities.remove(security)
    
+   
    public class FearAndGreedExampleAlgorithm : QCAlgorithm
 {
-    private List<string> _marketPairs;
-    
     public override void Initialize()
     {
-        SetStartDate(2020, 6, 1);
-        SetEndDate(2021, 2, 1);
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
         SetCash(100000);
-        Settings.FreePortfolioValuePercentage = 0.05m;
-        // Warm up the security with the last known price to avoid conversion error
-        Settings.SeedInitialPrices = true;
-        UniverseSettings.Asynchronous = true;
-        UniverseSettings.Resolution = Resolution.Minute;
-        // Add Crypto Universe Selection that select crypto pairs in Coinbase exchange
-        AddUniverse(CryptoUniverse.Coinbase(SelectAssets));
-        // Get the pairs on Coinbase that have USD as the quote currency.
-        _marketPairs = SymbolPropertiesDatabase.GetSymbolPropertiesList(Market.Coinbase)
-            .Where(x => x.Value.QuoteCurrency == AccountCurrency)
-            .Select(x => x.Key.Symbol)
-            .ToList();
-        AddAlpha( new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromHours(4), 0.025, null) );
-        // Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
+
+        // Trade SPY on a daily basis.
+        UniverseSettings.Resolution = Resolution.Daily;
+        var symbols = new[] { QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA) };
+        AddUniverseSelection(new ManualUniverseSelectionModel(symbols));
+        // Add a custom Alpha model that emits insights based on the Fear and Greed dataset.
+        AddAlpha(new FearAndGreedAlphaModel(this));
+        // Set the equal weighted PCM so the algorithm places trades.
         SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
     }
+}
 
-    private IEnumerable<Symbol> SelectAssets(IEnumerable<CryptoUniverse> universeDay)
+// Define the custom Alpha model.
+public class FearAndGreedAlphaModel : AlphaModel
+{
+    // Create a list to track the current universe.
+    private List<Security> _securities = new List<Security>();
+    private Symbol _datasetSymbol;
+
+    public FearAndGreedAlphaModel(QCAlgorithm algorithm)
     {
-        // Select pairs where the quote currency is USD.
-        return universeDay.Where(c => _marketPairs.Contains(c.Symbol.Value))
-            // Filter for materially traded crypto pairs with significant size and dollar volume, assuming higher capital flow in for higher return
-            .OrderBy(c => c.VolumeInUsd)
-            .TakeLast(3)
-            .Select(c => c.Symbol);
+        // Add the Fear and Greed dataset. 
+        _datasetSymbol = algorithm.AddData<FearGreedIndex>("FG").Symbol;
     }
 
-    public override void OnSecuritiesChanged(SecurityChanges changes)
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data)
     {
-        foreach(var security in changes.AddedSecurities)
+        // Wait until the Fear and Greed dataset has new data.
+        if (!data.ContainsKey(_datasetSymbol))
         {
-            // Historical data
-            var history = History(security.Symbol, 30, Resolution.Daily);
-            Debug($"We got {history.Count()} items from our history request");
+            return Enumerable.Empty<Insight>();
+        }
+        // Wait until the Fear and Greed index is extreme.
+        var value = data[_datasetSymbol].Value;
+        if (value >= 25 && value <= 75)
+        {
+            return Enumerable.Empty<Insight>();
+        }
+
+        var insights = new List<Insight>();
+        foreach (var security in _securities)
+        {
+            InsightDirection direction;
+            // Buy when the market is fearful.
+            if (!security.Invested && value < 25)
+            {
+                direction = InsightDirection.Up;
+            }
+            // Sell when the market is greedy.
+            else if (security.Invested && value > 75)
+            {
+                direction = InsightDirection.Flat;
+            }
+            else
+            {
+                continue;
+            }
+            insights.Add(Insight.Price(security.Symbol, TimeSpan.FromDays(2 * 365), direction));
+        }
+        return insights;
+    }
+
+    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
+    {
+        // When assets enter the universe, save them.
+        foreach (var security in changes.AddedSecurities)
+        {
+            _securities.Add(security);
+        }
+        // When assets leave the universe, remove them.
+        foreach (var security in changes.RemovedSecurities)
+        {
+            _securities.Remove(security);
         }
     }
 }
    
   
    
-  
    
-   Research Example
-  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
   
    
-   The following example lists crypto-currency pairs with the greatest dollar volume in the Coinbase exchange:
+   The Fear and Greed dataset provides
+   
+    FearGreedIndex
+   
+   objects, which have the following attributes:
   
    
-  
    
-   
    var qb = new QuantBook();
+  
    
+  
    
+ 
+
 
-// Add Cryptocurrency pair
-var symbol = qb.AddCrypto("BTCUSDT", market:Market.Coinbase).Symbol;
+
     
    
+
    Datasets > QuantConnect > International Future Universe
    
+
    
+    
    
+        
    QuantConnect
    
+        
    International Future Universe
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The International Future Universe dataset by QuantConnect lists the available International Future contracts, their daily trading volume, and Open Interest. The data covers 4 contracts (DAX, FESX, HSI, and NKD), starts in July 1998, and is delivered on daily frequency. This dataset is created by monitoring the trading activity on the EUREX, HKFE, and CME.
+  
    
+  
    
+   This dataset depends on the
+   
+    US Futures Security Master
+   
+   dataset because the US Futures Security Master dataset contains information on symbol changes of the contracts.
+  
    
+  
    
+   This dataset
+   
+    does not
+   
+   contain market data. For market data, see
+   
+    International Futures by TickData
+   
+   and
+   
+    US Futures by AlgoSeek
+   
+   for NKD.
+  
    
+  
    
+   For more information about the International Future Universe dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
 
-// Historical data
-var history = qb.History(symbol, 30, Resolution.Daily);
-foreach (var bar in history)
-{
-    Console.WriteLine($"{bar.EndTime} {bar}");
-}
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
 
-// Add Crypto Universe Selection
-IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
-{
-    return universeDay
-        .Where(x => x.VolumeInUsd != null && x.VolumeInUsd > 10000m)
-        .OrderByDescending(x => x.VolumeInUsd)
-        .Take(5)
-        .Select(x => x.Symbol);
-}
-var universe = qb.AddUniverse(CryptoUniverse.Coinbase(UniverseSelectionFilter));
+
    Getting Started
    
+
+ 
+  
    
+   The International Futures Universe dataset provides data for contract filtering/selection:
+  
    
+  
    
+   
    hsi = self.add_future(Futures.Indices.HANG_SENG, Resolution.MINUTE)           # "HSI"
+hsi.set_filter(0, 90)
+dax = self.add_future(Futures.Indices.DAX, Resolution.MINUTE)                 # "DAX"
+dax.set_filter(0, 90)
+fesx = self.add_future(Futures.Indices.EURO_STOXX_50, Resolution.MINUTE)      # "FESX"
+fesx.set_filter(0, 90)
+nkd = self.add_future(Futures.Indices.NIKKEI_225_DOLLAR, Resolution.MINUTE)   # "NKD"
+nkd.set_filter(0, 90)
    
+   
    var hsi= AddFuture(Futures.Indices.HangSeng, Resolution.Minute)           // "HSI";
+hsi.SetFilter(0, 90);
+var dax = AddFuture(Futures.Indices.Dax, Resolution.Minute)               // "DAX";
+dax.SetFilter(0, 90);
+var fesx = AddFuture(Futures.Indices.EuroStoxx50, Resolution.Minute)      // "FESX";
+fesx.SetFilter(0, 90);
+var nkd = AddFuture(Futures.Indices.Nikkei225Dollar, Resolution.Minute)   // "NKD";
+nkd.SetFilter(0, 90);
    
+  
    
+ 
+
 
-// Historical Universe data
-var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-foreach (var universeDay in universeHistory)
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      July 1998 (for details, see
+      
+       Supported Assets
+      
+      )
+     
    
+      Coverage
+     
+      4 Contracts
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Daily
+     
    
+      Timezone
+     
+      Various (for details, see
+      
+       Supported Assets
+      
+      )
+     
    
+      Market Hours
+     
+      
+       Regular and Extended
+      
+     
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add International Future Universe data to your algorithm, call the
+   
+    AddFuture
+   
+   
+    add_future
+   
+   method. Save a reference to the Future object so you can access the data later in your algorithm. To define which contracts should be in your universe, specify the filter when requesting the Future data.
+  
    
+  
    
+   The
+   
+    AddFuture
+   
+   
+    add_future
+   
+   method provides a daily stream of Future chain data. To get the most recent daily chain, call the
+   
+    FuturesChain
+   
+   
+    futures_chain
+   
+   method with the underlying Future Symbol. The
+   
+    FuturesChain
+   
+   
+    futures_chain
+   
+   method returns data on all the tradable contracts, not just the contracts that pass your universe filter.
+  
    
+  
    
+   
    class InternationalFuturesDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2013, 12, 20) 
+        self.set_end_date(2014, 2, 20) 
+        self.set_cash(1000000) 
+        self.universe_settings.asynchronous = True
+        self._future = self.add_future(Futures.Indices.HANG_SENG)
+        # Set our contract filter for this Future chain.
+        self._future.set_filter(lambda universe: universe.standards_only().front_month())
+        # Get the entire Futures chain for the current day.
+        chain = self.futures_chain(self._future.symbol, flatten=True).data_frame
    
+   
    public class InternationalFuturesDataAlgorithm : QCAlgorithm
 {
-    Console.WriteLine($"=== {universeDay.First().EndTime} ===");
-    foreach (CryptoUniverse universeItem in universeDay.OrderByDescending(x => x.VolumeInUsd))
+    private Future _future;
+        
+    public override void Initialize()
     {
-        Console.WriteLine($"{universeItem.Symbol}: {universeItem.VolumeInUsd}");
+        SetStartDate(2013, 12, 20);
+        SetEndDate(2014, 2, 20);
+        SetCash(1000000);
+        UniverseSettings.Asynchronous = true;
+        var future = AddFuture(Futures.Indices.HangSeng);
+        // Set our contract filter for this Future chain.
+        _future.SetFilter((universe) => universe.StandardsOnly().FrontMonth());
+        // Get the entire Futures chain for the current day.
+        var chain = FuturesChain(_future.Symbol);
     }
 }
    
-   
    qb = QuantBook()
-
-# Add Cryptocurrency pair
-symbol = qb.add_crypto("BTCUSDT", market=Market.COINBASE).symbol
-
-# Historical data
-history = qb.history(symbol, 30, Resolution.DAILY)
-for (symbol, time), row in history.iterrows():
-    print(f'{time} {symbol} {row.close}')
-
-# Add Crypto Universe Selection
-def universe_selection_filter(universe_day):
-    selected = sorted([x for x in universe_day
-        if x.volume_in_usd and x.volume_in_usd > 10000],
-        key=lambda x: x.volume_in_usd, reverse=True)[:5]
-    return [x.symbol for x in selected]
-
-universe = qb.add_universe(CryptoUniverse.coinbase(universe_selection_filter))
-
-# Historical Universe data
-history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (univere_symbol, time), universe_day in history.items():
-    print(f'=== {time} ===')
-    for universe_item in sorted(universe_day, key=lambda x: x.volume_in_usd, reverse=True):
-        print(f"{universe_item.symbol}: {universe_item.volume_in_usd}")
    
   
    
+  
    
+   For more information about creating Future Universes, see
+   
+    Futures
+   
+   .
+  
    
  
 
 
-
    Data Point Attributes
    
+
    Accessing Data
    
 
  
   
    
-   The Coinbase Crypto Price dataset provides
-   
-    TradeBar
-   
-   ,
-   
-    QuoteBar
+   For information about accessing International Future Universe data, see
+   
+    Futures
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical International Future Universe data in an algorithm and the Research Environment.
+  
    
+  
    
+   Historical Data In Algorithms
+  
    
+  
    
+   To get historical International Future Universe data in an algorithm, call the
+   
+    History<FutureUniverse>
    
-   ,
-   
-    Tick
+   
+    history
    
-   , and
+   method with continuous Future
    
-    CryptoUniverse
+    Symbol
    
-   objects.
+   and a lookback period. This method returns the all the available contracts for each trading day, not the subset of contracts that pass your universe filter. If there is no data for the period you requested, the history result is empty.
+  
    
+  
    
+   
    # Add the Future and save a reference to it.
+future = self.add_future(Futures.Indices.HANG_SENG)
+
+# DataFrame example where the columns are the FutureUniverse attributes:
+history_df = self.history(FutureUniverse, future.symbol, 5, flatten=True)
+
+# Series example where the values are lists of FutureUniverse objects: 
+history_series = self.history(FutureUniverse, future.symbol, 5, flatten=False)
+
+# FutureUniverse objects example:
+history = self.history[FutureUniverse](future.symbol, 5)
    
+   
    // Add the Future and save a reference to it.
+var future = AddFuture(Futures.Indices.HangSeng);
+
+// Get historical data.
+var history = History<FutureUniverse>(future.Symbol, 5);
    
+  
    
+  
    
+   For more information about historical International Future Universe data in algorithms, see
+   
+    Contracts
+   
+   .
   
    
   
    
-   TradeBar Attributes
+   Historical Data In Research
   
    
   
    
+   To get historical International Future Universe data in the Research Environment, call the
+   
+    History<FutureUniverse>
+   
+   
+    history
+   
+   method with continuous Future
    
-    TradeBar
+    Symbol
    
-   objects have the following attributes:
+   and a time period. This method returns the all the available contracts for each trading day, not the subset of contracts that pass your universe filter. If there is no data for the period you requested, the history result is empty.
   
    
-  
    
+  
    
+   
    # Add the Future and save a reference to it.
+future = qb.add_future(Futures.Indices.HANG_SENG)
+
+# DataFrame example where the columns are the FutureUniverse attributes:
+history_df = qb.history(FutureUniverse, future.symbol, datetime(2025, 1, 1), datetime(2025, 4, 1), flatten=True)
+
+# Series example where the values are lists of FutureUniverse objects: 
+history_series = qb.history(FutureUniverse, future.symbol, 5, flatten=False)
+
+# FutureUniverse objects example:
+history = qb.history[FutureUniverse](future.symbol, datetime(2025, 1, 1), datetime(2025, 4, 1))
    
+   
    // Add the Future and save a reference to it.
+var future = qb.AddFuture(Futures.Indices.HangSeng);
+
+// Get historical data.
+var history = qb.History<FutureUniverse>(future.Symbol, new DateTime(2025, 1, 1), new DateTime(2025, 4, 1));
    
   
    
+  
    
+   For more information about historical International Future Universe data in the Research Environment, see
+   
+    Daily Prices History
+   
+   .
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   The following table shows the available Futures:
+  
    
+  
+   
+    
+     
+     
+     
+     
+     
+    
+   
+   
+    
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+    
+   
+  
    
+      Ticker
+     
+      Future
+     
+      Start Date
+     
+      Time Zone
+     
+      Currency
+     
    
+      HSI
+     
+      Hang Seng Index Futures
+     
+      Jan 2010
+     
+      Asia/Hong Kong
+     
+      HKD
+     
    
+      DAX
+     
+      German Index Futures
+     
+      Jan 2010
+     
+      Europe/Berlin
+     
+      EUR
+     
    
+      FESX
+     
+      EURO STOXX 50 Index Futures
+     
+      Jul 1998
+     
+      Europe/Berlin
+     
+      EUR
+     
    
+      NKD
+     
+      Nikkei 225 Index Futures
+     
+      Jan 2007
+     
+      America/Chicago
+     
+      USD
+     
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The International Futures Universe dataset enables you to design Futures strategies accurately. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Buying the Futures contract with the most open interest to reduce slippage and market impact
+   
      • 
+   
    • 
+    Trade speculation on an International Index
+   
      • 
+   
    • 
+    Trading bull calendar spreads to reduce volatility and margin requirements
+   
      • 
+  
    
   
    
-   QuoteBar Attributes
+   Classic Algorithm Example
   
    
   
    
+   The following example algorithm uses the
+   
+    ZigZag indicator
+   
+   to determine the trend of Hang Seng Index. It then trades the Index with HSI Futures.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class InternationalFuturesDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        # Set the time zone to HKT to make it more comparable with the exchange.
+        self.set_time_zone(TimeZones.HONG_KONG)
+        # Set the account currency as HKD to trade HSI Futures.
+        self.set_account_currency("HKD", 1000000)
+        # Seed the last price of the contracts for filling.
+        self.settings.seed_initial_prices = True
+
+        # Request HSI Futures to trade. 
+        # Note that we will trade the contract with the highest open interest for liquidity.
+        self.hsi_future = self.add_future(
+            Futures.Indices.HANG_SENG,
+            extended_market_hours=True,
+            data_mapping_mode=DataMappingMode.LAST_TRADING_DAY,
+            contract_depth_offset=0
+        )
+        # Adds contracts that expiry within 90 days. We will trade the farthest contract
+        self.hsi_future.set_filter(0,90)
+        # Request the corresponding underlying Index for feeding indicator for trade signal generation.
+        hsi_index = self.add_index("HSI").symbol
+
+        # Create a ZigZag indicator to trade Hang Seng Index price pivot points.
+        self._zz = self.zz(hsi_index, 0.15, 5, Resolution.DAILY)
+        # Warm up indicator for immediate readiness to trade.
+        self.warm_up_indicator(hsi_index, self._zz, Resolution.DAILY)
+
+    def on_data(self, slice: Slice) -> None:
+        # Only place trade if the Future contracts is in market opening hours to avoid stale fills.
+        if self.is_market_open(self.hsi_future.symbol) and self._zz.is_ready:
+            pivot = self._zz.pivot_type
+            # If the last pivot point is a low point, the current trend is increasing after this low point.
+            if pivot == PivotPointType.LOW:
+                contracts = sorted([x.symbol for x in slice.future_chains.get(self.hsi_future.symbol)],
+                    key=lambda x: x.id.date)
+                self.set_holdings(contracts[-1], 0.2)
+            # If the last pivot point is a high point, the current trend is decreasing after this high point.
+            if pivot == PivotPointType.HIGH:
+                contracts = sorted([x.symbol for x in slice.future_chains.get(self.hsi_future.symbol)],
+                    key=lambda x: x.id.date)
+                self.set_holdings(contracts[-1], -0.2)
    
+   
    public class InternationalFuturesDataAlgorithm : QCAlgorithm
+{
+    private Future _hsiFuture;
+    private ZigZag _zz;
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        // Set the time zone to HKT to make it more comparable with the exchange.
+        SetTimeZone(TimeZones.HongKong);
+        // Set the account currency as HKD to trade HSI Futures.
+        SetAccountCurrency("HKD", 1000000);
+
+        // Seed the last price of the contracts for filling.
+        Settings.SeedInitialPrices = true;
+
+        // Request HSI Futures to trade. 
+        // Note that we will trade the contract with the highest open interest for liquidity.
+        _hsiFuture = AddFuture(
+            Futures.Indices.HangSeng,
+            extendedMarketHours: true,
+            dataMappingMode: DataMappingMode.LastTradingDay,
+            contractDepthOffset: 0
+        );
+        // Adds contracts that expiry within 90 days. We will trade the farthest contract
+        _hsiFuture.SetFilter(0,90);
+        // Request the corresponding underlying index for feeding indicators for trade signal generation.
+        var hsiIndex = AddIndex("HSI").Symbol;
+
+        // Create a ZigZag indicator to trade Hang Seng Index price pivot points.
+        _zz = ZZ(hsiIndex, 0.15m, 5, Resolution.Daily);
+        // Warm up indicator for immediate readiness to trade.
+        WarmUpIndicator(hsiIndex, _zz, Resolution.Daily);
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // Only place trade if the Future contracts is in market opening hours to avoid stale fills.
+        if (IsMarketOpen(_hsiFuture.Symbol) && _zz.IsReady)
+        {
+            var pivot = _zz.PivotType;
+            FuturesChain chain;
+            // If the last pivot point is low, the current trend is increasing after this low point.
+            if (pivot == PivotPointType.Low && slice.FutureChains.TryGetValue(_hsiFuture.Symbol, out chain))
+            {
+                var contract = chain.OrderBy(x => x.Expiry).LastOrDefault()?.Symbol;                    
+                SetHoldings(contract, 0.2m);
+            }
+            // If the last pivot point is high, the current trend decreases after this high point.
+            if (pivot == PivotPointType.High && slice.FutureChains.TryGetValue(_hsiFuture.Symbol, out chain))
+            {
+                var contract = chain.OrderBy(x => x.Expiry).LastOrDefault()?.Symbol;                    
+                SetHoldings(contract, -0.2m);
+            }
+        }
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The International Future Universe dataset provides
    
-    QuoteBar
+    FutureFilterUniverse
    
-   objects have the following attributes:
+   and
+   
+    FutureUniverse
+   
+   objects.
   
    
-  
    
-  
    
   
    
-   Tick Attributes
+   FutureFilterUniverse Attributes
   
    
   
    
    
-    Tick
+    FutureFilterUniverse
    
    objects have the following attributes:
   
    
-  
    
+  
    
   
    
   
    
-   CryptoUniverse Attributes
+   FutureUniverse Attributes
   
    
   
    
    
-    CryptoUniverse
+    FutureUniverse
    
    objects have the following attributes:
   
    
-  
    
+  
    
   
    
  
 
 
 
     
    
-
    Datasets > CoinAPI > Kraken Crypto Price Data
    
+
    Datasets > QuantConnect > Kraken Crypto Price Data
    
 
    
-    
    
-        
    CoinAPI
    
+    
    
+        
    QuantConnect
    
         
    Kraken Crypto Price Data
    
     
    
 
    
@@ -232179,7 +206432,7 @@ 
    Introduction
    
 
  
   
    
-   The Kraken Crypto Price Data by CoinAPI provides Cryptocurrency price and volume data points. The data covers 1,262 Cryptocurrency pairs, starts in October 2013, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on the Cryptocurrency markets/exchanges supported by QuantConnect.
+   The Kraken Crypto Price Data provides Cryptocurrency price and volume data points. The data covers 1,262 Cryptocurrency pairs, starts in October 2013, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on the Cryptocurrency markets/exchanges supported by QuantConnect.
   
    
   
    
    For more information about the Kraken Crypto Price Data dataset, including CLI commands and pricing, see the
@@ -232197,10 +206450,18 @@ 
    About the Provider
    
 
  
   
    
-   
-    CoinAPI
+   
+    Kraken
+   
+   was founded by Jesse Powell in 2011 with the goal to "accelerate the adoption of cryptocurrency so that you and the rest of the world can achieve financial freedom and inclusion". Kraken provides access to trading Crypto through spot and Futures markets for clients with a minimum deposit of around $0-$150 USD for
+   
+    currency
+   
+   and
+   
+    Crypto deposits
    
-   was founded by Artur Pietrzyk in 2016 with the goal of providing real-time and historical cryptocurrency market data, collected from hundreds of exchanges. CoinAPI provides access to Cryptocurrencies for traders, market makers, and developers building third-party applications.
+   . Kraken also provides staking services, educational content, and a developer grant program.
   
    
  
 
@@ -232308,9 +206569,12 @@ 
    Requesting Data
    
  
   
    
    To add Kraken Crypto Price data to your algorithm, call the
-   
+   
     AddCrypto
    
+   
+    add_crypto
+   
    method. Save a reference to the Crypto
    
     Symbol
@@ -236974,312 +211238,27985 @@ 
    Example Applications
    
 
  
   
    
-   The Kraken Crypto Price dataset enables you to accurately design strategies for Cryptocurrencies. Examples include the following strategies:
+   The Kraken Crypto Price dataset enables you to accurately design strategies for Cryptocurrencies. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Buy and hold
+   
      • 
+   
    • 
+    Trading Cryptocurrency volatility and price action
+   
      • 
+   
    • 
+    Allocating a small portion of your portfolio to Cryptocurrencies to hedge against inflation
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm buys and holds Bitcoin through the Kraken exchange:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class CoinAPIDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.universe_settings.asynchronous = True
+        self.set_cash(100_000)
+        # Kraken accepts both Cash and Margin account types. Select the
+        # one you need for the best reality modeling.
+        self.set_brokerage_model(BrokerageName.KRAKEN, AccountType.MARGIN)
+        # Add BTCUSD data.
+        self._btc = self.add_crypto("BTCUSD", Resolution.MINUTE, Market.KRAKEN)
+        # Historical data
+        history = self.history(self._btc.symbol, 30, Resolution.DAILY)
+        # Add a Crypto universe that selects Crypto pairs on Kraken.
+        universe = self.add_universe(CryptoUniverse.kraken(self._select_assets))
+        # Historical Universe data
+        universe_history = self.history(universe, 30, Resolution.DAILY)
+        for (universe_symbol, time), universe_day in universe_history.items():
+            for universe_item in universe_day:
+                symbol = universe_item.symbol
+                price = universe_item.close
+
+    def _select_assets(self, universe_day):
+        # Filter for materially traded Crypto pairs with significant 
+        # size and dollar volume, assuming higher capital flow in for 
+        # higher return.
+        return [universe_item.symbol for universe_item in universe_day
+                if universe_item.volume >= 100 
+                and universe_item.volume_in_usd > 10_000_000]
+
+    def on_data(self, slice: Slice) -> None:
+        # Speculate-invest all available free cash on BTCUSD, repecting 
+        # the order quantity restrictions to avoid invalid orders.
+        if self.portfolio.cash_book['BTC'].amount != 0:
+            return
+        # Define a buffer so that we can scale down the order size,
+        # avoiding trading errors.
+        buffer = max(
+            KrakenFeeModel.TAKER_TIER_1_CRYPTO_FEE, 
+            self.settings.free_portfolio_value_percentage
+        )
+        quantity = (
+            self.portfolio.cash_book['USD'].amount
+            / self._btc.ask_price
+            * (1-buffer)
+        )
+        # Check if the quantity exceeds the minimum order size. On 
+        # Kraken, the minimum order size is measured in the base
+        # currency (BTC in this case).
+        if abs(quantity) < self._btc.symbol_properties.minimum_order_size:
+            return
+        self.market_order(self._btc, quantity)
    
+   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+{
+    private Crypto _btc;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        UniverseSettings.Asynchronous = true;
+        SetCash(100000);
+        // Kraken accepts both Cash and Margin account types. Select the
+        // one you need for the best reality modeling.
+        SetBrokerageModel(BrokerageName.Kraken, AccountType.Margin);
+        // Add BTCUSD data.
+        _btc = AddCrypto("BTCUSD", Resolution.Minute, Market.Kraken);   
+        // Historical data
+        var history = History(_btc.Symbol, 30, Resolution.Daily);
+        // Add a Crypto universe that selects trading pairs on Kraken.
+        var universe = AddUniverse(CryptoUniverse.Kraken(SelectAssets));
+        // Historical Universe data
+        var universeHistory = History(universe, 30, Resolution.Daily);
+        foreach (var universeDay in universeHistory)
+        {
+            foreach (CryptoUniverse universeItem in universeDay)
+            {
+                var symbol = universeItem.Symbol;
+                var price = universeItem.Close;
+            }
+        }
+    }
+
+    private IEnumerable<Symbol> SelectAssets(IEnumerable<CryptoUniverse> universeDay)
+    {
+        // Filter for materially traded Crypto pairs with significant 
+        // size and dollar volume, assuming higher capital flow in for 
+        // higher return.
+        return from universeItem in universeDay
+                where universeItem.Volume >= 100m 
+                && universeItem.VolumeInUsd > 10000000m
+                select universeItem.Symbol;
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // Speculate-invest all available free cash on BTCUSD, repecting 
+        // the order quantity restrictions to avoid invalid orders.
+        if (Portfolio.CashBook["BTC"].Amount != 0)
+        {
+            return;
+        }
+        // Define a buffer so that we can scale down the order size,
+        // avoiding trading errors.
+        var buffer = Math.Max(KrakenFeeModel.TakerTier1CryptoFee, Settings.FreePortfolioValuePercentage);
+        var quantity = Portfolio.CashBook["USD"].Amount / _btc.AskPrice * (1-buffer);
+        // Check if the quantity exceeds the minimum order size. On 
+        // Kraken, the minimum order size is measured in the base 
+        // currency (BTC in this case).
+        if (Math.Abs(quantity) < _btc.SymbolProperties.MinimumOrderSize)
+        {
+            return;
+        }
+        MarketOrder(_btc, quantity);
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm creates a dynamic universe of Crypto pairs on the Kraken exchange and then forms a equal-weighted portfolio of all the pairs in the universe:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class CoinAPIDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        self.settings.free_portfolio_value_percentage = 0.05
+
+        # Kraken accepts both Cash and Margin type account, select the one you need for the best reality modeling.
+        self.set_brokerage_model(BrokerageName.KRAKEN, AccountType.MARGIN)
+
+        # Warm up the security with the last known price to avoid conversion error
+        self.settings.seed_initial_prices = True
+        
+        # Get the pairs on Kraken that have USD as the quote currency.
+        self._market_pairs = [
+            x.key.symbol 
+            for x in self.symbol_properties_database.get_symbol_properties_list(Market.KRAKEN) 
+            if x.value.quote_currency == self.account_currency
+        ]
+        # Add Crypto Universe Selection that select crypto pairs in Kraken exchange
+        self.add_universe(CryptoUniverse.kraken(self.universe_selection_filter))
+        
+        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(hours=4), 0.025, None))
+        # Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+
+    def universe_selection_filter(self, universe_day: List[CryptoUniverse]) -> List[Symbol]:
+        # Select pairs where the quote currency is USD.
+        pairs = [c for c in universe_day if c.symbol.value in self._market_pairs]
+        # Filter for materially traded crypto pairs with significant dollar volume, assuming higher capital flow in for higher return.
+        pairs = sorted(pairs, key=lambda c: c.volume_in_usd)[-3:]      
+        return [c.symbol for c in pairs]
+
+    def on_securities_changed(self, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            # Historical data
+            history = self.history(security.symbol, 30, Resolution.DAILY)
+            self.debug(f"We got {len(history)} items from our history request")
    
+   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+{
+    private List<string> _marketPairs;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+        Settings.FreePortfolioValuePercentage = 0.05m;
+
+        // Kraken accepts both Cash and Margin type account, select the one you need for the best reality modeling.            
+        SetBrokerageModel(BrokerageName.Kraken, AccountType.Margin);
+
+        // Warm up the security with the last known price to avoid conversion error
+        Settings.SeedInitialPrices = true;
+        
+        // Get the pairs on Kraken that have USD as the quote currency.
+        _marketPairs = SymbolPropertiesDatabase.GetSymbolPropertiesList(Market.Kraken)
+            .Where(x => x.Value.QuoteCurrency == AccountCurrency)
+            .Select(x => x.Key.Symbol)
+            .ToList();
+        // Add Crypto Universe Selection that select crypto pairs in Kraken exchange
+        AddUniverse(CryptoUniverse.Kraken(UniverseSelectionFilter));
+    
+        AddAlpha( new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromHours(4), 0.025, null) );
+        // Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+    }
+
+    private IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
+    {
+        // Select pairs where the quote currency is USD.
+        return universeDay.Where(c => _marketPairs.Contains(c.Symbol.Value))
+            // Filter for materially traded crypto pairs with significant size and dollar volume, assuming higher capital flow in for higher return
+            .OrderBy(c => c.VolumeInUsd)
+            .TakeLast(3)
+            .Select(c => c.Symbol);
+    }
+
+    public override void OnSecuritiesChanged(SecurityChanges changes)
+    {
+        foreach(var security in changes.AddedSecurities)
+        {
+            // Historical data
+            var history = History(security.Symbol, 30, Resolution.Daily);
+            Debug($"We got {history.Count()} items from our history request");
+        }
+    }
+}
    
+  
    
+  
    
+   Research Example
+  
    
+  
    
+   The following example lists crypto-currency pairs with the greatest dollar volume in the Kraken exchange:
+  
    
+  
    
+   
    var qb = new QuantBook();
+
+// Add Cryptocurrency pair
+var symbol = qb.AddCrypto("BTCUSDT", market:Market.Kraken).Symbol;
+
+// Historical data
+var history = qb.History(symbol, 30, Resolution.Daily);
+foreach (var bar in history)
+{
+    Console.WriteLine($"{bar.EndTime} {bar}");
+}
+
+// Add Crypto Universe Selection
+IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
+{
+    return universeDay
+        .Where(x => x.VolumeInUsd != null && x.VolumeInUsd > 10000m)
+        .OrderByDescending(x => x.VolumeInUsd)
+        .Take(5)
+        .Select(x => x.Symbol);
+}
+var universe = qb.AddUniverse(CryptoUniverse.Kraken(UniverseSelectionFilter));
+
+// Historical Universe data
+var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var universeDay in universeHistory)
+{
+    Console.WriteLine($"=== {universeDay.First().EndTime} ===");
+    foreach (CryptoUniverse universeItem in universeDay.OrderByDescending(x => x.VolumeInUsd))
+    {
+        Console.WriteLine($"{universeItem.Symbol}: {universeItem.VolumeInUsd}");
+    }
+}
    
+   
    qb = QuantBook()
+
+# Add Cryptocurrency pair
+symbol = qb.add_crypto("BTCUSDT", market=Market.KRAKEN).symbol
+
+# Historical data
+history = qb.history(symbol, 30, Resolution.DAILY)
+for (symbol, time), row in history.iterrows():
+    print(f'{time} {symbol} {row.close}')
+
+# Add Crypto Universe Selection
+def universe_selection_filter(universe_day):
+    selected = sorted([x for x in universe_day
+        if x.volume_in_usd and x.volume_in_usd > 10000],
+        key=lambda x: x.volume_in_usd, reverse=True)[:5]
+    return [x.symbol for x in selected]
+
+universe = qb.add_universe(CryptoUniverse.kraken(universe_selection_filter))
+
+# Historical Universe data
+history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+for (univere_symbol, time), universe_day in history.items():
+    print(f'=== {time} ===')
+    for universe_item in sorted(universe_day, key=lambda x: x.volume_in_usd, reverse=True):
+        print(f"{universe_item.symbol}: {universe_item.volume_in_usd}")
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The Kraken Crypto Price dataset provides
+   
+    TradeBar
+   
+   ,
+   
+    QuoteBar
+   
+   ,
+   
+    Tick
+   
+   , and
+   
+    CryptoUniverse
+   
+   objects.
+  
    
+  
    
+   TradeBar Attributes
+  
    
+  
    
+   
+    TradeBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   QuoteBar Attributes
+  
    
+  
    
+   
+    QuoteBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   Tick Attributes
+  
    
+  
    
+   
+    Tick
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   CryptoUniverse Attributes
+  
    
+  
    
+   
+    CryptoUniverse
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > US ETF Constituents
    
+
    
+    
    
+        
    QuantConnect
    
+        
    US ETF Constituents
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US ETF Constituents dataset by QuantConnect tracks the constituents and weighting of US Equities in 2,650 ETF listings. The data starts in June 2009 and is delivered on a daily basis (monthly basis before January 2015). This dataset is created by tracking the host ETF websites and can be delayed by up to 1 week.
+  
    
+  
    
+   This dataset depends on the
+   
+    US Equity Security Master
+   
+   dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
+  
    
+  
    
+   For more information about the US ETF Constituents dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 160,000 quants are served every month.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the US ETF Constituents dataset:
+  
    
+  
    
+   
    def initialize(self) -> None:
+    self.universe_settings.asynchronous = True
+    # Use the following method for a Classic Algorithm
+    self._universe = self.add_universe(self.universe.etf("SPY", Market.USA, self.universe_settings, self.etf_constituents_filter))
+
+    symbol = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
+    # Use the following method for a Framework Algorithm
+    self.add_universe_selection(ETFConstituentsUniverseSelectionModel(symbol, self.universe_settings, self.etf_constituents_filter))
+
+    def etf_constituents_filter(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
+        # Add all Symbols of the ETFConstituentUniverse
+       return [x.symbol for x in constituents]
    
+   
    public override void Initialize()
+{
+    UniverseSettings.Asynchronous = true;
+    // Use the following method for a Classic Algorithm
+    _universe = AddUniverse(Universe.ETF("SPY", Market.USA, UniverseSettings, ETFConstituentsFilter));
+
+    var symbol = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
+    // Use the following method for a Framework Algorithm
+    AddUniverseSelection(new ETFConstituentsUniverseSelectionModel(symbol, UniverseSettings, ETFConstituentsFilter));
+}
+private IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable <ETFConstituentUniverse> constituents)
+{
+    // Add all Symbols of the ETFConstituentUniverse
+    return constituents.Select(x => x.Symbol);
+}
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      June 2009
+     
    
+      Asset Coverage
+     
+      2,650 US ETF Listings
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Daily (Monthly before Jan 2015)
+     
    
+      Timezone
+     
+      New York
+     
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add US ETF Constituents data to your algorithm, call the
+   
+    AddUniverse
+   
+   
+    add_universe
+   
+   and
+   
+    Universe.ETF
+   
+   
+    universe.etf
+   
+   methods. To select which constituents occupy the universe, provide the ETF
+   
+    Symbol
+   
+   and a selection function.
+  
    
+  
    
+   
    class ETFConstituentUniverseAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2018, 1, 1)
+        self.set_end_date(2020, 8, 25)
+        self.set_cash(100000)
+        self.universe_settings.asynchronous = True
+        self._universe = self.add_universe(self.universe.etf("SPY", self.universe_settings, self.etf_constituents_filter))
    
+   
    public class ETFConstituentUniverseAlgorithm : QCAlgorithm
+{
+    private Universe _universe;
+
+    public override void Initialize()
+    {
+        SetStartDate(2018, 1, 1);
+        SetEndDate(2020, 8, 25);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = true;
+        _universe = AddUniverse(Universe.ETF("SPY", UniverseSettings, ETFConstituentsFilter));
+    }
+}
+
    
+  
    
+  
    
+   For more information about universe settings, see
+   
+    Settings
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To access the US ETF Constituent data, use the
+   
+    ETFConstituentUniverse
+   
+   objects in your selection function. The data is available in daily resolution. The
+   
+    Symbol
+   
+   objects you return from your selection function defines the universe constituents.
+  
    
+  
    
+   
    def etf_constituents_filter(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
+    for c in constituents:
+        self.debug(f'{c.end_time} :: {c.last_update} :: {c.weight} :: {c.shares_held} :: {c.market_value}')
+    return [x.symbol for x in constituents]
    
+   
    +public IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentUniverse> constituents)
+{
+    foreach (var c in constituents)
+    {
+        Debug($"{c.EndTime} :: {c.LastUpdate} :: {c.Weight} :: {c.SharesHeld} :: {c.MarketValue}");
+    }
+
+    return constituents.Select(c => c.Symbol);
+}
+
    
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical universe data in an algorithm and in the Research Environment.
+  
    
+  
    
+   Historical Universe Data in Algorithms
+  
    
+  
    
+   To get historical universe data in an algorithm, call the
+   
+    History
+   
+   
+    history
+   
+   method with the
+   
+    Universe
+   
+   object and the lookback period. If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    var history = History(_universe, 30, Resolution.Daily);
+foreach (var constituents in history)
+{
+    foreach (ETFConstituentUniverse constituent in constituents)
+    {
+        Log($"{constituent.Symbol} weight at {constituent.EndTime}: {constituent.Weight}");
+    }
+}
    
+   
    # DataFrame example where the columns are the ETFConstituentUniverse attributes: 
+df_history = self.history(self.universe, 30, Resolution.DAILY, flatten=True)
+
+# Series example where the values are lists of ETFConstituentUniverse objects: 
+series_history = self.history(self.universe, 30, Resolution.DAILY)
+for (universe_symbol, time), constituents in series_history.items():
+    for constituent in constituents:
+        self.log(f'{constituent.symbol} weight at {constituent.end_time}: {constituent.weight}')
    
+  
    
+  
    
+   Historical Universe Data in Research
+  
    
+  
    
+   To get historical universe data in research, call the
+   
+    UniverseHistory
+   
+   
+    universe_history
+   
+   method with the
+   
+    Universe
+   
+   object and the lookback period. The
+   
+    UniverseHistory
+   
+   
+    universe_history
+   
+   returns the filtered universe. If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var constituents in universeHistory )
+{
+    foreach (ETFConstituentUniverse constituent in constituents)
+    {
+        Console.WriteLine($"{constituent.Symbol} weight at {constituent.EndTime}: {constituent.Weight}");
+    }
+}
    
+   
    # DataFrame example where the columns are the ETFConstituentUniverse attributes: 
+df_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
+
+# Series example where the values are lists of ETFConstituentUniverse objects: 
+series_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+for (universe_symbol, time), constituents in series_history.items():
+    for constituent in constituents:
+       print(f"{constituent.symbol} weight at {constituent.end_time}: {constituent.weight}")
    
+  
    
+  
    
+   You can call the
+   
+    History
+   
+   
+    history
+   
+   method in Research.
+  
    
+ 
+
+
+
    Supported ETFs
    
+
+ 
+  
    
+   The following table shows the available ETFs:
+  
    
+  
+   
+    
+     
+    
+   
+   
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
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+   
+  
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The ETF Constituents dataset provides an excellent source of tradable universes for strategies without selection bias. When you use an ETF universe, the original ETF can serve as an excellent benchmark for your strategy performance. Other use cases include the following:
+  
    
+  
      
+   
    • 
+    Creating an index-tracking algorithm for customized passive portfolio management
+   
      • 
+   
    • 
+    Performing statistical arbitrage with the base ETF
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm creates a dynamic universe of the 10 largest US Equities in the SPY ETF. Each day, the algorithm forms a dollar-neutral and market-neutral portfolio by buying the 10 ETF constituents and shorting the SPY ETF.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class ETFConstituentUniverseAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        # Add the SPY to trade.
+        self._spy = self.add_equity("SPY").symbol
+        # Add an ETF constituents universe that selects the large caps 
+        # in SPY. Save the universe object so you can get history
+        # for the universe.
+        universe = self.add_universe(
+            self.universe.etf(
+                self._spy, 
+                universe_filter_func=self._select_assets
+            )
+        )
+        # Get historical universe data.
+        history = self.history(universe, 30, Resolution.DAILY, flatten=True)
+        # Show an example of wrangling the historical data.
+        etf_weights = history.weight.unstack(1)  
+        # Create a dictionary to store the ETF weights each day.
+        self._weight_by_symbol = {}
+        # Add a Scheduled Event to rebalance the portfolio each day.
+        self.schedule.on(
+            self.date_rules.every_day(self._spy),
+            self.time_rules.after_market_open(self._spy, 1),
+            self._rebalance
+        )
+
+    def _select_assets(
+            self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
+        # Select the 10 largest stocks in the SPY.
+        selected = sorted(
+            [c for c in constituents if c.weight], 
+            key=lambda c: c.weight
+        )[-10:]
+        # Save the weights for position sizing.
+        self._weight_by_symbol = {c.symbol: c.weight for c in selected}
+        # Return the selected assets.
+        return list(self._weight_by_symbol.keys())
+
+    def _rebalance(self) -> None:
+        # Create a long-short portfolio to earn the excess return of the 
+        # top 10 weighted stocks from SPY.
+        spy_weight = sum(self._weight_by_symbol.values())
+        targets = [PortfolioTarget(self._spy, -0.5)]
+        for symbol, weight in self._weight_by_symbol.items():
+            targets.append(PortfolioTarget(symbol, 0.5*weight/spy_weight))
+        # Liquidate the stocks that aren't in top 10.
+        self.set_holdings(targets, True)
+
    
+   
    public class ETFConstituentUniverseAlgorithm : QCAlgorithm
+{ 
+    private Symbol _spy;
+    // Create a dictionary to store the ETF weights each day.
+    private Dictionary<Symbol, decimal> _weightBySymbol = new();
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+        // Add the SPY to trade.
+        _spy = AddEquity("SPY").Symbol;
+        // Add an ETF constituents universe that selects the large caps 
+        // in SPY. Save the universe object so you can get history
+        // for the universe.
+        var universe = AddUniverse(Universe.ETF(_spy, universeFilterFunc: SelectAssets));
+        // Get historical universe data.
+        var history = History(universe, 30, Resolution.Daily);
+        // Show an example of iterating through the historical data.
+        foreach (var constituents in history)
+        {
+            var t = constituents.EndTime;
+            foreach (ETFConstituentUniverse constituent in constituents)
+            {
+                var symbol = constituent.Symbol;
+                var weight = constituent.Weight;
+            }
+        }
+        // Add a Scheduled Event to rebalance the portfolio each day.
+        Schedule.On(DateRules.EveryDay(_spy), TimeRules.AfterMarketOpen(_spy, 1), Rebalance);
+    }
+    
+    private IEnumerable<Symbol> SelectAssets(IEnumerable<ETFConstituentUniverse> constituents)
+    {
+        // Select the 10 largest stocks in the SPY. 
+        _weightBySymbol = constituents.OrderByDescending(c => c.Weight).Take(10)
+            // Save the weights for position sizing.
+            .ToDictionary(c => c.Symbol, c => c.Weight ?? 0m);
+        // Return the selected assets.
+        return _weightBySymbol.Keys;
+    }
+    
+    private void Rebalance()
+    {
+        // Create a long-short portfolio to earn the excess return of the 
+        // top 10 weighted stocks from SPY.
+        var spyWeight = _weightBySymbol.Values.Sum();
+        var targets = new List<PortfolioTarget>() { new PortfolioTarget(_spy, -0.5m) };
+        foreach (var kvp in _weightBySymbol)
+        {
+            targets.Add(new PortfolioTarget(kvp.Key, 0.5m * kvp.Value / spyWeight));
+        }
+        // Liquidate the stocks that aren't in top 10.
+        SetHoldings(targets, true);
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm creates a dynamic universe of the 10 largest US Equities in the SPY ETF. Each day, the algorithm forms a dollar-neutral and market-neutral portfolio by buying the 10 ETF constituents and shorting the SPY ETF.
+  
    
+  
    
+   
    class ETFConstituentUniverseFrameworkAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        self.universe_settings.asynchronous = False
+        self.universe_settings.resolution = Resolution.MINUTE
+        self.weight_by_symbol = {}
+        
+        # Add universe selection on SPY's constituents to select only from large cap stocks
+        # Save the universe to access its members for historical data call
+        spy = self.add_equity("SPY").symbol
+        self.add_universe_selection(ETFConstituentsUniverseSelectionModel(spy, self.universe_settings, self.etf_constituents_filter))
+
+        # Add alpha model that set normalized weight as investment insight
+        self.add_alpha(ETFConstituentsAlphaModel(self, spy))
+
+        # Set up portfolio construction model that invest by the insight weights
+        pcm = InsightWeightingPortfolioConstructionModel()
+        # Avoid excessive rebalance on insight changes
+        pcm.rebalance_on_insight_changes = False
+        self.set_portfolio_construction(pcm)
+
+        self.add_risk_management(NullRiskManagementModel())
+
+        self.set_execution(ImmediateExecutionModel())
+
+    def etf_constituents_filter(self, constituents: List[ETFConstituentUniverse]) -> List[Symbol]:
+        # The top 10 weighted securities are considered better active selections
+        # Save the weights for position sizing
+        selected = sorted([c for c in constituents if c.weight],
+            key=lambda c: c.weight, reverse=True)[:10]
+        self.weight_by_symbol = {c.symbol: c.weight for c in selected}
+
+        return list(self.weight_by_symbol.keys())
+
+class ETFConstituentsAlphaModel(AlphaModel):
+
+    def __init__(self, algorithm: QCAlgorithm, etf: Symbol) -> None:
+        self.algorithm = algorithm
+        self.etf = etf
+        self.day = -1
+
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        # Rebalance daily since selection is on daily basis
+        if self.day == algorithm.time.day:
+            return []
+
+        self.day = algorithm.time.day
+
+        insights = []
+
+        # Create a long-short portfolio to earn excess return of the top 10 weighted stocks from SPY
+        etf_weight = sum(self.algorithm.weight_by_symbol.values())
+        if etf_weight> 0:
+            # Invest half the portfolio by normalized weights of the top 10 constituents
+            for symbol, weight in self.algorithm.weight_by_symbol.items():
+                if algorithm.securities.contains_key(symbol):
+                    insights.append(Insight.price(symbol, Expiry.END_OF_DAY, InsightDirection.UP, weight=0.5*weight/etf_weight))
+            
+            # Short the other half with SPY, looking to profit from the active selection
+            insights.append(Insight.price(self.etf, Expiry.END_OF_DAY, InsightDirection.DOWN, weight=0.5))
+
+        return insights
+
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
+        # Liquidate the ones not in top 10 weights
+        symbols = [x.symbol for x in changes.removed_securities if x.invested]
+        algorithm.liquidate(symbols, tag='Removed From Universe')
    
+   
    public class ETFConstituentUniverseFrameworkAlgorithm : QCAlgorithm
+{ 
+    public Dictionary<Symbol, decimal> WeightBySymbol = [];
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = false;
+        UniverseSettings.Resolution = Resolution.Minute;
+
+        // Add universe selection on SPY's constituents to select only from large cap stocks
+        // Save the universe to access its members for historical data call
+        var spy = AddEquity("SPY").Symbol;
+        AddUniverseSelection(new ETFConstituentsUniverseSelectionModel(spy, UniverseSettings, ETFConstituentsFilter));
+        
+        // Add alpha model that set normalized weight as investment insight
+        AddAlpha(new ETFConstituentsAlphaModel(this, spy));
+
+        // Set up portfolio construction model that invest by the insight weights
+        SetPortfolioConstruction(new InsightWeightingPortfolioConstructionModel
+        {
+            // Avoid excessive rebalance on insight changes
+            RebalanceOnInsightChanges = false
+        });
+        
+        AddRiskManagement(new NullRiskManagementModel());
+        
+        SetExecution(new ImmediateExecutionModel());
+    }
+    
+    private IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentUniverse> constituents)
+    {
+        // The top 10 weighted securities are considered better active selections
+        // Save the weights for position sizing
+        WeightBySymbol = constituents.Where(c=> c.Weight.HasValue).OrderByDescending(c => c.Weight).Take(10)
+            .ToDictionary(c => c.Symbol, c => c.Weight.Value);
+
+        return WeightBySymbol.Keys;
+    }
+}
+
+public class ETFConstituentsAlphaModel : AlphaModel
+{
+    private int _day = -1;
+    private Symbol _etf;
+    private ETFConstituentUniverseFrameworkAlgorithm _algorithm;
+    
+    public ETFConstituentsAlphaModel(ETFConstituentUniverseFrameworkAlgorithm algorithm, Symbol etf)
+    {
+        _etf = etf;
+        _algorithm = algorithm;
+    }
+    
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    {
+        // Rebalance daily since selection is on daily basis
+        if (_day == algorithm.Time.Day)
+        {
+            return [];
+        }
+        
+        _day = algorithm.Time.Day;
+        
+        var insights = new List<Insight>();
+        
+        // Create a long-short portfolio to earn excess return of the top 10 weighted stocks from SPY
+        var etfWeight = (double)_algorithm.WeightBySymbol.Values.Sum();
+        if (etfWeight > 0)
+        {
+            // Invest half the portfolio by normalized weights of the top 10 constituents
+            foreach(var kvp in _algorithm.WeightBySymbol)
+            {
+                insights.Add(Insight.Price(kvp.Key, Expiry.EndOfDay, InsightDirection.Up, weight: (double)kvp.Value/etfWeight * 0.5));
+            }
+
+            // Short the other half with SPY, looking to profit from the active selection
+            insights.Add(Insight.Price(_etf, Expiry.EndOfDay, InsightDirection.Down, weight: 0.5));
+        }
+        
+        return insights;
+    }
+    
+    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
+    {
+        // Liquidate the ones not in top 10 weights
+        var symbols = changes.RemovedSecurities.Where(x => x.Invested).Select(x => x.Symbol);
+        algorithm.Liquidate(symbols, tag: "Removed From Universe");
+    }
+}
    
+  
    
+  
    
+   Research Example
+  
    
+  
    
+   The following example lists ETF constituents with the greatest weight in the SPY:
+  
    
+  
    
+   
    var qb = new QuantBook();
+
+// Add the ETF
+var symbol = qb.AddEquity("SPY").Symbol;
+
+// Add ETF Universe Selection
+IEnumerable<Symbol> ETFConstituentsFilter(IEnumerable<ETFConstituentUniverse> constituents)
+{
+    // Take the top 10 weighted constituents
+    return constituents
+        .OrderByDescending(c => c.Weight)
+        .Take(10)
+        .Select(c => c.Symbol);
+}
+
+var universe = qb.AddUniverse(qb.Universe.ETF(spy, qb.UniverseSettings, ETFConstituentsFilter)); 
+
+// Historical Universe data
+var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var constituents in universeHistory )
+{
+    foreach (ETFConstituentUniverse constituent in constituents)
+    {
+        Console.WriteLine($"{constituent.Symbol} weight at {constituent.EndTime}: {constituent.Weight}");
+    }
+}
    
+   
    qb = QuantBook()
+
+# Add the ETF
+qb.spy = qb.add_equity("SPY").symbol
+
+# Add ETF Universe Selection
+def etf_constituents_filter(constituents):
+    # Take the top 10 weighted constituents
+    selected = sorted([c for c in constituents if c.weight],
+        key=lambda c: c.weight, reverse=True)[:10]
+    return [c.symbol for c in selected]
+
+universe = qb.add_universe(qb.universe.etf(qb.spy, qb.universe_settings, etf_constituents_filter))
+
+# Historical Universe data
+universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+for (universe_symbol, time), constituents in universe_history.items():
+    for constituent in constituents:
+        print(f"{constituent.symbol} weight at {constituent.end_time}: {constituent.weight}")
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The ETF Constituents dataset provides
+   
+    ETFConstituentUniverse
+   
+   objects, which have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > US Equities Short Availability
    
+
    
+    
    
+        
    QuantConnect
    
+        
    US Equities Short Availability
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Equity Short Availability dataset provides the available shares for open short positions and their borrowing cost in the US Equity market. The data covers 10,500 US Equities, starts in January 2018, and is delivered on a daily frequency. This dataset is created using information from the exchanges.
+  
    
+  
    
+   This dataset depends on the
+   
+    US Equity Security Master
+   
+   dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
+  
    
+  
    
+   For more information about the US Equities Short Availability dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading.  Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippets demonstrate how to request data from the US Equities Short Availability dataset.
+  
    
+  
    
+   Interactive Brokers Data
+  
    
+  
    
+   
    security.set_shortable_provider(InteractiveBrokersShortableProvider())
    
+   
    security.SetShortableProvider(new InteractiveBrokersShortableProvider());
    
+  
    
+  
    
+   Axos Clearing Data
+  
    
+  
    
+   
    security.set_shortable_provider(LocalDiskShortableProvider("axos"))
    
+   
    security.SetShortableProvider(new LocalDiskShortableProvider("axos"));
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 2018
+     
    
+      Asset Coverage
+     
+      10,500 US Equities
+     
    
+      Data Density
+     
+      Sparse
+     
    
+      Resolution
+     
+      Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add US Equities Short Availability data to your algorithm, set the
+   
+    shortable provider
+   
+   of each US Equity in your algorithm.
+  
    
+  
    
+   
    class ShortAvailabilityDataAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2019, 1, 1)
+        security = self.add_equity("AAPL")
+        # Set shortable provider as IB
+        security.set_shortable_provider(InteractiveBrokersShortableProvider())
+        self._symbol = security.symbol
    
+   
    public class ShortAvailabilityDataAlgorithm : QCAlgorithm
+{
+    private Symbol _symbol;
+
+    public override void Initialize()
+    {
+        SetStartDate(2019, 1, 1);
+        var security = AddEquity("AAPL");
+        // Set shortable provider as IB
+        security.SetShortableProvider(new InteractiveBrokersShortableProvider());
+        _symbol = security.Symbol;
+    }
+}
    
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To check how many shares are available for a security to short, call the
+   
+    ShortableQuantity
+   
+   
+    shortable_quantity
+   
+   method of the
+   
+    ShortableProvider
+   
+   
+    shortable_provider
+   
+  
    
+  
    
+   
    var shortableProvider = Securities[_symbol].ShortableProvider;
+// Get the shortable quantity of the selected symbol at the selected time
+var shortableQuantity = shortableProvider.ShortableQuantity(_symbol, Time);
+
+// Check if there are a certain quantity of shares available
+var quantity = 100;
+var isShortableQuantity = Shortable(_symbol, quantity);
    
+   
    shortable_provider = self.securities[self._symbol].shortable_provider
+# Get the shortable quantity of the selected symbol at the selected time
+shortable_quantity = shortable_provider.shortable_quantity(self._symbol, self.time)
+
+# Check if there are a certain quantity of shares available
+quantity = 100;
+is_shortable_quantity = self.shortable(self._symbol, quantity)
    
+  
    
+  
    
+   To check borrowing cost, call the
+   
+    FeeRate
+   
+   
+    fee_rate
+   
+   or
+   
+    RebateRate
+   
+   
+    rebate_rate
+   
+   method of the
+   
+    ShortableProvider
+   
+   
+    shortable_provider
+   
+  
    
+  
    
+   
    var feeRate = shortableProvider.FeeRate(_symbol, Time);
+var rebateRate = shortableProvider.RebateRate(_symbol, Time);
    
+   
    fee_rate = shortable_provider.fee_rate(self._symbol, self.time);
+rebate_rate = shortable_provider.rebate_rate(self._symbol, self.time);
    
+  
    
+  To get valid borrowing rates, use the
+  
+   InteractiveBrokersShortableProvider
+  
+  
+   .
+  
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Equities Short Availability dataset enables you to accurately design strategies harnessing information about short availability. Examples include the following use cases:
+  
    
+  
      
+   
    • 
+    Avoiding short orders that the brokerage will reject
+   
      • 
+   
    • 
+    Selecting securities based on how many shares are available to short
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm shorts GameStop every day there are shares available to short. If the algorithm receives a margin call, it liquidates the position and start again on the next day.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class ShortAvailabilityDataAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(1000)
+        # Seed the security price as the last known price, such that the price data is immediately available at initial rebalance
+        self.settings.seed_initial_prices = True
+        self.add_security_initializer(self._custom_security_initializer)
+
+        self.equity = self.add_equity("GME")
+
+        # Set up daily rebalance scheduled event, since shortable quantity is updated daily
+        self.schedule.on(
+            self.date_rules.every_day(self.equity.symbol),
+            self.time_rules.after_market_open(self.equity.symbol, 10),
+            self.rebalance)
+
+    def rebalance(self) -> None:
+        symbol = self.equity.symbol
+        
+        # You can obtain the shortable quantity to decide the submission of a short order
+        shortable_quantity = self.equity.shortable_provider.shortable_quantity(symbol, self.time)
+        if not shortable_quantity:
+            shortable_quantity = 0
+        # Fee and rebate rate is also available, such that you can calculate the expected return and decide if the margin is worthwhile
+        self.plot('Total Shortable Quantity', symbol, shortable_quantity)
+        self.plot('Borrowing Cost', "Fee Rate", self.equity.shortable_provider.fee_rate(symbol, self.time))
+        self.plot('Borrowing Cost', "Rebate Rate", self.equity.shortable_provider.rebate_rate(symbol, self.time))
+
+        # Test whether we can short the desired quantity
+        quantity = self.calculate_order_quantity(symbol, -1)
+        if quantity and self.shortable(symbol, quantity):
+            self.market_order(symbol, quantity)
+
+    def on_margin_call_warning(self) -> None:
+        self.liquidate()
+
+    def _custom_security_initializer(self, security: Security) -> None:
+        # Set the shortable provider as your broker for accurate short reality modeling
+        security.set_shortable_provider(InteractiveBrokersShortableProvider())
    
+   
    public class ShortAvailabilityDataAlgorithm : QCAlgorithm
+{
+    private Equity _equity;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(1000);
+        // Seed the security price as the last known price, such that the price data is immediately available at initial rebalance
+        Settings.SeedInitialPrices = true;
+        AddSecurityInitializer(security =>
+        {
+            // Set the shortable provider as your broker for accurate short reality modeling
+            security.SetShortableProvider(new InteractiveBrokersShortableProvider());    
+        });
+        
+        _equity = AddEquity("GME");
+        
+        // Set up daily rebalance scheduled event, since shortable quantity is updated daily
+        Schedule.On(
+            DateRules.EveryDay(_equity.Symbol),
+            TimeRules.AfterMarketOpen(_equity.Symbol, 10),
+            Rebalance);
+    }
+    
+    public void Rebalance()
+    {
+        var symbol = _equity.Symbol;
+
+        // You can obtain the shortable quantity to decide the submission of a short order
+        // Fee and rebate rate is also available, such that you can calculate the expected return and decide if the margin is worthwhile
+        Plot("Total Shortable Quantity", symbol, _equity.ShortableProvider.ShortableQuantity(symbol, Time) ?? 0m);
+        Plot("Borrowing Cost", "Fee Rate", _equity.ShortableProvider.FeeRate(symbol, Time));
+        Plot("Borrowing Cost", "Rebate Rate", _equity.ShortableProvider.RebateRate(symbol, Time));
+
+        // Test whether we can short the desired quantity
+        var quantity = CalculateOrderQuantity(symbol, -1m);
+        if (quantity != 0 && Shortable(symbol, quantity))
+        {
+            MarketOrder(symbol, quantity);
+        }
+    }
+
+    public override void OnMarginCallWarning()
+    {
+        Liquidate();
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm shorts GameStop every day there are shares available to short. If the algorithm receives a margin call, it liquidates the position and start again on the next day.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class ShortAvailabilityDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(1000)
+        # Seed the security price as the last known price, such that the price data is immediately available at initial rebalance
+        self.settings.seed_initial_prices = True
+        self.add_security_initializer(self._custom_security_initializer)
+
+        self.set_universe_selection(ManualUniverseSelectionModel(
+            [Symbol.create("GME", SecurityType.EQUITY, Market.USA)]))           
+
+        # Emit down-direction insights to short all securities in the universe            
+        self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.DOWN, timedelta(1)))
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+        self.set_execution(ShortableExecutionModel())
+
+    # On Margin Call, emit flat-direction insights to liquidate the positions
+    def on_margin_call_warning(self) -> None:
+        self.emit_insights([Insight.price(kvp.key, timedelta(1), InsightDirection.FLAT)
+            for kvp in self.securities if kvp.value.invested])
+
+    def _custom_security_initializer(self, security: Security) -> None:
+        # Set the shortable provider as IB
+        security.set_shortable_provider(InteractiveBrokersShortableProvider())
+
+class ShortableExecutionModel(ExecutionModel):
+    def __init__(self) -> None:
+        self.targets_collection = PortfolioTargetCollection()
+
+    def execute(self, algorithm: QCAlgorithm, targets: List[PortfolioTarget]) -> None:
+        '''Immediately submits orders for the specified portfolio targets.
+        Args:
+            algorithm: The algorithm instance
+            targets: The portfolio targets to be ordered'''
+
+        # for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
+        self.targets_collection.add_range(targets)
+        if self.targets_collection.count > 0:
+            for target in self.targets_collection.order_by_margin_impact(algorithm):
+                # calculate remaining quantity to be ordered
+                quantity = OrderSizing.get_unordered_quantity(algorithm, target)
+                # If the quantity is negative, ensure that the security is shortable
+                if quantity > 0 or algorithm.shortable(target.symbol, quantity):
+                    algorithm.market_order(target.symbol, quantity)
+
+            self.targets_collection.clear_fulfilled(algorithm)
    
+   
    public class ShortAvailabilityDataAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(1000);
+        // Seed the security price as the last known price, such that the price data is immediately available at initial rebalance
+        Settings.SeedInitialPrices = true;
+        AddSecurityInitializer(security =>
+        {
+            // Set the shortable provider as your broker for accurate short reality modeling
+            security.SetShortableProvider(new InteractiveBrokersShortableProvider());    
+        });
+
+        SetUniverseSelection(new ManualUniverseSelectionModel(
+            QuantConnect.Symbol.Create("GME", SecurityType.Equity, Market.USA)));            
+        
+        // Emit down-direction insights to short all securities in the universe            
+        SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Down, TimeSpan.FromDays(1)));
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+        SetExecution(new ShortableExecutionModel());
+    }
+
+    // On Margin Call, emit flat-direction insights to liquidate the positions
+    public override void OnMarginCallWarning()
+    {
+        EmitInsights(Securities
+            .Where(kvp => kvp.Value.Invested)
+            .Select(kvp => Insight.Price(kvp.Key, TimeSpan.FromDays(1), InsightDirection.Flat))
+            .ToArray());
+    }
+
+    public class ShortableExecutionModel : ImmediateExecutionModel
+    {
+        private readonly PortfolioTargetCollection _targetsCollection = new PortfolioTargetCollection();
+
+        public override void Execute(QCAlgorithm algorithm, IPortfolioTarget[] targets)
+        {
+            _targetsCollection.AddRange(targets);
+            // for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
+            if (_targetsCollection.Count > 0)
+            {
+                foreach (var target in _targetsCollection.OrderByMarginImpact(algorithm))
+                {
+                    // calculate remaining quantity to be ordered
+                    var quantity = OrderSizing.GetUnorderedQuantity(algorithm, target);
+                    // If the quantity is negative, ensure that the security is shortable
+                    if (quantity > 0 || algorithm.Shortable(target.Symbol, quantity))
+                    {
+                        algorithm.MarketOrder(target.Symbol, quantity);
+                    }
+                }
+
+                _targetsCollection.ClearFulfilled(algorithm);
+            }
+        }
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Equities Short Availability data is a
+   
+    Symbol
+   
+   /decimal pair for
+   
+    ShortQuantity
+   
+   ,
+   
+    FeeRate
+   
+   , 
+ and
+   
+    RebateRate
+   
+   .
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > US Equity Coarse Universe
    
+
    
+    
    
+        
    QuantConnect
    
+        
    US Equity Coarse Universe
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Equity Coarse Universe dataset by QuantConnect is a daily universe of all trading stocks in the US for a given day with the end of day price and volume. The data covers 30,000 US Equities in total, with approximately 8,000 Equities per day. The data starts in January 1998 and is delivered each trading day. This dataset is created by taking the closing auction price tick from the daily L1 trade and quote exchange dumps.
+  
    
+  
    
+   This dataset depends on the
+   
+    US Equities by AlgoSeek
+   
+   dataset because
+   
+    universe selection
+   
+   adds and removes market data subscriptions and on the
+   
+    US Equity Security Master
+   
+   dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
+  
    
+  
    
+   QuantConnect/LEAN combines the data of this dataset with
+   
+    MorningStar Fundamental data
+   
+   in runtime. You can use this dataset in local development without the Morningstar counterpart.
+  
    
+  
    
+   For more information about the US Equity Coarse Universe dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the US Equity Coarse Universe dataset:
+  
    
+  
      
+   
    • 
+    Direct Access using the Security object
+   
      • 
+  
    
+  
    
+   
    equity = self.add_equity("IBM")
+ibm_fundamental = equity.fundamentals
    
+   
    var equity = AddEquity("IBM");
+var ibmFundamental = equity.Fundamentals;
    
+  
    
+  
      
+   
    • 
+    Direct Access using the Symbol object
+   
      • 
+  
    
+  
    
+   
    ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
+ibm_fundamental = self.fundamentals
    
+   
    var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
+var ibmFundamental = Fundamentals(ibm);
    
+  
    
+  
      
+   
    • 
+    Universe Selection
+   
      • 
+  
    
+  
    
+   
    def initialize(self) -> None:
+    # Universe selection
+    self._universe = self.add_universe(self.fundamental_filter_function)
+
+def fundamental_filter_function(self, fundamental: List[Fundamental]):
+    # Sort all equities with price above $10 by dollar volume
+    sorted_by_dollar_volume = sorted([f for f in fundamental if f.price > 10],
+                                key=lambda f: f.dollar_volume, reverse=True)
+    # Take the top 10
+    return [f.symbol for f in sorted_by_dollar_volume[:10]]
    
+   
    public override void Initialize()
+{
+    // Universe selection
+    _universe = AddUniverseSelection(new FundamentalUniverseSelectionModel(FundamentalFilterFunction));
+}
+
+public override List<Symbol> FundamentalFilterFunction(List<Fundamental> fundamental)
+{
+    // Sort all equities with price above $10 by dollar volume, take the top 10
+    return (from f in fundamental
+            where f.Price > 10
+            orderby f.DollarVolume descending
+            select f.Symbol).Take(10);
+}
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 1998
+     
    
+      Asset Coverage
+     
+      30,000 US Equities
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+  
    
+   This dataset doesn't include Over-the-Counter (OTC) stocks.
+  
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   You don't need any special code to request US Coarse Fundamental Data. You can access the current and historical fundamental data for any of the US Equities that this dataset includes.
+  
    
+  
    
+   
    class CoarseFundamentalDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2021, 1, 1)
+        self.set_end_date(2021, 7, 1)
+        self.set_cash(100000) 
+        self.universe_settings.asynchronous = True
+
+        # Option 1: Subscribe to individual US Equity assets
+        self.add_equity("IBM")  
+
+        # Option 2A: Create a fundamental universe (classic version)
+        self._universe = self.add_universe(self.fundamental_function)
+
+        # Option 2B: Create a fundamental universe (framework version)
+        self.add_universe_selection(FundamentalUniverseSelectionModel(self.fundamental_function))
    
+   
    public class CoarseFundamentalDataAlgorithm : QCAlgorithm
+{
+    private Universe _universe;
+    public override void Initialize()
+    {
+        SetStartDate(2021, 1, 1);
+        SetEndDate(2021, 7, 1);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = true;
+        
+        // Option 1: Subscribe to individual US Equity assets
+        AddEquity("IBM");
+
+        // Option 2A: Create a fundamental universe (classic version)
+        _universe = AddUniverse(FundamentalFilterFunction);
+
+        // Option 2B: Create a fundamental universe (framework version)
+        AddUniverseSelection(new FundamentalUniverseSelectionModel(FundamentalFilterFunction));
+    }
+}
    
+  
    
+  
    
+   For more information about universe settings, see
+   
+    Settings
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   If you add a
+   
+    fundamental universe
+   
+   to your algorithm, you can access fundamental data in the universe selection function.
+  
    
+  
    
+   
    def fundamental_function(self, fundamental: List[Fundamental]) -> List[Symbol]:
+    sorted_by_dollar_volume = sorted(fundamental, key=lambda x: x.dollar_volume, reverse=True)[:3]
+    for cf in sorted_by_dollar_volume:
+        self.debug(f"{cf.end_time} :: {cf.symbol} : {cf.adjusted_price} :: {cf.dollar_volume}")
+
+    return [ x.symbol for x in sorted_by_dollar_volume]
    
+   
    public IEnumerable<Symbol> FundamentalFunction(IEnumerable<Fundamental> fundamental)
+{
+    var sortedByDollarVolume = fundamental
+        .OrderByDescending(x => x.DollarVolume)
+        .Take(3).ToList();
+
+    foreach (var cf in sortedByDollarVolume)
+    {
+        Debug($"{cf.EndTime} :: {cf.Symbol} : {cf.AdjustedPrice} :: {cf.DollarVolume}");
+    }
+
+    return sortedByDollarVolume.Select(x => x.Symbol);
+}
    
+  
    
+  
    
+   To get fundamental data for Equities in your algorithm, use the
+   
+    Fundamentals
+   
+   
+    fundamentals
+   
+   property of the
+   
+    Equity
+   
+   objects. The fundamental data represent the corporate fundamentals for the current algorithm time.
+  
    
+  
    
+   
    fundamentals = self.securities[symbol].fundamentals
    
+   
    var fundamentals = Securities[symbol].Fundamentals;
    
+  
    
+  
    
+   To get fundamental data for Equities, regardless of whether or not you have subscribed to them in your algorithm, call the
+   
+    Fundamentals
+   
+   
+    fundamentals
+   
+   method. If you pass one
+   
+    Symbol
+   
+   , the method returns a
+   
+    Fundamental
+   
+   object. If you pass a list of
+   
+    Symbol
+   
+   objects, the method returns a list of
+   
+    Fundamental
+   
+   objects.
+  
    
+  
    
+   
    // Single asset 
+var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
+var ibmFundamental = Fundamentals(ibm);
+
+// Multiple assets
+var nb = QuantConnect.Symbol.Create("NB", SecurityType.Equity, Market.USA);
+var fundamentals = Fundamentals(new List<Symbol>{ nb, ibm }).ToList();
    
+   
    # Single asset
+ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
+ibm_fundamental = self.fundamentals(ibm)
+
+# Multiple assets
+nb = Symbol.create("NB", SecurityType.EQUITY, Market.USA)
+fundamentals = self.fundamentals([ nb, ibm ])
    
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical fundamental data in an algorithm and in the Research Environment.
+  
    
+  
    
+   Historical Data in Algorithms
+  
    
+  
    
+   To get historical fundamental data in an algorithm, call the
+   
+    History
+   
+   
+    history
+   
+   method with
+   
+    Fundamental
+   
+   type and the Equity
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
+
+// Fundamental objects
+var fundamentalHistory = History<Fundamental>(ibm, TimeSpan.FromDays(30));
+
+// Fundamentals objects for all US Equities (including delisted companies)
+var fundamentalsHistory = History<Fundamentals>(TimeSpan.FromDays(30));
+
+// Collection of Fundamental objects for all US Equities (including delisted companies)
+var collectionHistory = History(_universe, 30, Resolution.Daily);
+foreach (var fundamental in collectionHistory)
+{
+    // Cast to Fundamental is required
+    var mostLiquid = fundamental.OfType<Fundamental>().OrderByDescending(x => x.DollarVolume).Take(5);
+}
    
+   
    ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
+
+# DataFrame of fundamental data for a given asset
+df_history = self.history(Fundamental, ibm, timedelta(30), flatten=True)
+
+# Fundamental objects
+fundamental_history = self.history[Fundamental](ibm, timedelta(30))
+
+# Fundamentals objects for all US Equities (including delisted companies)
+fundamentals_history = self.history[Fundamentals](timedelta(30))
+
+# DataFrame of fundamental data for universe constituents
+df_history = self.history(self._universe, 30, Resolution.DAILY, flatten=True)
+
+# Series of fundamental data for universe constituents
+series_history = self.history(self._universe, 30, Resolution.DAILY)
+for (universe_symbol, time), fundamental in series_history.items():
+    most_liquid = sorted(fundamental, key=lambda x: x.dollar_volume)[-5:]
    
+  
    
+  
    
+   Historical Universe Data in Research
+  
    
+  
    
+   To get historical universe data in the Research Environment, call the
+   
+    UniverseHistory
+   
+   
+    universe_history
+   
+   method with the
+   
+    Universe
+   
+   object and the lookback period. This method returns the filtered universe. If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var fundamentals in universeHistory)
+{
+    foreach (Fundamental fundamental in fundamentals)
+    {
+        Console.WriteLine($"{fundamental.Symbol} dollar volume at {fundamental.EndTime}: {fundamental.DollarVolume}");
+    }
+}
    
+   
    # DataFrame of fundamental data for universe constituents
+df_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
+
+# Series of fundamental data for universe constituents
+series_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+for (universe_symbol, time), fundamentals in series_history.items():
+    for fundamental in fundamentals:
+        print(f"{fundamental.symbol} dollar volume at {fundamental.end_time}: {fundamental.dollar_volume}")
    
+  
    
+  
    
+   For more information about historical US Equity fundamental data, see
+   
+    Equity Fundamental Data
+   
+   .
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Equity Coarse Universe dataset enables you to accurately design a universe of US Equities. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Selecting securities with the largest dollar volume
+   
      • 
+   
    • 
+    Selecting securities within a specific price range
+   
      • 
+   
    • 
+    Selecting securities that have fundamental data available (together with
+    
+     MorningStar Fundamental data
+    
+    in
+    
+     Fundamental
+    
+    objects)
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm creates a dynamic universe of the three most liquid US Equities. Each time the universe changes, the algorithm forms an equal-weighted portfolio of the three companies.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class USEquityCoarseUniverseConstituentsDataAlgorithm(QCAlgorithm):
+
+    _number_of_symbols = 3
+    _changes = None
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        # Set Asynchronous to True to improve speed performance.
+        self.universe_settings.asynchronous = True
+        # Add a universe of US Equities.
+        universe = self.add_universe(self._select_assets)
+        # Get historical data for the universe.
+        history = self.history(universe, 30, Resolution.DAILY, flatten=True)
+
+    def _select_assets(self, fundamental: List[Fundamental]) -> List[Symbol]:
+        # Select the top traded stocks, since they are the most popular 
+        # with high capital flow.
+        return [ 
+            x.symbol for x in sorted(
+                fundamental, key=lambda x: x.dollar_volume
+            )[-self._number_of_symbols:] 
+        ]
+
+    def on_data(self, slice: Slice) -> None:
+        # If we have no changes, do nothing.
+        if not slice.bars or self._changes is None: 
+            return
+        # Liquidate removed securities, since they are not the most 
+        # popular stocks.
+        for security in self._changes.removed_securities:
+            if security.invested:
+                self.liquidate(security.symbol)
+        # We want 1/N allocation for each security in our universe to
+        # evenly dissipate risk.
+        for security in self._changes.added_securities:
+            self.set_holdings(security.symbol, 1 / self._number_of_symbols)
+        self._changes = None
+
+    def on_securities_changed(self, changes: SecurityChanges) -> None:
+        self._changes = changes
+
    
+   
    public class USEquityCoarseUniverseConstituentsDataAlgorithm : QCAlgorithm
+{
+    private int _numberOfSymbols = 3;
+    private SecurityChanges _changes = SecurityChanges.None;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+    
+        // Set Asynchronous to true to improve speed performance 
+        UniverseSettings.Asynchronous = true;
+        // Requesting data
+        var universe = AddUniverse(SelectAssets);
+        // Get historical data for the universe.
+        var history = History(universe, 30, Resolution.Daily);
+        foreach (var fundamentals in history)
+        {
+            // Iterate through each asset in the universe on this day 
+            // and access its data point attributes.
+            foreach (Fundamental f in fundamentals)
+            {
+                var symbol = f.Symbol;
+                var t = f.EndTime;
+                var dollarVolume = f.DollarVolume;
+            }
+        }
+    }
+
+    public IEnumerable<Symbol> SelectAssets(IEnumerable<Fundamental> fundamental)
+    {
+        // Select the top traded stocks, since they are the most popular 
+        // with high capital flow.
+        return fundamental.OrderByDescending(x => x.DollarVolume)
+            .Take(_numberOfSymbols).Select(x => x.Symbol);
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // If we have no changes, do nothing.
+        if (slice.Bars.Count == 0 || _changes == SecurityChanges.None) return;
+        // Liquidate removed securities, since they are not the most 
+        // popular stocks.
+        foreach (var security in _changes.RemovedSecurities)
+        {
+            if (security.Invested)
+            {
+                Liquidate(security.Symbol);
+            }
+        }
+        // We want 1/N allocation for each security in our universe to
+        // evenly dissipate risk.
+        foreach (var security in _changes.AddedSecurities)
+        {
+            SetHoldings(security.Symbol, 1m / _numberOfSymbols);
+        }
+        _changes = SecurityChanges.None;
+    }
+    
+    public override void OnSecuritiesChanged(SecurityChanges changes)
+    {
+        _changes = changes;
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm creates a dynamic universe of the three most liquid US Equities and forms an equal-weighted portfolio with them.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class USEquityCoarseUniverseConstituentsDataAlgorithm(QCAlgorithm):
+
+    _number_of_symbols = 3
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+
+        # Set Asynchronous to True to improve speed performance
+        self.universe_settings.asynchronous = True
+            
+        # Requesting data
+        self.set_universe_selection(
+            FundamentalUniverseSelectionModel(self.fundamental_selection_function))
+        
+        # we want 1/N allocation in each security in our universe to evenly dissipate risk
+        self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(1)))
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+         
+        self.add_risk_management(NullRiskManagementModel())
+        
+        self.set_execution(ImmediateExecutionModel())
+
+    def fundamental_selection_function(self, fundamental: List[Fundamental]) -> List[Symbol]:
+        # Select the top traded stocks, since they are the most popular with high capital flow
+        sorted_by_dollar_volume = sorted(fundamental, key=lambda x: x.dollar_volume, reverse=True)
+        return [ x.symbol for x in sorted_by_dollar_volume[:self._number_of_symbols] ]
    
+   
    public class USEquityCoarseUniverseConstituentsDataAlgorithm : QCAlgorithm
+{
+    private int _numberOfSymbols = 3;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+
+        // Set Asynchronous to true to improve speed performance
+        UniverseSettings.Asynchronous = true;
+
+        // Requesting data
+        SetUniverseSelection(
+            new FundamentalUniverseSelectionModel(FundamentalSelectionFunction));
+
+        // we want 1/N allocation in each security in our universe to evenly dissipate risk
+        SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+        
+        AddRiskManagement(new NullRiskManagementModel());
+        
+        SetExecution(new ImmediateExecutionModel());
+    }
+
+    public IEnumerable<Symbol> FundamentalSelectionFunction(IEnumerable<Fundamental> fundamental)
+    {
+        // Select the top traded stocks, since they are the most popular with high capital flow
+        return fundamental.OrderByDescending(x => x.DollarVolume)
+            .Take(_numberOfSymbols).Select(x => x.Symbol);
+    }
+}
    
+  
    
+  
    
+   Research Example
+  
    
+  
    
+   The following example lists the three most liquid US Equities:
+  
    
+  
    
+   
    var qb = new QuantBook();
+
+// Add Fundamental Universe Selection
+IEnumerable<Symbol> FundamentalSelectionFunction(IEnumerable<Fundamental> fundamentals)
+{
+    return fundamentals.OrderByDescending(x => c.DollarVolume).Take(10).Select(x => c.Symbol);
+}
+
+var universe = qb.AddUniverse(FundamentalSelectionFunction); 
+
+// Historical Universe data
+var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var fundamentals in universeHistory)
+{
+    foreach (Fundamental fundamental in fundamentals)
+    {
+        Console.WriteLine($"{fundamental.Symbol} dollar volume at {fundamental.EndTime}: {fundamental.DollarVolume}");
+    }
+}
    
+   
    qb = QuantBook()
+
+# Add Fundamental Universe Selection
+def fundamental_selection_function(fundamentals):
+    selected = sorted(fundamentals, key=lambda x: x.dollar_volume, reverse=True)[:10]
+    return [x.symbol for x in selected]
+
+universe = qb.add_universe(fundamental_selection_function)
+
+# Historical Universe data
+universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+for (universe_symbol, time), fundamentals in universe_history.items():
+    for fundamental in fundamentals:
+        print(f"{fundamental.symbol} dollar volume at {fundamental.end_time}: {fundamental.dollar_volume}")
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Equity Coarse Universe dataset provides its data within
+   
+    Fundamental
+   
+   objects, which have the following . The attributes from
+   
+    market_cap
+   
+   
+    MarketCap
+   
+   to
+   
+    asset_classification
+   
+   
+    AssetClassification
+   
+   listed in the following widget are NaN until this dataset is combined with the
+   
+    MorningStar Fundamental
+   
+   dataset.
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > US Equity Option Universe
    
+
    
+    
    
+        
    QuantConnect
    
+        
    US Equity Option Universe
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Equity Option Universe dataset by QuantConnect lists the available US Equity Options contracts and the current Implied Volatility and Greeks. The data covers 4,000 Symbols, starts in January 2012, and is delivered on a daily update frequency. To create this dataset, we use
+   
+    our implementation
+   
+   of the forward tree pricing model, which accounts for the interest rate, dividend payments, and daily closing prices. The values in this dataset are the same values you can get from daily
+   
+    indicators
+   
+   with mirror Options.
+  
    
+  
    
+   This dataset depends on the
+   
+    US Equity Security Master
+   
+   dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes of the underlying security.
+  
    
+  
    
+   This dataset
+   
+    does not
+   
+   contain market data. For market data, see
+   
+    US Equity Options by AlgoSeek
+   
+   .
+  
    
+  
    
+   For more information about the US Equity Option Universe dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the US Equity Options Universe dataset:
+  
    
+  
    
+   
    option = self.add_option("GOOG")
+self.option_symbol = option.symbol
+option.set_filter(lambda universe: universe.delta(0.4, 0.6))
    
+   
    var option = AddOption("GOOG");
+_optionSymbol = option.Symbol;
+option.SetFilter(universe => universe.delta(0.4m, 0.6m));
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 2012
+     
    
+      Asset Coverage
+     
+      4,000 Symbols
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add US Equity Options Universe data to your algorithm, call the
+   
+    AddOption
+   
+   
+    add_option
+   
+   method. Save a reference to the Equity Option
+   
+    Symbol
+   
+   so you can access the data later in your algorithm. To define which contracts should be in your universe, call the
+   
+    SetFilter
+   
+   
+    set_filter
+   
+   method of the Option object.
+  
    
+  
    
+   The
+   
+    AddOption
+   
+   
+    add_option
+   
+   method provides a daily stream of Option chain data. To get the most recent daily chain, call the
+   
+    OptionChain
+   
+   
+    option_chain
+   
+   method with the underlying Equity Symbol. The
+   
+    OptionChain
+   
+   
+    option_chain
+   
+   method returns data on all the tradable contracts, not just the contracts that pass your universe filter.
+  
    
+  
    
+   
    class USEquityOptionsDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2020, 6, 1)
+        self.set_end_date(2021, 6, 1)
+        self.set_cash(100000)
+        self.universe_settings.asynchronous = True
+        option = self.add_option("GOOG")
+        self.option_symbol = option.symbol
+        # Set our strike/expiry filter for this option chain
+        option.set_filter(self._option_filter)
+        # Get the entire Option chain for the current day.
+        chain = self.option_chain(option.symbol.underlying, flatten=True).data_frame
+
+    def _option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
+        # Contracts can be filtered by greeks, implied volatility, open interest:
+        return universe \
+            .delta(0.5, 1.5) \
+            .gamma(0.0001, 0.0006) \
+            .vega(0.01, 1.5) \
+            .theta(-2.0, -0.5) \
+            .rho(0.5, 3.0) \
+            .implied_volatility(1, 3) \
+            .open_interest(100,500)
+
    
+   
    public class USEquityOptionsDataAlgorithm : QCAlgorithm
+{
+    private Symbol _optionSymbol;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2020, 6, 1);
+        SetEndDate(2021, 6, 1);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = true;
+        // Requesting data
+        var option = AddOption("GOOG");
+        _optionSymbol = option.Symbol;
+        // Set our strike/expiry filter for this option chain
+        option.SetFilter(OptionFilter);
+        // Get the entire Option chain for the current day.
+        var chain = OptionChain(option.Symbol.Underlying);
+    }
+
+    private OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
+    {
+        // Contracts can be filtered by greeks, implied volatility, open interest:
+        return universe
+            .Delta(0.5m, 1.5m)
+            .Gamma(0.0001m, 0.0006m)
+            .Vega(0.01m, 1.5m)
+            .Theta(-2.0m, -0.5m)
+            .Rho(0.5m, 3.0m)
+            .ImpliedVolatility(1.0m, 3.0m)
+            .OpenInterest(100, 500);
+    }
+}
    
+  
    
+  
    
+   The Equity resolution must be less than or equal to the Equity Option resolution. For example, if you set the Equity resolution to minute, then you must set the Equity Option resolution to minute, hour, or daily.
+  
    
+  
    
+   For more information about creating US Equity Option Universes, see
+   
+    Equity Options
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   For information about accessing US Equity Options Universe data, see
+   
+    Equity Options
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical US Equity Options Universe data in an algorithm and the Research Environment.
+  
    
+  
    
+   Historical Data In Algorithms
+  
    
+  
    
+   To get historical US Equity Options Universe data in an algorithm, call the
+   
+    History<OptionUniverse>
+   
+   
+    history
+   
+   method with the canonical Equity Option
+   
+    Symbol
+   
+   . This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter. If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame
+history_df = self.history(self.option_symbol, timedelta(3), flatten=True)
+
+# OptionUniverse objects
+history = self.history[OptionUniverse](self.option_symbol, timedelta(3))
    
+   
    // OptionUniverse objects 
+var history = History<OptionUniverse>(_optionSymbol, TimeSpan.FromDays(3)).ToList();
    
+  
    
+  
    
+   For more information about historical Equity Options Universe data in algorithms, see
+   
+    Historical Data
+   
+   .
+  
    
+  
    
+   Historical Data In Research
+  
    
+  
    
+   To get historical US Equity Options Universe data in the Research Environment, call the
+   
+    History<OptionUniverse>
+   
+   
+    history
+   
+   method with the canonical Option
+   
+    Symbol
+   
+   . This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter.
+  
    
+  
    
+   
    qb = QuantBook()
+option = qb.add_option("GOOG")
+history = qb.history(option.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5), flatten=True)
    
+   
    var qb = new QuantBook();
+var option = qb.AddOption("GOOG");
+var history = qb.History<OptionUniverse>(option.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 6));
+foreach (var chain in history)
+{
+    var endTime = chain.EndTime;
+    var filteredContracts = chain.Data
+        .Select(contract => contract as OptionUniverse)
+        .Where(contract => contract.Greeks.Delta > 0.3m);
+    foreach (var contract in filteredContracts)
+    {
+        var price = contract.Price;
+        var iv = contract.ImpliedVolatility;
+    }
+}
    
+  
    
+  
    
+   For more information about historical Equity Options Universe data in the Research Environment, see
+   
+    Universes
+   
+   .
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   To view the supported assets in the US Equity Options Universe dataset, see the
+   
+    Data Explorer
+   
+   .
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Equity Options Universe dataset enables you to accurately design Option strategies. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Buying put Options to hedge against downward price movement in positive Equity positions
+   
      • 
+   
    • 
+    Exploiting arbitrage opportunities that arise when the price of Option contracts deviate from their theoretical value
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm subscribes to Google put options that fall within delta range between -1 and -0.95, open interest range between 10 and 1000, and expire within seven days. Within this Option chain, the algorithm holds the put Option contract that has the minimum delta (closest to -1) during market hour to hedge the underlying intra-day movement completely. It avoid volatility from sentiment and only earns from inter-day movement from longer-term factors. When the contract expires, the algorithm rolls over to the next contract that meets this criteria.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class USEquityOptionsUniverseAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(10000000)
+
+        # Asynchronous can use computational resources efficiently
+        self.universe_settings.asynchronous = True
+        # Subscribe to the underlying for the underlying position
+        # Set the data normalization mode to raw for strike price comparability
+        self.equity = self.add_equity("GOOG", data_normalization_mode=DataNormalizationMode.RAW).symbol
+        # Requesting option data and filter for the hedge candidates
+        option = self.add_option(self.equity)
+        option.set_filter(self.option_filter)
+        self.option_symbol = option.symbol
+
+        # Set scheduled event to buy a hedge option contract at market open to eliminate the intra-day movement
+        self.schedule.on(
+            self.date_rules.every_day(self.equity),
+            self.time_rules.after_market_open(self.equity, 1),
+            self.buy_hedge_contract
+        )
+
+        # Set a scheduled event to sell the hedge contract before market close, since we want to earn from inter-day movement
+        # Leave 2 minutes contingency to fill
+        self.schedule.on(
+            self.date_rules.every_day(self.equity),
+            self.time_rules.before_market_close(self.equity, 2),
+            self.sell_hedge_contract
+        )
+
+        self.hedge = None
+        
+    def option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
+        # Select the contracts with delta very close to -1 and high open interest
+        # This can effectively hedge most of the price change of the underlying and ensure the liquidity
+        # Make sure the contract is expiring close for its tradbility
+        return universe.puts_only().expiration(2, 7).delta(-1, -0.95).open_interest(10, 1000)
+
+    def buy_hedge_contract(self) -> None:
+        chain = self.current_slice.option_chains.get(self.option_symbol)
+        if chain:
+            # Order the underlying if not hold, the order size should match the option contract
+            # Order only if option chain data ready for hedging
+            if not self.portfolio[self.equity].invested:
+                self.market_order(self.equity, self.securities[self.option_symbol].symbol_properties.contract_multiplier)
+
+            # Get the contract with delta closest to -1 (lowest possible delta)
+            contract = sorted(chain, key=lambda x: x.greeks.delta)[0]
+            self.hedge = contract.symbol
+            # Buy 1 deep ITM put with delta close to -1 to eliminate the intraday movement
+            self.market_order(self.hedge, 1)
+        
+    def sell_hedge_contract(self) -> None:
+        # Check if any hedge contract position, if so, liquidate before market close to expose to underlying overnight movement
+        if self.hedge:
+            self.liquidate(self.hedge)
+            self.hedge = None
    
+   
    public class USEquityOptionsUniverseAlgorithm : QCAlgorithm
+{
+    private Symbol _equity, _optionSymbol, _hedge;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(10000000);
+        // Asynchronous can use computational resources efficiently
+        UniverseSettings.Asynchronous = true;
+
+        // Subscribe to the underlying for the underlying position
+        // Set the data normalization mode to raw for strike price comparability
+        _equity = AddEquity("GOOG", dataNormalizationMode: DataNormalizationMode.Raw).Symbol;
+        // Requesting option data and filter for the hedge candidates
+        var option = AddOption(_equity);
+        _optionSymbol = option.Symbol;
+        option.SetFilter(OptionFilter);
+
+        // Set scheduled event to buy a hedge option contract at market open to eliminate the intra-day movement
+        Schedule.On(
+            DateRules.EveryDay(_equity),
+            TimeRules.AfterMarketOpen(_equity, 1),
+            BuyHedgeContract
+        );
+
+        // Set a scheduled event to sell the hedge contract before market close, since we want to earn from inter-day movement
+        // Leave 2 minutes contingency to fill
+        Schedule.On(
+            DateRules.EveryDay(_equity),
+            TimeRules.BeforeMarketClose(_equity, 2),
+            SellHedgeContract
+        );
+    }
+
+    private OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
+    {
+        // Select the contracts with delta very close to -1 and high open interest
+        // This can effectively hedge most of the price change of the underlying and ensure the liquidity
+        // Make sure the contract is expiring close for its tradbility
+        return universe
+            .PutsOnly()
+            .Expiration(2, 7)
+            .Delta(-1m, -0.95m)
+            .OpenInterest(10, 1000);
+    }
+
+    private void BuyHedgeContract()
+    {
+        if (CurrentSlice.OptionChains.TryGetValue(_optionSymbol, out var chain))
+        {
+            // Order the underlying if not hold, the order size should match the option contract
+            // Order only if option chain data ready for hedging
+            if (!Portfolio[_equity].Invested)
+            {
+                MarketOrder(_equity, Securities[_optionSymbol].SymbolProperties.ContractMultiplier);
+            }
+
+            // Get the contract with delta closest to -1 (lowest possible delta)
+            var contract = chain.MinBy(x => x.Greeks.Delta);
+            _hedge = contract.Symbol;
+            // Buy 1 deep ITM put with delta close to -1 to eliminate the intraday movement
+            MarketOrder(_hedge, 1);
+        }
+    }
+
+    private void SellHedgeContract()
+    {
+        // Check if any hedge contract position, if so, liquidate before market close to expose to underlying overnight movement
+        if (_hedge != null)
+        {
+            Liquidate(_hedge);
+            _hedge = null;
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm demonstrating a Gamma Scalping strategy through framework algorithm. It filters NVDA options with expiration between 30 to 90 days and open interest between 50 and 1000, due to liquidity concern and lower Gamma fluctuation. Assuming a significant upward trend is expecting, it orders a long straddle strategy from the strike price with Delta-neutral and the highest Gamma to earn the highest profit from upward underlying movement.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class USEquityOptionsUniverseFrameworkAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        # Asynchronous can use computational resources efficiently
+        self.universe_settings.asynchronous = True
+
+        # Universe selection that select based on liquidity of the option contracts (by open interest)
+        self.add_universe_selection(EquityOptionsUniverseSelectionModel())
+        # Custom alpha model that use Delta and Gamma to signal insights
+        self.add_alpha(OptionDeltaGammaAlphaModel())
+        # To maintain long-short size equal, use a PCM that order a single contract
+        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
+
+class EquityOptionsUniverseSelectionModel(OptionUniverseSelectionModel):
+    def __init__(self) -> None:
+        # Daily update with the select_option_chain_symbols function
+        super().__init__(timedelta(1), self.select_option_chain_symbols)
+
+    def select_option_chain_symbols(self, dt: datetime) -> List[Symbol]:
+        # Select only NVDA options as our focus, which is a highly traded volatile equity with great upward momentum
+        return [Symbol.create("NVDA", SecurityType.OPTION, Market.USA)]
+
+    def filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
+        # To ensure the liquidity and tradability, make sure the option is not 0-DTE and have a fair open interest
+        # A longer TTM will have lower Gamma thus more stable in the local delta-neutral position
+        return universe.expiration(30, 90).open_interest(50, 1000)
+
+class OptionDeltaGammaAlphaModel(AlphaModel):
+    def __init__(self) -> None:
+        # A day count variable to control the alpha model only trade once a day
+        self._day = -1
+
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        insights = []
+
+        if self._day != slice.time.day:
+            # We open position for each option underlying as separate bet
+            for _, chain in slice.option_chains.items():
+                # For theta-neutral, select the same expiry for both call and put
+                expiry = min(x.expiry for x in chain)
+                contracts = [x for x in chain if x.expiry == expiry]
+
+                # Calculate delta and gamma per strike price group for later filtering, ensure both call and put available for option strategy
+                delta_gamma_symbols = []
+                strikes = set(x.strike for x in contracts if len([y for y in contracts if y.strike == x.strike]) == 2)
+                for strike in strikes:
+                    # Get both call and put for their aggregated delta and gamma
+                    call = next(filter(lambda x: x.right == OptionRight.CALL and x.strike == strike, contracts))
+                    put = next(filter(lambda x: x.right == OptionRight.PUT and x.strike == strike, contracts))
+                    delta_gamma_symbols.append((call.greeks.delta + put.greeks.delta, call.greeks.gamma + put.greeks.gamma, call.symbol, put.symbol))
+                
+                if len(delta_gamma_symbols) == 0:
+                    continue
+
+                # We want a delta-neutral position, so it is likely to be ATM (like a long straddle)
+                # Less than 2d.p. difference is non-significant, which we can risk for better reward
+                # Assuming the market direction is up in most scenario, we try to get the strike providing max overall Gamma
+                # Make sure the aggregated gamma to be positive to bet on large uptrend
+                # So it will earn more when the price really go up higher and higher, but locally immune for small noise
+                filtered = [item for item in delta_gamma_symbols if round(item[0], 2) <= 0.01 and item[1] > 0]
+                if len(filtered) == 0:
+                    continue
+                selected = sorted(filtered, key=lambda x: x[1], reverse=True)[0]
+
+                # Provide trade signal and roll the day count to let the position stay for the whole day
+                selected_call = selected[2]
+                selected_put = selected[3]
+                insights.extend([
+                    Insight.price(selected_call, Expiry.END_OF_DAY, InsightDirection.UP),
+                    Insight.price(selected_put, Expiry.END_OF_DAY, InsightDirection.UP)
+                ])
+                self._day = slice.time.day
+        
+        return insights
+
+class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
+    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
+        targets = []
+        for insight in insights:
+            if algorithm.securities[insight.symbol].is_tradable:
+                # Use a whole number target to order the exact number of share for size-matching
+                targets.append(PortfolioTarget(insight.symbol, insight.direction))
+        return targets
    
+   
    public class USEquityOptionsUniverseFrameworkAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+        // Asynchronous can use computational resources efficiently
+        UniverseSettings.Asynchronous = true;
+
+        // Universe selection that select based on liquidity of the option contracts (by open interest)
+        AddUniverseSelection(new EquityOptionsUniverseSelectionModel());
+        // Custom alpha model that use Delta and Gamma to signal insights
+        AddAlpha(new OptionDeltaGammaAlphaModel());
+        // To maintain long-short size equal, use a PCM that order a single contract
+        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
+    }
+}       
+    
+class EquityOptionsUniverseSelectionModel : OptionUniverseSelectionModel
+{
+    // Daily update with the SelectOptionChainSymbols function
+    public EquityOptionsUniverseSelectionModel()
+            : base(TimeSpan.FromDays(1), SelectOptionChainSymbols) {}
+    
+    private static IEnumerable<Symbol> SelectOptionChainSymbols(DateTime utcTime)
+    {
+        // Select only NVDA options as our focus, which is a highly traded volatile equity with great upward momentum
+        return new[] {QuantConnect.Symbol.Create("NVDA", SecurityType.Option, Market.USA)};
+    }
+
+    protected override OptionFilterUniverse Filter(OptionFilterUniverse filter)
+    {
+        // To ensure the liquidity and tradability, make sure the option is not 0-DTE and have a fair open interest
+        // A longer TTM will have lower Gamma thus more stable in the local delta-neutral position
+        return filter
+            .Expiration(30, 90)
+            .OpenInterest(50, 1000);
+    }
+}
+    
+class OptionDeltaGammaAlphaModel : AlphaModel
+{
+    // A day count variable to control the alpha model only trade once a day
+    private int _day = -1;
+
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    {
+        var insights = new List<Insight>();
+        
+        if (_day != slice.Time.Day)
+        {
+            // We open position for each option underlying as separate bet
+            foreach (var kvp in slice.OptionChains)
+            {
+                var chain = kvp.Value;
+
+                // For theta-neutral, select the same expiry for both call and put
+                var expiry = chain.Min(x => x.Expiry);
+                var contracts = chain.Where(x => x.Expiry == expiry).ToList();
+
+                // Calculate delta and gamma per strike price group for later filtering, ensure both call and put available for option strategy
+                var deltaGammaSymbols = new List<(decimal, decimal, Symbol, Symbol)>();
+                var strikes = contracts.Select(x => x.Strike)
+                    .Where(x => contracts.Count(y => y.Strike == x) == 2)
+                    .Distinct();
+                foreach (var strike in strikes)
+                {
+                    // Get both call and put for their aggregated delta and gamma
+                    var call = contracts.Single(x => x.Right == OptionRight.Call && x.Strike == strike);
+                    var put = contracts.Single(x => x.Right == OptionRight.Put && x.Strike == strike);
+                    deltaGammaSymbols.Add((call.Greeks.Delta + put.Greeks.Delta, call.Greeks.Gamma + put.Greeks.Gamma, call.Symbol, put.Symbol));
+                }
+
+                if (deltaGammaSymbols.Count == 0)
+                {
+                    continue;
+                }
+
+                // We want a delta-neutral position, so it is likely to be ATM (like a long straddle)
+                // Less than 2d.p. difference is non-significant, which we can risk for better reward
+                // Assuming the market direction is up in most scenario, we try to get the strike providing max overall Gamma
+                // Make sure the aggregated gamma to be positive to bet on large uptrend
+                // So it will earn more when the price really go up higher and higher, but locally immune for small noise
+                var filtered = deltaGammaSymbols.Where(item => Math.Round(item.Item1, 2) <= 0.01m && item.Item2 > 0).ToList();
+                if (filtered.Count == 0)
+                {
+                    continue;
+                }
+                var selected = filtered.OrderByDescending(item => item.Item2).First();
+
+                // Provide trade signal and roll the day count to let the position stay for the whole day
+                var selectedCall = selected.Item3;
+                var selectedPut = selected.Item4;
+                insights.AddRange(new[] {
+                    Insight.Price(selectedCall, Expiry.EndOfDay, InsightDirection.Up),
+                    Insight.Price(selectedPut, Expiry.EndOfDay, InsightDirection.Up)
+                });
+                _day = slice.Time.Day;
+            }
+        }
+
+        return insights;
+    }
+}
+    
+class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
+{
+    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
+    {
+        var targets = new List<PortfolioTarget>();
+        foreach (var insight in insights)
+        {
+            if (algorithm.Securities[insight.Symbol].IsTradable)
+            {
+                // Use a whole number target to order the exact number of share for size-matching
+                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
+            }
+        }
+        return targets;
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Equity Options Universe dataset provides
+   
+    OptionFilterUniverse
+   
+   ,
+   
+    OptionUniverse
+   
+   , and
+   
+    OptionChain
+   
+   objects.
+  
    
+  
    
+   OptionFilterUniverse Attributes
+  
    
+  
    
+   
+    OptionFilterUniverse
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   OptionUniverse Attributes
+  
    
+  
    
+   
+    OptionUniverse
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   OptionChain Attributes
+  
    
+  
    
+   
+    OptionChain
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > US Equity Security Master
    
+
    
+    
    
+        
    QuantConnect
    
+        
    US Equity Security Master
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Equity Security Master dataset by QuantConnect tracks US Equity corporate actions, including splits, dividends, delistings, mergers, and ticker changes through history. The data covers approximately 27,500 US Equities, starts in January 1998, and is delivered on a daily update frequency. You can easily download and install the dataset with the LEAN CLI so it's ready to use by LEAN. LEAN automatically handles all corporate actions and passes them into your algorithm as
+   
+    events
+   
+   .
+  
    
+  
    
+   This is
+   
+    NOT
+   
+   the underlying Equity data (
+   
+    US Equities
+   
+   dataset), which you need to purchase separately with a license from AlgoSeek. This security master dataset is required to purchase the US Equities or US Equities Options datasets.
+  
    
+  
    
+   For more information about the US Equity Security Master dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading.  Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   Data is delivered as a daily updated zip archive of map and factor files. The data is designed to be used in the LEAN Engine and cannot be consumed another way. The following table shows the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 1998
+     
    
+      Data Points
+     
+      Splits, Dividends, Mergers, IPO, & Delistings
+     
    
+      Asset Coverage
+     
+      27,500 US Equities
+     
    
+      Resolution
+     
+      Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+  
    
+   This is
+   
+    not
+   
+   the underlying Equity data (
+   
+    US Equities
+   
+   dataset), which you need to purchase separately with a license from AlgoSeek. This security master dataset is required to purchase the US Equities or US Equities Options datasets.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   You don't need any special code to utilize the US Equity Security Master. It automatically loads when you
+   
+    request US Equities data
+   
+   .
+  
    
+ 
+
+
+
    Accessing Splits
    
+
+ 
+  
    
+   To get the current split data, index the
+   
+    Splits
+   
+   
+    splits
+   
+   property of the current
+   
+    
+     Slice
+    
+   
+   with the Equity
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your security at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Check if any splits for the symbol
+    if slice.splits.contains_key(self._symbol):
+        # If so, get the mapped split object
+        split = slice.splits[self._symbol]
+        split_type = {0: "Warning", 1: "SplitOccurred"}.get(split.type)
+        self.log(f"Split: {split.symbol}\t{split.split_factor}\t{split.reference_price}\t{split_type}")
    
+   
    public override void OnData(Slice slice)
+{
+    // Check if any splits for the symbol
+    if (slice.Splits.ContainsKey(_symbol))
+    {
+        // If so, get the mapped split object
+        var split = slice.Splits[_symbol];
+        Log($"Split: {split.Symbol}\t{split.SplitFactor}\t{split.ReferencePrice}\t{split.Type}");
+    }
+}
    
+  
    
+  
    
+   For more information about accessing splits, see
+   
+    Splits
+   
+   .
+  
    
+ 
+
+
+
    Accessing Dividends
    
+
+ 
+  
    
+   To get the current dividend data, index the
+   
+    Dividends
+   
+   
+    dividends
+   
+   property of the current
+   
+    Slice
+   
+   with the Equity
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your security at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Check if any dividend for the symbol
+    if slice.dividends.contains_key(self._symbol):
+        # If so, get the mapped dividend object
+        dividend = slice.dividends[self._symbol]
+        self.log(f'Dividend: {dividend.symbol}\t{dividend.distribution}\t{dividend.reference_price}')
    
+   
    public override void OnData(Slice slice)
+{
+    // Check if any dividend for the symbol
+    if (slice.Dividends.ContainsKey(_symbol))
+    {
+        // If so, get the mapped dividend object
+        var dividend = slice.Dividends[_symbol];
+        Log($"Dividend: {dividend.Symbol}\t{dividend.Distribution}\t{dividend.ReferencePrice}");
+    }
+}
    
+  
    
+  
    
+   For more information about accessing dividends, see
+   
+    Dividends
+   
+   .
+  
    
+ 
+
+
+
    Accessing Delistings
    
+
+ 
+  
    
+   To get the current Delistings data, index the
+   
+    Delistings
+   
+   
+    delistings
+   
+   property of the current
+   
+    Slice
+   
+   with the Equity
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your security at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Check if any delisting for the symbol
+    if slice.delistings.contains_key(self._symbol):
+        # If so, get the mapped delisting object
+        delisting = slice.delistings[self._symbol]
+        delisting_type = {0: "Warning", 1: "Delisted"}.get(delisting.type)
+        self.log(f'Delistings: {delisting_type}')
    
+   
    public override void OnData(Slice slice)
+{
+    // Check if any delisting for the symbol
+    if (slice.Delistings.ContainsKey(_symbol))
+    {
+        // If so, get the mapped delisting object
+        var delisting = slice.Delistings[_symbol];
+        Log($"Delistings: {delisting.Type}");
+    }
+}
    
+  
    
+  
    
+   For more information about accessing delistings, see
+   
+    Delistings
+   
+   .
+  
    
+ 
+
+
+
    Accessing Symbol Change Events
    
+
+ 
+  
    
+   To get the current Symbol change events, index the
+   
+    SymbolChangedEvents
+   
+   
+    symbol_changed_events
+   
+   property of the current
+   
+    Slice
+   
+   with the Equity
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your security at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Check if any symbol change event for the symbol
+    if slice.symbol_changed_events.contains_key(self._symbol):
+        # If so, get the mapped SymbolChangeEvent object
+        symbol_changed_event = slice.symbol_changed_events[self._symbol]
+        self.log(f"Symbol changed: {symbol_changed_event.old_symbol} -> {symbol_changed_event.new_symbol}")
+
    
+   
    +public override void OnData(Slice slice){
+    // Check if any symbol change event for the symbol
+    if (slice.SymbolChangedEvents.ContainsKey(_symbol))
+    {
+        // If so, get the mapped SymbolChangeEvent object
+        var symbolChangedEvent = slice.SymbolChangedEvents[_symbol];
+        Log($"Symbol changed: {symbolChangedEvent.OldSymbol} -> {symbolChangedEvent.NewSymbol}");
+    }
+}
    
+  
    
+  
    
+   For more information about accessing Symbol change events, see
+   
+    Symbol Changes
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   To get historical US Equity Security Master data, call the
+   
+    History
+   
+   
+    history
+   
+   method with the data type and the Equity
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # Splits
+split_history_df = self.history(Split, self._symbol, timedelta(5*365))
+split_history = self.history[Split](self._symbol, timedelta(5*365))
+
+# Dividends
+dividend_history_df = self.history(Dividend, self._symbol, timedelta(5*365))
+dividend_history = self.history[Dividend](self._symbol, timedelta(5*365))
+
+# Symbol Changes
+symbol_change_history_df = self.history(SymbolChangedEvent, self._symbol, timedelta(5*365))
+symbol_change_history = self.history[SymbolChangedEvent](self._symbol, timedelta(5*365))
+
+# Delistings
+delisting_history_df = self.history(Delisting, self._symbol, timedelta(5*365))
+delisting_history = self.history[Delisting](self._symbol, timedelta(5*365))
    
+   
    // Splits
+var splitHistory = History<Split>(_symbol, TimeSpan.FromDays(5*365));
+
+// Dividends 
+var dividendHistory = History<Dividend>(_symbol, TimeSpan.FromDays(5*365));
+
+// Symbol Changes
+var symbolChangeHistory = History<SymbolChangedEvent>(_symbol, TimeSpan.FromDays(5*365));
+
+// Delistings
+var delistingHistory = History<Delisting>(_symbol, TimeSpan.FromDays(5*365));
    
+  
    
+  
    
+   For more information about historical data, see
+   
+    History Requests
+   
+   .
+  
    
+ 
+
+
+
    Live Trading Considerations
    
+
+ 
+  
    
+   In backtesting, corporate actions occurs at midnight (ET). In live trading, the live data for corporate actions arrives at 6/7 AM ET, so that's when they occur.
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   To view the supported assets in the US Equity Security Master dataset, see the
+   
+    Data Explorer
+   
+   . This dataset doesn't include Over-the-Counter (OTC) stocks.
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Security Master enables you to accurately design strategies harnessing any core corporate actions. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Post-dividend announcement trading strategies.
+   
      • 
+   
    • 
+    Trading on new Equities by monitoring for IPOs.
+   
      • 
+   
    • 
+    Harnessing split announcements for reverse-split announcement momentum.
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm logs the
+   
+    Split
+   
+   ,
+   
+    Dividend
+   
+   ,
+   
+    Delisting
+   
+   , and
+   
+    SymbolChangedEvent
+   
+   objects of Apple:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class USEquitySecurityMasterAlgorithm (QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(1000000)
+        
+        self.equity = self.add_equity("AAPL", Resolution.DAILY).symbol
+        
+    def on_data(self, slice: Slice) -> None:
+        # Accessing Data - Splits
+        split = slice.splits.get(self.equity)
+        if split:
+            self.debug(f"{self.time} >> SPLIT >> {split.symbol} - {split.split_factor} - {self.portfolio.cash} - {self.portfolio[self.equity].price}")
+        
+        # Accessing Data - Dividends
+        dividend = slice.dividends.get(self.equity)
+        if dividend:
+            self.debug(f"{self.time} >> DIVIDEND >> {dividend.symbol} - {dividend.distribution} - {self.portfolio.cash} - {self.portfolio[self.equity].price}")
+
+        # Accessing Data - Delisting
+        delisting = slice.delistings.get(self.equity)
+        if delisting:
+            delistingType = {0: "Warning", 1: "Delisted"}.get(delisting.type)
+            self.debug(f"{self.time} >> DELISTING >> {delisting.symbol} - {delistingType}")
+            
+        # Accessing Data - Symbol Changed Event
+        symbolChangedEvent = slice.symbol_changed_events.get(self.equity)
+        if symbolChangedEvent:
+            self.debug(f"{self.time} >> SYMBOL CHANGED >> {symbolChangedEvent.old_symbol} -> {symbolChangedEvent.new_symbol}")
+
    
+   
    public class USEquitySecurityMasterAlgorithm : QCAlgorithm
+{
+    private Symbol _equity;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(1000000);
+        
+        _equity = AddEquity("AAPL", Resolution.Daily).Symbol;
+    }
+    
+    public override void OnData(Slice slice)
+    {
+        // Accessing Data - Splits
+        if (slice.Splits.ContainsKey(_equity))
+        {
+            var split = slice.Splits[_equity];
+            Debug($"Split: {split.Symbol}\t{split.SplitFactor}\t{split.ReferencePrice}\t{split.Type}");
+        }
+        
+        // Accessing Data - Dividends
+        if (slice.Dividends.ContainsKey(_equity))
+        {
+            var dividend = slice.Dividends[_equity];
+            Log($"Dividend: {dividend.Symbol}\t{dividend.Distribution}\t{dividend.ReferencePrice}");
+        }
+        
+        // Accessing Data - Delisting
+        if (slice.Delistings.ContainsKey(_equity))
+        {
+            var delisting = slice.Delistings[_equity];
+            Log($"Delistings: {delisting.Type}");
+        }
+        
+        // Accessing Data - Symbol Changed Event
+        if (slice.SymbolChangedEvents.ContainsKey(_equity))
+        {
+            var symbolChangedEvent = slice.SymbolChangedEvents[_equity];
+            Log($"Symbol changed: {symbolChangedEvent.OldSymbol} -> {symbolChangedEvent.NewSymbol}");
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following algorithm demonstrates the payments for cash dividends in backtesting. When the data normalization mode is
+   
+    Raw
+   
+   , your portfolio receives cash dividends.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class PaymentAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+
+        # this will use the Tradier Brokerage open order split behavior
+        # forward split will modify open order to maintain order value
+        # reverse split open orders will be canceled
+        self.set_brokerage_model(BrokerageName.TRADIER_BROKERAGE)
+
+        self.universe_settings.resolution = Resolution.DAILY
+        self.universe_settings.data_normalization_mode = DataNormalizationMode.RAW
+        
+        # MSFT: Splits and Dividends
+        # GOOG: Symbol Changed Event
+        # AAA.1: Delisting
+        
+        self.set_universe_selection(ManualUniverseSelectionModel(
+            Symbol.create("MSFT", SecurityType.EQUITY, Market.USA)))
+
+        self.set_alpha(PaymentAlphaModel())
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+        self.set_execution(BracketExecutionModel())
+            
+class PaymentAlphaModel(AlphaModel):
+
+    symbol = Symbol.EMPTY
+    
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        # Accessing Data - Splits
+        split = slice.splits.get(self.symbol)
+        if split:
+            algorithm.debug(f"{algorithm.time} >> SPLIT >> {split.symbol} - {split.split_factor} - {algorithm.portfolio.cash} - {algorithm.portfolio[self.symbol].price}")
+
+        # Accessing Data - Dividends
+        dividend = slice.dividends.get(self.symbol)
+        if dividend:
+            algorithm.debug(f"{algorithm.time} >> DIVIDEND >> {dividend.symbol} - {dividend.distribution} - {algorithm.portfolio.cash} - {algorithm.portfolio[self.symbol].price}")
+
+        # Accessing Data - Delistings
+        delisting = slice.delistings.get(self.symbol)
+        if delisting:
+            delistingType = {0: "Warning", 1: "Delisted"}.get(delisting.type)
+            algorithm.debug(f"{algorithm.time} >> DELISTING >> {delisting.symbol} - {delistingType}")
+
+        # Accessing Data - Symbol Changed Events
+        symbolChangedEvent = slice.symbol_changed_events.get(self.symbol)
+        if symbolChangedEvent:
+            algorithm.debug(f"{algorithm.time} >> SYMBOL CHANGED >> {symbolChangedEvent.old_symbol} -> {symbolChangedEvent.new_symbol}")
+
+        bar = slice.bars.get(self.symbol)
+        return [Insight.price(self.symbol, timedelta(1), InsightDirection.UP)] if bar else []
+
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
+        self.symbol = list(changes.added_securities)[0].symbol
+
+class BracketExecutionModel(ExecutionModel):
+    
+    def __init__(self) -> None:
+        '''Initializes a new instance of the ImmediateExecutionModel class'''
+        self.targets_collection = PortfolioTargetCollection()
+
+    def execute(self, algorithm: QCAlgorithm, targets: List[PortfolioTarget]) -> None:
+
+        # for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
+        self.targets_collection.add_range(targets)
+        if self.targets_collection.count > 0:
+            for target in self.targets_collection.order_by_margin_impact(algorithm):
+                # calculate remaining quantity to be ordered
+                quantity = OrderSizing.get_unordered_quantity(algorithm, target)
+                if quantity != 0 and algorithm.transactions.orders_count == 0:
+                    bar = algorithm.securities[target.symbol].get_last_data()
+                    algorithm.market_order(target.symbol, quantity)
+                    # place some orders that won't fill, when the split comes in they'll get modified to reflect the split
+                    algorithm.stop_market_order(target.symbol, -quantity, bar.low/2)
+                    algorithm.limit_order(target.symbol, -quantity, bar.high*2)
+
+            self.targets_collection.clear_fulfilled(algorithm)
+
    
+   
    public class PaymentsAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+            
+        // this will use the Tradier Brokerage open order split behavior
+        // forward split will modify open order to maintain order value
+        // reverse split open orders will be canceled
+        SetBrokerageModel(BrokerageName.TradierBrokerage);
+        
+        UniverseSettings.Resolution = Resolution.Daily;
+        UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
+        
+        // MSFT: Splits and Dividends
+        // GOOG: Symbol Changed Event
+        // AAA.1: Delisting
+        SetUniverseSelection(new ManualUniverseSelectionModel(
+            QuantConnect.Symbol.Create("MSFT", SecurityType.Equity, Market.USA)));
+
+        SetAlpha(new PaymentAlphaModel());
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+        SetExecution(new BracketExecutionModel());
+    }
+}
+
+public class PaymentAlphaModel : AlphaModel
+{
+    private Symbol _symbol = Symbol.Empty;
+    
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    {
+        // Accessing Data - Splits
+        if (slice.Splits.ContainsKey(_symbol))
+        {
+            var split = slice.Splits[_symbol];
+            algorithm.Debug($"{split.Time.ToIso8601Invariant()} >> SPLIT >> {split.Symbol} - " +
+                $"{split.SplitFactor.ToStringInvariant()} - " +
+                $"{algorithm.Portfolio.Cash.ToStringInvariant()} - " +
+                $"{algorithm.Portfolio[_symbol].Quantity.ToStringInvariant()}");
+        }
+        
+        // Accessing Data - Dividends
+        if (slice.Dividends.ContainsKey(_symbol))
+        {
+            var dividend = slice.Dividends[_symbol];
+            algorithm.Debug($"{dividend.Time.ToStringInvariant("o")} >> DIVIDEND >> {dividend.Symbol} - " +
+                $"{dividend.Distribution.ToStringInvariant("C")} - {algorithm.Portfolio.Cash} - " +
+                $"{algorithm.Portfolio[_symbol].Price.ToStringInvariant("C")}");
+        }
+
+        // Accessing Data - Delisting
+        if (slice.Delistings.ContainsKey(_symbol))
+        {
+            var delisting = slice.Delistings[_symbol];
+            algorithm.Debug($"{delisting.Time.ToStringInvariant("o")} >> DELISTING >> {delisting.Type}");
+        }
+
+        // Accessing Data - Symbol Changed Event
+        if (slice.SymbolChangedEvents.ContainsKey(_symbol))
+        {
+            var symbolChangedEvent = slice.SymbolChangedEvents[_symbol];
+            algorithm.Debug($"{symbolChangedEvent.Time.ToStringInvariant("o")} >> Symbol Changed Event >> " +
+                $"{symbolChangedEvent.OldSymbol} -> {symbolChangedEvent.OldSymbol}");
+        }
+        
+        return slice.Bars.ContainsKey(_symbol)
+            ? new [] { Insight.Price(_symbol, TimeSpan.FromDays(1), InsightDirection.Up) }
+            : Enumerable.Empty<Insight>();
+    }
+    
+    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
+    {
+        _symbol = changes.AddedSecurities.First().Symbol;
+    }
+}
+
+public class BracketExecutionModel : ExecutionModel
+{
+    private readonly PortfolioTargetCollection _targetsCollection = new PortfolioTargetCollection();
+
+    public override void Execute(QCAlgorithm algorithm, IPortfolioTarget[] targets)
+    {
+        _targetsCollection.AddRange(targets);
+        // for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call
+        if (_targetsCollection.Count > 0)
+        {
+            foreach (var target in _targetsCollection.OrderByMarginImpact(algorithm))
+            {
+                // calculate remaining quantity to be ordered
+                var quantity = OrderSizing.GetUnorderedQuantity(algorithm, target);
+                if (quantity != 0 && algorithm.Transactions.OrdersCount == 0)
+                {
+                    var bar = algorithm.Securities[target.Symbol].GetLastData() as TradeBar;
+                    algorithm.MarketOrder(target.Symbol, quantity);
+                    // place some orders that won't fill, when the split comes in they'll get modified to reflect the split
+                    algorithm.StopMarketOrder(target.Symbol, -quantity, bar.Low/2);
+                    algorithm.LimitOrder(target.Symbol, -quantity, bar.High*2);
+                }
+            }
+
+            _targetsCollection.ClearFulfilled(algorithm);
+        }
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Equity Security Master dataset provides
+   
+    Split
+   
+   ,
+   
+    Dividend
+   
+   ,
+   
+    Delisting
+   
+   , and
+   
+    SymbolChangedEvent
+   
+   objects.
+  
    
+  
    
+   Split Attributes
+  
    
+  
    
+   When a split or merger occurs, we pass the previous
+   
+    Symbol
+   
+   data into your algorithm.
+   
+    Split
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   Dividend Attributes
+  
    
+  
    
+   Dividend events are triggered on the payment date.
+   
+    Dividend
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   Delisting Attributes
+  
    
+  
    
+   When a security is delisted, we notify your algorithm.
+   
+    Delisting
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   SymbolChangedEvent Attributes
+  
    
+  
    
+   When a security changes their ticker, we notify your algorithm.
+   
+    SymbolChangedEvent
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > US Future Option Universe
    
+
    
+    
    
+        
    QuantConnect
    
+        
    US Future Option Universe
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Future Option Universe dataset by QuantConnect lists the available US Future Options contracts and the current open interest. The data covers 15 Monthly Future contracts, starts in January 2012, and is delivered on a daily update frequency. This dataset is created by monitoring the trading activity on the CME, CBOT, NYMEX, and COMEX markets.
+  
    
+  
    
+   The US Future Option Universe dataset depends on the
+   
+    US Future Universe
+   
+   dataset because the US Future Universe dataset contains the universe of underlying Futures contracts. 
+  This dataset also depends on the
+   
+    US Futures Security Master
+   
+   dataset because the US Futures Security Master dataset contains information on symbol changes of the contracts.
+  
    
+  
    
+   This dataset
+   
+    does not
+   
+   contain market data. For market data, see
+   
+    US Future Options by AlgoSeek
+   
+   .
+  
    
+  
    
+   For more information about the US Future Option Universe dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the US Future Options Universe dataset:
+  
    
+  
    
+   
    future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
+future.set_filter(0, 90)
+self.add_future_option(future.symbol, lambda universe: universe.strikes(-1, 1))
    
+   
    var future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
+future.SetFilter(0, 90);
+AddFutureOption(future.Symbol, universe => universe.Strikes(-1, 1));
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 2012
+     
    
+      Asset Coverage
+     
+      15 Monthly Future Contracts. Standard expires only. No weeklies or 0DTE contracts.
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+      Market Hours
+     
+      
+       Regular and Extended
+      
+     
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add US Future Options Universe data to your algorithm, call the
+   
+    AddFutureOption
+   
+   
+    add_future_option
+   
+   method. To define which contracts should be in your universe, specify the filter when requesting the Future Option data.
+  
    
+  
    
+   The
+   
+    AddFutureOption
+   
+   
+    add_future_option
+   
+   method provides a daily stream of Option chain data. To get the most recent daily chain, call the
+   
+    OptionChain
+   
+   
+    option_chain
+   
+   method with the underlying Future Symbol. The
+   
+    OptionChain
+   
+   
+    option_chain
+   
+   method returns data on all the tradable contracts, not just the contracts that pass your universe filter.
+  
    
+  
    
+   
    class USFutureOptionsDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2020, 6, 1)
+        self.set_end_date(2021, 6, 1)
+        self.set_cash(100000)
+        self.universe_settings.asynchronous = True
+        
+        self.future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
+        self.future.set_filter(0, 90)
+        # Set our strike/expiry filter for this option chain
+        self.add_future_option(self.future.symbol, self._option_filter)
+
+    def on_data(self, slice: Slice) -> None:
+        # Get the entire Option chain for the current day.
+        symbol = Symbol.create_canonical_option(self.future.mapped)
+        chain = self.option_chain(symbol, flatten=True).data_frame
+
+    def _option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
+        # Contracts can be filtered by strike, and expiration
+        return universe.strikes(-1, 1)
    
+   
    public class USFutureOptionsDataAlgorithm : QCAlgorithm
+{
+    private Future _future;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2020, 6, 1);
+        SetEndDate(2021, 6, 1);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = true;
+        
+        _future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
+        _future.SetFilter(0, 90);
+        // Set our strike/expiry filter for this option chain
+        AddFutureOption(_future.Symbol, OptionFilter);
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // Create canonical symbol for the mapped future contract, since option chains are mapped by canonical symbol.
+        var symbol = QuantConnect.Symbol.CreateCanonicalOption(_future.Mapped);
+        // Get the entire Option chain for the current day.
+        var chain = OptionChain(symbol);
+    }
+
+    private OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
+    {
+        // Contracts can be filtered by strike, and expiration
+        return universe.Strikes(-1, 1);
+    }
+}
    
+  
    
+  
    
+   The Future resolution must be less than or equal to the Future Option resolution. For example, if you set the Future resolution to minute, then you must set the Future Option resolution to minute, hour, or daily.
+  
    
+  
    
+   For more information about creating US Future Option universes, see
+   
+    Future Options
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   For information about accessing US Future Options Universe data, see
+   
+    Future Options
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical US Future Options Universe data in an algorithm and the Research Environment.
+  
    
+  
    
+   Historical Data In Algorithms
+  
    
+  
    
+   To get historical US Future Options Universe data in an algorithm, call the
+   
+    History<OptionUniverse>
+   
+   
+    history
+   
+   method with the canonical mapped Future Option
+   
+    Symbol
+   
+   . This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter. If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    future_option_symbol = Symbol.create_canonical_option(self.future.mapped)
+# DataFrame
+history_df = self.history(future_option_symbol, timedelta(10), flatten=True)
+# OptionUniverse objects
+history = self.history[OptionUniverse](future_option_symbol, timedelta(10))
    
+   
    var futureOptionSymbol = QuantConnect.Symbol.CreateCanonicalOption(_future.Mapped);
+var history = History<OptionUniverse>(futureOptionSymbol, TimeSpan.FromDays(10)).ToList();
    
+  
    
+  
    
+   For more information about historical US Future Options Universe data in algorithms, see
+   
+    Historical Data
+   
+   .
+  
    
+  
    
+   Historical Data In Research
+  
    
+  
    
+   To get historical US Future Options Universe data in the Research Environment, call the
+   
+    History<OptionUniverse>
+   
+   
+    history
+   
+   method with the canonical Option
+   
+    Symbol
+   
+   . This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter.
+  
    
+  
    
+   
    qb = QuantBook()
+future = qb.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
+future.set_filter(0, 90)
+symbol = Symbol.create_canonical_option(future.mapped)
+history = qb.history(symbol, datetime(2020, 6, 1), datetime(2020, 6, 5), flatten=True)
    
+   
    var qb = new QuantBook();
+var future = qb.AddFuture(Futures.Metals.Gold, Resolution.Minute);
+var symbol = QuantConnect.Symbol.CreateCanonicalOption(future.Mapped);
+var history = qb.History<OptionUniverse>(symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 6));
+foreach (var chain in history)
+{
+    var endTime = chain.EndTime;
+    var filteredContracts = chain.Data
+        .Select(contract => contract as OptionUniverse)
+        .Where(contract => contract.Greeks.Delta > 0.3m);
+    foreach (var contract in filteredContracts)
+    {
+        var price = contract.Price;
+        var iv = contract.ImpliedVolatility;
+    }
+}
    
+  
    
+  
    
+   For more information about historical Future Options Universe data in the Research Environment, see
+   
+    Universes
+   
+   .
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   The following list shows the available (15) Futures Options:
+  
    
+  
      
+   
    • 
+    
+     Futures.Energy.CrudeOilWTI
+    
+    
+     Futures.Energy.CRUDE_OIL_WTI
+    
+    : Crude Oil WTI Futures (NYMEX: LO | Underlying: CL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.Gasoline
+    
+    
+     Futures.Energy.GASOLINE
+    
+    : Gasoline RBOB Futures (NYMEX: OB | Underlying: RB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.HeatingOil
+    
+    
+     Futures.Energy.HEATING_OIL
+    
+    : Heating Oil Futures (NYMEX: OH | Underlying: HO)
+   
      • 
+   
    • 
+    
+     Futures.Energy.NaturalGas
+    
+    
+     Futures.Energy.NATURAL_GAS
+    
+    : Natural Gas Futures (NYMEX: ON | Underlying: NG)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y10TreasuryNote
+    
+    
+     Futures.Financials.Y_10_TREASURY_NOTE
+    
+    : 10Y U.S. Treasury Note Futures (CBOT: OZN | Underlying: ZN)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y2TreasuryNote
+    
+    
+     Futures.Financials.Y_2_TREASURY_NOTE
+    
+    : 2Y U.S. Treasury Note Futures (CBOT: OZT | Underlying: ZT)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y30TreasuryBond
+    
+    
+     Futures.Financials.Y_30_TREASURY_BOND
+    
+    : 30Y U.S. Treasury Bond Futures (CBOT: OZB | Underlying: ZB)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Corn
+    
+    
+     Futures.Grains.CORN
+    
+    : Corn Futures (CBOT: OZC | Underlying: ZC)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Soybeans
+    
+    
+     Futures.Grains.SOYBEANS
+    
+    : Soybeans Futures (CBOT: OZS | Underlying: ZS)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Wheat
+    
+    
+     Futures.Grains.WHEAT
+    
+    : Default wheat contract is SRWWheat (CBOT: OZW | Underlying: ZW)
+   
      • 
+   
    • 
+    
+     Futures.Indices.NASDAQ100EMini
+    
+    
+     Futures.Indices.NASDAQ_100_E_MINI
+    
+    : E-mini NASDAQ 100 Futures (CME: NQ)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SP500EMini
+    
+    
+     Futures.Indices.SP_500_E_MINI
+    
+    : E-mini S&P 500 Futures (CME: ES)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Copper
+    
+    
+     Futures.Metals.COPPER
+    
+    : Copper Futures (COMEX: HXE | Underlying: HG)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Gold
+    
+    
+     Futures.Metals.GOLD
+    
+    : Gold Futures (COMEX: OG | Underlying: GC)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Silver
+    
+    
+     Futures.Metals.SILVER
+    
+    : Silver Futures (COMEX: SO | Underlying: SI)
+   
      • 
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Future Options dataset enables you to accurately design Future Option strategies. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Selling out of the money Future Option contracts to collect the premium that the Option buyer pays
+   
      • 
+   
    • 
+    Buying put Options to hedge against downward price movement in Future contracts you bought
+   
      • 
+   
    • 
+    Exploiting arbitrage opportunities that arise when the price of Option contracts deviate from their theoretical value
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example demonstrates a bull call spread Option strategy using universe filtering.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class FutureOptionAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        # Filter the underlying continuous Futures to narrow the FOP spectrum.
+        self.underlying = self.add_future(Futures.Indices.SP_500_E_MINI,
+            extended_market_hours=True,
+            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
+            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
+            contract_depth_offset=0)
+        self.underlying.set_filter(0, 182)
+        # Use CallSpread filter to obtain the 2 best-matched contracts that forms a call spread.
+        # It simplifies from further filtering and reduce computation on redundant subscription.
+        self.add_future_option(self.underlying.symbol, lambda u: u.call_spread(5, 5, -5))
+    
+    def on_data(self, slice: Slice) -> None:
+        if self.portfolio.invested:
+            return
+        # Create canonical symbol for the mapped future contract, since we need that to access the option chain.
+        symbol = Symbol.create_canonical_option(self.underlying.mapped)
+    
+        # Get option chain data for the mapped future only.
+        # It requires 2 contracts with different strikes to form a call spread, so we make sure at least 2 contracts are present.
+        chain = slice.option_chains.get(symbol)
+        if not chain or len(list(chain)) < 2:
+            return
+            
+        # Separate the contracts by strike, as we need to access their strike.
+        expiry = min([x.expiry for x in chain])
+        sorted_by_strike = sorted([x.strike for x in chain])
+        itm_strike = sorted_by_strike[0]
+        otm_strike = sorted_by_strike[-1]
+        
+        # Use abstraction method to order a bull call spread to avoid manual error.
+        option_strategy = OptionStrategies.bull_call_spread(symbol, itm_strike, otm_strike, expiry)
+        self.buy(option_strategy, 1)
    
+   
    public class FutureOptionAlgorithm : QCAlgorithm
+{
+    private Future _underlying;
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        // Filter the underlying continuous Futures to narrow the FOP spectrum.
+        _underlying = AddFuture(Futures.Indices.SP500EMini,
+            extendedMarketHours: true,
+            dataMappingMode: DataMappingMode.OpenInterest,
+            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
+            contractDepthOffset: 0);
+        _underlying.SetFilter(0, 182);
+        // Use CallSpread filter to obtain the 2 best-matched contracts that forms a call spread.
+        // It simplifies from further filtering and reduce computation on redundant subscription.
+        AddFutureOption(_underlying.Symbol, (u) => u.CallSpread(5, 5, -5));
+    }
+    
+    public override void OnData(Slice slice)
+    {
+        if (Portfolio.Invested)
+            return;
+
+        // Create canonical symbol for the mapped future contract, since we need that to access the option chain.
+        var symbol = QuantConnect.Symbol.CreateCanonicalOption(_underlying.Mapped);
+    
+        // Get option chain data for the mapped future only.
+        // It requires 2 contracts with different strikes to form a call spread, so we make sure at least 2 contracts are present.
+        if (!slice.OptionChains.TryGetValue(symbol, out var chain) || chain.Count() < 2)
+            return;
+    
+        // Separate the contracts by strike, as we need to access their strike.
+        var expiry = chain.Min(x => x.Expiry);
+        var itmStrike = chain.Min(x => x.Strike);
+        var otmStrike = chain.Max(x => x.Strike);
+    
+        // Use abstraction method to order a bull call spread to avoid manual error.
+        var optionStrategy = OptionStrategies.BullCallSpread(symbol, itmStrike, otmStrike, expiry);
+        Buy(optionStrategy, 1);
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Future Options Universe dataset provides
+   
+    OptionFilterUniverse
+   
+   and
+   
+    OptionUniverse
+   
+   objects.
+  
    
+  
    
+   OptionFilterUniverse Attributes
+  
    
+  
    
+   
+    OptionFilterUniverse
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   OptionUniverse Attributes
+  
    
+  
    
+   
+    OptionUniverse
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > US Future Universe
    
+
    
+    
    
+        
    QuantConnect
    
+        
    US Future Universe
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Future Universe dataset by QuantConnect lists the available US Future contracts, their daily trading volume, and Open Interest. The data covers the 157 most liquid contracts, starts in May 2009, and is delivered on daily frequency. This dataset is created by monitoring the trading activity on the CFE, CBOT, CME, COMEX, NYMEX, and ICE
+   
+    *
+   
+   .
+  
    
+  
    
+   This dataset depends on the
+   
+    US Futures Security Master
+   
+   dataset because the US Futures Security Master dataset contains information on symbol changes of the contracts.
+  
    
+  
    
+   This dataset
+   
+    does not
+   
+   contain market data. For market data, see
+   
+    US Futures by AlgoSeek
+   
+   .
+  
    
+  
    
+   For more information about the US Future Universe dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the US Future Universe dataset:
+  
    
+  
    
+   
    future = self.add_future(Futures.Metals.GOLD)
+future.set_filter(0, 90)
    
+   
    var future = AddFuture(Futures.Metals.Gold);
+future.SetFilter(0, 90);
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      May 2009
+     
    
+      Asset Coverage
+     
+      157 Futures
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Daily
+     
    
+      Timezone
+     
+      
      
+       
    • 
+        Chicago (CFE, CME or CBOT)
+       
      • 
+       
    • 
+        New York (ICE, NYMEX or COMEX)
+       
      • 
+      
    
+     
    
+      Market Hours
+     
+      
+       Regular and Extended
+      
+     
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add US Future Universe data to your algorithm, call the
+   
+    AddFuture
+   
+   
+    add_future
+   
+   method. Save a reference to the Future object so you can access the data later in your algorithm. To define which contracts should be in your universe, specify the filter when requesting the Future data.
+  
    
+  
    
+   The
+   
+    AddFuture
+   
+   
+    add_future
+   
+   method provides a daily stream of Future chain data. To get the most recent daily chain, call the
+   
+    FuturesChain
+   
+   
+    futures_chain
+   
+   method with the underlying Future Symbol. The
+   
+    FuturesChain
+   
+   
+    futures_chain
+   
+   method returns data on all the tradable contracts, not just the contracts that pass your universe filter.
+  
    
+  
    
+   
    class USFutureDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2020, 6, 1)
+        self.set_end_date(2021, 6, 1)
+        self.set_cash(100000)
+        self.universe_settings.asynchronous = True
+        self._future = self.add_future(Futures.Metals.GOLD)
+        # Set our contract filter for this Future chain.
+        self._future.set_filter(lambda universe: universe.standards_only().front_month())
+        # Get the entire Futures chain for the current day.
+        chain = self.futures_chain(self._future.symbol, flatten=True).data_frame
    
+   
    public class USFutureDataAlgorithm : QCAlgorithm
+{
+    private Future _future;
+        
+    public override void Initialize()
+    {
+        SetStartDate(2020, 6, 1);
+        SetEndDate(2021, 6, 1);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = true;
+        _future = AddFuture(Futures.Metals.Gold);
+        // Set our contract filter for this Future chain.
+        _future.SetFilter((universe) => universe.StandardsOnly().FrontMonth());
+        // Get the entire Futures chain for the current day.
+        var chain = FuturesChain(_future.Symbol);
+    }
+}
    
+  
    
+  
    
+   For more information about creating US Future universes, see
+   
+    Futures
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   For information about accessing US Future Universe data, see
+   
+    Futures
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical US Future Universe data in an algorithm and the Research Environment.
+  
    
+  
    
+   Historical Data In Algorithms
+  
    
+  
    
+   To get historical US Future Universe data in an algorithm, call the
+   
+    History<FutureUniverse>
+   
+   
+    history
+   
+   method with continuous Future
+   
+    Symbol
+   
+   and a lookback period. This method returns the all the available contracts for each trading day, not the subset of contracts that pass your universe filter. If there is no data for the period you requested, the history result is empty.
+  
    
+  
    
+   
    # Add the Future and save a reference to it.
+future = self.add_future(Futures.Metals.GOLD)
+
+# DataFrame example where the columns are the FutureUniverse attributes:
+history_df = self.history(FutureUniverse, future.symbol, 5, flatten=True)
+
+# Series example where the values are lists of FutureUniverse objects: 
+history_series = self.history(FutureUniverse, future.symbol, 5, flatten=False)
+
+# FutureUniverse objects example:
+history = self.history[FutureUniverse](future.symbol, 5)
    
+   
    // Add the Future and save a reference to it.
+var future = AddFuture(Futures.Metals.Gold);
+
+// Get historical data.
+var history = History<FutureUniverse>(future.Symbol, 5);
    
+  
    
+  
    
+   For more information about historical US Future Universe data in algorithms, see
+   
+    Contracts
+   
+   .
+  
    
+  
    
+   Historical Data In Research
+  
    
+  
    
+   To get historical US Future Universe data in the Research Environment, call the
+   
+    History<FutureUniverse>
+   
+   
+    history
+   
+   method with continuous Future
+   
+    Symbol
+   
+   and a time period. This method returns the all the available contracts for each trading day, not the subset of contracts that pass your universe filter. If there is no data for the period you requested, the history result is empty.
+  
    
+  
    
+   
    # Add the Future and save a reference to it.
+future = qb.add_future(Futures.Metals.GOLD)
+
+# DataFrame example where the columns are the FutureUniverse attributes:
+history_df = qb.history(FutureUniverse, future.symbol, datetime(2025, 1, 1), datetime(2025, 4, 1), flatten=True)
+
+# Series example where the values are lists of FutureUniverse objects: 
+history_series = qb.history(FutureUniverse, future.symbol, 5, flatten=False)
+
+# FutureUniverse objects example:
+history = qb.history[FutureUniverse](future.symbol, datetime(2025, 1, 1), datetime(2025, 4, 1))
    
+   
    // Add the Future and save a reference to it.
+var future = qb.AddFuture(Futures.Metals.Gold);
+
+// Get historical data.
+var history = qb.History<FutureUniverse>(future.Symbol, new DateTime(2025, 1, 1), new DateTime(2025, 4, 1));
    
+  
    
+  
    
+   For more information about historical US Future Universe data in the Research Environment, see
+   
+    Daily Prices History
+   
+   .
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   The following list shows the available (157) Futures:
+  
    
+  
      
+   
    • 
+    
+     Futures.Currencies.AUD
+    
+    : Australian Dollar Futures (CME: 6A)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.AUDCAD
+    
+    : Australian Dollar/Canadian Dollar Futures (CME: ACD)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.AUDJPY
+    
+    : Australian Dollar/Japanese Yen Futures (CME: AJY)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.AUDNZD
+    
+    : Australian Dollar/New Zealand Dollar Futures (CME: ANE)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.BRL
+    
+    : Brazillian Real Futures (CME: 6L)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.BTC
+    
+    : Bitcoin Futures (CME: BTC)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.BTICMicroBTC
+    
+    
+     Futures.Currencies.BTIC_MICRO_BTC
+    
+    : BTIC on Micro Bitcoin Futures (CME: MIB)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.BTICMicroEther
+    
+    
+     Futures.Currencies.BTIC_MICRO_ETHER
+    
+    : BTIC on Micro Ether Futures (CME: MRB)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.CAD
+    
+    : Canadian Dollar Futures (CME: 6C)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.CADJPY
+    
+    : Canadian Dollar/Japanese Yen Futures (CME: CJY)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.CHF
+    
+    : Swiss Franc Futures (CME: 6S)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.ETH
+    
+    : Ether Futures (CME: ETH)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EUR
+    
+    : Euro FX Futures (CME: 6E)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EURAUD
+    
+    : Euro/Australian Dollar Futures (CME: EAD)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EURCAD
+    
+    : Euro/Canadian Dollar Futures (CME: ECD)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EuroFXEmini
+    
+    
+     Futures.Currencies.EURO_FX_EMINI
+    
+    : E-mini Euro FX Futures (CME: E7)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EURSEK
+    
+    : Euro/Swedish Krona Futures (CME: ESK)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.GBP
+    
+    : British Pound Futures (CME: 6B)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.JapaneseYenEmini
+    
+    
+     Futures.Currencies.JAPANESE_YEN_EMINI
+    
+    : E-mini Japanese Yen Futures (CME: J7)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.JPY
+    
+    : Japanese Yen Futures (CME: 6J)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroAUD
+    
+    
+     Futures.Currencies.MICRO_AUD
+    
+    : Micro AUD/USD Futures (CME: M6A)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroBTC
+    
+    
+     Futures.Currencies.MICRO_BTC
+    
+    : Micro Bitcoin Futures (CME: MBT)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroCAD
+    
+    
+     Futures.Currencies.MICRO_CAD
+    
+    : Micro USD/CAD Futures (CME: M6C)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroCADUSD
+    
+    
+     Futures.Currencies.MICRO_CADUSD
+    
+    : Micro CAD/USD Futures (CME: MCD)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroCHF
+    
+    
+     Futures.Currencies.MICRO_CHF
+    
+    : Micro CHF/USD Futures (CME: MSF)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroEther
+    
+    
+     Futures.Currencies.MICRO_ETHER
+    
+    : Micro Ether Futures (CME: MET)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroEUR
+    
+    
+     Futures.Currencies.MICRO_EUR
+    
+    : Micro EUR/USD Futures (CME: M6E)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroGBP
+    
+    
+     Futures.Currencies.MICRO_GBP
+    
+    : Micro GBP/USD Futures (CME: M6B)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroINRUSD
+    
+    
+     Futures.Currencies.MICRO_INRUSD
+    
+    : Micro INR/USD Futures (CME: MIR)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroJPY
+    
+    
+     Futures.Currencies.MICRO_JPY
+    
+    : Micro JPY/USD Futures (CME: MJY)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroUSDCHF
+    
+    
+     Futures.Currencies.MICRO_USDCHF
+    
+    : Micro USD/CHF Futures (CME: M6S)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroUSDCNH
+    
+    
+     Futures.Currencies.MICRO_USDCNH
+    
+    : Micro USD/CNH Futures (CME: MNH)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroUSDJPY
+    
+    
+     Futures.Currencies.MICRO_USDJPY
+    
+    : Micro USD/JPY Futures (CME: M6J)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MXN
+    
+    : Mexican Peso Futures (CME: 6M)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.NZD
+    
+    : New Zealand Dollar Futures (CME: 6N)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.RUB
+    
+    : Russian Ruble Futures (CME: 6R)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.StandardSizeUSDOffshoreRMBCNH
+    
+    
+     Futures.Currencies.STANDARD_SIZE_USD_OFFSHORE_RMBCNH
+    
+    : Standard-Size USD/Offshore RMB (CNH) Futures (CME: CNH)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.ZAR
+    
+    : South African Rand Futures (CME: 6Z)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ArgusLLSvsWTIArgusTradeMonth
+    
+    
+     Futures.Energy.ARGUS_LL_SVS_WTI_ARGUS_TRADE_MONTH
+    
+    : Argus LLS vs. WTI (Argus) Trade Month Futures (NYMEX: AE5)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ArgusPropaneFarEastIndex
+    
+    
+     Futures.Energy.ARGUS_PROPANE_FAR_EAST_INDEX
+    
+    : Argus Propane Far East Index Futures (NYMEX: A7E)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ArgusPropaneSaudiAramco
+    
+    
+     Futures.Energy.ARGUS_PROPANE_SAUDI_ARAMCO
+    
+    : Argus Propane (Saudi Aramco) Futures (NYMEX: A9N)
+   
      • 
+   
    • 
+    
+     Futures.Energy.BrentCrudeOilVsDubaiCrudeOilPlatts
+    
+    
+     Futures.Energy.BRENT_CRUDE_OIL_VS_DUBAI_CRUDE_OIL_PLATTS
+    
+    : Brent Crude Oil vs. Dubai Crude Oil (Platts) Futures (NYMEX: ADB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.BrentLastDayFinancial
+    
+    
+     Futures.Energy.BRENT_LAST_DAY_FINANCIAL
+    
+    : Brent Last Day Financial Futures (NYMEX: BZ)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ChicagoEthanolPlatts
+    
+    
+     Futures.Energy.CHICAGO_ETHANOL_PLATTS
+    
+    : Chicago Ethaanol (Platts) Futures (NYMEX: CU)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ConwayPropaneOPIS
+    
+    
+     Futures.Energy.CONWAY_PROPANE_OPIS
+    
+    : Conway Propane (OPIS) Futures (NYMEX: A8K)
+   
      • 
+   
    • 
+    
+     Futures.Energy.CrudeOilWTI
+    
+    
+     Futures.Energy.CRUDE_OIL_WTI
+    
+    : Crude Oil WTI Futures (NYMEX: CL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.DubaiCrudeOilPlattsFinancial
+    
+    
+     Futures.Energy.DUBAI_CRUDE_OIL_PLATTS_FINANCIAL
+    
+    : Dubai Crude Oil (Platts) Financial Futures (NYMEX: DCB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EastWestGasolineSpreadPlattsArgus
+    
+    
+     Futures.Energy.EAST_WEST_GASOLINE_SPREAD_PLATTS_ARGUS
+    
+    : East-West Gasoline Spread (Platts-Argus) Futures (NYMEX: EWG)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EastWestNaphthaJapanCFvsCargoesCIFNWESpreadPlatts
+    
+    
+     Futures.Energy.EAST_WEST_NAPHTHA_JAPAN_C_FVS_CARGOES_CIFNWE_SPREAD_PLATTS
+    
+    : East-West Naphtha: Japan C&F vs. Cargoes CIF NWE Spread (Platts) Futures (NYMEX: EWN)
+   
      • 
+   
    • 
+    
+     Futures.Energy.Ethanol
+    
+    
+     Futures.Energy.ETHANOL
+    
+    : Ethanol Futures (CBOT: EH)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EthanolT2FOBRdamIncludingDutyPlatts
+    
+    
+     Futures.Energy.ETHANOL_T_2_FOB_RDAM_INCLUDING_DUTY_PLATTS
+    
+    : Ethanol T2 FOB Rdam Including Duty (Platts) Futures (NYMEX: AZ1)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EuropeanNaphthaPlattsCrackSpread
+    
+    
+     Futures.Energy.EUROPEAN_NAPHTHA_PLATTS_CRACK_SPREAD
+    
+    : European Naphtha (Platts) Crack Spread Futures (NYMEX: EN)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EuropeanPropaneCIFARAArgus
+    
+    
+     Futures.Energy.EUROPEAN_PROPANE_CIFARA_ARGUS
+    
+    : European Propane CIF ARA (Argus) Futures (NYMEX: APS)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EuropeanPropaneCIFARAArgusVsNaphthaCargoesCIFNWEPlatts
+    
+    
+     Futures.Energy.EUROPEAN_PROPANE_CIFARA_ARGUS_VS_NAPHTHA_CARGOES_CIFNWE_PLATTS
+    
+    : European Propane CIF ARA (Argus) vs. Naphtha Cargoes CIF NWE (Platts) Futures (NYMEX: EPN)
+   
      • 
+   
    • 
+    
+     Futures.Energy.FreightRouteTC14Baltic
+    
+    
+     Futures.Energy.FREIGHT_ROUTE_TC_14_BALTIC
+    
+    : Freight Route TC14 (Baltic) Futures (NYMEX: FRC)
+   
      • 
+   
    • 
+    
+     Futures.Energy.Gasoline
+    
+    
+     Futures.Energy.GASOLINE
+    
+    : Gasoline RBOB Futures (NYMEX: RB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GasolineEurobobOxyNWEBargesArgus
+    
+    
+     Futures.Energy.GASOLINE_EUROBOB_OXY_NWE_BARGES_ARGUS
+    
+    : Gasoline Euro-bob Oxy NWE Barges (Argus) Futures (NYMEX: B7H)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GroupThreeSuboctaneGasolinePlattsVsRBOB
+    
+    
+     Futures.Energy.GROUP_THREE_SUBOCTANE_GASOLINE_PLATTS_VS_RBOB
+    
+    : Group Three Sub-octane Gasoliine (Platts) vs. RBOB Futures (NYMEX: AA8)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GroupThreeULSDPlattsVsNYHarborULSD
+    
+    
+     Futures.Energy.GROUP_THREE_ULSD_PLATTS_VS_NY_HARBOR_ULSD
+    
+    : Group Three ULSD (Platts) vs. NY Harbor ULSD Futures (NYMEX: AA6)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GulfCoastCBOBGasolineA2PlattsVsRBOBGasoline
+    
+    
+     Futures.Energy.GULF_COAST_CBOB_GASOLINE_A_2_PLATTS_VS_RBOB_GASOLINE
+    
+    : Gulf Coast CBOB Gasoline A2 (Platts) vs. RBOB Gasoline Futures (NYMEX: CRB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GulfCoastHSFOPlattsVsEuropeanThreePointFivePercentFuelOilBargesFOBRdamPlatts
+    
+    
+     Futures.Energy.GULF_COAST_HSFO_PLATTS_VS_EUROPEAN_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
+    
+    : Gulf Coast HSFO (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: GCU)
+   
      • 
+   
    • 
+    
+     Futures.Energy.HeatingOil
+    
+    
+     Futures.Energy.HEATING_OIL
+    
+    : Heating Oil Futures (NYMEX: HO)
+   
      • 
+   
    • 
+    
+     Futures.Energy.LosAngelesCARBOBGasolineOPISvsRBOBGasoline
+    
+    
+     Futures.Energy.LOS_ANGELES_CARBOB_GASOLINE_OPI_SVS_RBOB_GASOLINE
+    
+    : Los Angeles CARBOB Gasoline (OPIS) vs. RBOB Gasoline Futures (NYMEX: AJL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.LosAngelesCARBDieselOPISvsNYHarborULSD
+    
+    
+     Futures.Energy.LOS_ANGELES_CARB_DIESEL_OPI_SVS_NY_HARBOR_ULSD
+    
+    : Los Angeles CARB Diesel (OPIS) vs. NY Harbor ULSD Futures (NYMEX: AKL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.LosAngelesJetOPISvsNYHarborULSD
+    
+    
+     Futures.Energy.LOS_ANGELES_JET_OPI_SVS_NY_HARBOR_ULSD
+    
+    : Los Angeles Jet (OPIS) vs. NY Harbor ULSD Futures (NYMEX: AJS)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MarsArgusVsWTIFinancial
+    
+    
+     Futures.Energy.MARS_ARGUS_VS_WTI_FINANCIAL
+    
+    : Mars (Argus) vs. WTI Financial Futures (NYMEX: AYX)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MarsArgusVsWTITradeMonth
+    
+    
+     Futures.Energy.MARS_ARGUS_VS_WTI_TRADE_MONTH
+    
+    : Mars (Argus) vs. WTI Trade Month Futures (NYMEX: AYV)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroCrudeOilWTI
+    
+    
+     Futures.Energy.MICRO_CRUDE_OIL_WTI
+    
+    : Micro WTI Crude Oil Futures (NYMEX: MCL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroEuropeanFOBRdamMarineFuelZeroPointFivePercentBargesPlatts
+    
+    
+     Futures.Energy.MICRO_EUROPEAN_FOB_RDAM_MARINE_FUEL_ZERO_POINT_FIVE_PERCENT_BARGES_PLATTS
+    
+    : Micro European FOB Rdam Marine Fuel 0.5% Barges (Platts) Futures (NYMEX: R5O)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroEuropeanThreePointFivePercentOilBargesFOBRdamPlatts
+    
+    
+     Futures.Energy.MICRO_EUROPEAN_THREE_POINT_FIVE_PERCENT_OIL_BARGES_FOB_RDAM_PLATTS
+    
+    : Micro European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: MEF)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroGasoilZeroPointOnePercentBargesFOBARAPlatts
+    
+    
+     Futures.Energy.MICRO_GASOIL_ZERO_POINT_ONE_PERCENT_BARGES_FOBARA_PLATTS
+    
+    : Micro Gasoil 0.1% Barges FOB ARA (Platts) Futures (NYMEX: M1B)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroSingaporeFOBMarineFuelZeroPointFivePercetPlatts
+    
+    
+     Futures.Energy.MICRO_SINGAPORE_FOB_MARINE_FUEL_ZERO_POINT_FIVE_PERCET_PLATTS
+    
+    : Micro Singapore FOB Marine Fuel 0.5% (Platts) Futures (NYMEX: S5O)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroSingaporeFuelOil380CSTPlatts
+    
+    
+     Futures.Energy.MICRO_SINGAPORE_FUEL_OIL_380_CST_PLATTS
+    
+    : Micro Singapore Fuel Oil 380CST (Platts) Futures (NYMEX: MAF)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MiniEuropeanThreePointPercentFiveFuelOilBargesPlatts
+    
+    
+     Futures.Energy.MINI_EUROPEAN_THREE_POINT_PERCENT_FIVE_FUEL_OIL_BARGES_PLATTS
+    
+    : Mini European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: A0D)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MiniSingaporeFuelOil180CstPlatts
+    
+    
+     Futures.Energy.MINI_SINGAPORE_FUEL_OIL_180_CST_PLATTS
+    
+    : Mini Singapore Fuel Oil 180 cst (Platts) Futures (NYMEX: A0F)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MontBelvieuEthaneOPIS
+    
+    
+     Futures.Energy.MONT_BELVIEU_ETHANE_OPIS
+    
+    : Mont Belvieu Ethane (OPIS) Futures (NYMEX: AC0)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MontBelvieuLDHPropaneOPIS
+    
+    
+     Futures.Energy.MONT_BELVIEU_LDH_PROPANE_OPIS
+    
+    : Mont Belvieu LDH Propane (OPIS) Futures (NYMEX: B0)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MontBelvieuNaturalGasolineOPIS
+    
+    
+     Futures.Energy.MONT_BELVIEU_NATURAL_GASOLINE_OPIS
+    
+    : Mont Belvieu Natural Gasoline (OPIS) Futures (NYMEX: A7Q)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MontBelvieuNormalButaneOPIS
+    
+    
+     Futures.Energy.MONT_BELVIEU_NORMAL_BUTANE_OPIS
+    
+    : Mont Belvieu Normal Butane (OPIS) Futures (NYMEX: AD0)
+   
      • 
+   
    • 
+    
+     Futures.Energy.NaturalGas
+    
+    
+     Futures.Energy.NATURAL_GAS
+    
+    : Natural Gas Futures (NYMEX: NG)
+   
      • 
+   
    • 
+    
+     Futures.Energy.NaturalGasHenryHubLastDayFinancial
+    
+    
+     Futures.Energy.NATURAL_GAS_HENRY_HUB_LAST_DAY_FINANCIAL
+    
+    : Natural Gas (Henry Hub) Last-day Financial Futures (NYMEX: HH)
+   
      • 
+   
    • 
+    
+     Futures.Energy.NaturalGasHenryHubPenultimateFinancial
+    
+    
+     Futures.Energy.NATURAL_GAS_HENRY_HUB_PENULTIMATE_FINANCIAL
+    
+    : Natural Gas (Henry Hub) Penultimate Financial Futures (NYMEX: HP)
+   
      • 
+   
    • 
+    
+     Futures.Energy.OnePercentFuelOilCargoesFOBNWEPlattsVsThreePointFivePercentFuelOilBargesFOBRdamPlatts
+    
+    
+     Futures.Energy.ONE_PERCENT_FUEL_OIL_CARGOES_FOBNWE_PLATTS_VS_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
+    
+    : 1% Fuel Oil Cargoes FOB NWE (Platts) vs. 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: FSS)
+   
      • 
+   
    • 
+    
+     Futures.Energy.PremiumUnleadedGasoline10ppmFOBMEDPlatts
+    
+    
+     Futures.Energy.PREMIUM_UNLEADED_GASOLINE_10_PPM_FOBMED_PLATTS
+    
+    : Premium Unleaded Gasoline 10 ppm FOB MED (Platts) Futures (NYMEX: A3G)
+   
      • 
+   
    • 
+    
+     Futures.Energy.PropaneNonLDHMontBelvieuOPIS
+    
+    
+     Futures.Energy.PROPANE_NON_LDH_MONT_BELVIEU_OPIS
+    
+    : Propane Non-LDH Mont Belvieu (OPIS) Futures (NYMEX: A1R)
+   
      • 
+   
    • 
+    
+     Futures.Energy.RBOBGasolineCrackSpread
+    
+    
+     Futures.Energy.RBOB_GASOLINE_CRACK_SPREAD
+    
+    : RBOB Gasoline Crack Spread Futures (NYMEX: ARE)
+   
      • 
+   
    • 
+    
+     Futures.Energy.RBOBGasolineVsEurobobOxyNWEBargesArgusThreeHundredFiftyThousandGallons
+    
+    
+     Futures.Energy.RBOB_GASOLINE_VS_EUROBOB_OXY_NWE_BARGES_ARGUS_THREE_HUNDRED_FIFTY_THOUSAND_GALLONS
+    
+    : RBOB Gasoline vs. Euro-bob Oxy NWE Barges (Argus) (350,000 gallons) Futures (NYMEX: EXR)
+   
      • 
+   
    • 
+    
+     Futures.Energy.SingaporeFuelOil380cstPlattsVsEuropeanThreePointFivePercentFuelOilBargesFOBRdamPlatts
+    
+    
+     Futures.Energy.SINGAPORE_FUEL_OIL_380_CST_PLATTS_VS_EUROPEAN_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
+    
+    : Singapore Fuel Oil 380 cst (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: EVC)
+   
      • 
+   
    • 
+    
+     Futures.Energy.SingaporeGasoilPlattsVsLowSulphurGasoilFutures
+    
+    
+     Futures.Energy.SINGAPORE_GASOIL_PLATTS_VS_LOW_SULPHUR_GASOIL_FUTURES
+    
+    : Singapore Gasoil (Platts) vs. Low Sulphur Gasoil Futures (NYMEX: AGA)
+   
      • 
+   
    • 
+    
+     Futures.Energy.SingaporeMogas92UnleadedPlattsBrentCrackSpread
+    
+    
+     Futures.Energy.SINGAPORE_MOGAS_92_UNLEADED_PLATTS_BRENT_CRACK_SPREAD
+    
+    : Singapore Mogas 92 Unleaded (Platts) Brent Crack Spread Futures (NYMEX: D1N)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ThreePointFivePercentFuelOilBargesFOBRdamPlattsCrackSpread
+    
+    
+     Futures.Energy.THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS_CRACK_SPREAD
+    
+    : 3.5% Fuel Oil Barges FOB Rdam (Platts) Crack Spread Futures (NYMEX: FO)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ThreePointFivePercentFuelOilBargesFOBRdamPlattsCrackSpread1000mt
+    
+    
+     Futures.Energy.THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS_CRACK_SPREAD_1000_MT
+    
+    : 3.5% Fuel Oil Barges FOB Rdam (Platts) Crack Spread (1000mt) Futures (NYMEX: BOO)
+   
      • 
+   
    • 
+    
+     Futures.Energy.WTIBrentFinancial
+    
+    
+     Futures.Energy.WTI_BRENT_FINANCIAL
+    
+    : WTI-Brent Financial Futures (NYMEX: BK)
+   
      • 
+   
    • 
+    
+     Futures.Energy.WTIFinancial
+    
+    
+     Futures.Energy.WTI_FINANCIAL
+    
+    : WTI Financial Futures (NYMEX: CSX)
+   
      • 
+   
    • 
+    
+     Futures.Energy.WTIHoustonArgusVsWTITradeMonth
+    
+    
+     Futures.Energy.WTI_HOUSTON_ARGUS_VS_WTI_TRADE_MONTH
+    
+    : WTI Houston (Argus) vs. WTI Trade Month Futures (NYMEX: HTT)
+   
      • 
+   
    • 
+    
+     Futures.Energy.WTIHoustonCrudeOil
+    
+    
+     Futures.Energy.WTI_HOUSTON_CRUDE_OIL
+    
+    : WTI Houston Crude Oil Futures (NYMEX: HCL)
+   
      • 
+   
    • 
+    
+     Futures.Financials.EuroDollar
+    
+    
+     Futures.Financials.EURO_DOLLAR
+    
+    : EuroDollar Futures (CME: GE)
+   
      • 
+   
    • 
+    
+     Futures.Financials.FiveYearUSDMACSwap
+    
+    
+     Futures.Financials.FIVE_YEAR_USDMAC_SWAP
+    
+    : 5-Year USD MAC Swap Futures (CBOT: F1U)
+   
      • 
+   
    • 
+    
+     Futures.Financials.MicroY10TreasuryNote
+    
+    
+     Futures.Financials.MICRO_Y_10_TREASURY_NOTE
+    
+    : Micro 10-Year Yield Futures (CBOT: 10Y)
+   
      • 
+   
    • 
+    
+     Futures.Financials.MicroY2TreasuryBond
+    
+    
+     Futures.Financials.MICRO_Y_2_TREASURY_BOND
+    
+    : Micro 2-Year Yield Futures (CBOT: 2YY)
+   
      • 
+   
    • 
+    
+     Futures.Financials.MicroY30TreasuryBond
+    
+    
+     Futures.Financials.MICRO_Y_30_TREASURY_BOND
+    
+    : Micro 30-Year Yield Futures (CBOT: 30Y)
+   
      • 
+   
    • 
+    
+     Futures.Financials.MicroY5TreasuryBond
+    
+    
+     Futures.Financials.MICRO_Y_5_TREASURY_BOND
+    
+    : Micro 5-Year Yield Futures (CBOT: 5YY)
+   
      • 
+   
    • 
+    
+     Futures.Financials.UltraTenYearUSTreasuryNote
+    
+    
+     Futures.Financials.ULTRA_TEN_YEAR_US_TREASURY_NOTE
+    
+    : Ultra 10-Year U.S. Treasury Note Futures (CBOT: TN)
+   
      • 
+   
    • 
+    
+     Futures.Financials.UltraUSTreasuryBond
+    
+    
+     Futures.Financials.ULTRA_US_TREASURY_BOND
+    
+    : Ultra U.S. Treasury Bond Futures (CBOT: UB)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y10TreasuryNote
+    
+    
+     Futures.Financials.Y_10_TREASURY_NOTE
+    
+    : 10Y U.S. Treasury Note Futures (CBOT: ZN)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y2TreasuryNote
+    
+    
+     Futures.Financials.Y_2_TREASURY_NOTE
+    
+    : 2Y U.S. Treasury Note Futures (CBOT: ZT)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y30TreasuryBond
+    
+    
+     Futures.Financials.Y_30_TREASURY_BOND
+    
+    : 30Y U.S. Treasury Bond Futures (CBOT: ZB)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y5TreasuryNote
+    
+    
+     Futures.Financials.Y_5_TREASURY_NOTE
+    
+    : 5Y U.S. Treasury Note Futures (CBOT: ZF)
+   
      • 
+   
    • 
+    
+     Futures.Forestry.Lumber
+    
+    
+     Futures.Forestry.LUMBER
+    
+    : Lumber Futures (CME: LBR)
+   
      • 
+   
    • 
+    
+     Futures.Forestry.RandomLengthLumber
+    
+    
+     Futures.Forestry.RANDOM_LENGTH_LUMBER
+    
+    : Random Length Lumber Futures (CME: LBS)
+   
      • 
+   
    • 
+    
+     Futures.Grains.BlackSeaCornFinanciallySettledPlatts
+    
+    
+     Futures.Grains.BLACK_SEA_CORN_FINANCIALLY_SETTLED_PLATTS
+    
+    : Black Sea Corn Financially Settled (Platts) Futures (CBOT: BCF)
+   
      • 
+   
    • 
+    
+     Futures.Grains.BlackSeaWheatFinanciallySettledPlatts
+    
+    
+     Futures.Grains.BLACK_SEA_WHEAT_FINANCIALLY_SETTLED_PLATTS
+    
+    : Black Sea Wheat Financially Settled (Platts) Futures (CBOT: BWF)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Corn
+    
+    
+     Futures.Grains.CORN
+    
+    : Corn Futures (CBOT: ZC)
+   
      • 
+   
    • 
+    
+     Futures.Grains.HRWWheat
+    
+    
+     Futures.Grains.HRW_WHEAT
+    
+    : KC HRW Wheat Futures (CBOT: KE)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Oats
+    
+    
+     Futures.Grains.OATS
+    
+    : Oats Futures (CBOT: ZO)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Soybeans
+    
+    
+     Futures.Grains.SOYBEANS
+    
+    : Soybeans Futures (CBOT: ZS)
+   
      • 
+   
    • 
+    
+     Futures.Grains.SoybeanMeal
+    
+    
+     Futures.Grains.SOYBEAN_MEAL
+    
+    : Soybean Meal Futures (CBOT: ZM)
+   
      • 
+   
    • 
+    
+     Futures.Grains.SoybeanOil
+    
+    
+     Futures.Grains.SOYBEAN_OIL
+    
+    : Soybean Oil Futures (CBOT: ZL)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Wheat
+    
+    
+     Futures.Grains.WHEAT
+    
+    : Default wheat contract is SRWWheat (CBOT: ZW)
+   
      • 
+   
    • 
+    
+     Futures.Indices.BloombergCommodityIndex
+    
+    
+     Futures.Indices.BLOOMBERG_COMMODITY_INDEX
+    
+    : Bloomberg Commodity Index Futures (CBOT: AW)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Dow30EMini
+    
+    
+     Futures.Indices.DOW_30_E_MINI
+    
+    : E-mini Dow Indu 30 Futures (CBOT: YM)
+   
      • 
+   
    • 
+    
+     Futures.Indices.DowJonesRealEstate
+    
+    
+     Futures.Indices.DOW_JONES_REAL_ESTATE
+    
+    : Dow Jones Real Estate futures on CME (CME: RX)
+   
      • 
+   
    • 
+    
+     Futures.Indices.FTSEEmergingEmini
+    
+    
+     Futures.Indices.FTSE_EMERGING_EMINI
+    
+    : E-mini FTSE Emerging Index Futures (CME: EI)
+   
      • 
+   
    • 
+    
+     Futures.Indices.MicroDow30EMini
+    
+    
+     Futures.Indices.MICRO_DOW_30_E_MINI
+    
+    : Micro E-mini Dow Jones Industrial Average Index Futures (CBOT: MYM)
+   
      • 
+   
    • 
+    
+     Futures.Indices.MicroNASDAQ100EMini
+    
+    
+     Futures.Indices.MICRO_NASDAQ_100_E_MINI
+    
+    : Micro E-mini Nasdaq-100 Index Futures (CME: MNQ)
+   
      • 
+   
    • 
+    
+     Futures.Indices.MicroRussell2000EMini
+    
+    
+     Futures.Indices.MICRO_RUSSELL_2000_E_MINI
+    
+    : Micro E-mini Russell 2000 Index Futures (CME: M2K)
+   
      • 
+   
    • 
+    
+     Futures.Indices.MicroSP500EMini
+    
+    
+     Futures.Indices.MICRO_SP_500_E_MINI
+    
+    : Micro E-mini S&P 500 Index Futures (CME: MES)
+   
      • 
+   
    • 
+    
+     Futures.Indices.NASDAQ100BiotechnologyEMini
+    
+    
+     Futures.Indices.NASDAQ_100_BIOTECHNOLOGY_E_MINI
+    
+    : E-mini Nasdaq-100 Biotechnology Index Futures (CME: BIO)
+   
      • 
+   
    • 
+    
+     Futures.Indices.NASDAQ100EMini
+    
+    
+     Futures.Indices.NASDAQ_100_E_MINI
+    
+    : E-mini NASDAQ 100 Futures (CME: NQ)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Nikkei225Dollar
+    
+    
+     Futures.Indices.NIKKEI_225_DOLLAR
+    
+    : Nikkei-225 Dollar Futures (CME: NKD)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Nikkei225YenCME
+    
+    
+     Futures.Indices.NIKKEI_225_YEN_CME
+    
+    : Nikkei-225 Yen denominated Futures on CME (CME: NIY)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Russell1000EMini
+    
+    
+     Futures.Indices.RUSSELL_1000_E_MINI
+    
+    : E-mini Russell 1000 futures on CME (CME: RS1)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Russell2000EMini
+    
+    
+     Futures.Indices.RUSSELL_2000_E_MINI
+    
+    : E-mini Russell 2000 Futures (CME: RTY)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SPGSCICommodity
+    
+    
+     Futures.Indices.SPGSCI_COMMODITY
+    
+    : S&P-GSCI Commodity Index Futures (CME: GD)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SP400MidCapEmini
+    
+    
+     Futures.Indices.SP_400_MID_CAP_EMINI
+    
+    : E-mini S&P MidCap 400 Futures (CME: EMD)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SP500AnnualDividendIndex
+    
+    
+     Futures.Indices.SP_500_ANNUAL_DIVIDEND_INDEX
+    
+    :  (CME: SDA)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SP500EMini
+    
+    
+     Futures.Indices.SP_500_E_MINI
+    
+    : E-mini S&P 500 Futures (CME: ES)
+   
      • 
+   
    • 
+    
+     Futures.Indices.TOPIXYEN
+    
+    : YEN Denominated Topix Index Futures on CME (CME: TPY)
+   
      • 
+   
    • 
+    
+     Futures.Indices.USDDenominatedIbovespa
+    
+    
+     Futures.Indices.USD_DENOMINATED_IBOVESPA
+    
+    : USD-Denominated Ibovespa Index Futures (CME: IBV)
+   
      • 
+   
    • 
+    
+     Futures.Indices.VIX
+    
+    : CBOE Volatility Index Futures (CFE: VX)
+   
      • 
+   
    • 
+    
+     Futures.Meats.FeederCattle
+    
+    
+     Futures.Meats.FEEDER_CATTLE
+    
+    : Feeder Cattle Futures (CME: GF)
+   
      • 
+   
    • 
+    
+     Futures.Meats.LeanHogs
+    
+    
+     Futures.Meats.LEAN_HOGS
+    
+    : Lean Hogs Futures (CME: HE)
+   
      • 
+   
    • 
+    
+     Futures.Meats.LiveCattle
+    
+    
+     Futures.Meats.LIVE_CATTLE
+    
+    : Live Cattle Futures (CME: LE)
+   
      • 
+   
    • 
+    
+     Futures.Metals.AluminiumEuropeanPremiumDutyPaidMetalBulletin
+    
+    
+     Futures.Metals.ALUMINIUM_EUROPEAN_PREMIUM_DUTY_PAID_METAL_BULLETIN
+    
+    : Aluminium European Premium Duty-Paid (Metal Bulletin) Futures (COMEX: EDP)
+   
      • 
+   
    • 
+    
+     Futures.Metals.AluminumMWUSTransactionPremiumPlatts25MT
+    
+    
+     Futures.Metals.ALUMINUM_MWUS_TRANSACTION_PREMIUM_PLATTS_25_MT
+    
+    : Aluminum MW U.S. Transaction Premium Platts (25MT) Futures (COMEX: AUP)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Copper
+    
+    
+     Futures.Metals.COPPER
+    
+    : Copper Futures (COMEX: HG)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Gold
+    
+    
+     Futures.Metals.GOLD
+    
+    : Gold Futures (COMEX: GC)
+   
      • 
+   
    • 
+    
+     Futures.Metals.MicroGold
+    
+    
+     Futures.Metals.MICRO_GOLD
+    
+    : Micro Gold Futures (COMEX: MGC)
+   
      • 
+   
    • 
+    
+     Futures.Metals.MicroGoldTAS
+    
+    
+     Futures.Metals.MICRO_GOLD_TAS
+    
+    : Micro Gold TAS Futures (COMEX: MGT)
+   
      • 
+   
    • 
+    
+     Futures.Metals.MicroPalladium
+    
+    
+     Futures.Metals.MICRO_PALLADIUM
+    
+    : Micro Palladium Futures (NYMEX: PAM)
+   
      • 
+   
    • 
+    
+     Futures.Metals.MicroSilver
+    
+    
+     Futures.Metals.MICRO_SILVER
+    
+    : Micro Silver Futures (COMEX: SIL)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Palladium
+    
+    
+     Futures.Metals.PALLADIUM
+    
+    : Palladium Futures (NYMEX: PA)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Platinum
+    
+    
+     Futures.Metals.PLATINUM
+    
+    : Platinum Futures (NYMEX: PL)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Silver
+    
+    
+     Futures.Metals.SILVER
+    
+    : Silver Futures (COMEX: SI)
+   
      • 
+   
    • 
+    
+     Futures.Metals.USMidwestDomesticHotRolledCoilSteelCRUIndex
+    
+    
+     Futures.Metals.US_MIDWEST_DOMESTIC_HOT_ROLLED_COIL_STEEL_CRU_INDEX
+    
+    : U.S. Midwest Domestic Hot-Rolled Coil Steel (CRU) Index Futures (NYMEX: HRC)
+   
      • 
+   
    • 
+    
+     Futures.Softs.Sugar11
+    
+    
+     Futures.Softs.SUGAR_11
+    
+    : Sugar #11 Futures ICE (ICE: SB)
+   
      • 
+   
    • 
+    
+     Futures.Softs.Sugar11CME
+    
+    
+     Futures.Softs.SUGAR_11_CME
+    
+    : Sugar #11 Futures CME (NYMEX: YO)
+   
      • 
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Future Universe dataset enables you to accurately design Futures strategies. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Buying the Futures contract with the most open interest to reduce slippage and market impact
+   
      • 
+   
    • 
+    Trading bull calendar spreads to reduce volatility and margin requirements
+   
      • 
+   
    • 
+    Trading bull calendar spreads to speculate contango
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm selects and buys the front-month Mini Gold Futures contract and sells the front-month Micro Gold Futures contract. When the front-month contract changes, the algorithm rebalances the portfolio.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class USFuturesDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(1000000) 
+        self.universe_settings.asynchronous = True
+        # Requesting data on gold and micro-gold contracts.
+        # Filter the universe to trade only the contracts expiring within 90 days to ensure liquidity.
+        self.mini_gold = self.add_future(Futures.Metals.GOLD) 
+        self.mini_gold.set_filter(0, 90)
+        
+        self.micro_gold = self.add_future(Futures.Metals.MICRO_GOLD) 
+        self.micro_gold.set_filter(0, 90)
+        
+        # Save a cache of the mapped symbol to trade.
+        self.contract = {self.mini_gold.symbol: None, self.micro_gold.symbol: None}
+    
+    def on_data(self, slice: Slice) -> None:
+        for symbol, chain in slice.future_chains.items():
+            if symbol in self.contract:
+                # Select the contract with the greatest open interest to trade with the most efficiency.
+                most_liquid_contract = sorted(chain, key=lambda contract: contract.open_interest, reverse=True)[0]
+                
+                if self.contract[symbol] is None or most_liquid_contract.symbol != self.contract[symbol].symbol:
+                    # Liquidate any unmapped contracts.
+                    if self.contract[symbol] is not None:
+                        self.liquidate(self.contract[symbol].symbol)
+                    self.contract[symbol] = most_liquid_contract
+                    
+                    # Buy mini-gold and short micro-gold contracts as planned.
+                    if symbol == self.mini_gold.symbol:
+                        self.market_order(self.contract[symbol].symbol, 1)
+                    elif symbol == self.micro_gold.symbol:
+                        self.market_order(self.contract[symbol].symbol, -1)
    
+   
    public class USFuturesDataAlgorithm : QCAlgorithm
+{
+    private Future _miniGold;
+    private Future _microGold;
+    // Save a cache of the mapped symbol to trade.
+    private Dictionary<Symbol, FuturesContract?> _contract = new ();
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(1000000);
+        UniverseSettings.Asynchronous = true;
+
+        // Requesting data on gold and micro-gold contracts.
+        // Filter the universe to trade only the contracts expiring within 90 days to ensure liquidity.
+        _miniGold = AddFuture(Futures.Metals.Gold);
+        _miniGold.SetFilter(0, 90);
+        _contract.Add(_miniGold.Symbol, null);
+        
+        _microGold = AddFuture(Futures.Metals.MicroGold);
+        _microGold.SetFilter(0, 90);
+        _contract.Add(_microGold.Symbol, null);
+    }
+
+    public override void OnData(Slice slice)
+    {
+        foreach (var (symbol, chain) in slice.FutureChains)
+        {
+            if (_contract.ContainsKey(symbol))
+            {
+                // Select the contract with the greatest open interest to trade with the most efficiency.
+                var mostLiquidContract = chain.OrderBy(x => x.OpenInterest).Last();
+                
+                if (_contract[symbol] == null || mostLiquidContract.Symbol != _contract[symbol].Symbol)
+                {
+                    // Liquidate any unmapped contracts.
+                    if (_contract[symbol] != null)
+                    {
+                        Liquidate(_contract[symbol].Symbol);
+                    }
+                    _contract[symbol] = mostLiquidContract;
+
+                    // Buy mini-gold and short micro-gold contracts as planned.
+                    if (symbol == _miniGold.Symbol)
+                    {
+                        MarketOrder(_contract[symbol].Symbol, 1);
+                    }
+                    else if (symbol == _microGold.Symbol)
+                    {
+                        MarketOrder(_contract[symbol].Symbol, -1);
+                    }
+                }
+            }
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm selects and buys the front-month Mini Gold Futures contract and sells the front-month Micro Gold Futures contract. When the front-month contract changes, the algorithm rebalances the portfolio.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class USFuturesDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        self.universe_settings.asynchronous = True
+        # Set up an universe selection model that selects the front month contract
+        self.set_universe_selection(FrontMonthFutureUniverseSelectionModel())
+        self.add_alpha(ConstantFuturesAlphaModel())
+        # A portfolio construction model that only order a single share per insight signal
+        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
+        
+class FrontMonthFutureUniverseSelectionModel(FutureUniverseSelectionModel):
+    def __init__(self,) -> None:
+        # Daily updating with select_future_chain_symbols function
+        super().__init__(timedelta(1), self.select_future_chain_symbols)
+
+    def select_future_chain_symbols(self, utcTime: datetime) -> List[Symbol]:
+        # Select gold and micro gold contracts for the strategy need
+        future_pairs = [
+            (Futures.Metals.GOLD, Market.COMEX),
+            (Futures.Metals.MICRO_GOLD, Market.COMEX)
+        ]
+        return [Symbol.create(pair[0], SecurityType.FUTURE, pair[1]) for pair in future_pairs]
+
+    def filter(self, filter: FutureFilterUniverse) -> FutureFilterUniverse:
+        # Filter only front month contract for liquidity and most informed information
+        return filter.front_month().only_apply_filter_at_market_open()
+
+class ConstantFuturesAlphaModel(AlphaModel):
+    # Long gold and short micro gold in this strategy
+    long_symbol = Symbol.create(Futures.Metals.GOLD, SecurityType.FUTURE, Market.COMEX)
+    short_symbol = Symbol.create(Futures.Metals.MICRO_GOLD, SecurityType.FUTURE, Market.COMEX)
+        
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        if algorithm.portfolio.invested:
+            return []
+        
+        insights = []
+        # For both gold and micro gold, select the front month contract (only contract) in the chain
+        for kvp in slice.future_chains:
+            chain = [contract for contract in kvp.value]
+            contract = chain[0]
+            
+            # Long gold and short micro gold as planned
+            if kvp.key == self.long_symbol:
+                insights.append(Insight.price(contract.symbol, contract.expiry + timedelta(days=1), InsightDirection.UP))
+            elif kvp.key == self.short_symbol:
+                insights.append(Insight.price(contract.symbol, contract.expiry + timedelta(days=1), InsightDirection.DOWN))
+        
+        return insights
+        
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            # Historical data
+            history = algorithm.history(security.symbol, 10, Resolution.MINUTE)
+            algorithm.debug(f"We got {len(history)} from our history request for {security.symbol}")
+
+class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
+    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
+        targets = []
+        for insight in insights:
+            if algorithm.securities[insight.symbol].is_tradable:
+                # Single share only using integer portfolio target
+                targets.append(PortfolioTarget(insight.symbol, insight.direction))
+        return targets
    
+   
    public class USFuturesDataAlgorithm : QCAlgorithm
+{        
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(1000000);
+        UniverseSettings.Asynchronous = true;
+        // Set up an universe selection model that selects the front month contract
+        SetUniverseSelection(new FrontMonthFutureUniverseSelectionModel());
+        SetAlpha(new ConstantFuturesAlphaModel());
+        // A portfolio construction model that only order a single share per insight signal
+        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
+    }
+}
+    
+class FrontMonthFutureUniverseSelectionModel : FutureUniverseSelectionModel
+{
+    // Daily updating with select_future_chain_symbols function
+    public FrontMonthFutureUniverseSelectionModel()
+        : base(TimeSpan.FromDays(1), SelectFutureChainSymbols) {}
+
+    private static IEnumerable<Symbol> SelectFutureChainSymbols(DateTime utcTime)
+    {
+        //Select gold and micro gold contracts for the strategy need
+        return new List<Symbol> {
+            QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX),
+            QuantConnect.Symbol.Create(Futures.Metals.MicroGold, SecurityType.Future, Market.COMEX)
+        };
+    }
+
+    protected override FutureFilterUniverse Filter(FutureFilterUniverse filter)
+    {
+        // Filter only front month contract for liquidity and most informed information
+        return filter.FrontMonth().OnlyApplyFilterAtMarketOpen();
+    }
+}
+
+
+class ConstantFuturesAlphaModel : AlphaModel
+{
+    // Long gold and short micro gold in this strategy
+    private Symbol 
+        _longSymbol = QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX), 
+        _shortSymbol = QuantConnect.Symbol.Create(Futures.Metals.MicroGold, SecurityType.Future, Market.COMEX);
+    
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    {
+        var insights = new List<Insight>();
+
+        if (algorithm.Portfolio.Invested)
+        {
+            return insights;
+        }
+        
+        // For both gold and micro gold, select the front month contract (only contract) in the chain
+        foreach (var kvp in slice.FutureChains)
+        {
+            var symbol = kvp.Key;
+            var chain = kvp.Value;
+            var contract = chain.First();
+            
+            // Long gold and short micro gold as planned
+            if (symbol == _longSymbol)
+            {
+                insights.Add(Insight.Price(contract.Symbol, contract.Expiry + TimeSpan.FromDays(1), InsightDirection.Up));
+            }
+            else if (symbol == _shortSymbol)
+            {
+                insights.Add(Insight.Price(contract.Symbol, contract.Expiry + TimeSpan.FromDays(1), InsightDirection.Down));
+            }
+        }
+
+        return insights;
+    }
+    
+    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
+    {
+        foreach (var security in changes.AddedSecurities)
+        {
+            // Historical data
+            var history = algorithm.History(security.Symbol, 100, Resolution.Minute);
+            algorithm.Debug($"We got {history.Count()} from our history request for {security.Symbol}");
+        }
+    }
+}
+    
+    
+class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
+{
+    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
+    {
+        var targets = new List<PortfolioTarget>();
+        foreach (var insight in insights)
+        {
+            if (algorithm.Securities[insight.Symbol].IsTradable)
+            {
+                // Single share only using integer portfolio target
+                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
+            }
+        }
+        return targets;
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Future Universe dataset provides
+   
+    FutureFilterUniverse
+   
+   and
+   
+    FutureUniverse
+   
+   objects.
+  
    
+  
    
+   FutureFilterUniverse Attributes
+  
    
+  
    
+   
+    FutureFilterUniverse
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   FutureUniverse Attributes
+  
    
+  
    
+   
+    FutureUniverse
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > US Futures Security Master
    
+
    
+    
    
+        
    QuantConnect
    
+        
    US Futures Security Master
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Futures Security Master dataset by QuantConnect provides mapping reference data for the most liquid contracts of the CME Group exchanges, calculated with popular rolling techniques. The data covers 157 root Future contracts, starts in May 2009, and is delivered on a daily frequency with a zip file with all the contract mappings. This dataset is created by daily processing of the US historical Future chains.
+  
    
+  
    
+   This dataset, paired with the US Futures dataset, supports the following rolling techniques: forward panama canal, backwards panama canal, and backwards ratio. You can set the specific rolling date to occur on the last trading day, first day month, or the day when the contract with the greatest open interest changes.
+  
    
+  
    
+   VIX Futures don't support continous contract rolling with open interest.
+  
    
+  
    
+   This is
+   
+    NOT
+   
+   the underlying Futures data (
+   
+    US Futures
+   
+   dataset), which you need to purchase separately with a license from AlgoSeek. This security master dataset is required to purchase the US Futures or US Future Options datasets.
+  
    
+  
    
+   For more information about the US Futures Security Master dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   You don't need any special code to utilize the US Futures Security Master. It automatically loads when you
+   
+    request US Futures data
+   
+   .
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      May 2009
+     
    
+      Asset Coverage
+     
+      157 Liquid Futures
+     
    
+      Data Density
+     
+      Regular
+     
    
+      Resolution
+     
+      Daily
+     
    
+  
    
+   VIX Futures don't support continous contract rolling with open interest.
+  
    
+ 
+
+
+
    Key Concept
    
+
+ 
+  
    
+   The US Futures Security Master lets you to construct continuous Futures, allowing the access of normalized historical data of the underlying assets, as well as trading the “lead” Future contracts for those assets.
+  
    
+  
    
+   Continuous Futures refer to sets of rolling lead Future contracts during their actively trading periods. Since Future contracts expire at their maturities, to analyze the historical price of a Future and to apply technical indicators, you need the continuous Future price series.
+  
    
+  
    
+   To access the continuous Future, use the Future's
+   
+    Symbol
+   
+   
+    symbol
+   
+   property.
+  
    
+  
    
+   
    # Set the data normalization mode and data mapping mode to select the contract as the continuous contract with price adjustment for smooth price series
+future = self.add_future(Futures.Energy.CRUDE_OIL_WTI,
+    data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
+    data_mapping_mode=DataMappingMode.OPEN_INTEREST,
+    contract_depth_offset=0)
+self.continuous_contract_symbol = future.symbol
    
+   
    // Set the data normalization mode and data mapping mode to select the contract as the continuous contract with price adjustment for smooth price series
+var future = AddFuture(Futures.Energy.CrudeOilWTI,
+    dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
+    dataMappingMode: DataMappingMode.OpenInterest,
+    contractDepthOffset: 0
+);
+_continuousFutureSymbol = future.Symbol;
    
+  
    
+  
    
+   The
+   
+    dataNormalizationMode
+   
+   and
+   
+    dataMappingMode
+   
+   arguments makes the transition of the underlying contracts seemless. However, the Future
+   
+    Symbol
+   
+   doesn't map to an underlying Future contract. It works fine to trade within backtests, but could be subjected to friction costs during live trading since the order price could be a normalized price. For more information about this topic, see the
+   
+    Live Trading Considerations
+   
+   section.
+  
    
+ 
+
+
+
    Data Normalization Modes
    
+
+ 
+  
    
+   The data normalization mode defines how the price series of two contracts are stitched together when the contract rollovers occur. The
+   
+    DataNormalizatoinMode
+   
+   enumeration has the following members available for continuous contracts:
+  
    
+  
    
+  
    
+  
    
+   If you use a data normalization mode that's not in the preceding list, LEAN automatically converts it to
+   
+    DataNormalizationMode.BackwardsRatio
+   
+   
+    DataNormalizationMode.BACKWARDS_RATIO
+   
+   .
+  
    
+ 
+
+
+
    Data Mapping Modes
    
+
+ 
+  
    
+   The data mapping mode defines when contract rollovers occur. The
+   
+    DataMappingMode
+   
+   enumeration has the following members:
+  
    
+  
    
+  
    
+  
    
+   VIX Futures don't support continous contract rolling with
+   
+    DataMappingMode.OpenInterest
+   
+   
+    DataMappingMode.OPEN_INTEREST
+   
+   and
+   
+    DataMappingMode.OpenInterestAnnuak
+   
+   
+    DataMappingMode.OPEN_INTEREST_ANNUAL
+   
+   .
+  
    
+ 
+
+
+
    Tracking Contract Changes
    
+
+ 
+  
    
+   As the contracts roll over, the
+   
+    Mapped
+   
+   
+    mapped
+   
+   property of the
+   
+    Future
+   
+   object references the next contract in the series and you receive a
+   
+    SymbolChangedEvent
+   
+   . To get the current
+   
+    Symbol
+   
+   change events, index the
+   
+    SymbolChangedEvents
+   
+   
+    symbol_changed_events
+   
+   property of the current
+   
+    
+     Slice
+    
+   
+   with the continuous Futures
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your Future at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Get Symbol Change Event of the Continuous Future (change in mapped contract)
+    changed_event = slice.symbol_changed_events.get(self.continuous_future_symbol)
+    if changed_event:
+        old_symbol = changed_event.old_symbol
+        new_symbol = self.add_future_contract(changed_event.new_symbol).symbol
+        tag = f"Rollover - Symbol changed at {self.time}: {old_symbol} -> {new_symbol}"
+        quantity = self.portfolio[old_symbol].quantity
+
+        # Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
+        self.liquidate(old_symbol, tag = tag)
+        if quantity != 0: self.market_order(new_symbol, quantity, tag = tag)
+        self.log(tag)
    
+   
    public override void OnData(Slice slice)
+{
+    // Get Symbol Change Event of the Continuous Future (change in mapped contract)
+    if (slice.SymbolChangedEvents.TryGetValue(_continuousFutureSymbol, out var changedEvent))
+    {
+        var oldSymbol = changedEvent.OldSymbol;
+        var newSymbol = AddFutureContract(changedEvent.NewSymbol).Symbol;
+        var tag = $"Rollover - Symbol changed at {Time}: {oldSymbol} -> {newSymbol}";
+        var quantity = Portfolio[oldSymbol].Quantity;
+        // Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
+        Liquidate(oldSymbol, tag: tag);
+        if (quantity != 0) MarketOrder(newSymbol, quantity, tag: tag);
+        Log(tag);
+    }
+}
    
+  
    
+  
    
+   
+    SymbolChangedEvent
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
    Live Trading Considerations
    
+
+ 
+  
    
+   You can trade continuous Futures, but the continuous Future
+   
+    Symbol
+   
+   doesn't map to a single underlying Future contract. Instead, it represents a set of rolling contracts. Thus, the prices could be frictional during a contract rollover, which could be catastrophic in live trading! For live trading, you should place your orders directly on the underlying contracts. To get the current underlying contract in the continuous Future series, use the
+   
+    Mapped
+   
+   
+    mapped
+   
+   property.
+  
    
+  
    
+   
    # Get the mapped symbol
+current_contract = self.continuous_contract.mapped
+self.buy(current_contract, 1)
    
+   
    // Get the mapped symbol
+var currentContract = _continuousContract.Mapped;
+Buy(currentContract, 1);
    
+  
    
+ 
+
+
+
    Data Format
    
+
+ 
+  
    
+   If you download the files in the US Futures Security Master, you get a factor file and a map file for each of the exchanges with supported continuous Futures. To view the files, see the
+   
+    \data\future\<exchange_name>
+   
+   directory under your LEAN CLI base directory.
+  
    
+  
    
+   For the factor file, it is a
+   
+    .zip
+   
+   collection of REST API styled
+   
+    .csv
+   
+   files for each Future
+   
+    Symbol
+   
+   , including the date, scaling factors for each type of data normalization and the data mapping mode that indicates the Symbol changing event is on that day for that mapping mode. The following line is an example line in the
+   
+    .csv
+   
+   file:
+  
    
+  
+   {"Date":"2009-10-31T00:00:00","BackwardsRatioScale":[0.9914163090128755364806866953,1.0,1.0],"BackwardsPanamaCanalScale":[-2.0,0.0,0.0],"ForwardPanamaCanalScale":[0.0,0.0,0.0],"DataMappingMode":1}
+  
+  
    
+  
    
+   For the map file, it is a
+   
+    .zip
+   
+   collection of
+   
+    .csv
+   
+   files for each Future
+   
+    Symbol
+   
+   , including the date, new underlying contract Symbol, the exchange code, and the data mapping mode that indicates the Symbol changing event is on that day for that mapping mode. The following line is an example line in the
+   
+    .csv
+   
+   file:
+  
    
+  
+   20091130,aw uii3j0m6zbj9,CBOT,1
+  
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical US Future Security Master data in an algorithm and the Research Environment.
+  
    
+  
    
+   Historical Data In Algorithms
+  
    
+  
    
+   To get historical US Future Security Master data in an algorithm, call the
+   
+    History<SymbolChangedEvent>
+   
+   
+    history
+   
+   method with continuous Future
+   
+    Symbol
+   
+   and a lookback period. This method returns the old and new contract of each rollover. If there is no data for the period you requested, the history result is empty.
+  
    
+  
    
+   
    # Add the Future and define its rollover settings.
+future = self.add_future(
+    Futures.Indices.SP_500_E_MINI,
+    data_mapping_mode=DataMappingMode.OPEN_INTEREST,
+    data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
+    contract_depth_offset=0
+)
+
+# DataFrame example where the columns are the SymbolChangedEvent attributes:
+history_df = self.history(SymbolChangedEvent, future.symbol, timedelta(365))
+
+# SymbolChangedEvent objects example:
+history = self.history[SymbolChangedEvent](future.symbol, timedelta(365))
    
+   
    // Add the Future and define its rollover settings.
+var future = AddFuture(
+    Futures.Indices.SP500EMini,
+    dataMappingMode: DataMappingMode.OpenInterest,
+    dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
+    contractDepthOffset: 0
+);
+
+// Get historical data.
+var history = History<SymbolChangedEvent>(future.Symbol, TimeSpan.FromDays(365));
    
+  
    
+  
    
+   For more information about historical US Future Security Master data in algorithms, see
+   
+    Symbol Changes
+   
+   .
+  
    
+  
    
+   Historical Data In Research
+  
    
+  
    
+   To get historical US Future Security Master data in the Research Environment, call the
+   
+    History<SymbolChangedEvent>
+   
+   
+    history
+   
+   method with continuous Future
+   
+    Symbol
+   
+   and a time period. This method returns the old and new contract of each rollover. If there is no data for the period you requested, the history result is empty.
+  
    
+  
    
+   
    # Add the Future and define its rollover settings.
+future = qb.add_future(
+    Futures.Indices.SP_500_E_MINI,
+    data_mapping_mode=DataMappingMode.OPEN_INTEREST,
+    data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
+    contract_depth_offset=0
+)
+
+# DataFrame example where the columns are the SymbolChangedEvent attributes:
+history_df = qb.history(SymbolChangedEvent, future.symbol, datetime(2023, 1, 1), datetime(2025, 1, 1))
+
+# SymbolChangedEvent objects example:
+history = qb.history[SymbolChangedEvent](future.symbol, datetime(2023, 1, 1), datetime(2025, 1, 1))
    
+   
    // Add the Future and define its rollover settings.
+var future = qb.AddFuture(
+    Futures.Indices.SP500EMini,
+    dataMappingMode: DataMappingMode.OpenInterest,
+    dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
+    contractDepthOffset: 0
+);
+
+// Get historical data.
+var history = qb.History<SymbolChangedEvent>(future.Symbol, new DateTime(2023, 1, 1), new DateTime(2025, 1, 1));
    
+  
    
+  
    
+   For more information about historical US Future Security Master data in the Research Environment, see
+   
+    Symbol Changes History
+   
+   .
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   The following list shows the available (157) Futures:
+  
    
+  
      
+   
    • 
+    
+     Futures.Currencies.AUD
+    
+    : Australian Dollar Futures (CME: 6A)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.AUDCAD
+    
+    : Australian Dollar/Canadian Dollar Futures (CME: ACD)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.AUDJPY
+    
+    : Australian Dollar/Japanese Yen Futures (CME: AJY)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.AUDNZD
+    
+    : Australian Dollar/New Zealand Dollar Futures (CME: ANE)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.BRL
+    
+    : Brazillian Real Futures (CME: 6L)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.BTC
+    
+    : Bitcoin Futures (CME: BTC)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.BTICMicroBTC
+    
+    
+     Futures.Currencies.BTIC_MICRO_BTC
+    
+    : BTIC on Micro Bitcoin Futures (CME: MIB)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.BTICMicroEther
+    
+    
+     Futures.Currencies.BTIC_MICRO_ETHER
+    
+    : BTIC on Micro Ether Futures (CME: MRB)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.CAD
+    
+    : Canadian Dollar Futures (CME: 6C)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.CADJPY
+    
+    : Canadian Dollar/Japanese Yen Futures (CME: CJY)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.CHF
+    
+    : Swiss Franc Futures (CME: 6S)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.ETH
+    
+    : Ether Futures (CME: ETH)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EUR
+    
+    : Euro FX Futures (CME: 6E)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EURAUD
+    
+    : Euro/Australian Dollar Futures (CME: EAD)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EURCAD
+    
+    : Euro/Canadian Dollar Futures (CME: ECD)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EuroFXEmini
+    
+    
+     Futures.Currencies.EURO_FX_EMINI
+    
+    : E-mini Euro FX Futures (CME: E7)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EURSEK
+    
+    : Euro/Swedish Krona Futures (CME: ESK)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.GBP
+    
+    : British Pound Futures (CME: 6B)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.JapaneseYenEmini
+    
+    
+     Futures.Currencies.JAPANESE_YEN_EMINI
+    
+    : E-mini Japanese Yen Futures (CME: J7)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.JPY
+    
+    : Japanese Yen Futures (CME: 6J)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroAUD
+    
+    
+     Futures.Currencies.MICRO_AUD
+    
+    : Micro AUD/USD Futures (CME: M6A)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroBTC
+    
+    
+     Futures.Currencies.MICRO_BTC
+    
+    : Micro Bitcoin Futures (CME: MBT)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroCAD
+    
+    
+     Futures.Currencies.MICRO_CAD
+    
+    : Micro USD/CAD Futures (CME: M6C)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroCADUSD
+    
+    
+     Futures.Currencies.MICRO_CADUSD
+    
+    : Micro CAD/USD Futures (CME: MCD)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroCHF
+    
+    
+     Futures.Currencies.MICRO_CHF
+    
+    : Micro CHF/USD Futures (CME: MSF)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroEther
+    
+    
+     Futures.Currencies.MICRO_ETHER
+    
+    : Micro Ether Futures (CME: MET)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroEUR
+    
+    
+     Futures.Currencies.MICRO_EUR
+    
+    : Micro EUR/USD Futures (CME: M6E)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroGBP
+    
+    
+     Futures.Currencies.MICRO_GBP
+    
+    : Micro GBP/USD Futures (CME: M6B)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroINRUSD
+    
+    
+     Futures.Currencies.MICRO_INRUSD
+    
+    : Micro INR/USD Futures (CME: MIR)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroJPY
+    
+    
+     Futures.Currencies.MICRO_JPY
+    
+    : Micro JPY/USD Futures (CME: MJY)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroUSDCHF
+    
+    
+     Futures.Currencies.MICRO_USDCHF
+    
+    : Micro USD/CHF Futures (CME: M6S)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroUSDCNH
+    
+    
+     Futures.Currencies.MICRO_USDCNH
+    
+    : Micro USD/CNH Futures (CME: MNH)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroUSDJPY
+    
+    
+     Futures.Currencies.MICRO_USDJPY
+    
+    : Micro USD/JPY Futures (CME: M6J)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MXN
+    
+    : Mexican Peso Futures (CME: 6M)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.NZD
+    
+    : New Zealand Dollar Futures (CME: 6N)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.RUB
+    
+    : Russian Ruble Futures (CME: 6R)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.StandardSizeUSDOffshoreRMBCNH
+    
+    
+     Futures.Currencies.STANDARD_SIZE_USD_OFFSHORE_RMBCNH
+    
+    : Standard-Size USD/Offshore RMB (CNH) Futures (CME: CNH)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.ZAR
+    
+    : South African Rand Futures (CME: 6Z)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ArgusLLSvsWTIArgusTradeMonth
+    
+    
+     Futures.Energy.ARGUS_LL_SVS_WTI_ARGUS_TRADE_MONTH
+    
+    : Argus LLS vs. WTI (Argus) Trade Month Futures (NYMEX: AE5)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ArgusPropaneFarEastIndex
+    
+    
+     Futures.Energy.ARGUS_PROPANE_FAR_EAST_INDEX
+    
+    : Argus Propane Far East Index Futures (NYMEX: A7E)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ArgusPropaneSaudiAramco
+    
+    
+     Futures.Energy.ARGUS_PROPANE_SAUDI_ARAMCO
+    
+    : Argus Propane (Saudi Aramco) Futures (NYMEX: A9N)
+   
      • 
+   
    • 
+    
+     Futures.Energy.BrentCrudeOilVsDubaiCrudeOilPlatts
+    
+    
+     Futures.Energy.BRENT_CRUDE_OIL_VS_DUBAI_CRUDE_OIL_PLATTS
+    
+    : Brent Crude Oil vs. Dubai Crude Oil (Platts) Futures (NYMEX: ADB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.BrentLastDayFinancial
+    
+    
+     Futures.Energy.BRENT_LAST_DAY_FINANCIAL
+    
+    : Brent Last Day Financial Futures (NYMEX: BZ)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ChicagoEthanolPlatts
+    
+    
+     Futures.Energy.CHICAGO_ETHANOL_PLATTS
+    
+    : Chicago Ethaanol (Platts) Futures (NYMEX: CU)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ConwayPropaneOPIS
+    
+    
+     Futures.Energy.CONWAY_PROPANE_OPIS
+    
+    : Conway Propane (OPIS) Futures (NYMEX: A8K)
+   
      • 
+   
    • 
+    
+     Futures.Energy.CrudeOilWTI
+    
+    
+     Futures.Energy.CRUDE_OIL_WTI
+    
+    : Crude Oil WTI Futures (NYMEX: CL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.DubaiCrudeOilPlattsFinancial
+    
+    
+     Futures.Energy.DUBAI_CRUDE_OIL_PLATTS_FINANCIAL
+    
+    : Dubai Crude Oil (Platts) Financial Futures (NYMEX: DCB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EastWestGasolineSpreadPlattsArgus
+    
+    
+     Futures.Energy.EAST_WEST_GASOLINE_SPREAD_PLATTS_ARGUS
+    
+    : East-West Gasoline Spread (Platts-Argus) Futures (NYMEX: EWG)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EastWestNaphthaJapanCFvsCargoesCIFNWESpreadPlatts
+    
+    
+     Futures.Energy.EAST_WEST_NAPHTHA_JAPAN_C_FVS_CARGOES_CIFNWE_SPREAD_PLATTS
+    
+    : East-West Naphtha: Japan C&F vs. Cargoes CIF NWE Spread (Platts) Futures (NYMEX: EWN)
+   
      • 
+   
    • 
+    
+     Futures.Energy.Ethanol
+    
+    
+     Futures.Energy.ETHANOL
+    
+    : Ethanol Futures (CBOT: EH)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EthanolT2FOBRdamIncludingDutyPlatts
+    
+    
+     Futures.Energy.ETHANOL_T_2_FOB_RDAM_INCLUDING_DUTY_PLATTS
+    
+    : Ethanol T2 FOB Rdam Including Duty (Platts) Futures (NYMEX: AZ1)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EuropeanNaphthaPlattsCrackSpread
+    
+    
+     Futures.Energy.EUROPEAN_NAPHTHA_PLATTS_CRACK_SPREAD
+    
+    : European Naphtha (Platts) Crack Spread Futures (NYMEX: EN)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EuropeanPropaneCIFARAArgus
+    
+    
+     Futures.Energy.EUROPEAN_PROPANE_CIFARA_ARGUS
+    
+    : European Propane CIF ARA (Argus) Futures (NYMEX: APS)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EuropeanPropaneCIFARAArgusVsNaphthaCargoesCIFNWEPlatts
+    
+    
+     Futures.Energy.EUROPEAN_PROPANE_CIFARA_ARGUS_VS_NAPHTHA_CARGOES_CIFNWE_PLATTS
+    
+    : European Propane CIF ARA (Argus) vs. Naphtha Cargoes CIF NWE (Platts) Futures (NYMEX: EPN)
+   
      • 
+   
    • 
+    
+     Futures.Energy.FreightRouteTC14Baltic
+    
+    
+     Futures.Energy.FREIGHT_ROUTE_TC_14_BALTIC
+    
+    : Freight Route TC14 (Baltic) Futures (NYMEX: FRC)
+   
      • 
+   
    • 
+    
+     Futures.Energy.Gasoline
+    
+    
+     Futures.Energy.GASOLINE
+    
+    : Gasoline RBOB Futures (NYMEX: RB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GasolineEurobobOxyNWEBargesArgus
+    
+    
+     Futures.Energy.GASOLINE_EUROBOB_OXY_NWE_BARGES_ARGUS
+    
+    : Gasoline Euro-bob Oxy NWE Barges (Argus) Futures (NYMEX: B7H)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GroupThreeSuboctaneGasolinePlattsVsRBOB
+    
+    
+     Futures.Energy.GROUP_THREE_SUBOCTANE_GASOLINE_PLATTS_VS_RBOB
+    
+    : Group Three Sub-octane Gasoliine (Platts) vs. RBOB Futures (NYMEX: AA8)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GroupThreeULSDPlattsVsNYHarborULSD
+    
+    
+     Futures.Energy.GROUP_THREE_ULSD_PLATTS_VS_NY_HARBOR_ULSD
+    
+    : Group Three ULSD (Platts) vs. NY Harbor ULSD Futures (NYMEX: AA6)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GulfCoastCBOBGasolineA2PlattsVsRBOBGasoline
+    
+    
+     Futures.Energy.GULF_COAST_CBOB_GASOLINE_A_2_PLATTS_VS_RBOB_GASOLINE
+    
+    : Gulf Coast CBOB Gasoline A2 (Platts) vs. RBOB Gasoline Futures (NYMEX: CRB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GulfCoastHSFOPlattsVsEuropeanThreePointFivePercentFuelOilBargesFOBRdamPlatts
+    
+    
+     Futures.Energy.GULF_COAST_HSFO_PLATTS_VS_EUROPEAN_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
+    
+    : Gulf Coast HSFO (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: GCU)
+   
      • 
+   
    • 
+    
+     Futures.Energy.HeatingOil
+    
+    
+     Futures.Energy.HEATING_OIL
+    
+    : Heating Oil Futures (NYMEX: HO)
+   
      • 
+   
    • 
+    
+     Futures.Energy.LosAngelesCARBOBGasolineOPISvsRBOBGasoline
+    
+    
+     Futures.Energy.LOS_ANGELES_CARBOB_GASOLINE_OPI_SVS_RBOB_GASOLINE
+    
+    : Los Angeles CARBOB Gasoline (OPIS) vs. RBOB Gasoline Futures (NYMEX: AJL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.LosAngelesCARBDieselOPISvsNYHarborULSD
+    
+    
+     Futures.Energy.LOS_ANGELES_CARB_DIESEL_OPI_SVS_NY_HARBOR_ULSD
+    
+    : Los Angeles CARB Diesel (OPIS) vs. NY Harbor ULSD Futures (NYMEX: AKL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.LosAngelesJetOPISvsNYHarborULSD
+    
+    
+     Futures.Energy.LOS_ANGELES_JET_OPI_SVS_NY_HARBOR_ULSD
+    
+    : Los Angeles Jet (OPIS) vs. NY Harbor ULSD Futures (NYMEX: AJS)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MarsArgusVsWTIFinancial
+    
+    
+     Futures.Energy.MARS_ARGUS_VS_WTI_FINANCIAL
+    
+    : Mars (Argus) vs. WTI Financial Futures (NYMEX: AYX)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MarsArgusVsWTITradeMonth
+    
+    
+     Futures.Energy.MARS_ARGUS_VS_WTI_TRADE_MONTH
+    
+    : Mars (Argus) vs. WTI Trade Month Futures (NYMEX: AYV)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroCrudeOilWTI
+    
+    
+     Futures.Energy.MICRO_CRUDE_OIL_WTI
+    
+    : Micro WTI Crude Oil Futures (NYMEX: MCL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroEuropeanFOBRdamMarineFuelZeroPointFivePercentBargesPlatts
+    
+    
+     Futures.Energy.MICRO_EUROPEAN_FOB_RDAM_MARINE_FUEL_ZERO_POINT_FIVE_PERCENT_BARGES_PLATTS
+    
+    : Micro European FOB Rdam Marine Fuel 0.5% Barges (Platts) Futures (NYMEX: R5O)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroEuropeanThreePointFivePercentOilBargesFOBRdamPlatts
+    
+    
+     Futures.Energy.MICRO_EUROPEAN_THREE_POINT_FIVE_PERCENT_OIL_BARGES_FOB_RDAM_PLATTS
+    
+    : Micro European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: MEF)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroGasoilZeroPointOnePercentBargesFOBARAPlatts
+    
+    
+     Futures.Energy.MICRO_GASOIL_ZERO_POINT_ONE_PERCENT_BARGES_FOBARA_PLATTS
+    
+    : Micro Gasoil 0.1% Barges FOB ARA (Platts) Futures (NYMEX: M1B)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroSingaporeFOBMarineFuelZeroPointFivePercetPlatts
+    
+    
+     Futures.Energy.MICRO_SINGAPORE_FOB_MARINE_FUEL_ZERO_POINT_FIVE_PERCET_PLATTS
+    
+    : Micro Singapore FOB Marine Fuel 0.5% (Platts) Futures (NYMEX: S5O)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroSingaporeFuelOil380CSTPlatts
+    
+    
+     Futures.Energy.MICRO_SINGAPORE_FUEL_OIL_380_CST_PLATTS
+    
+    : Micro Singapore Fuel Oil 380CST (Platts) Futures (NYMEX: MAF)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MiniEuropeanThreePointPercentFiveFuelOilBargesPlatts
+    
+    
+     Futures.Energy.MINI_EUROPEAN_THREE_POINT_PERCENT_FIVE_FUEL_OIL_BARGES_PLATTS
+    
+    : Mini European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: A0D)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MiniSingaporeFuelOil180CstPlatts
+    
+    
+     Futures.Energy.MINI_SINGAPORE_FUEL_OIL_180_CST_PLATTS
+    
+    : Mini Singapore Fuel Oil 180 cst (Platts) Futures (NYMEX: A0F)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MontBelvieuEthaneOPIS
+    
+    
+     Futures.Energy.MONT_BELVIEU_ETHANE_OPIS
+    
+    : Mont Belvieu Ethane (OPIS) Futures (NYMEX: AC0)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MontBelvieuLDHPropaneOPIS
+    
+    
+     Futures.Energy.MONT_BELVIEU_LDH_PROPANE_OPIS
+    
+    : Mont Belvieu LDH Propane (OPIS) Futures (NYMEX: B0)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MontBelvieuNaturalGasolineOPIS
+    
+    
+     Futures.Energy.MONT_BELVIEU_NATURAL_GASOLINE_OPIS
+    
+    : Mont Belvieu Natural Gasoline (OPIS) Futures (NYMEX: A7Q)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MontBelvieuNormalButaneOPIS
+    
+    
+     Futures.Energy.MONT_BELVIEU_NORMAL_BUTANE_OPIS
+    
+    : Mont Belvieu Normal Butane (OPIS) Futures (NYMEX: AD0)
+   
      • 
+   
    • 
+    
+     Futures.Energy.NaturalGas
+    
+    
+     Futures.Energy.NATURAL_GAS
+    
+    : Natural Gas Futures (NYMEX: NG)
+   
      • 
+   
    • 
+    
+     Futures.Energy.NaturalGasHenryHubLastDayFinancial
+    
+    
+     Futures.Energy.NATURAL_GAS_HENRY_HUB_LAST_DAY_FINANCIAL
+    
+    : Natural Gas (Henry Hub) Last-day Financial Futures (NYMEX: HH)
+   
      • 
+   
    • 
+    
+     Futures.Energy.NaturalGasHenryHubPenultimateFinancial
+    
+    
+     Futures.Energy.NATURAL_GAS_HENRY_HUB_PENULTIMATE_FINANCIAL
+    
+    : Natural Gas (Henry Hub) Penultimate Financial Futures (NYMEX: HP)
+   
      • 
+   
    • 
+    
+     Futures.Energy.OnePercentFuelOilCargoesFOBNWEPlattsVsThreePointFivePercentFuelOilBargesFOBRdamPlatts
+    
+    
+     Futures.Energy.ONE_PERCENT_FUEL_OIL_CARGOES_FOBNWE_PLATTS_VS_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
+    
+    : 1% Fuel Oil Cargoes FOB NWE (Platts) vs. 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: FSS)
+   
      • 
+   
    • 
+    
+     Futures.Energy.PremiumUnleadedGasoline10ppmFOBMEDPlatts
+    
+    
+     Futures.Energy.PREMIUM_UNLEADED_GASOLINE_10_PPM_FOBMED_PLATTS
+    
+    : Premium Unleaded Gasoline 10 ppm FOB MED (Platts) Futures (NYMEX: A3G)
+   
      • 
+   
    • 
+    
+     Futures.Energy.PropaneNonLDHMontBelvieuOPIS
+    
+    
+     Futures.Energy.PROPANE_NON_LDH_MONT_BELVIEU_OPIS
+    
+    : Propane Non-LDH Mont Belvieu (OPIS) Futures (NYMEX: A1R)
+   
      • 
+   
    • 
+    
+     Futures.Energy.RBOBGasolineCrackSpread
+    
+    
+     Futures.Energy.RBOB_GASOLINE_CRACK_SPREAD
+    
+    : RBOB Gasoline Crack Spread Futures (NYMEX: ARE)
+   
      • 
+   
    • 
+    
+     Futures.Energy.RBOBGasolineVsEurobobOxyNWEBargesArgusThreeHundredFiftyThousandGallons
+    
+    
+     Futures.Energy.RBOB_GASOLINE_VS_EUROBOB_OXY_NWE_BARGES_ARGUS_THREE_HUNDRED_FIFTY_THOUSAND_GALLONS
+    
+    : RBOB Gasoline vs. Euro-bob Oxy NWE Barges (Argus) (350,000 gallons) Futures (NYMEX: EXR)
+   
      • 
+   
    • 
+    
+     Futures.Energy.SingaporeFuelOil380cstPlattsVsEuropeanThreePointFivePercentFuelOilBargesFOBRdamPlatts
+    
+    
+     Futures.Energy.SINGAPORE_FUEL_OIL_380_CST_PLATTS_VS_EUROPEAN_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
+    
+    : Singapore Fuel Oil 380 cst (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: EVC)
+   
      • 
+   
    • 
+    
+     Futures.Energy.SingaporeGasoilPlattsVsLowSulphurGasoilFutures
+    
+    
+     Futures.Energy.SINGAPORE_GASOIL_PLATTS_VS_LOW_SULPHUR_GASOIL_FUTURES
+    
+    : Singapore Gasoil (Platts) vs. Low Sulphur Gasoil Futures (NYMEX: AGA)
+   
      • 
+   
    • 
+    
+     Futures.Energy.SingaporeMogas92UnleadedPlattsBrentCrackSpread
+    
+    
+     Futures.Energy.SINGAPORE_MOGAS_92_UNLEADED_PLATTS_BRENT_CRACK_SPREAD
+    
+    : Singapore Mogas 92 Unleaded (Platts) Brent Crack Spread Futures (NYMEX: D1N)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ThreePointFivePercentFuelOilBargesFOBRdamPlattsCrackSpread
+    
+    
+     Futures.Energy.THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS_CRACK_SPREAD
+    
+    : 3.5% Fuel Oil Barges FOB Rdam (Platts) Crack Spread Futures (NYMEX: FO)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ThreePointFivePercentFuelOilBargesFOBRdamPlattsCrackSpread1000mt
+    
+    
+     Futures.Energy.THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS_CRACK_SPREAD_1000_MT
+    
+    : 3.5% Fuel Oil Barges FOB Rdam (Platts) Crack Spread (1000mt) Futures (NYMEX: BOO)
+   
      • 
+   
    • 
+    
+     Futures.Energy.WTIBrentFinancial
+    
+    
+     Futures.Energy.WTI_BRENT_FINANCIAL
+    
+    : WTI-Brent Financial Futures (NYMEX: BK)
+   
      • 
+   
    • 
+    
+     Futures.Energy.WTIFinancial
+    
+    
+     Futures.Energy.WTI_FINANCIAL
+    
+    : WTI Financial Futures (NYMEX: CSX)
+   
      • 
+   
    • 
+    
+     Futures.Energy.WTIHoustonArgusVsWTITradeMonth
+    
+    
+     Futures.Energy.WTI_HOUSTON_ARGUS_VS_WTI_TRADE_MONTH
+    
+    : WTI Houston (Argus) vs. WTI Trade Month Futures (NYMEX: HTT)
+   
      • 
+   
    • 
+    
+     Futures.Energy.WTIHoustonCrudeOil
+    
+    
+     Futures.Energy.WTI_HOUSTON_CRUDE_OIL
+    
+    : WTI Houston Crude Oil Futures (NYMEX: HCL)
+   
      • 
+   
    • 
+    
+     Futures.Financials.EuroDollar
+    
+    
+     Futures.Financials.EURO_DOLLAR
+    
+    : EuroDollar Futures (CME: GE)
+   
      • 
+   
    • 
+    
+     Futures.Financials.FiveYearUSDMACSwap
+    
+    
+     Futures.Financials.FIVE_YEAR_USDMAC_SWAP
+    
+    : 5-Year USD MAC Swap Futures (CBOT: F1U)
+   
      • 
+   
    • 
+    
+     Futures.Financials.MicroY10TreasuryNote
+    
+    
+     Futures.Financials.MICRO_Y_10_TREASURY_NOTE
+    
+    : Micro 10-Year Yield Futures (CBOT: 10Y)
+   
      • 
+   
    • 
+    
+     Futures.Financials.MicroY2TreasuryBond
+    
+    
+     Futures.Financials.MICRO_Y_2_TREASURY_BOND
+    
+    : Micro 2-Year Yield Futures (CBOT: 2YY)
+   
      • 
+   
    • 
+    
+     Futures.Financials.MicroY30TreasuryBond
+    
+    
+     Futures.Financials.MICRO_Y_30_TREASURY_BOND
+    
+    : Micro 30-Year Yield Futures (CBOT: 30Y)
+   
      • 
+   
    • 
+    
+     Futures.Financials.MicroY5TreasuryBond
+    
+    
+     Futures.Financials.MICRO_Y_5_TREASURY_BOND
+    
+    : Micro 5-Year Yield Futures (CBOT: 5YY)
+   
      • 
+   
    • 
+    
+     Futures.Financials.UltraTenYearUSTreasuryNote
+    
+    
+     Futures.Financials.ULTRA_TEN_YEAR_US_TREASURY_NOTE
+    
+    : Ultra 10-Year U.S. Treasury Note Futures (CBOT: TN)
+   
      • 
+   
    • 
+    
+     Futures.Financials.UltraUSTreasuryBond
+    
+    
+     Futures.Financials.ULTRA_US_TREASURY_BOND
+    
+    : Ultra U.S. Treasury Bond Futures (CBOT: UB)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y10TreasuryNote
+    
+    
+     Futures.Financials.Y_10_TREASURY_NOTE
+    
+    : 10Y U.S. Treasury Note Futures (CBOT: ZN)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y2TreasuryNote
+    
+    
+     Futures.Financials.Y_2_TREASURY_NOTE
+    
+    : 2Y U.S. Treasury Note Futures (CBOT: ZT)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y30TreasuryBond
+    
+    
+     Futures.Financials.Y_30_TREASURY_BOND
+    
+    : 30Y U.S. Treasury Bond Futures (CBOT: ZB)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y5TreasuryNote
+    
+    
+     Futures.Financials.Y_5_TREASURY_NOTE
+    
+    : 5Y U.S. Treasury Note Futures (CBOT: ZF)
+   
      • 
+   
    • 
+    
+     Futures.Forestry.Lumber
+    
+    
+     Futures.Forestry.LUMBER
+    
+    : Lumber Futures (CME: LBR)
+   
      • 
+   
    • 
+    
+     Futures.Forestry.RandomLengthLumber
+    
+    
+     Futures.Forestry.RANDOM_LENGTH_LUMBER
+    
+    : Random Length Lumber Futures (CME: LBS)
+   
      • 
+   
    • 
+    
+     Futures.Grains.BlackSeaCornFinanciallySettledPlatts
+    
+    
+     Futures.Grains.BLACK_SEA_CORN_FINANCIALLY_SETTLED_PLATTS
+    
+    : Black Sea Corn Financially Settled (Platts) Futures (CBOT: BCF)
+   
      • 
+   
    • 
+    
+     Futures.Grains.BlackSeaWheatFinanciallySettledPlatts
+    
+    
+     Futures.Grains.BLACK_SEA_WHEAT_FINANCIALLY_SETTLED_PLATTS
+    
+    : Black Sea Wheat Financially Settled (Platts) Futures (CBOT: BWF)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Corn
+    
+    
+     Futures.Grains.CORN
+    
+    : Corn Futures (CBOT: ZC)
+   
      • 
+   
    • 
+    
+     Futures.Grains.HRWWheat
+    
+    
+     Futures.Grains.HRW_WHEAT
+    
+    : KC HRW Wheat Futures (CBOT: KE)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Oats
+    
+    
+     Futures.Grains.OATS
+    
+    : Oats Futures (CBOT: ZO)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Soybeans
+    
+    
+     Futures.Grains.SOYBEANS
+    
+    : Soybeans Futures (CBOT: ZS)
+   
      • 
+   
    • 
+    
+     Futures.Grains.SoybeanMeal
+    
+    
+     Futures.Grains.SOYBEAN_MEAL
+    
+    : Soybean Meal Futures (CBOT: ZM)
+   
      • 
+   
    • 
+    
+     Futures.Grains.SoybeanOil
+    
+    
+     Futures.Grains.SOYBEAN_OIL
+    
+    : Soybean Oil Futures (CBOT: ZL)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Wheat
+    
+    
+     Futures.Grains.WHEAT
+    
+    : Default wheat contract is SRWWheat (CBOT: ZW)
+   
      • 
+   
    • 
+    
+     Futures.Indices.BloombergCommodityIndex
+    
+    
+     Futures.Indices.BLOOMBERG_COMMODITY_INDEX
+    
+    : Bloomberg Commodity Index Futures (CBOT: AW)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Dow30EMini
+    
+    
+     Futures.Indices.DOW_30_E_MINI
+    
+    : E-mini Dow Indu 30 Futures (CBOT: YM)
+   
      • 
+   
    • 
+    
+     Futures.Indices.DowJonesRealEstate
+    
+    
+     Futures.Indices.DOW_JONES_REAL_ESTATE
+    
+    : Dow Jones Real Estate futures on CME (CME: RX)
+   
      • 
+   
    • 
+    
+     Futures.Indices.FTSEEmergingEmini
+    
+    
+     Futures.Indices.FTSE_EMERGING_EMINI
+    
+    : E-mini FTSE Emerging Index Futures (CME: EI)
+   
      • 
+   
    • 
+    
+     Futures.Indices.MicroDow30EMini
+    
+    
+     Futures.Indices.MICRO_DOW_30_E_MINI
+    
+    : Micro E-mini Dow Jones Industrial Average Index Futures (CBOT: MYM)
+   
      • 
+   
    • 
+    
+     Futures.Indices.MicroNASDAQ100EMini
+    
+    
+     Futures.Indices.MICRO_NASDAQ_100_E_MINI
+    
+    : Micro E-mini Nasdaq-100 Index Futures (CME: MNQ)
+   
      • 
+   
    • 
+    
+     Futures.Indices.MicroRussell2000EMini
+    
+    
+     Futures.Indices.MICRO_RUSSELL_2000_E_MINI
+    
+    : Micro E-mini Russell 2000 Index Futures (CME: M2K)
+   
      • 
+   
    • 
+    
+     Futures.Indices.MicroSP500EMini
+    
+    
+     Futures.Indices.MICRO_SP_500_E_MINI
+    
+    : Micro E-mini S&P 500 Index Futures (CME: MES)
+   
      • 
+   
    • 
+    
+     Futures.Indices.NASDAQ100BiotechnologyEMini
+    
+    
+     Futures.Indices.NASDAQ_100_BIOTECHNOLOGY_E_MINI
+    
+    : E-mini Nasdaq-100 Biotechnology Index Futures (CME: BIO)
+   
      • 
+   
    • 
+    
+     Futures.Indices.NASDAQ100EMini
+    
+    
+     Futures.Indices.NASDAQ_100_E_MINI
+    
+    : E-mini NASDAQ 100 Futures (CME: NQ)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Nikkei225Dollar
+    
+    
+     Futures.Indices.NIKKEI_225_DOLLAR
+    
+    : Nikkei-225 Dollar Futures (CME: NKD)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Nikkei225YenCME
+    
+    
+     Futures.Indices.NIKKEI_225_YEN_CME
+    
+    : Nikkei-225 Yen denominated Futures on CME (CME: NIY)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Russell1000EMini
+    
+    
+     Futures.Indices.RUSSELL_1000_E_MINI
+    
+    : E-mini Russell 1000 futures on CME (CME: RS1)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Russell2000EMini
+    
+    
+     Futures.Indices.RUSSELL_2000_E_MINI
+    
+    : E-mini Russell 2000 Futures (CME: RTY)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SPGSCICommodity
+    
+    
+     Futures.Indices.SPGSCI_COMMODITY
+    
+    : S&P-GSCI Commodity Index Futures (CME: GD)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SP400MidCapEmini
+    
+    
+     Futures.Indices.SP_400_MID_CAP_EMINI
+    
+    : E-mini S&P MidCap 400 Futures (CME: EMD)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SP500AnnualDividendIndex
+    
+    
+     Futures.Indices.SP_500_ANNUAL_DIVIDEND_INDEX
+    
+    :  (CME: SDA)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SP500EMini
+    
+    
+     Futures.Indices.SP_500_E_MINI
+    
+    : E-mini S&P 500 Futures (CME: ES)
+   
      • 
+   
    • 
+    
+     Futures.Indices.TOPIXYEN
+    
+    : YEN Denominated Topix Index Futures on CME (CME: TPY)
+   
      • 
+   
    • 
+    
+     Futures.Indices.USDDenominatedIbovespa
+    
+    
+     Futures.Indices.USD_DENOMINATED_IBOVESPA
+    
+    : USD-Denominated Ibovespa Index Futures (CME: IBV)
+   
      • 
+   
    • 
+    
+     Futures.Indices.VIX
+    
+    : CBOE Volatility Index Futures (CFE: VX)
+   
      • 
+   
    • 
+    
+     Futures.Meats.FeederCattle
+    
+    
+     Futures.Meats.FEEDER_CATTLE
+    
+    : Feeder Cattle Futures (CME: GF)
+   
      • 
+   
    • 
+    
+     Futures.Meats.LeanHogs
+    
+    
+     Futures.Meats.LEAN_HOGS
+    
+    : Lean Hogs Futures (CME: HE)
+   
      • 
+   
    • 
+    
+     Futures.Meats.LiveCattle
+    
+    
+     Futures.Meats.LIVE_CATTLE
+    
+    : Live Cattle Futures (CME: LE)
+   
      • 
+   
    • 
+    
+     Futures.Metals.AluminiumEuropeanPremiumDutyPaidMetalBulletin
+    
+    
+     Futures.Metals.ALUMINIUM_EUROPEAN_PREMIUM_DUTY_PAID_METAL_BULLETIN
+    
+    : Aluminium European Premium Duty-Paid (Metal Bulletin) Futures (COMEX: EDP)
+   
      • 
+   
    • 
+    
+     Futures.Metals.AluminumMWUSTransactionPremiumPlatts25MT
+    
+    
+     Futures.Metals.ALUMINUM_MWUS_TRANSACTION_PREMIUM_PLATTS_25_MT
+    
+    : Aluminum MW U.S. Transaction Premium Platts (25MT) Futures (COMEX: AUP)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Copper
+    
+    
+     Futures.Metals.COPPER
+    
+    : Copper Futures (COMEX: HG)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Gold
+    
+    
+     Futures.Metals.GOLD
+    
+    : Gold Futures (COMEX: GC)
+   
      • 
+   
    • 
+    
+     Futures.Metals.MicroGold
+    
+    
+     Futures.Metals.MICRO_GOLD
+    
+    : Micro Gold Futures (COMEX: MGC)
+   
      • 
+   
    • 
+    
+     Futures.Metals.MicroGoldTAS
+    
+    
+     Futures.Metals.MICRO_GOLD_TAS
+    
+    : Micro Gold TAS Futures (COMEX: MGT)
+   
      • 
+   
    • 
+    
+     Futures.Metals.MicroPalladium
+    
+    
+     Futures.Metals.MICRO_PALLADIUM
+    
+    : Micro Palladium Futures (NYMEX: PAM)
+   
      • 
+   
    • 
+    
+     Futures.Metals.MicroSilver
+    
+    
+     Futures.Metals.MICRO_SILVER
+    
+    : Micro Silver Futures (COMEX: SIL)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Palladium
+    
+    
+     Futures.Metals.PALLADIUM
+    
+    : Palladium Futures (NYMEX: PA)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Platinum
+    
+    
+     Futures.Metals.PLATINUM
+    
+    : Platinum Futures (NYMEX: PL)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Silver
+    
+    
+     Futures.Metals.SILVER
+    
+    : Silver Futures (COMEX: SI)
+   
      • 
+   
    • 
+    
+     Futures.Metals.USMidwestDomesticHotRolledCoilSteelCRUIndex
+    
+    
+     Futures.Metals.US_MIDWEST_DOMESTIC_HOT_ROLLED_COIL_STEEL_CRU_INDEX
+    
+    : U.S. Midwest Domestic Hot-Rolled Coil Steel (CRU) Index Futures (NYMEX: HRC)
+   
      • 
+   
    • 
+    
+     Futures.Softs.Sugar11
+    
+    
+     Futures.Softs.SUGAR_11
+    
+    : Sugar #11 Futures ICE (ICE: SB)
+   
      • 
+   
    • 
+    
+     Futures.Softs.Sugar11CME
+    
+    
+     Futures.Softs.SUGAR_11_CME
+    
+    : Sugar #11 Futures CME (NYMEX: YO)
+   
      • 
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Futures Security Master enables you to design strategies harnessing continuous Futures contracts. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Trading cyclical patterns in commodity Futures.
+   
      • 
+   
    • 
+    Buying gold Futures as an inflation hedge with automatic contract roll-overs.
+   
      • 
+   
    • 
+    Detecting arbitrage opportunities between index Futures and Equities.
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm buys the continuous Future contract for Crude Oil when its price rises above its simple moving average. When its price drops below its simple moving average, the algorithm sells the continuous contract. Each time the continuous Future contract rolls over, the algorithm logs the event.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class USFuturesSecurityMasterDataClassicAlgorithm(QCAlgorithm):
+    # Define a threshold for the SMA cross.
+    _threshold = 0.01
+    
+    def initialize(self) -> None:
+        self.set_cash(1000000)
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        # Seed the price of each asset with its last known price to 
+        # avoid trading errors.
+        self.settings.seed_initial_prices = True
+        # Add a Future. Set the continuous contract mapping criteria to
+        # the contract with highest open interest to provide the best 
+        # price information for trend estimation.
+        self._future = self.add_future(
+            Futures.Energy.CRUDE_OIL_WTI,
+            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
+            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
+            contract_depth_offset=0
+        )                      
+        # Historical data: Get the contract rollovers for the trailing 
+        # year.
+        history = self.history(
+            SymbolChangedEvent, self._future.symbol, timedelta(365)
+        )
+        # Set up an SMA indicator for to estimate the trend direction.
+        self._sma = self.sma(self._future.symbol, 10, Resolution.DAILY)
+        # Warm up the SMA indicator so we can trade right away.
+        self.warm_up_indicator(self._future.symbol, self._sma)
+
+    def on_data(self, slice: Slice) -> None:
+        # Wait until we have the latest TradeBar and the indicator is 
+        # ready.
+        if not (self._future.symbol in slice.bars and self._sma.is_ready):
+            return
+        contract = self.portfolio[self._future.mapped]
+        # Long during an uptrend.
+        if (not contract.is_long and 
+            self._future.price > self._sma.current.value * (1+self._threshold)):
+            quantity = 1
+        # Short during an downtrend.
+        elif (not contract.is_short and 
+            self._future.price < self._sma.current.value * (1-self._threshold)):
+            quantity = -1
+        else:
+            return
+        self.market_order(self._future.mapped, quantity - contract.quantity)
+
+    def on_symbol_changed_events(self, symbol_changed_events):
+        for symbol, changed_event in symbol_changed_events.items():
+            old_symbol = changed_event.old_symbol
+            new_symbol = changed_event.new_symbol
+            # The quantity to roll over should be consistent.
+            quantity = self.portfolio[old_symbol].quantity
+            if not quantity:
+                continue
+            # Rolling over: Liquidate the old mapped contract and switch
+            # to the new mapped contract.
+            tag = f"Rollover: {old_symbol} -> {new_symbol}"
+            self.liquidate(old_symbol, tag=tag)
+            self.market_order(new_symbol, quantity, tag=tag)
    
+   
    public class USFuturesSecurityMasterDataClassicAlgorithm  : QCAlgorithm
+{
+    private Future _future;
+    // Define a threshold for the SMA cross.
+    private decimal _threshold = 0.01m;
+    private SimpleMovingAverage _sma;
+    
+    public override void Initialize()
+    {
+        SetCash(1000000);
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        // Seed the price of each asset with its last known price to 
+        // avoid trading errors.
+        Settings.SeedInitialPrices = true;
+        // Add a Future. Set the continuous contract mapping criteria to
+        // the contract with highest open interest to provide the best 
+        // price information for trend estimation.
+        _future = AddFuture(
+            Futures.Energy.CrudeOilWTI,
+            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
+            dataMappingMode: DataMappingMode.OpenInterest,
+            contractDepthOffset: 0
+        );
+        // Historical data: Get the contract rollovers for the trailing 
+        // year.
+        var history = History<SymbolChangedEvent>(_future.Symbol, TimeSpan.FromDays(365));
+        foreach (var symbolChangedEvent in history)
+        {
+            var t = symbolChangedEvent.EndTime;
+            var oldSymbol = symbolChangedEvent.OldSymbol;
+            var newSymbol = symbolChangedEvent.NewSymbol;
+        }
+        // Set up an SMA indicator for to estimate the trend direction.
+        _sma = SMA(_future.Symbol, 10, Resolution.Daily);
+        // Warm up the SMA indicator so we can trade right away.
+        WarmUpIndicator(_future.Symbol, _sma);
+    }
+    
+    public override void OnData(Slice slice)
+    {       
+        // Wait until we have the latest TradeBar and the indicator is 
+        // ready.
+        if (!(slice.Bars.ContainsKey(_future.Symbol) && _sma.IsReady))
+        {
+            return;
+        }
+        var contract = Portfolio[_future.Mapped];
+        int quantity;
+        // Long during an uptrend.
+        if (!contract.IsLong && _future.Price > _sma.Current.Value * (1+_threshold))
+        {
+            quantity = 1;
+        }
+        // Short during an downtrend.
+        else if (!contract.IsShort && _future.Price < _sma.Current.Value * (1-_threshold))
+        {
+            quantity = -1;
+        }
+        else
+        {
+            return;
+        }
+        MarketOrder(_future.Mapped, quantity - contract.Quantity);
+    }
+
+    public override void OnSymbolChangedEvents(SymbolChangedEvents symbolChangedEvents)
+    {
+        foreach (var (symbol, changedEvent) in symbolChangedEvents)
+        {
+            var oldSymbol = changedEvent.OldSymbol;
+            var newSymbol = changedEvent.NewSymbol;
+            // The quantity to roll over should be consistent.
+            var quantity = Portfolio[oldSymbol].Quantity;
+            if (quantity == 0)
+            {
+                continue;
+            }
+            // Rolling over: Liquidate the old mapped contract and switch 
+            // to the new mapped contract.
+            var tag = $"Rollover: {oldSymbol} -> {newSymbol}";
+            Liquidate(oldSymbol, tag: tag);
+            MarketOrder(newSymbol, quantity, tag: tag);
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm buys the continuous Future contract for Crude Oil when its price rises above its simple moving average. When its price drops below its simple moving average, the algorithm sells the continuous contract. Each time the contract rolls over, the algorithm logs the event.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class USFuturesSecurityMasterDataFrameworkAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_cash(1000000)
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+
+        # Setting the continuous contract mapping criteria, the contract with highest open interest provide the best price information for trend estimation
+        self.add_future(Futures.Energies.CRUDE_OIL_WTI,
+                       data_normalization_mode = DataNormalizationMode.BACKWARDS_RATIO,
+                       data_mapping_mode = DataMappingMode.OPEN_INTEREST,
+                       contract_depth_offset = 0)
+        self.add_alpha(ContinuousFuturesAlphaModel())
+        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
+
+class ContinuousFuturesAlphaModel(AlphaModel):
+    # 1% margin to reassure trend direction
+    threshold = 0.01
+    symbol = None
+    continuous_contract = None
+    
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        insights = []
+        # Check if contract data available, since all trades are based on that only
+        if self.continuous_contract_symbol is None or self.continuous_contract is None:
+            return insights 
+        
+        # Up-to-date handling of switching the mapped contract for trade liquidity
+        for symbol, changed_event in slice.symbol_changed_events.items():
+            old_symbol = changed_event.old_symbol
+            if algorithm.insights.has_active_insights(old_symbol, algorithm.utc_time):
+                new_symbol = changed_event.new_symbol
+                tag = f"Rollover - Symbol changed at {algorithm.time}: {old_symbol} -> {new_symbol}"
+                last_insight = sorted(algorithm.insights[old_symbol], key=lambda x: x.close_time_utc)[-1]
+                insights.append(Insight.price(new_symbol, last_insight.close_time_utc, last_insight.direction, tag= tag))
+                algorithm.insights.clear([old_symbol])
+                algorithm.log(tag)
+         
+        mapped_symbol = self.continuous_contract.mapped
+        # Make sure trade decisions are based on newly received data
+        if not slice.bars.contains_key(self.continuous_contract_symbol) or not self.sma.is_ready or not mapped_symbol:
+            return insights 
+        
+        direction = None
+        # Long if trend up by threshold to follow the trend
+        if slice.bars[self.continuous_contract_symbol].price > self.sma.current.value * (1+self.threshold) and not algorithm.portfolio[mapped_symbol].is_long:
+            direction = InsightDirection.UP
+        # Short if trend down by threshold to follow the trend
+        elif slice.bars[self.continuous_contract_symbol].price < self.sma.current.value * (1-self.threshold) and not algorithm.portfolio[mapped_symbol].is_short:
+            direction = InsightDirection.DOWN
+        
+        if direction:
+            insights.append(Insight.price(mapped_symbol, timedelta(days=14), direction))
+        
+        return insights
+        
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            symbol = security.symbol
+            # Set up SMA indicator for trend direction estimator, only for the canonical symbol
+            if symbol.is_canonical():
+                self.continuous_contract = security
+                self.continuous_contract_symbol = symbol
+                self.sma = algorithm.SMA(self.continuous_contract_symbol, 10, Resolution.DAILY)
+            
+                # Historical data
+                history = algorithm.history(symbol, 60*24*10, Resolution.MINUTE)
+                algorithm.debug(f"We got {len(history)} from our history request for {symbol}")
+                
+                if history.empty:
+                    continue
+                # Warm up the SMA indicator for its readiness for immediate use
+                for time, row in history.droplevel(0).loc[self.continuous_contract_symbol].iterrows():
+                    self.sma.update(IndicatorDataPoint(time, row.close))
+
+class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
+    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
+        targets = []
+        for insight in insights:
+            if algorithm.securities[insight.symbol].is_tradable:
+                targets.append(PortfolioTarget(insight.symbol, insight.direction))
+        return targets
    
+   
    public class USFuturesSecurityMasterDataFrameworkAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetCash(1000000);
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+
+        // Setting the continuous contract mapping criteria, the contract with highest open interest provide the best price information for trend estimation
+        AddFuture(Futures.Energies.CrudeOilWTI,
+            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
+            dataMappingMode: DataMappingMode.OpenInterest,
+            contractDepthOffset: 0
+        );
+        AddAlpha(new ContinuousFuturesAlphaModel());
+        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
+    }
+    
+    class ContinuousFuturesAlphaModel : AlphaModel
+    {
+        private Future _continuousContract;
+        private Symbol _symbol;
+        // 1% margin to reassure trend direction
+        private decimal _threshold = 0.01m;
+        private SimpleMovingAverage _sma;
+    
+        public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+        {
+            var insights = new List<Insight>();
+            
+            //  Check if contract data available, since all trades are based on that only
+            if (_symbol == null || _continuousContract == null)
+            {
+                return insights;
+            }
+            
+            // Up-to-date handling of switching the mapped contract for trade liquidity
+            foreach (var (symbol, changedEvent) in slice.SymbolChangedEvents)
+            {
+                var oldSymbol = changedEvent.OldSymbol;
+                if (algorithm.Insights.HasActiveInsights(oldSymbol, algorithm.UtcTime))
+                {
+                    var newSymbol = changedEvent.NewSymbol;
+                    var tag = $"Rollover - Symbol changed at {algorithm.Time}: {oldSymbol} -> {newSymbol}";
+                    var lastInsight = algorithm.Insights[oldSymbol].OrderBy(x => x.CloseTimeUtc).LastOrDefault();
+                    insights.Add(Insight.Price(newSymbol, lastInsight.CloseTimeUtc, lastInsight.Direction, tag: tag));
+                    algorithm.Insights.Clear(new Symbol[] { oldSymbol });
+                    algorithm.Log(tag);
+                }
+            }
+            
+            var mappedSymbol = _continuousContract.Mapped;
+            // Make sure trade decisions are based on newly received data
+            if (!slice.Bars.ContainsKey(_symbol) || !_sma.IsReady || mappedSymbol == null)
+            {
+                return insights;
+            }
+            
+            // Long if trend up by threshold to follow the trend
+            if (slice.Bars[_symbol].Price > _sma.Current.Value * (1+_threshold) && !algorithm.Portfolio[mappedSymbol].IsLong)
+            {
+                insights.Add(Insight.Price(mappedSymbol, TimeSpan.FromDays(14), InsightDirection.Up));
+            }
+            // Short if trend down by threshold to follow the trend
+            else if (slice.Bars[_symbol].Price < _sma.Current.Value * (1-_threshold) && !algorithm.Portfolio[mappedSymbol].IsShort)
+            {
+                insights.Add(Insight.Price(mappedSymbol, TimeSpan.FromDays(14), InsightDirection.Down));
+            }
+            
+            return insights;
+        }
+    
+        public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
+        {
+            foreach (var security in changes.AddedSecurities)
+            {
+                var symbol = security.Symbol;
+                // Set up SMA indicator for trend direction estimator, only for the canonical symbol
+                if (symbol.IsCanonical())
+                {
+                    _continuousContract = (Future)security;
+                    _symbol = symbol;
+                    _sma = algorithm.SMA(_symbol, 10, Resolution.Daily);
+                    
+                    // Historical data 
+                    var history = algorithm.History(symbol, 60*24*10, Resolution.Minute);
+                    algorithm.Debug($"We got {history.Count()} from our history request for {symbol}");
+                    
+                    // Warm up the SMA indicator for its readiness for immediate use
+                    foreach (var bar in history)
+                    {
+                        _sma.Update(new IndicatorDataPoint(bar.Time, bar.Close));
+                    }
+                }
+            }
+        }
+    }
+    
+    class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
+    {
+        public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
+        {
+            var targets = new List<PortfolioTarget>();
+            foreach (var insight in insights)
+            {
+                if (algorithm.Securities[insight.Symbol].IsTradable)
+                {
+                    targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
+                }
+            }
+            return targets;
+        }
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Futures Security Master dataset provides
+   
+    SymbolChangedEvent
+   
+   objects, which have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > QuantConnect > US Index Option Universe
    
+
    
+    
    
+        
    QuantConnect
    
+        
    US Index Option Universe
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Index Option Universe dataset by QuantConnect lists the available US Index Options contracts and the current Implied Volatility and Greeks. The data covers European Option contracts for 3 US Indices: SPX, VIX, and NDX. It starts in January 2012 and is delivered on a daily update frequency. To create this dataset, we use
+   
+    our implementation
+   
+   of the forward tree pricing model, which accounts for the interest rate, dividend payments, and daily closing prices. The values in this dataset are the same values you can get from daily
+   
+    indicators
+   
+   with mirror Options.
+  
    
+  
    
+   This dataset
+   
+    does not
+   
+   contain market data. For market data, see
+   
+    US Index Options by AlgoSeek
+   
+   .
+  
    
+  
    
+   For more information about the US Index Option Universe dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   QuantConnect was founded in 2012 to serve quants everywhere with the best possible algorithmic trading technology. Seeking to disrupt a notoriously closed-source industry, QuantConnect takes a radically open-source approach to algorithmic trading. Through the QuantConnect web platform, more than 50,000 quants are served every month.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the US Index Options Universe dataset:
+  
    
+  
    
+   
    option = self.add_index_option('VIX')
+option.set_filter(lambda universe: universe.delta(0.4, 0.6))
+self.option_symbol = option.symbol
    
+   
    var option = AddIndexOption("VIX");
+option.SetFilter(universe => universe.delta(0.4m, 0.6m));
+_optionSymbol = option.Symbol;
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 2012
+     
    
+      Asset Coverage
+     
+      7 Index Options
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+ 
+
+
+
    Specification Over Time
    
+
+ 
+  
    
+   According to the SPX Options
+   
+    contract specification
+   
+   , some SPX contracts expire every month and SPXW contracts expires every day. Before 2021, you could only trade SPX contracts with the following expiration dates:
+  
    
+  
      
+   
    • 
+    Expires within the next 4 months
+   
      • 
+   
    • 
+    Expires in September within the next 14 months
+   
      • 
+   
    • 
+    Expires in January, March, or June within the next 2 years
+   
      • 
+   
    • 
+    Expires in December within the next 3 years
+   
      • 
+  
    
+  
    
+   During this time, SPXW didn't have 0DTE every day.
+  
    
+  
    
+   
+    Sources
+   
+   :
+   
    
+   -
+   
+    Cboe Options Exchange to List Three Long-Dated SPX Options Expirations, Beginning November 1, 2021
+   
+   
    
+   -
+   
+    S&P 500 Weekly Options Now Expire Five Days a Week
+   
+  
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add US Index Options Universe data to your algorithm, call the
+   
+    AddIndexOption
+   
+   
+    add_index_option
+   
+   method. Save a reference to the Index Option
+   
+    Symbol
+   
+   so you can access the data later in your algorithm. To define which contracts should be in your universe, call the
+   
+    SetFilter
+   
+   
+    set_filter
+   
+   method of the
+   
+    IndexOption
+   
+   object.
+  
    
+  
    
+   The
+   
+    AddIndexOption
+   
+   
+    add_index_option
+   
+   method provides a daily stream of Option chain data. To get the most recent daily chain, call the
+   
+    OptionChain
+   
+   
+    option_chain
+   
+   method with the canonical Index Option Symbol. The
+   
+    OptionChain
+   
+   
+    option_chain
+   
+   method returns data on all the tradable contracts, not just the contracts that pass your universe filter.
+  
    
+  
    
+   
    class IndexOptionsDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2021, 1, 1)
+        self.set_end_date(2021, 6, 1)
+        self.set_cash(1_000_000)
+        self.universe_settings.asynchronous = True
+        self.index_symbol = self.add_index('VIX').symbol
+        
+        standard_option = self.add_index_option(self.index_symbol)
+        standard_option.set_filter(self._option_filter)
+        self.standard_option_symbol = standard_option.symbol
+        standard_chain = self.option_chain(standard_option.symbol, flatten=True).data_frame
+
+        weekly_option = self.add_index_option(self.index_symbol, "VIXW")
+        weekly_option.set_filter(self._option_filter)
+        self.weekly_option_symbol = weekly_option.symbol
+        weekly_chain = self.option_chain(weekly_option.symbol, flatten=True).data_frame
+        weekly_chain = weekly_chain[
+            weekly_chain.index.map(lambda symbol: not IndexOptionSymbol.is_standard(symbol))
+        ]
+        
+
+    def _option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
+        # Contracts can be filtered by greeks, implied volatility, open interest:
+        return universe \
+            .delta(0.5, 1.5) \
+            .gamma(0.0001, 0.0006) \
+            .vega(0.01, 1.5) \
+            .theta(-2.0, -0.5) \
+            .rho(0.5, 3.0) \
+            .implied_volatility(1, 3) \
+            .open_interest(100,500)
    
+   
    public class IndexOptionsDataAlgorithm : QCAlgorithm
+{
+    private Symbol _indexSymbol, _standardOptionSymbol, _weeklyOptionSymbol;
+
+    public override void Initialize()
+    {
+        SetStartDate(2021, 1, 1);
+        SetEndDate(2021, 6, 1);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = true;
+        _indexSymbol = AddIndex("VIX").Symbol;
+
+        var standardOption = AddIndexOption(_indexSymbol);
+        standardOption.SetFilter(OptionFilter);
+        _standardOptionSymbol = standardOption.Symbol;
+        var standardChain = OptionChain(_standardOptionSymbol);
+
+        var weeklyOption = AddIndexOption(_indexSymbol, "VIXW");
+        weeklyOption.SetFilter(OptionFilter);
+        _weeklyOptionSymbol = weeklyOption.Symbol;
+        var weeklyChain = OptionChain(_weeklyOptionSymbol)
+            .Where(contract => !IndexOptionSymbol.IsStandard(contract.Symbol));
+    }
+
+    private OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
+    {
+        // Contracts can be filtered by greeks, implied volatility, open interest:
+        return universe
+            .Delta(0.5m, 1.5m)
+            .Gamma(0.0001m, 0.0006m)
+            .Vega(0.01m, 1.5m)
+            .Theta(-2.0m, -0.5m)
+            .Rho(0.5m, 3.0m)
+            .ImpliedVolatility(1.0m, 3.0m)
+            .OpenInterest(100, 500);
+    }
+}
    
+  
    
+  
    
+   The Index resolution must be less than or equal to the Index Option resolution. For example, if you set the Index resolution to minute, then you must set the Index Option resolution to minute, hour, or daily.
+  
    
+  
    
+   For more information about creating US Index Option subscriptions, see
+   
+    Index Options
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   For information about accessing US Equity Options data, see
+   
+    Handling Data
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical US Index Options data in an algorithm and the Research Environment.
+  
    
+  
    
+   Historical Data In Algorithms
+  
    
+  
    
+   To get historical US Index Options Universe data in an algorithm, call the
+   
+    History<OptionUniverse>
+   
+   
+    history
+   
+   method with the canonical Index Option
+   
+    Symbol
+   
+   . This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter. If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame
+history_df = self.history(self.standard_option_symbol, timedelta(3))
+
+# OptionUniverse objects
+history = self.history[OptionUniverse](self.standard_option_symbol, timedelta(3))
    
+   
    // OptionUniverse objects 
+var history = History<OptionUniverse>(_standardOptionSymbol, TimeSpan.FromDays(3)).ToList();
    
+  
    
+  
    
+   For more information about Index Options Universe data in algorithms, see
+   
+    Historical Data
+   
+   .
+  
    
+  
    
+   Historical Data In Research
+  
    
+  
    
+   To get historical US Index Options Universe data in the Research Environment, call the
+   
+    History<OptionUniverse>
+   
+   
+    history
+   
+   method with the canonical Option
+   
+    Symbol
+   
+   . This method returns data on all of the tradable contracts, not just the contracts that pass your universe filter.
+  
    
+  
    
+   
    qb = QuantBook()
+index_symbol = qb.add_index('VIX').symbol
+option = qb.add_index_option(index_symbol) # or qb.add_index_option(index_symbol, "VIXW")
+history = qb.history(option.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5))
    
+   
    var qb = new QuantBook();
+var indexSymbol = qb.AddIndex("VIX").Symbol;
+var option = qb.AddIndexOption(indexSymbol); // or qb.AddIndexOption(indexSymbol, "VIXW");
+var history = qb.History<OptionUniverse>(option.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 6));
+foreach (var chain in history)
+{
+    var endTime = chain.EndTime;
+    var filteredContracts = chain.Data
+        .Select(contract => contract as OptionUniverse)
+        .Where(contract => contract.Greeks.Delta > 0.3m);
+    foreach (var contract in filteredContracts)
+    {
+        var price = contract.Price;
+        var iv = contract.ImpliedVolatility;
+    }
+}
    
+  
    
+  
    
+   For more information about historical Index Options Universe data in the Research Environment, see
+   
+    Universes
+   
+   .
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   The following table shows the available Index Options:
+  
    
+  
+   
+    
+     
+     
+     
+     
+     
+     
+    
+   
+   
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+   
+  
    
+      Underlying Index
+     
+      Underlying Ticker
+     
+      Target Ticker
+     
+      Standard Contracts
+     
+      Weekly Contracts
+     
+      Tradable on Expiry Day
+     
    
+      NASDAQ-100
+     
+      NDX
+     
+     
+      green check
+     
+     
+     
    
+      NASDAQ-100
+     
+      NDX
+     
+      NDXP
+     
+      green check
+     
+     
+      green check
+     
    
+      NASDAQ-100
+     
+      NDX
+     
+      NQX
+     
+      green check
+     
+      green check
+     
+      green check
+     
    
+      Russell 2000
+     
+      RUT
+     
+     
+      green check
+     
+     
+     
    
+      Russell 2000
+     
+      RUT
+     
+      RUTW
+     
+     
+      green check
+     
+      green check
+     
    
+      S&P500
+     
+      SPX
+     
+     
+      green check
+     
+     
+     
    
+      S&P500
+     
+      SPX
+     
+      SPXW
+     
+     
+      green check
+     
+      green check
+     
    
+      S&P500
+     
+      VIX
+     
+     
+      green check
+     
+     
+     
    
+      S&P500
+     
+      VIX
+     
+      VIXW
+     
+     
+      green check
+     
+     
    
+  
    
+   For more information about each underlying Index, see
+   
+    Supported Indices
+   
+   .
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Index Options Universe dataset enables you to accurately design strategies for Index Options. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Buying VIX call Options to hedge against upcoming volatility
+   
      • 
+   
    • 
+    Buying VIX put Options to capture the natural downward price movement in the VIX index
+   
      • 
+   
    • 
+    Buying SPX put Options to protect against downward price movement in the S&P 500
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm subscribes to SPX put options that fall within delta range between -1 and -0.95, open interest range between 10 and 1000, and expire within seven days. Within this Option chain, the algorithm holds the put Option contract that has the minimum delta (closest to -1) during market hour to hedge the underlying intra-day movement completely. It avoid volatility from sentiment and only earns from inter-day movement from longer-term factors. When the contract expires, the algorithm rolls over to the next contract that meets this criteria.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class IndexOptionsUniverseAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(10000000)
+
+        # Asynchronous can use computational resources efficiently
+        self.universe_settings.asynchronous = True
+        # Subscribe to the underlying for the underlying position
+        # Set the data normalization mode to raw for strike price comparability
+        self.index = self.add_index("SPX").symbol
+        # Requesting option data and filter for the hedge candidates
+        option = self.add_index_option(self.index)
+        option.set_filter(self.option_filter)
+        self.option_symbol = option.symbol
+
+        # Set scheduled event to buy a hedge option contract at market open to eliminate the intra-day movement
+        self.schedule.on(
+            self.date_rules.every_day(self.index),
+            self.time_rules.after_market_open(self.index, 1),
+            self.buy_hedge_contract
+        )
+
+        # Set a scheduled event to sell the hedge contract before market close, since we want to earn from inter-day movement
+        # Leave 2 minutes contingency to fill
+        self.schedule.on(
+            self.date_rules.every_day(self.index),
+            self.time_rules.before_market_close(self.index, 2),
+            self.sell_hedge_contract
+        )
+
+        self.hedge = None
+        
+    def option_filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
+        # Select the contracts with delta very close to -1 and high open interest
+        # This can effectively hedge most of the price change of the underlying and ensure the liquidity
+        # Make sure the contract is expiring close for its tradbility
+        return universe.puts_only().expiration(2, 7).delta(-1, -0.95).open_interest(10, 1000)
+
+    def buy_hedge_contract(self) -> None:
+        chain = self.current_slice.option_chains.get(self.option_symbol)
+        if chain:
+            # Order the underlying if not hold, the order size should match the option contract
+            # Order only if option chain data ready for hedging
+            if not self.portfolio[self.index].invested:
+                self.market_order(self.index, self.securities[self.option_symbol].symbol_properties.contract_multiplier)
+
+            # Get the contract with delta closest to -1 (lowest possible delta)
+            contract = sorted(chain, key=lambda x: x.greeks.delta)[0]
+            self.hedge = contract.symbol
+            # Buy 1 deep ITM put with delta close to -1 to eliminate the intraday movement
+            self.market_order(self.hedge, 1)
+        
+    def sell_hedge_contract(self) -> None:
+        # Check if any hedge contract position, if so, liquidate before market close to expose to underlying overnight movement
+        if self.hedge:
+            self.liquidate(self.hedge)
+            self.hedge = None
    
+   
    public class IndexOptionsUniverseAlgorithm : QCAlgorithm
+{
+    private Symbol _index, _optionSymbol, _hedge;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(10000000);
+        // Asynchronous can use computational resources efficiently
+        UniverseSettings.Asynchronous = true;
+
+        // Subscribe to the underlying for the underlying position
+        // Set the data normalization mode to raw for strike price comparability
+        _index = AddIndex("SPX").Symbol;
+        // Requesting option data and filter for the hedge candidates
+        var option = AddIndexOption(_index);
+        _optionSymbol = option.Symbol;
+        option.SetFilter(OptionFilter);
+
+        // Set scheduled event to buy a hedge option contract at market open to eliminate the intra-day movement
+        Schedule.On(
+            DateRules.EveryDay(_index),
+            TimeRules.AfterMarketOpen(_index, 1),
+            BuyHedgeContract
+        );
+
+        // Set a scheduled event to sell the hedge contract before market close, since we want to earn from inter-day movement
+        // Leave 2 minutes contingency to fill
+        Schedule.On(
+            DateRules.EveryDay(_index),
+            TimeRules.BeforeMarketClose(_index, 2),
+            SellHedgeContract
+        );
+    }
+
+    private OptionFilterUniverse OptionFilter(OptionFilterUniverse universe)
+    {
+        // Select the contracts with delta very close to -1 and high open interest
+        // This can effectively hedge most of the price change of the underlying and ensure the liquidity
+        // Make sure the contract is expiring close for its tradbility
+        return universe
+            .PutsOnly()
+            .Expiration(2, 7)
+            .Delta(-1m, -0.95m)
+            .OpenInterest(10, 1000);
+    }
+
+    private void BuyHedgeContract()
+    {
+        if (CurrentSlice.OptionChains.TryGetValue(_optionSymbol, out var chain))
+        {
+            // Order the underlying if not hold, the order size should match the option contract
+            // Order only if option chain data ready for hedging
+            if (!Portfolio[_index].Invested)
+            {
+                MarketOrder(_index, Securities[_optionSymbol].SymbolProperties.ContractMultiplier);
+            }
+
+            // Get the contract with delta closest to -1 (lowest possible delta)
+            var contract = chain.MinBy(x => x.Greeks.Delta);
+            _hedge = contract.Symbol;
+            // Buy 1 deep ITM put with delta close to -1 to eliminate the intraday movement
+            MarketOrder(_hedge, 1);
+        }
+    }
+
+    private void SellHedgeContract()
+    {
+        // Check if any hedge contract position, if so, liquidate before market close to expose to underlying overnight movement
+        if (_hedge != null)
+        {
+            Liquidate(_hedge);
+            _hedge = null;
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm demonstrating a Gamma Scalping strategy through framework algorithm. It filters SPX options with expiration between 30 to 90 days and open interest between 50 and 1000, due to liquidity concern and lower Gamma fluctuation. Assuming a significant upward trend is expecting, it orders a long straddle strategy from the strike price with Delta-neutral and the highest Gamma to earn the highest profit from upward underlying movement.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class IndexOptionsUniverseFrameworkAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2024, 12, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(1000000)
+        # Asynchronous can use computational resources efficiently
+        self.universe_settings.asynchronous = True
+
+        # Universe selection that select based on liquidity of the option contracts (by open interest)
+        self.add_universe_selection(IndexOptionsUniverseSelectionModel())
+        # Custom alpha model that use Delta and Gamma to signal insights
+        self.add_alpha(OptionDeltaGammaAlphaModel())
+        # To maintain long-short size equal, use a PCM that order a single contract
+        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
+
+class IndexOptionsUniverseSelectionModel(OptionUniverseSelectionModel):
+    def __init__(self) -> None:
+        # Daily update with the select_option_chain_symbols function
+        super().__init__(timedelta(1), self.select_option_chain_symbols)
+
+    def select_option_chain_symbols(self, dt: datetime) -> List[Symbol]:
+        # Select only SPX options as our focus, which is a highly traded volatile index with great upward momentum
+        return [Symbol.create("SPX", SecurityType.INDEX_OPTION, Market.USA)]
+
+    def filter(self, universe: OptionFilterUniverse) -> OptionFilterUniverse:
+        # To ensure the liquidity and tradability, make sure the option is not 0-DTE and have a fair open interest
+        # A longer TTM will have lower Gamma thus more stable in the local delta-neutral position
+        return universe.expiration(30, 90).open_interest(50, 1000)
+
+class OptionDeltaGammaAlphaModel(AlphaModel):
+    def __init__(self) -> None:
+        # A day count variable to control the alpha model only trade once a day
+        self._day = -1
+
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        insights = []
+
+        if self._day != slice.time.day:
+            # We open position for each option underlying as separate bet
+            for _, chain in slice.option_chains.items():
+                # For theta-neutral, select the same expiry for both call and put
+                expiry = min(x.expiry for x in chain)
+                contracts = [x for x in chain if x.expiry == expiry]
+
+                # Calculate delta and gamma per strike price group for later filtering, ensure both call and put available for option strategy
+                delta_gamma_symbols = []
+                strikes = set(x.strike for x in contracts if len([y for y in contracts if y.strike == x.strike]) == 2)
+                for strike in strikes:
+                    # Get both call and put for their aggregated delta and gamma
+                    call = next(filter(lambda x: x.right == OptionRight.CALL and x.strike == strike, contracts))
+                    put = next(filter(lambda x: x.right == OptionRight.PUT and x.strike == strike, contracts))
+                    delta_gamma_symbols.append((call.greeks.delta + put.greeks.delta, call.greeks.gamma + put.greeks.gamma, call.symbol, put.symbol))
+                
+                if len(delta_gamma_symbols) == 0:
+                    continue
+
+                # We want a delta-neutral position, so it is likely to be ATM (like a long straddle)
+                # Less than 2d.p. difference is non-significant, which we can risk for better reward
+                # Assuming the market direction is up in most scenario, we try to get the strike providing max overall Gamma
+                # Make sure the aggregated gamma to be positive to bet on large uptrend
+                # So it will earn more when the price really go up higher and higher, but locally immune for small noise
+                filtered = [item for item in delta_gamma_symbols if round(item[0], 2) <= 0.01 and item[1] > 0]
+                if len(filtered) == 0:
+                    continue
+                selected = sorted(filtered, key=lambda x: x[1])[0]
+
+                # Provide trade signal and roll the day count to let the position stay for the whole day
+                selected_call = selected[2]
+                selected_put = selected[3]
+                insights.extend([
+                    Insight.price(selected_call, Expiry.END_OF_DAY, InsightDirection.UP),
+                    Insight.price(selected_put, Expiry.END_OF_DAY, InsightDirection.UP)
+                ])
+                self._day = slice.time.day
+        
+        return insights
+
+class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
+    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
+        targets = []
+        for insight in insights:
+            if algorithm.securities[insight.symbol].is_tradable:
+                # Use a whole number target to order the exact number of share for size-matching
+                targets.append(PortfolioTarget(insight.symbol, insight.direction))
+        return targets
    
+   
    public class IndexOptionsUniverseFrameworkAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetStartDate(2024, 12, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(1000000);
+        // Asynchronous can use computational resources efficiently
+        UniverseSettings.Asynchronous = true;
+
+        // Universe selection that select based on liquidity of the option contracts (by open interest)
+        AddUniverseSelection(new IndexOptionsUniverseSelectionModel());
+        // Custom alpha model that use Delta and Gamma to signal insights
+        AddAlpha(new OptionDeltaGammaAlphaModel());
+        // To maintain long-short size equal, use a PCM that order a single contract
+        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
+    }
+}       
+    
+class IndexOptionsUniverseSelectionModel : OptionUniverseSelectionModel
+{
+    // Daily update with the SelectOptionChainSymbols function
+    public IndexOptionsUniverseSelectionModel()
+            : base(TimeSpan.FromDays(1), SelectOptionChainSymbols) {}
+    
+    private static IEnumerable<Symbol> SelectOptionChainSymbols(DateTime utcTime)
+    {
+        // Select only SPX options as our focus, which is a highly traded volatile index with great upward momentum
+        return new[] {QuantConnect.Symbol.Create("SPX", SecurityType.IndexOption, Market.USA)};
+    }
+
+    protected override OptionFilterUniverse Filter(OptionFilterUniverse filter)
+    {
+        // To ensure the liquidity and tradability, make sure the option is not 0-DTE and have a fair open interest
+        // A longer TTM will have lower Gamma thus more stable in the local delta-neutral position
+        return filter
+            .Expiration(30, 90)
+            .OpenInterest(50, 1000);
+    }
+}
+    
+class OptionDeltaGammaAlphaModel : AlphaModel
+{
+    // A day count variable to control the alpha model only trade once a day
+    private int _day = -1;
+
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    {
+        var insights = new List<Insight>();
+        
+        if (_day != slice.Time.Day)
+        {
+            // We open position for each option underlying as separate bet
+            foreach (var kvp in slice.OptionChains)
+            {
+                var chain = kvp.Value;
+
+                // For theta-neutral, select the same expiry for both call and put
+                var expiry = chain.Min(x => x.Expiry);
+                var contracts = chain.Where(x => x.Expiry == expiry).ToList();
+
+                // Calculate delta and gamma per strike price group for later filtering, ensure both call and put available for option strategy
+                var deltaGammaSymbols = new List<(decimal, decimal, Symbol, Symbol)>();
+                var strikes = contracts.Select(x => x.Strike)
+                    .Where(x => contracts.Count(y => y.Strike == x) == 2)
+                    .Distinct();
+                foreach (var strike in strikes)
+                {
+                    // Get both call and put for their aggregated delta and gamma
+                    var call = contracts.Single(x => x.Right == OptionRight.Call && x.Strike == strike);
+                    var put = contracts.Single(x => x.Right == OptionRight.Put && x.Strike == strike);
+                    deltaGammaSymbols.Add((call.Greeks.Delta + put.Greeks.Delta, call.Greeks.Gamma + put.Greeks.Gamma, call.Symbol, put.Symbol));
+                }
+
+                if (deltaGammaSymbols.Count == 0)
+                {
+                    continue;
+                }
+
+                // We want a delta-neutral position, so it is likely to be ATM (like a long straddle)
+                // Less than 2d.p. difference is non-significant, which we can risk for better reward
+                // Assuming the market direction is up in most scenario, we try to get the strike providing max overall Gamma
+                // Make sure the aggregated gamma to be positive to bet on large uptrend
+                // So it will earn more when the price really go up higher and higher, but locally immune for small noise
+                var filtered = deltaGammaSymbols.Where(item => Math.Round(item.Item1, 2) <= 0.01m && item.Item2 > 0).ToList();
+                if (filtered.Count == 0)
+                {
+                    continue;
+                }
+                var selected = filtered.OrderByDescending(item => item.Item2).First();
+
+                // Provide trade signal and roll the day count to let the position stay for the whole day
+                var selectedCall = selected.Item3;
+                var selectedPut = selected.Item4;
+                insights.AddRange(new[] {
+                    Insight.Price(selectedCall, Expiry.EndOfDay, InsightDirection.Up),
+                    Insight.Price(selectedPut, Expiry.EndOfDay, InsightDirection.Up)
+                });
+                _day = slice.Time.Day;
+            }
+        }
+
+        return insights;
+    }
+}
+    
+class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
+{
+    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
+    {
+        var targets = new List<PortfolioTarget>();
+        foreach (var insight in insights)
+        {
+            if (algorithm.Securities[insight.Symbol].IsTradable)
+            {
+                // Use a whole number target to order the exact number of share for size-matching
+                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
+            }
+        }
+        return targets;
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Index Options Universe dataset provides
+   
+    OptionFilterUniverse
+   
+   ,
+   
+    OptionUniverse
+   
+   , and
+   
+    OptionChain
+   
+   objects.
+  
    
+  
    
+   OptionFilterUniverse Attributes
+  
    
+  
    
+   
+    OptionFilterUniverse
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   OptionUniverse Attributes
+  
    
+  
    
+   
+    OptionUniverse
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   OptionChain Attributes
+  
    
+  
    
+   
+    OptionChain
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > AlgoSeek
    
+
    
+    
    
+        
    Datasets
    
+        
    AlgoSeek
    
+    
    
+
    
+
+ 
+  
    
+   
    
+    
    
+     AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering Equities, Futures, Options, cash FOREX, and Cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning.  For more information about AlgoSeek, visit
+     
+      algoseek.com
+     
+     .
+    
    
+   
    
+   
    
+    
    
+     
+      
    
+       
    
+        
    
+         US Equities
+        
    
+       
    
+      
    
+         
+    
    
+    
    
+     
+      
    
+       
    
+        
    
+         US Equity Options
+        
    
+       
    
+      
    
+         
+    
    
+    
    
+     
+      
    
+       
    
+        
    
+         US Future Options
+        
    
+       
    
+      
    
+         
+    
    
+    
    
+     
+      
    
+       
    
+        
    
+         US Futures
+        
    
+       
    
+      
    
+         
+    
    
+    
    
+     
+      
    
+       
    
+        
    
+         US Index Options
+        
    
+       
    
+      
    
+         
+    
    
+   
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > AlgoSeek > US Equities
    
+
    
+    
    
+        
    AlgoSeek
    
+        
    US Equities
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Equities dataset by AlgoSeek is survivorship bias-free daily coverage of every stock traded in the US Securities Information Processors (SIP) CTA/UTP feed since 1998. The dataset covers approximately 27,500 securities, starts in January 1998, and is delivered in any resolution from tick to daily. The Data is collected from the full SIP feed via our Equinix co-located servers, including all trades and quotes published to every exchange as well as FINRA. Over-the-Counter (OTC) trades are not included.
+  
    
+  
    
+   This dataset depends on the
+   
+    US Equity Security Master
+   
+   dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
+  
    
+  
    
+   For more information about the US Equities dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering Equities, Futures, Options, cash FOREX, and Cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning.  For more information about AlgoSeek, visit
+   
+    algoseek.com
+   
+   .
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   AlgoSeek is the default US Equities dataset on QuantConnect. The following snippet demonstrates how to request data from the US Equities dataset:
+  
    
+  
    
+   
    self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
    
+   
    _aapl = AddEquity("AAPL", Resolution.Daily).Symbol;
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 1998
+     
    
+      Asset Coverage
+     
+      27,500 US Equities
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Tick, Second, Minute, Hourly, & Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+      Market Hours
+     
+      
+       Regular and Extended
+      
+     
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add US Equities data to your algorithm, call the
+   
+    AddEquity
+   
+   
+    add_equity
+   
+   method. Save a reference to the Equity
+   
+    Symbol
+   
+   so you can access the data later in your algorithm.
+  
    
+  
    
+   
    class USEquityDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2018, 1, 1)
+        self.set_end_date(2021, 6, 1)
+        self.set_cash(100000) 
+        # Subscribe to AAPL data
+        self.aapl = self.add_equity("AAPL", Resolution.MINUTE).symbol
    
+   
    public class USEquityDataAlgorithm : QCAlgorithm
+{
+    private Symbol _symbol; 
+    
+    public override void Initialize()
+    {
+        SetStartDate(2018, 1, 1);
+        SetEndDate(2021, 6, 1);
+        SetCash(100000);
+        // Subscribe to AAPL data
+        _symbol = AddEquity("AAPL", Resolution.Minute).Symbol;
+    }
+}
    
+  
    
+  
    
+   For more information about creating US Equity subscriptions, see
+   
+    Requesting Data
+   
+   or
+   
+    US Equity Universes
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current US Equities data, index the
+   
+    Bars
+   
+   
+    bars
+   
+   ,
+   
+    QuoteBars
+   
+   
+    quote_bars
+   
+   , or
+   
+    Ticks
+   
+   
+    ticks
+   
+   properties of the current
+   
+    
+     Slice
+    
+   
+   with the Equity
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your security at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Access data: TradeBar data
+    if self.aapl in slice.bars:
+        trade_bar = slice.bars[self.aapl]
+        self.log(f"{self.aapl} close at {slice.time}: {trade_bar.close}")
+
+    # Access data: QuoteBar data
+    if self.aapl in slice.quote_bars:
+        quote_bar = slice.quote_bars[self.aapl]
+        self.log(f"{self.aapl} bid at {slice.time}: {quote_bar.bid.close}")
+
+    # Access data: Ticks data
+    if self.aapl in slice.ticks:
+        ticks = slice.ticks[self.aapl]
+        for tick in ticks:
+            self.log(f"{self.aapl} price at {slice.time}: {tick.price}")
    
+   
    public override void OnData(Slice slice)
+{
+    // Access data: TradeBar data
+    if (slice.Bars.ContainsKey(_symbol))
+    {
+        var tradeBar = slice.Bars[_symbol];
+        Log($"{_symbol} price at {slice.Time}: {tradeBar.Close}");
+    }
+
+    // Access data: QuoteBar data
+    if (slice.QuoteBars.ContainsKey(_symbol))
+    {
+        var quoteBar = slice.QuoteBars[_symbol];
+        Log($"{_symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
+    }
+
+    // Access data: Ticks data
+    if (slice.Ticks.ContainsKey(_symbol))
+    {
+        var ticks = slice.Ticks[_symbol];
+        foreach (var tick in ticks)
+        {
+            Log($"{_symbol} price at {slice.Time}: {tick.Price}");
+        }
+    }
+}
+
    
+  
    
+  
    
+   You can also iterate through all of the data objects in the current
+   
+    Slice
+   
+   .
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Iterate all TradeBar received
+    for symbol, trade_bar in slice.bars.items():
+        self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")
+
+    # Iterate all QuoteBar received
+    for symbol, quote_bar in slice.quote_bars.items():
+        self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
+
+    # Iterate all Ticks received
+    for symbol, ticks in slice.ticks.items():
+        for tick in ticks:
+            self.log(f"{symbol} price at {slice.time}: {tick.price}")
    
+   
    public override void OnData(Slice slice)
+{
+    // Iterate all TradeBar received
+    foreach (var kvp in slice.Bars)
+    {
+        var symbol = kvp.Key;
+        var tradeBar = kvp.Value;
+        Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
+    }
+
+    // Iterate all QuoteBar received
+    foreach (var kvp in slice.QuoteBars)
+    {
+        var symbol = kvp.Key;
+        var quoteBar = kvp.Value;
+        Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
+    }
+
+    // Iterate all Ticks received
+    foreach (var kvp in slice.Ticks)
+    {
+        var symbol = kvp.Key;
+        var ticks = kvp.Value;
+        foreach (var tick in ticks)
+        {
+            Log($"{symbol} price at {slice.Time}: {tick.Price}");
+        }
+    }
+}
+
    
+  
    
+  
    
+   For more information about accessing US Equities data, see
+   
+    Handling Data
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   To get historical US Equity data, call the
+   
+    History
+   
+   
+    history
+   
+   method with the Equity
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame
+history_df = self.history(self.aapl, 100, Resolution.DAILY)
+
+# TradeBar objects
+history_trade_bars = self.history[TradeBar](self.aapl, 100, Resolution.DAILY)
+
+# QuoteBar objects
+history_quote_bars = self.history[QuoteBar](self.aapl, 100, Resolution.MINUTE)
+
+# Tick objects
+history_ticks = self.history[Tick](self.aapl, timedelta(seconds=10), Resolution.TICK)
    
+   
    // TradeBar objects 
+var historyTradeBars = History(_symbol, 100, Resolution.Daily);
+
+// QuoteBar objects 
+var historyQuoteBars = History<QuoteBar>(_symbol, 100, Resolution.Minute);
+
+// Tick objects 
+var historyTicks = History<Tick>(_symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
+  
    
+  
    
+   For more information about historical data, see
+   
+    History Requests
+   
+   .
+  
    
+ 
+
+
+
    Universe Selection
    
+
+ 
+  
    
+   To select a universe of US Equities, see
+   
+    Equity Universes
+   
+   .
+  
    
+ 
+
+
+
    Remove Subscriptions
    
+
+ 
+  
    
+   To unsubscribe from a US Equity that you added with the
+   
+    AddEquity
+   
+   
+    add_equity
+   
+   method, call the
+   
+    RemoveSecurity
+   
+   
+    remove_security
+   
+   method.
+  
    
+  
    
+   
    self.remove_security(self.aapl)
    
+   
    RemoveSecurity(_symbol);
    
+  
    
+  
    
+   The
+   
+    RemoveSecurity
+   
+   
+    remove_security
+   
+   method cancels your open orders for the security and liquidates your holdings.
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   To view the supported assets in the US Equities dataset, see the
+   
+    Data Explorer
+   
+   . This dataset doesn't include Over-the-Counter (OTC) stocks.
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Equities dataset enables you to accurately design Equity trading strategies. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Momentum strategies using historical returns on the premise that the momentum will continue
+   
      • 
+   
    • 
+    Value strategies using fundamental factors on the premise that the price of undervalued securities will rise
+   
      • 
+   
    • 
+    Factor investing with periodic rebalancing
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm buys and holds Apple stock:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class USEquityDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        
+        # Requesting single equity data, since we only trade AAPL
+        self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
+        
+        # Historical data
+        history = self.history(self.aapl, 60, Resolution.DAILY)
+        self.debug(f"We got {len(history)} items from our history request")
+
+    def on_data(self, slice: Slice) -> None:
+        # Check if the current slice containing AAPL and if we hold any position
+        # As we make use of the most updated price data to decide the order size
+        if slice.contains_key(self.aapl) and slice[self.aapl] is not None and not self.portfolio.invested:
+            self.set_holdings(self.aapl, 1)
    
+   
    public class USEquityDataAlgorithm : QCAlgorithm
+{
+    private Symbol _symbol;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+
+        // Requesting single equity data, since we only trade AAPL
+        _symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
+        
+        // Historical data
+        var history = History(_symbol, 60, Resolution.Daily);
+        Debug($"We got {history.Count()} items from our history request");
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // Check if the current slice containing AAPL and if we hold any position
+        // As we make use of the most updated price data to decide the order size
+        if (slice.ContainsKey(_symbol) && slice[_symbol] != null && !Portfolio.Invested)
+        {
+            SetHoldings(_symbol, 1);
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm buys and holds Apple stock:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class USEquityDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        
+        self.universe_settings.resolution = Resolution.DAILY
+        # To select only AAPL, use a manual selection universe
+        symbols = [Symbol.create("AAPL", SecurityType.EQUITY, Market.USA)]
+        self.add_universe_selection(ManualUniverseSelectionModel(symbols))    
+        
+        # Constant investment signal
+        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(days=7), 0.025, None))
+        
+        # Invest in all members equally
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
    
+   
    public class USEquityDataAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+        
+        UniverseSettings.Resolution = Resolution.Daily;
+        // To select only AAPL, use a manual selection universe
+        var symbols = new[] {QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA)};
+        AddUniverseSelection(new ManualUniverseSelectionModel(symbols));
+        
+        // Constant investment signal
+        AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(7), 0.025, null));
+
+        // Invest in all members equally
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Equities dataset provides
+   
+    TradeBar
+   
+   ,
+   
+    QuoteBar
+   
+   , and
+   
+    Tick
+   
+   objects.
+  
    
+  
    
+   TradeBar Attributes
+  
    
+  
    
+   
+    TradeBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   QuoteBar Attributes
+  
    
+  
    
+   
+    QuoteBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   Tick Attributes
+  
    
+  
    
+   
+    Tick
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > AlgoSeek > US Equity Options
    
+
    
+    
    
+        
    AlgoSeek
    
+        
    US Equity Options
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Equity Options data by AlgoSeek provides Option data, including prices, strikes, expires, and open interest. The data covers 4,000 Symbols, starts in January 2012, and is delivered on a minute frequency. This dataset is created by monitoring Options Price Reporting Authority (OPRA) data feed, which consolidates last sale and quotation information originating from the national securities exchanges that have been approved by the Securities and Exchange Commission.
+  
    
+  
    
+   This dataset depends on the following datasets:
+  
    
+  
+  
    
+   For more information about the US Equity Options dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   
+    AlgoSeek
+   
+   was in 2014 with the goal of providing the highest quality, most accurate, ready-to-use data in the financial data industry. AlgoSeek provides access to Equities, ETFs, ETNs, Equity Indices, Equity Options, Futures, and Future Options for quantitative firms and traders.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the US Equity Options dataset:
+  
    
+  
    
+   
    option = self.add_option("GOOG")
+self.option_symbol = option.symbol
+option.set_filter(-2, +2, 0, 180)
    
+   
    var option = AddOption("GOOG");
+_optionSymbol = option.Symbol;
+option.SetFilter(-2, +2, 0, 180);
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 2012*
+     
    
+      Asset Coverage
+     
+      4,000 Symbols
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Minute, Hourly, & Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+      Market Hours
+     
+      
+       Regular Only
+      
+     
    
+  
    
+   * Some data is available before this date. In 2012, AlgoSeek started to fetch data from 48 OPRA channels instead of 24, increasing the quality of the data.
+  
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add US Equity Options data to your algorithm, call the
+   
+    AddOption
+   
+   
+    add_option
+   
+   method. Save a reference to the Equity Option
+   
+    Symbol
+   
+   so you can access the data later in your algorithm.
+  
    
+  
    
+   
    class USEquityOptionsDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2020, 6, 1)
+        self.set_end_date(2021, 6, 1)
+        self.set_cash(100000)
+        self.universe_settings.asynchronous = True
+        # Request GOOG option data
+        option = self.add_option("GOOG")
+        self.option_symbol = option.symbol
+        # Set our strike/expiry filter for this option chain
+        option.set_filter(-2, +2, 0, 180)
+
    
+   
    public class USEquityOptionsDataAlgorithm : QCAlgorithm
+{
+    private Symbol _optionSymbol;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2020, 6, 1);
+        SetEndDate(2021, 6, 1);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = true;
+        // Request GOOG option data
+        var option = AddOption("GOOG");
+        _optionSymbol = option.Symbol;
+        // Set our strike/expiry filter for this option chain
+        option.SetFilter(-2, +2, 0, 180);
+    }
+}
    
+  
    
+  
    
+   The Equity resolution must be less than or equal to the Equity Option resolution. For example, if you set the Equity resolution to minute, then you must set the Equity Option resolution to minute, hour, or daily.
+  
    
+  
    
+   For more information about creating US Equity Option subscriptions, see
+   
+    Requesting Data
+   
+   or
+   
+    Equity Options Universes
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current US Equity Options data, index the
+   
+    OptionChains
+   
+   
+    option_chains
+   
+   property of the current
+   
+    
+     Slice
+    
+   
+   with the canonical Equity Option
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your Index Option at every time step. To avoid issues, call the
+   
+    Get
+   
+   
+    get
+   
+   method.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Get the wanted option chain with the canonical symbol
+    chain = slice.option_chains.get(self.option_symbol)
+    if chain:
+        # Iterate the option contracts in chain
+        for contract in chain:
+            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
+
    
+   
    public override void OnData(Slice slice)
+{
+    // Get the wanted option chain with the canonical symbol
+    if (slice.OptionChains.TryGetValue(_optionSymbol, out var chain))
+    {
+        // Iterate the option contracts in chain
+        foreach (var contract in chain)
+        {
+            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
+        }
+    }
+}
    
+  
    
+  
    
+   You can also iterate through all of the
+   
+    OptionChain
+   
+   objects in the current
+   
+    Slice
+   
+   .
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Iterate all option chains of all symbols
+    for canonical_symbol, chain in slice.option_chains.items():
+        # Iterate the option contracts in chain
+        for contract in chain:
+            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
+
    
+   
    public override void OnData(Slice slice)
+{
+    // Iterate all option chains of all symbols
+    foreach (var kvp in slice.OptionChains)
+    {
+        var canonicalSymbol = kvp.Key;
+        var chain = kvp.Value;
+        // Iterate the option contracts in chain
+        foreach (var contract in chain)
+        {
+            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
+        }
+    }
+}
    
+  
    
+  
    
+   For more information about accessing US Equity Options data, see
+   
+    Handling Data
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical US Equity Options data in an algorithm and the Research Environment.
+  
    
+  
    
+   Historical Data In Algorithms
+  
    
+  
    
+   To get historical US Equity Options data in an algorithm, call the
+   
+    History
+   
+   
+    history
+   
+   method with the Equity Option contract
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame of trade and quote data
+history_df = self.history(contract.symbol, 100, Resolution.MINUTE)
+
+# DataFrame of open interest data
+history_oi_df = self.history(OpenInterest, contract.symbol, 100, Resolution.MINUTE)
+
+# TradeBar objects
+history_trade_bars = self.history[TradeBar](contract.symbol, 100, Resolution.MINUTE)
+
+# QuoteBar objects
+history_quote_bars = self.history[QuoteBar](contract.symbol, 100, Resolution.MINUTE)
+
+# OpenInterest objects
+history_oi = self.history[OpenInterest](contract.symbol, 100, Resolution.MINUTE)
    
+   
    // TradeBar objects 
+var historyTradeBars = History(contract.Symbol, 100, Resolution.Minute);
+
+// QuoteBar objects 
+var historyQuoteBars = History<QuoteBar>(contract.Symbol, 100, Resolution.Minute);
+
+// OpenInterest objects
+var historyOpenInterest = History<OpenInterest>(contract.Symbol, 100, Resolution.Minute);
    
+  
    
+  
    
+   For more information about historical data in algorithms, see
+   
+    History Requests
+   
+   .
+  
    
+  
    
+   Historical Data In Research
+  
    
+  
    
+   To get historical US Equity Options data in the Research Environment, call the
+   
+    History
+   
+   
+    history
+   
+   or
+   
+    OptionHistory
+   
+   
+    option_history
+   
+   method. The
+   
+    History
+   
+   
+    history
+   
+   method returns the price, volume, and open interest history for some given Option contract(s). The
+   
+    OptionHistory
+   
+   
+    option_history
+   
+   method returns the price and volume history for the contracts that pass your daily universe filter.
+  
    
+  
    
+   
    qb = QuantBook()
+option = qb.add_option("GOOG") 
+option.set_filter(-2, 2, 0, 90)
+history = qb.option_history(option.symbol.underlying, datetime(2020, 6, 1), datetime(2020, 6, 5))
+history_df = history.data_frame
+expiries = history.get_expiry_dates() 
+strikes = history.get_strikes()
    
+   
    var qb = new QuantBook();
+var option = qb.AddOption("GOOG");
+option.SetFilter(-2, 2, 0, 90);
+var history = qb.OptionHistory(option.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 5));
+
+var contracts = history
+    .SelectMany(x => x.OptionChains.SelectMany(y => y.Value.Contracts.Keys))
+    .Distinct().ToList();
+var expiries = contracts.Select(x => x.ID.Date).Distinct().ToList();
+var strikes = contracts.Select(x => x.ID.StrikePrice).Distinct().ToList();
    
+  
    
+  
    
+   To get historical data for arbitrary US Equity Option contracts instead of just the that pass your universe filter, call the
+   
+    History
+   
+   
+    history
+   
+   method like you would in an algorithm, but on the
+   
+    QuantBook
+   
+   object. For more information about historical data in the Research Environment, see
+   
+    Key Concepts
+   
+   .
+  
    
+  
    
+   Historical Greeks and IV Data
+  
    
+  
    
+   To get historical data for the Greeks and implied volatility of Equity Options, see the
+   
+    US Equity Option Universe
+   
+   dataset.
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   To view the supported assets in the US Equity Options dataset, see the
+   
+    Data Explorer
+   
+   .
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Equity Options dataset enables you to accurately design Option strategies. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Buying put Options to hedge against downward price movement in positive Equity positions
+   
      • 
+   
    • 
+    Exploiting arbitrage opportunities that arise when the price of Option contracts deviates from their theoretical value
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm subscribes to Google Options that fall within two strikes of the underlying stock price and expire within seven days. Within this Option chain, the algorithm buys the call Option contract that has the furthest expiry and has its strike price closest to the underlying stock price. When the contract expires, the algorithm rolls over to the next contract that meets this criteria.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class USEquityOptionsDataAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        self.universe_settings.asynchronous = True
+        # Requesting data
+        self.underlying = self.add_equity("GOOG").symbol
+        option = self.add_option("GOOG")
+        self.option_symbol = option.symbol
+        # To speculate trade the underlying with a low cost, filter for the ATM calls that expiring in the current week
+        # -2/+2 strike buffer is given for small price change
+        option.set_filter(lambda u: u.calls_only().strikes(-2, +2).expiration(0, 6))
+        
+        self.contract = None
+
+    def on_data(self, slice: Slice) -> None:
+        # If the Option contract is exercised, close the underlying position.
+        if self.portfolio[self.underlying].invested:
+            self.liquidate(self.underlying)
+        # If the Option contract expires, rollover to the next contract.
+        if self.contract and not self.portfolio[self.contract.symbol].invested:
+            self.contract = None
+        # Select with the lastest option chain data only
+        chain = slice.option_chains.get(self.option_symbol)
+        if not self.contract and chain:
+            # Select the call contracts with the furthest expiration (week end)
+            furthest_expiry = max([c.expiry for c in chain])
+            furthest_expiry_calls = [contract for contract in chain if contract.expiry == furthest_expiry]
+            # Get the ATM call for speculate trade with low cost and limited loss
+            self.contract = sorted(furthest_expiry_calls, key = lambda x: abs(chain.underlying.price - x.strike))[0]
+            self.market_order(self.contract.symbol, 1)
+            
+    def on_securities_changed(self, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            # Historical data
+            history = self.history(security.symbol, 10, Resolution.MINUTE)
+            self.debug(f"We got {len(history)} from our history request for {security.symbol}")
    
+   
    public class USEquityOptionsDataAlgorithm : QCAlgorithm
+{
+    private Symbol _underlying, _optionSymbol;
+    private OptionContract? _contract = null;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = true;
+        // Requesting data
+        _underlying = AddEquity("GOOG").Symbol;
+        var option = AddOption("GOOG");
+        _optionSymbol = option.Symbol;
+        // To speculate trade the underlying with a low cost, filter for the ATM calls that expiring in the current week
+        // -2/+2 strike buffer is given for small price change
+        option.SetFilter((u) => u.CallsOnly().Strikes(-2, +2).Expiration(0, 7));
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // If the Option contract is exercised, close the underlying position.
+        if (Portfolio[_underlying].Invested)
+        {
+            Liquidate(_underlying);
+        }
+        // If the Option contract expires, rollover to the next contract.
+        if (_contract != null && !Portfolio[_contract.Symbol].Invested)
+        {
+            _contract = null;
+        }
+        // Select with the lastest option chain data only
+        if (_contract == null && slice.OptionChains.TryGetValue(_optionSymbol, out var chain))
+        {
+            // Select the call contracts with the furthest expiration (week end)
+            var furthestExpiry = chain.Max(c => c.Expiry);
+            var furthestExpiryCalls = chain.Where(c => c.Expiry == furthestExpiry);
+            // Get the ATM call for speculate trade with low cost and limited loss
+            _contract = furthestExpiryCalls.OrderByDescending(x => Math.Abs(chain.Underlying.Price - x.Strike)).Last();
+            MarketOrder(_contract.Symbol, 1);
+        }
+    }
+    
+    public override void OnSecuritiesChanged(SecurityChanges changes)
+    {
+        foreach (var security in changes.AddedSecurities)
+        {
+            // Historical data
+            var history = History(security.Symbol, 100, Resolution.Minute);
+            Debug($"We got {history.Count()} from our history request for {security.Symbol}");
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm buys a call Option contract for Google that falls within one strike of the underlying stock price and expires within seven days. When the contract expires, the algorithm rolls over to the next contract that meets this criteria.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class USEquityOptionsDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        self.universe_settings.asynchronous = True
+        # Requesting data
+        self.set_universe_selection(EarliestExpiringWeeklyAtTheMoneyCallOptionUniverseSelectionModel())
+        
+        self.set_alpha(ConstantOptionsAlphaModel())
+
+        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
+        
+        
+class EarliestExpiringWeeklyAtTheMoneyCallOptionUniverseSelectionModel(OptionUniverseSelectionModel):
+
+    def __init__(self) -> None:
+        # Daily update with the select_option_chain_symbols function
+        super().__init__(timedelta(1), self.select_option_chain_symbols)
+
+    def select_option_chain_symbols(self, utcTime: datetime) -> List[Symbol]:
+        # Always select only GOOG options as our focus
+        return [ Symbol.create("GOOG", SecurityType.OPTION, Market.USA) ]
+
+    def filter(self, filter: OptionFilterUniverse) -> OptionFilterUniverse:
+        # To speculate trade the underlying with a low cost, filter for the ATM calls that expiring in the current week
+        # -1/+1 strike buffer is given for small price change
+        return (filter.calls_only()
+                      .strikes(-1, -1)
+                      .expiration(0, 7))  
+        
+
+class ConstantOptionsAlphaModel(AlphaModel):
+
+    underlying = None
+    contract = None
+
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        
+        insights = []
+        
+        # Liquidate the underlying if the option is being exercised/assigned
+        if algorithm.portfolio[self.underlying].invested:
+            insights.append(Insight.price(self.underlying, timedelta(days=7), InsightDirection.FLAT))
+
+        if self.contract is not None and algorithm.portfolio[self.contract.symbol].invested:
+            return insights
+
+        # Get the ATM call for speculate trade with low cost and limited loss that expires at week end
+        for kvp in slice.option_chains:
+            chain = kvp.value
+            expiry = max(x.expiry for x in chain)
+            self.contract = sorted([x for x in chain if x.expiry == expiry],
+                                               key=lambda x: abs(x.strike - x.underlying_last_price))[0]
+            insights.append(Insight.price(self.contract.symbol, self.contract.expiry + timedelta(days=1), InsightDirection.UP))
+        
+        return insights
+        
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            if security.type == SecurityType.EQUITY:
+                self.underlying = security.symbol
+            else:
+                # Historical data
+                history = algorithm.history(security.symbol, 10, Resolution.MINUTE)
+                algorithm.debug(f"We got {len(history)} from our history request for {security.symbol}")
+
+
+class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
+
+    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
+        targets = []
+        for insight in insights:
+            if algorithm.securities[insight.symbol].is_tradable:
+                targets.append(PortfolioTarget(insight.symbol, insight.direction))
+        return targets
    
+   
    public class USEquityOptionsDataAlgorithm : QCAlgorithm
+{        
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = true;
+        // Requesting data
+        SetUniverseSelection(new EarliestExpiringWeeklyAtTheMoneyCallOptionUniverseSelectionModel());
+        
+        SetAlpha(new ConstantOptionsAlphaModel());
+        
+        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
+    }
+}   
+    
+class EarliestExpiringWeeklyAtTheMoneyCallOptionUniverseSelectionModel : OptionUniverseSelectionModel
+{
+    // Daily update with the SelectOptionChainSymbols function
+    public EarliestExpiringWeeklyAtTheMoneyCallOptionUniverseSelectionModel()
+            : base(TimeSpan.FromDays(1), SelectOptionChainSymbols) {}
+    
+    private static IEnumerable<Symbol> SelectOptionChainSymbols(DateTime utcTime)
+    {
+        // Select only GOOG options as our focus
+        return new[] {QuantConnect.Symbol.Create("GOOG", SecurityType.Option, Market.USA)};
+    }
+
+    protected override OptionFilterUniverse Filter(OptionFilterUniverse filter)
+    {
+        // To speculate trade the underlying with a low cost, filter for the ATM calls that expiring in the current week
+        // -2/+2 strike buffer is given for small price change
+        return filter.CallsOnly()
+            .Strikes(-1, -1)
+            .Expiration(0, 7);
+    }
+}
+    
+class ConstantOptionsAlphaModel : AlphaModel
+{ 
+    private Symbol? _underlying = null;
+    private OptionContract? _contract = null;
+    
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    {
+        var insights = new List<Insight>();
+        
+        // Liquidate the underlying if the option is being exercised/assigned
+        if (algorithm.Portfolio[_underlying].Invested)
+        {
+            insights.Add(Insight.Price(_underlying, TimeSpan.FromDays(7), InsightDirection.Flat));
+        }
+            
+        if (_contract != null && algorithm.Portfolio[_contract.Symbol].Invested)
+        {
+            return insights;
+        }
+        
+        // Get the ATM call for speculate trade with low cost and limited loss that expires at week end
+        foreach (var kvp in slice.OptionChains)
+        {
+            var chain = kvp.Value;
+            var expiry = chain.Max(x => x.Expiry);
+            _contract = chain.Where(x => x.Expiry == expiry)
+                .OrderBy(x => Math.Abs(x.Strike - x.UnderlyingLastPrice))
+                .First();
+            insights.Add(Insight.Price(_contract.Symbol, _contract.Expiry + TimeSpan.FromDays(1), InsightDirection.Up));
+        }    
+        return insights;
+    }
+
+    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
+    {
+        foreach (var security in changes.AddedSecurities)
+        {
+            if (security.Type == SecurityType.Equity)
+            {
+                _underlying = security.Symbol;
+            }
+            else {
+                // Historical data
+                var history = algorithm.History(security.Symbol, 100, Resolution.Minute);
+                algorithm.Debug($"We got {history.Count()} from our history request for {security.Symbol}");    
+            }
+        }
+    }
+}
+    
+    
+class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
+{
+    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
+    {
+        var targets = new List<PortfolioTarget>();
+        foreach (var insight in insights)
+        {
+            if (algorithm.Securities[insight.Symbol].IsTradable)
+            {
+                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
+            }
+        }
+        return targets;
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Equity Options dataset provides
+   
+    TradeBar
+   
+   ,
+   
+    QuoteBar
+   
+   , and
+   
+    OpenInterest
+   
+   objects.
+  
    
+  
    
+   TradeBar Attributes
+  
    
+  
    
+   
+    TradeBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   QuoteBar Attributes
+  
    
+  
    
+   
+    QuoteBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   OpenInterest Attributes
+  
    
+  
    
+   
+    OpenInterest
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > AlgoSeek > US Future Options
    
+
    
+    
    
+        
    AlgoSeek
    
+        
    US Future Options
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Future Options dataset by AlgoSeek provides Option data on US Future contracts, including prices, strikes, and expires. The data covers 15 Monthly Future contracts, starts in January 2012, and is delivered on a minute frequency. This dataset is created by monitoring the trading activity on the CME, CBOT, NYMEX, and COMEX markets.
+  
    
+  
    
+   This dataset depends on the following datasets:
+  
    
+  
+  
    
+   For more information about the US Future Options dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering equities, futures, options, cash forex, and cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning.  For more information about AlgoSeek, visit
+   
+    algoseek.com
+   
+   .
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the US Future Options dataset:
+  
    
+  
    
+   
    future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
+future.set_filter(0, 90)
+self.add_future_option(future.symbol, lambda universe: universe.strikes(-5, +5))
    
+   
    var future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
+future.SetFilter(0, 90);
+AddFutureOption(future.Symbol, universe => universe.Strikes(-5, +5));
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 2012
+     
    
+      Asset Coverage
+     
+      15 Monthly Future Contracts. Standard expires only*.
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Minute, Hourly, & Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+      Market Hours
+     
+      
+       Regular and Extended
+      
+     
    
+  * No weeklies or 0DTE contracts.
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add US Future Options data to your algorithm, call the
+   
+    AddFutureOption
+   
+   
+    add_future_option
+   
+   method.
+  
    
+  
    
+   
    class FutureOptionDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2020, 1, 28)
+        self.set_end_date(2020, 6, 1)
+        self.set_cash(100000)
+        self.universe_settings.asynchronous = True
+        future = self.add_future(Futures.Metals.GOLD, Resolution.MINUTE)
+        future.set_filter(0, 90)
+        self.add_future_option(future.symbol, lambda universe: universe.strikes(-5, +5))
    
+   
    public class FutureOptionDataAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetStartDate(2020, 1, 28);
+        SetEndDate(2020, 6, 1);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = true;
+        var future = AddFuture(Futures.Metals.Gold, Resolution.Minute);
+        future.SetFilter(0, 90);
+        AddFutureOption(future.Symbol, universe => universe.Strikes(-5, +5));
+    }
+}
    
+  
    
+  
    
+   The Future resolution must be less than or equal to the Future Option resolution. For example, if you set the Future resolution to minute, then the Future Option resolution must be minute, hour, or daily.
+  
    
+  
    
+   For more information about creating Future Options subscriptions, see
+   
+    Requesting Data
+   
+   or
+   
+    Future Options Universes
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current Future Options data, iterate through the
+   
+    OptionChains
+   
+   
+    option_chains
+   
+   property of the current
+   
+    
+     Slice
+    
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your Future Options at every time step.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    for canonical_fop_symbol, chain in slice.option_chains.items():
+        for contract in chain:
+            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
    
+   
    public override void OnData(Slice slice)
+{
+    foreach (var kvp in slice.OptionChains)
+    {
+        var canonicalFOPSymbol = kvp.Key;
+        var chain = kvp.Value;
+        foreach (var contract in chain)
+        {
+            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
+        }
+    }
+}
    
+  
    
+  
    
+   You can also iterate through the
+   
+    FuturesChains
+   
+   
+    futures_chains
+   
+   in the current
+   
+    Slice
+   
+   first.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    for continuous_future_symbol, futures_chain in slice.futures_chains.items():
+        # Select a Future Contract and create its canonical FOP Symbol
+        futures_contract = [contract for contract in futures_chain][0]
+        canonical_fop_symbol = Symbol.create_canonical_option(futures_contract.symbol)
+        option_chain = slice.option_chains.get(canonical_fop_symbol)
+        if option_chain:
+            for fop_contract in option_chain:
+                 self.log(f"{fop_contract.symbol} price at {slice.time}: {fop_contract.last_price}")
    
+   
    public override void OnData(Slice slice)
+{
+    foreach (var kvp in slice.FuturesChains)
+    {
+        var continuousContractSymbol = kvp.Key;
+        var futuresChain = kvp.Value;
+        
+        // Select a Future Contract and create its canonical FOP Symbol
+        var futuresContract = futuresChain.First();
+        var canonicalFOPSymbol = QuantConnect.Symbol.CreateCanonicalOption(futuresContract.Symbol);
+        if (slice.OptionChains.TryGetValue(canonicalFOPSymbol, out var optionChain))
+        {
+            foreach (var fopContract in optionChain)
+            {
+                Log($"{fopContract.Symbol} price at {slice.Time}: {fopContract.LastPrice}");
+            }
+        }
+    }
+}
    
+  
    
+  
    
+   For more information about accessing Future Options data, see
+   
+    Handling Data
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical US Future Options data in an algorithm and the Research Environment.
+  
    
+  
    
+   Historical Data In Algorithms
+  
    
+  
    
+   To get historical US Future Options data in an algorithm, call the
+   
+    History
+   
+   
+    history
+   
+   method with the Future Option contract
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame of trade and quote data
+history_df = self.history(contract.symbol, 100, Resolution.MINUTE)
+
+# DataFrame of open interest data
+history_oi_df = self.history(OpenInterest, contract.symbol, 100, Resolution.MINUTE)
+
+# TradeBar objects
+history_trade_bars = self.history[TradeBar](contract.symbol, 100, Resolution.MINUTE)
+
+# QuoteBar objects
+history_quote_bars = self.history[QuoteBar](contract.symbol, 100, Resolution.MINUTE)
+
+# OpenInterest objects
+history_oi = self.history[OpenInterest](contract.symbol, 100, Resolution.MINUTE)
+
    
+   
    // TradeBar objects 
+var historyTradeBars = History(contract.Symbol, 100, Resolution.Minute);
+
+// QuoteBar objects 
+var historyQuoteBars = History<QuoteBar>(contract.Symbol, 100, Resolution.Minute);
+
+// OpenInterest objects 
+var historyOpenInterest = History<OpenInterest >(contract.Symbol, 100, Resolution.Minute);
    
+  
    
+  
    
+   For more information about historical data in algorithms, see
+   
+    History Requests
+   
+   .
+  
    
+  
    
+   Historical Data In Research
+  
    
+  
    
+   To get historical US Future Options data in the Research Environment, call the
+   
+    History
+   
+   
+    history
+   
+   or
+   
+    OptionHistory
+   
+   
+    option_history
+   
+   method. The
+   
+    History
+   
+   
+    history
+   
+   method returns the price, volume, and open interest history for some given Future Option contract(s). The
+   
+    OptionHistory
+   
+   
+    option_history
+   
+   method returns the price and volume history for the contracts that pass your daily universe filter.
+  
    
+  
    
+   
    qb = QuantBook()
+future = qb.add_future(Futures.Indices.SP_500_E_MINI)
+start_date = datetime(2024, 1, 2)
+future_contract_symbol = sorted(
+    qb.future_chain_provider.get_future_contract_list(future.symbol, start_date), 
+    key=lambda s: s.id.date
+)[0]
+history = qb.option_history(
+    future_contract_symbol, start_date, future_contract_symbol.id.date, Resolution.HOUR
+)
+history_df = history.data_frame
+expiries = history.get_expiry_dates()
+strikes = history.get_strikes()
    
+   
    var qb = new QuantBook();
+var future = qb.AddFuture(Futures.Indices.SP500EMini);
+var startDate = new DateTime(2024, 1, 2);
+var futureContractSymbol = qb.FutureChainProvider.GetFutureContractList(future.Symbol, startDate)
+    .OrderBy(x => x.ID.Date)
+    .First();
+var history = qb.OptionHistory(
+    futureContractSymbol, startDate, futureContractSymbol.ID.Date, Resolution.Hour
+);
+
+var contracts = history
+    .SelectMany(x => x.OptionChains.SelectMany(y => y.Value.Contracts.Keys))
+    .Distinct().ToList();
+var expiries = contracts.Select(x => x.ID.Date).Distinct().ToList();
+var strikes = contracts.Select(x => x.ID.StrikePrice).Distinct().ToList();
    
+  
    
+  
    
+   To get historical data for arbitrary US Equity Option contracts instead of just the that pass your universe filter, call the
+   
+    History
+   
+   
+    history
+   
+   method like you would in an algorithm, but on the
+   
+    QuantBook
+   
+   object. For more information about historical data in the Research Environment, see
+   
+    Key Concepts
+   
+   .
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   The following list shows the available (15) Futures Options:
+  
    
+  
      
+   
    • 
+    
+     Futures.Energy.CrudeOilWTI
+    
+    
+     Futures.Energy.CRUDE_OIL_WTI
+    
+    : Crude Oil WTI Futures (NYMEX: LO | Underlying: CL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.Gasoline
+    
+    
+     Futures.Energy.GASOLINE
+    
+    : Gasoline RBOB Futures (NYMEX: OB | Underlying: RB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.HeatingOil
+    
+    
+     Futures.Energy.HEATING_OIL
+    
+    : Heating Oil Futures (NYMEX: OH | Underlying: HO)
+   
      • 
+   
    • 
+    
+     Futures.Energy.NaturalGas
+    
+    
+     Futures.Energy.NATURAL_GAS
+    
+    : Natural Gas Futures (NYMEX: ON | Underlying: NG)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y10TreasuryNote
+    
+    
+     Futures.Financials.Y_10_TREASURY_NOTE
+    
+    : 10Y U.S. Treasury Note Futures (CBOT: OZN | Underlying: ZN)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y2TreasuryNote
+    
+    
+     Futures.Financials.Y_2_TREASURY_NOTE
+    
+    : 2Y U.S. Treasury Note Futures (CBOT: OZT | Underlying: ZT)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y30TreasuryBond
+    
+    
+     Futures.Financials.Y_30_TREASURY_BOND
+    
+    : 30Y U.S. Treasury Bond Futures (CBOT: OZB | Underlying: ZB)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Corn
+    
+    
+     Futures.Grains.CORN
+    
+    : Corn Futures (CBOT: OZC | Underlying: ZC)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Soybeans
+    
+    
+     Futures.Grains.SOYBEANS
+    
+    : Soybeans Futures (CBOT: OZS | Underlying: ZS)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Wheat
+    
+    
+     Futures.Grains.WHEAT
+    
+    : Default wheat contract is SRWWheat (CBOT: OZW | Underlying: ZW)
+   
      • 
+   
    • 
+    
+     Futures.Indices.NASDAQ100EMini
+    
+    
+     Futures.Indices.NASDAQ_100_E_MINI
+    
+    : E-mini NASDAQ 100 Futures (CME: NQ)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SP500EMini
+    
+    
+     Futures.Indices.SP_500_E_MINI
+    
+    : E-mini S&P 500 Futures (CME: ES)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Copper
+    
+    
+     Futures.Metals.COPPER
+    
+    : Copper Futures (COMEX: HXE | Underlying: HG)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Gold
+    
+    
+     Futures.Metals.GOLD
+    
+    : Gold Futures (COMEX: OG | Underlying: GC)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Silver
+    
+    
+     Futures.Metals.SILVER
+    
+    : Silver Futures (COMEX: SO | Underlying: SI)
+   
      • 
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Future Options dataset enables you to accurately design Future Option strategies. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Selling out of the money Future Option contracts to collect the premium that the Option buyer pays
+   
      • 
+   
    • 
+    Buying put Options to hedge against downward price movement in Future contracts you bought
+   
      • 
+   
    • 
+    Exploiting arbitrage opportunities that arise when the price of Option contracts deviates from their theoretical value
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example demonstrates a weekly-renewing
+   
+    covered call
+   
+   strategy to collect credit of selling the option. It filters the ATM call contract that expires within the current week at week start using
+   
+    SetFilter
+   
+   filtering function.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class FutureOptionExampleAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(5_000_000)
+        # Subscribe the underlying since the updated price is needed for filtering
+        self.underlying = self.add_future(Futures.Indices.SP_500_E_MINI,
+            extended_market_hours=True,
+            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
+            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
+            contract_depth_offset=0)
+        # Filter the underlying continuous Futures to narrow the FOP spectrum
+        self.underlying.set_filter(0, 182)
+        # Filter for the current-week-expiring calls to formulate a covered call that expires at the end of week
+        self.add_future_option(self.underlying.symbol, lambda u: u.calls_only().expiration(0, 5))
+
+    def on_data(self, slice: Slice) -> None:
+        # Create canonical symbol for the mapped future contract, since option chains are mapped by canonical symbol
+        symbol = Symbol.create_canonical_option(self.underlying.mapped)
+
+        # Get option chain data for the mapped future, as both the underlying and FOP have the highest liquidity among all other contracts
+        chain = slice.option_chains.get(symbol)
+        if not self.portfolio.invested and chain:
+            # Obtain the ATM call that expires at the end of week, such that both underlying and the FOP expires the same time
+            expiry = max(x.expiry for x in chain)
+            atm_call = sorted([x for x in chain if x.expiry == expiry],
+                key=lambda x: abs(x.strike - x.underlying_last_price))[0]
+
+            # Use abstraction method to order a covered call to avoid manual error
+            option_strategy = OptionStrategies.covered_call(symbol, atm_call.strike,expiry)
+            self.buy(option_strategy, 1)
+        
+    def on_securities_changed(self, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            if security.type == SecurityType.FUTURE_OPTION:
+                # Historical data
+                history = self.history(security.symbol, 10, Resolution.MINUTE)
    
+   
    public class FutureOptionExampleAlgorithm : QCAlgorithm
+{
+    private Future _underlying;
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(5000000);
+        // Subscribe the underlying since the updated price is needed for filtering
+        _underlying = AddFuture(Futures.Indices.SP500EMini,
+            extendedMarketHours: true,
+            dataMappingMode: DataMappingMode.OpenInterest,
+            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
+            contractDepthOffset: 0);
+        // Filter the underlying continuous Futures to narrow the FOP spectrum
+        _underlying.SetFilter(0, 182);
+        // Filter for the current-week-expiring calls to formulate a covered call that expires at the end of week
+        AddFutureOption(_underlying.Symbol, (u) => u.CallsOnly().Expiration(0, 5));
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // Create canonical symbol for the mapped future contract, since option chains are mapped by canonical symbol
+        var symbol = QuantConnect.Symbol.CreateCanonicalOption(_underlying.Mapped);
+
+        // Get option chain data for the mapped future, as both the underlying and FOP have the highest liquidity among all other contracts
+        if (!Portfolio.Invested && 
+            slice.OptionChains.TryGetValue(symbol, out var chain))
+        {
+            // Obtain the ATM call that expires at the end of week, such that both underlying and the FOP expires the same time
+            var expiry = chain.Max(x => x.Expiry);
+            var atmCall = chain.Where(x => x.Expiry == expiry)
+                .OrderBy(x => Math.Abs(x.Strike - x.UnderlyingLastPrice))
+                .First();
+
+            // Use abstraction method to order a covered call to avoid manual error
+            var optionStrategy = OptionStrategies.CoveredCall(symbol, atmCall.Strike, expiry);
+            Buy(optionStrategy, 1);
+        }
+    }
+
+    public override void OnSecuritiesChanged(SecurityChanges changes)
+    {
+        foreach (var security in changes.AddedSecurities)
+        {
+            if (security.Type == SecurityType.FutureOption)
+            {
+                // Historical data
+                var history = History(security.Symbol, 10, Resolution.Minute);
+            }
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example demonstrates a daily renewing speculation trade on upward movement of Gold with FOP using algorithm framework. Using FOP, traders can achieve lower cost to get similar absolute profit, given the FOP is selected to be deep ITM with high Delta value.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class USFuturesDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        # Override an option universe selection model to select the FOPs
+        self.set_universe_selection(GoldFOPUniverseSelectionModel(self))
+        # Handle the FOP trading logic in an alpha model
+        self.add_alpha(ConstantFutureOptionsAlphaModel())
+        # To order a single contract per insight, use a custom portfolio construction model
+        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
+        
+
+class GoldFOPUniverseSelectionModel(OptionUniverseSelectionModel):
+
+    # Daily renewed universe since option contract list is updated on a daily basis
+    def __init__(self, algorithm) -> None:
+        self._algorithm = algorithm
+        super().__init__(timedelta(1), self.select_fop_symbols)
+
+    def select_fop_symbols(self, utc_time: datetime) -> List[Symbol]:
+        #  Create the underlying symbol to get the FOP contracts later
+        future_symbol = Symbol.create(Futures.Metals.GOLD, SecurityType.FUTURE, Market.COMEX)
+        # Get all gold FOP contracts of the front month gold contract expiring within 3 months, since their liquidity is the highest
+        future_contract_list = self._algorithm.future_chain_provider.get_future_contract_list(future_symbol, self._algorithm.time)
+        return [Symbol.create_canonical_option(x) for x in future_contract_list 
+                    if x.id.date <= self._algorithm.time + timedelta(90)]
+
+    def filter(self, option_filter_universe: OptionFilterUniverse) -> OptionFilterUniverse:
+        # Filter for the ATM calls that expires latest (same expiry as the underlying)
+        # +/-5 strike range buffer for price movement
+        return option_filter_universe.back_month().strikes(-5, +5).calls_only()
+
+
+class ConstantFutureOptionsAlphaModel(AlphaModel):
+
+    # A dictionary to cache the mapped FOP of the underlying Future for filtering wanted option chain data and check if invested
+    option_contract_by_future_underlying_contract = {}
+
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        insights = []
+
+        for kvp in slice.option_chains:
+            # Liquidate underlying Future contract after Option assignment
+            underlying_future_contract = kvp.key.underlying
+            if algorithm.portfolio[underlying_future_contract].invested:
+                algorithm.insights.cancel([underlying_future_contract])
+                del self.option_contract_by_future_underlying_contract[underlying_future_contract]
+
+            # Do not repeatly invest in the same underlying's FOP
+            chain = [contract for contract in kvp.value if algorithm.securities[contract.symbol].is_tradable]
+            if not chain or underlying_future_contract in self.option_contract_by_future_underlying_contract:
+                continue
+            
+            # Select the Option contract with the lowest strike price to speculate trade the underlying with lowest cost and highest delta
+            contract = sorted(chain, key=lambda x: x.strike)[0]
+            insights.append(Insight.price(contract.symbol, contract.expiry + timedelta(1), InsightDirection.UP))
+            self.option_contract_by_future_underlying_contract[underlying_future_contract] = contract
+        
+        return insights
+
+
+class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
+
+    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
+        targets = []
+        for insight in insights:
+            if algorithm.securities[insight.symbol].is_tradable:
+                # Use integer target to create a portfolio target to trade a single contract
+                targets.append(PortfolioTarget(insight.symbol, insight.direction))
+        return targets
    
+   
    public class FutureOptionDataAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+        // Override an option universe selection model to select the FOPs
+        AddUniverseSelection(new GoldFOPUniverseSelectionModel(this));
+        // Handle the FOP trading logic in an alpha model
+        SetAlpha(new ConstantFutureOptionsAlphaModel());
+        // To order a single contract per insight, use a custom portfolio construction model
+        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
+    }
+}
+
+public class GoldFOPUniverseSelectionModel : OptionUniverseSelectionModel
+{
+    // Daily renewed universe since option contract list is updated on a daily basis
+    public GoldFOPUniverseSelectionModel(QCAlgorithm algorithm)
+        : base(TimeSpan.FromDays(1), _ => OptionChainSymbolSelector(algorithm, _))
+    {
+    }
+
+    private static IEnumerable<Symbol> OptionChainSymbolSelector(QCAlgorithm algorithm, DateTime utcTime)
+    {
+        // Create the underlying symbol to get the FOP contracts later
+        var futureSymbol = QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX);
+        // Get all gold FOP contracts of the front month gold contract expiring within 3 months, since their liquidity is the highest
+        return algorithm.FutureChainProvider.GetFutureContractList(futureSymbol, algorithm.Time)
+            .Where(futureContractSymbol => futureContractSymbol.ID.Date <= algorithm.Time + TimeSpan.FromDays(90))
+            .Select(futureContractSymbol => QuantConnect.Symbol.CreateCanonicalOption(futureContractSymbol));
+    }
+
+    protected override OptionFilterUniverse Filter(OptionFilterUniverse optionFilterUniverse)
+    {
+        // Filter for the ATM calls that expires latest (same expiry as the underlying)
+        // +/-5 strike range buffer for price movement
+        return optionFilterUniverse.BackMonth().Strikes(-5, +5).CallsOnly();
+    }
+}
+
+
+class ConstantFutureOptionsAlphaModel : AlphaModel
+{
+    // A dictionary to cache the mapped FOP of the underlying Future for filtering wanted option chain data and check if invested
+    private Dictionary<Symbol, OptionContract> optionContractByUnderlyingFutureContract = new Dictionary<Symbol, OptionContract>();
+    
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    {
+        var insights = new List<Insight>();
+        
+        foreach (var kvp in slice.OptionChains)
+        {
+            // Liquidate underlying Future contract after Option assignment
+            var underlyingFutureContract = kvp.Key.Underlying;
+            if (algorithm.Portfolio[underlyingFutureContract].Invested)
+            {
+                algorithm.Insights.Cancel(new[] { underlyingFutureContract });
+                optionContractByUnderlyingFutureContract.Remove(underlyingFutureContract);
+            }
+            
+            // Do not repeatly invest in the same underlying's FOP
+            var chain = kvp.Value.Where(contract => algorithm.Securities[contract.Symbol].IsTradable);
+            if (chain.Count() == 0 || optionContractByUnderlyingFutureContract.ContainsKey(underlyingFutureContract))
+            {
+                continue;
+            }
+
+            // Select the Option contract with the lowest strike price to speculate trade the underlying with lowest cost and highest delta
+            var contract = chain.MinBy(contract => contract.Strike);
+            insights.Add(Insight.Price(contract.Symbol, contract.Expiry.AddDays(1), InsightDirection.Up));
+            optionContractByUnderlyingFutureContract.Add(kvp.Key.Underlying, contract);
+        }            
+        return insights;
+    }
+}
+
+
+class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
+{
+    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
+    {
+        var targets = new List<PortfolioTarget>();
+        foreach (var insight in insights)
+        {
+            if (algorithm.Securities[insight.Symbol].IsTradable)
+            {
+                // Use integer target to create a portfolio target to trade a single contract
+                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
+            }
+        }
+        return targets;
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Future Options dataset provides
+   
+    TradeBar
+   
+   ,
+   
+    QuoteBar
+   
+   , and
+   
+    OpenInterest
+   
+   objects.
+  
    
+  
    
+   TradeBar Attributes
+  
    
+  
    
+   
+    TradeBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   QuoteBar Attributes
+  
    
+  
    
+   
+    QuoteBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   OpenInterest Attributes
+  
    
+  
    
+   
+    OpenInterest
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > AlgoSeek > US Futures
    
+
    
+    
    
+        
    AlgoSeek
    
+        
    US Futures
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Futures dataset by AlgoSeek provides Futures data, including price, volume, open interest, and expiry. The data covers the 157 most liquid contracts, starts in May 2009, and is delivered on any frequency from tick to daily. This dataset is created by monitoring the trading activity on the CFE
+   
+    *
+   
+   , CBOT, CME, COMEX, NYMEX, and ICE
+   
+    *
+   
+   .
+  
    
+  
    
+   This dataset also depends on the
+   
+    US Futures Security Master
+   
+   because the US Futures Security Master dataset contains information to construct continuous Futures.
+  
    
+  
    
+   For more information about the US Futures dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering equities, futures, options, cash forex, and cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning.  For more information about AlgoSeek, visit
+   
+    algoseek.com
+   
+   .
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the US Futures dataset:
+  
    
+  
    
+   
    future = self.add_future(Futures.Metals.GOLD)
+future.set_filter(0, 90)
    
+   
    var future = AddFuture(Futures.Metals.Gold);
+future.SetFilter(0, 90);
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      May 2009
+     
    
+      Asset Coverage
+     
+      157 Futures
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Tick, Second, Minute, Hour, & Daily
+     
    
+      Timezone
+     
+      
      
+       
    • 
+        Chicago (CFE, CME or CBOT)
+       
      • 
+       
    • 
+        New York (ICE, NYMEX or COMEX)
+       
      • 
+      
    
+     
    
+      Market Hours
+     
+      
+       Regular and Extended
+      
+     
    
+  
    
+   This dataset only includes Sugar (SB) from the ICE exchange.
+  
    
+  
    
+   This data provider doesn't include a live data feed for the CFE market (VIX).
+  
    
+  
    
+   LBS (Random Length Lumber) was deprecated for LBR (Lumber).
+  
    
+ 
+
+
+
    Volume Discrepancies
    
+
+ 
+  
    
+   The volume of the daily trade bars from this dataset is different from the daily volume that CME and other platforms like Yahoo Finance report because the CME includes pit trades in their daily volume calculation. In contrast, the volume of the daily trade bars in this dataset (from AlgoSeek) doesn't include pit trades because pit trades are over-the-counter, which algorithms can't trade.
+  
    
+  
    
+   Another discrepancy occurs from the start and end times of the daily bars. Daily bars from the CME and Yahoo Finance span from 5 PM Central Time (CT) to 4 PM CT on the following day. In contrast, QuantConnect consolidates daily bars from from 12 AM Eastern Time (ET) to 12 AM ET the following day. Therefore, to calculate the daily volume in the same way that CME does (excluding the pit trades), sum the volume of intraday bars that span 5 PM CT to 4 PM CT on the following day. For an example implementation, see
+   
+    Examples
+   
+   .
+  
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add US Futures data to your algorithm, call the
+   
+    AddFuture
+   
+   
+    add_future
+   
+   method. Save a reference to the Future so you can access the data later in your algorithm.
+  
    
+  
    
+   
    class USFuturesDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2013, 12, 20) 
+        self.set_end_date(2014, 2, 20) 
+        self.set_cash(1000000) 
+        self.universe_settings.asynchronous = True
+        # Request Gold Future data
+        future = self.add_future(Futures.Metals.GOLD) 
+        # Set filter to obtain the contracts expire within 90 days
+        future.set_filter(0, 90)
+        self.future_symbol = future.symbol
    
+   
    public class USFuturesDataAlgorithm : QCAlgorithm
+{
+    private Symbol _futureSymbol;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2013, 12, 20);
+        SetEndDate(2014, 2, 20);
+        SetCash(1000000);
+        UniverseSettings.Asynchronous = true;
+        // Request Gold Future data
+        var future = AddFuture(Futures.Metals.Gold);
+        // Set filter to obtain the contracts expire within 90 days
+        future.SetFilter(0, 90);
+        _futureSymbol = future.Symbol;
+    }
+}
+
    
+  
    
+  
    
+   For more information about creating Future subscriptions, see
+   
+    Requesting Data
+   
+   or
+   
+    Futures Universes
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current US Futures data, index the
+   
+    Bars
+   
+   
+    bars
+   
+   ,
+   
+    QuoteBars
+   
+   
+    quote_bars
+   
+   , or
+   
+    Ticks
+   
+   
+    ticks
+   
+   properties of the current
+   
+    
+     Slice
+    
+   
+   with the Future
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your security at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Access data: TradeBar data
+    if self.future_symbol in slice.bars:
+        trade_bar = slice.bars[self.future_symbol]
+        self.log(f"{self.future_symbol} close at {slice.time}: {trade_bar.close}")
+
+    # Access data: QuoteBar data
+    if self.future_symbol in slice.quote_bars:
+        quote_bar = slice.quote_bars[self.future_symbol]
+        self.log(f"{self.future_symbol} bid at {slice.time}: {quote_bar.bid.close}")
+
+    # Access data: Ticks data
+    if self.future_symbol in slice.ticks:
+        ticks = slice.ticks[self.future_symbol]
+        for tick in ticks:
+            self.log(f"{self.future_symbol} price at {slice.time}: {tick.price}")
    
+   
    public override void OnData(Slice slice)
+{
+    // Access data: TradeBar data
+    if (slice.Bars.TryGetValue(_futureSymbol, out var tradeBar))
+    {
+        Log($"{_futureSymbol} price at {slice.Time}: {tradeBar.Close}");
+    }
+
+    // Access data: QuoteBar data
+    if (slice.QuoteBars.TryGetValue(_futureSymbol, out var quoteBar))
+    {
+        Log($"{_futureSymbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
+    }
+
+    // Access data: Ticks data
+    if (slice.Ticks.TryGetValue(_futureSymbol, out var ticks))
+    {
+        foreach (var tick in ticks)
+        {
+            Log($"{_futureSymbol} price at {slice.Time}: {tick.Price}");
+        }
+    }
+}
+
    
+  
    
+  
    
+   You can also iterate through all of the data objects in the current
+   
+    Slice
+   
+   .
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Iterate all TradeBar received
+    for symbol, trade_bar in slice.bars.items():
+        self.log(f"{symbol} close at {slice.time}: {trade_bar.close}")
+
+    # Iterate all QuoteBar received
+    for symbol, quote_bar in slice.quote_bars.items():
+        self.log(f"{symbol} bid at {slice.time}: {quote_bar.bid.close}")
+
+    # Iterate all Ticks received
+    for symbol, ticks in slice.ticks.items():
+        for tick in ticks:
+            self.log(f"{symbol} price at {slice.time}: {tick.price}")
    
+   
    public override void OnData(Slice slice)
+{
+    // Iterate all TradeBar received
+    foreach (var kvp in slice.Bars)
+    {
+        var symbol = kvp.Key;
+        var tradeBar = kvp.Value;
+        Log($"{symbol} price at {slice.Time}: {tradeBar.Close}");
+    }
+
+    // Iterate all QuoteBar received
+    foreach (var kvp in slice.QuoteBars)
+    {
+        var symbol = kvp.Key;
+        var quoteBar = kvp.Value;
+        Log($"{symbol} bid at {slice.Time}: {quoteBar.Bid.Close}");
+    }
+
+    // Iterate all Ticks received
+    foreach (var kvp in slice.Ticks)
+    {
+        var symbol = kvp.Key;
+        var ticks = kvp.Value;
+        foreach (var tick in ticks)
+        {
+            Log($"{symbol} price at {slice.Time}: {tick.Price}");
+        }
+    }
+}
+
    
+  
    
+  
    
+   For more information about accessing US Futures data, see
+   
+    Handling Data
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical US Futures data in an algorithm and the Research Environment.
+  
    
+  
    
+   Historical Data In Algorithms
+  
    
+  
    
+   To get historical US Futures data in an algorithm, call the
+   
+    History
+   
+   
+    history
+   
+   method with the canonical Futures
+   
+    Symbol
+   
+   or a Futures contract
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame objects
+contract_history_df = self.history(contract.symbol, 100, Resolution.MINUTE)
+continuous_history_df = self.history(self.future_symbol,
+    start=self.time - timedelta(days=15), 
+    end=self.time, 
+    resolution=Resolution.MINUTE, 
+    fill_forward=False, 
+    extended_market_hours=False,
+    data_mapping_mode=DataMappingMode.OPEN_INTEREST, 
+    data_normalization_mode=DataNormalizationMode.RAW, 
+    contract_depth_offset=0)
+
+# TradeBar objects
+contract_history_trade_bars = self.history[TradeBar](contract.symbol, 100, Resolution.MINUTE)
+continous_history_trade_bars = self.history[TradeBar](self.future_symbol, 100, Resolution.MINUTE)
+
+# QuoteBar objects
+contract_history_quote_bars = self.history[QuoteBar](contract.symbol, 100, Resolution.MINUTE)
+continous_history_quote_bars = self.history[QuoteBar](self.future_symbol, 100, Resolution.MINUTE)
+
+# Tick objects
+contract_history_ticks = self.history[Tick](contract.symbol, timedelta(seconds=10), Resolution.TICK)
+continous_history_ticks = self.history[Tick](self.future_symbol, timedelta(seconds=10), Resolution.TICK)
    
+   
    // TradeBar objects
+var contractHistoryTradeBars = History(contract.Symbol, 100, Resolution.Minute);
+var continuousHistoryTradeBars = History(
+    symbols: new[] {_futureSymbol}, 
+    start: Time - TimeSpan.FromDays(15),
+    end: Time,
+    resolution: Resolution.Minute,
+    fillForward: false,
+    extendedMarketHours: false,
+    dataMappingMode: DataMappingMode.OpenInterest,
+    dataNormalizationMode: DataNormalizationMode.Raw,
+    contractDepthOffset: 0);
+
+// QuoteBar objects
+var contractHistoryQuoteBars = History<QuoteBar>(contract.Symbol, 100, Resolution.Minute);
+var continuousHistoryQuoteBars = History<QuoteBar>(_futureSymbol, 100, Resolution.Minute);
+
+// Tick objects
+var contractHistoryTicks = History<Tick>(contract.Symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
+var continuousHistoryTicks = History<Tick>(_futureSymbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
+  
    
+  
    
+   For more information about historical data in algorithms, see
+   
+    History Requests
+   
+   . For more information about the price adjustments for continuous contracts, see
+   
+    Continous Contracts
+   
+   .
+  
    
+  
    
+   Historical Data In Research
+  
    
+  
    
+   To get historical US Futures data in the Research Environment for an entire Futures chain, call the
+   
+    FutureHistory
+   
+   
+    future_history
+   
+   method with the canonical Future
+   
+    Symbol
+   
+   .
+  
    
+  
    
+   
    qb = QuantBook()
+future = qb.add_future(Futures.Metals.GOLD) 
+future.set_filter(0, 90)
+history = qb.future_history(future.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5))
+history_df = history.data_frame
+all_history = history.get_all_data()
+expiries = history.get_expiry_dates()
    
+   
    var qb = new QuantBook();
+var future = qb.AddFuture(Futures.Metals.Gold);
+future.SetFilter(0, 90);
+var history = qb.FutureHistory(future.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 5));
+    
+var contracts = history.SelectMany(x => x.OptionChains.SelectMany(y => y.Value.Contracts.Keys)).Distinct().ToList();
+var expiries = contracts.Select(x => x.ID.Date).Distinct().ToList();
    
+  
    
+  
    
+   To get historical data for a single US Futures contract or the continuous Futures contract, call the
+   
+    History
+   
+   
+    history
+   
+   method like you would in an algorithm but on the
+   
+    QuantBook
+   
+   object. For more information about historical data in the Research Environment, see
+   
+    Futures
+   
+   .
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   The following list shows the available (157) Futures:
+  
    
+  
      
+   
    • 
+    
+     Futures.Currencies.AUD
+    
+    : Australian Dollar Futures (CME: 6A)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.AUDCAD
+    
+    : Australian Dollar/Canadian Dollar Futures (CME: ACD)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.AUDJPY
+    
+    : Australian Dollar/Japanese Yen Futures (CME: AJY)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.AUDNZD
+    
+    : Australian Dollar/New Zealand Dollar Futures (CME: ANE)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.BRL
+    
+    : Brazillian Real Futures (CME: 6L)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.BTC
+    
+    : Bitcoin Futures (CME: BTC)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.BTICMicroBTC
+    
+    
+     Futures.Currencies.BTIC_MICRO_BTC
+    
+    : BTIC on Micro Bitcoin Futures (CME: MIB)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.BTICMicroEther
+    
+    
+     Futures.Currencies.BTIC_MICRO_ETHER
+    
+    : BTIC on Micro Ether Futures (CME: MRB)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.CAD
+    
+    : Canadian Dollar Futures (CME: 6C)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.CADJPY
+    
+    : Canadian Dollar/Japanese Yen Futures (CME: CJY)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.CHF
+    
+    : Swiss Franc Futures (CME: 6S)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.ETH
+    
+    : Ether Futures (CME: ETH)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EUR
+    
+    : Euro FX Futures (CME: 6E)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EURAUD
+    
+    : Euro/Australian Dollar Futures (CME: EAD)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EURCAD
+    
+    : Euro/Canadian Dollar Futures (CME: ECD)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EuroFXEmini
+    
+    
+     Futures.Currencies.EURO_FX_EMINI
+    
+    : E-mini Euro FX Futures (CME: E7)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.EURSEK
+    
+    : Euro/Swedish Krona Futures (CME: ESK)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.GBP
+    
+    : British Pound Futures (CME: 6B)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.JapaneseYenEmini
+    
+    
+     Futures.Currencies.JAPANESE_YEN_EMINI
+    
+    : E-mini Japanese Yen Futures (CME: J7)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.JPY
+    
+    : Japanese Yen Futures (CME: 6J)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroAUD
+    
+    
+     Futures.Currencies.MICRO_AUD
+    
+    : Micro AUD/USD Futures (CME: M6A)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroBTC
+    
+    
+     Futures.Currencies.MICRO_BTC
+    
+    : Micro Bitcoin Futures (CME: MBT)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroCAD
+    
+    
+     Futures.Currencies.MICRO_CAD
+    
+    : Micro USD/CAD Futures (CME: M6C)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroCADUSD
+    
+    
+     Futures.Currencies.MICRO_CADUSD
+    
+    : Micro CAD/USD Futures (CME: MCD)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroCHF
+    
+    
+     Futures.Currencies.MICRO_CHF
+    
+    : Micro CHF/USD Futures (CME: MSF)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroEther
+    
+    
+     Futures.Currencies.MICRO_ETHER
+    
+    : Micro Ether Futures (CME: MET)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroEUR
+    
+    
+     Futures.Currencies.MICRO_EUR
+    
+    : Micro EUR/USD Futures (CME: M6E)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroGBP
+    
+    
+     Futures.Currencies.MICRO_GBP
+    
+    : Micro GBP/USD Futures (CME: M6B)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroINRUSD
+    
+    
+     Futures.Currencies.MICRO_INRUSD
+    
+    : Micro INR/USD Futures (CME: MIR)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroJPY
+    
+    
+     Futures.Currencies.MICRO_JPY
+    
+    : Micro JPY/USD Futures (CME: MJY)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroUSDCHF
+    
+    
+     Futures.Currencies.MICRO_USDCHF
+    
+    : Micro USD/CHF Futures (CME: M6S)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroUSDCNH
+    
+    
+     Futures.Currencies.MICRO_USDCNH
+    
+    : Micro USD/CNH Futures (CME: MNH)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MicroUSDJPY
+    
+    
+     Futures.Currencies.MICRO_USDJPY
+    
+    : Micro USD/JPY Futures (CME: M6J)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.MXN
+    
+    : Mexican Peso Futures (CME: 6M)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.NZD
+    
+    : New Zealand Dollar Futures (CME: 6N)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.RUB
+    
+    : Russian Ruble Futures (CME: 6R)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.StandardSizeUSDOffshoreRMBCNH
+    
+    
+     Futures.Currencies.STANDARD_SIZE_USD_OFFSHORE_RMBCNH
+    
+    : Standard-Size USD/Offshore RMB (CNH) Futures (CME: CNH)
+   
      • 
+   
    • 
+    
+     Futures.Currencies.ZAR
+    
+    : South African Rand Futures (CME: 6Z)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ArgusLLSvsWTIArgusTradeMonth
+    
+    
+     Futures.Energy.ARGUS_LL_SVS_WTI_ARGUS_TRADE_MONTH
+    
+    : Argus LLS vs. WTI (Argus) Trade Month Futures (NYMEX: AE5)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ArgusPropaneFarEastIndex
+    
+    
+     Futures.Energy.ARGUS_PROPANE_FAR_EAST_INDEX
+    
+    : Argus Propane Far East Index Futures (NYMEX: A7E)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ArgusPropaneSaudiAramco
+    
+    
+     Futures.Energy.ARGUS_PROPANE_SAUDI_ARAMCO
+    
+    : Argus Propane (Saudi Aramco) Futures (NYMEX: A9N)
+   
      • 
+   
    • 
+    
+     Futures.Energy.BrentCrudeOilVsDubaiCrudeOilPlatts
+    
+    
+     Futures.Energy.BRENT_CRUDE_OIL_VS_DUBAI_CRUDE_OIL_PLATTS
+    
+    : Brent Crude Oil vs. Dubai Crude Oil (Platts) Futures (NYMEX: ADB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.BrentLastDayFinancial
+    
+    
+     Futures.Energy.BRENT_LAST_DAY_FINANCIAL
+    
+    : Brent Last Day Financial Futures (NYMEX: BZ)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ChicagoEthanolPlatts
+    
+    
+     Futures.Energy.CHICAGO_ETHANOL_PLATTS
+    
+    : Chicago Ethaanol (Platts) Futures (NYMEX: CU)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ConwayPropaneOPIS
+    
+    
+     Futures.Energy.CONWAY_PROPANE_OPIS
+    
+    : Conway Propane (OPIS) Futures (NYMEX: A8K)
+   
      • 
+   
    • 
+    
+     Futures.Energy.CrudeOilWTI
+    
+    
+     Futures.Energy.CRUDE_OIL_WTI
+    
+    : Crude Oil WTI Futures (NYMEX: CL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.DubaiCrudeOilPlattsFinancial
+    
+    
+     Futures.Energy.DUBAI_CRUDE_OIL_PLATTS_FINANCIAL
+    
+    : Dubai Crude Oil (Platts) Financial Futures (NYMEX: DCB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EastWestGasolineSpreadPlattsArgus
+    
+    
+     Futures.Energy.EAST_WEST_GASOLINE_SPREAD_PLATTS_ARGUS
+    
+    : East-West Gasoline Spread (Platts-Argus) Futures (NYMEX: EWG)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EastWestNaphthaJapanCFvsCargoesCIFNWESpreadPlatts
+    
+    
+     Futures.Energy.EAST_WEST_NAPHTHA_JAPAN_C_FVS_CARGOES_CIFNWE_SPREAD_PLATTS
+    
+    : East-West Naphtha: Japan C&F vs. Cargoes CIF NWE Spread (Platts) Futures (NYMEX: EWN)
+   
      • 
+   
    • 
+    
+     Futures.Energy.Ethanol
+    
+    
+     Futures.Energy.ETHANOL
+    
+    : Ethanol Futures (CBOT: EH)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EthanolT2FOBRdamIncludingDutyPlatts
+    
+    
+     Futures.Energy.ETHANOL_T_2_FOB_RDAM_INCLUDING_DUTY_PLATTS
+    
+    : Ethanol T2 FOB Rdam Including Duty (Platts) Futures (NYMEX: AZ1)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EuropeanNaphthaPlattsCrackSpread
+    
+    
+     Futures.Energy.EUROPEAN_NAPHTHA_PLATTS_CRACK_SPREAD
+    
+    : European Naphtha (Platts) Crack Spread Futures (NYMEX: EN)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EuropeanPropaneCIFARAArgus
+    
+    
+     Futures.Energy.EUROPEAN_PROPANE_CIFARA_ARGUS
+    
+    : European Propane CIF ARA (Argus) Futures (NYMEX: APS)
+   
      • 
+   
    • 
+    
+     Futures.Energy.EuropeanPropaneCIFARAArgusVsNaphthaCargoesCIFNWEPlatts
+    
+    
+     Futures.Energy.EUROPEAN_PROPANE_CIFARA_ARGUS_VS_NAPHTHA_CARGOES_CIFNWE_PLATTS
+    
+    : European Propane CIF ARA (Argus) vs. Naphtha Cargoes CIF NWE (Platts) Futures (NYMEX: EPN)
+   
      • 
+   
    • 
+    
+     Futures.Energy.FreightRouteTC14Baltic
+    
+    
+     Futures.Energy.FREIGHT_ROUTE_TC_14_BALTIC
+    
+    : Freight Route TC14 (Baltic) Futures (NYMEX: FRC)
+   
      • 
+   
    • 
+    
+     Futures.Energy.Gasoline
+    
+    
+     Futures.Energy.GASOLINE
+    
+    : Gasoline RBOB Futures (NYMEX: RB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GasolineEurobobOxyNWEBargesArgus
+    
+    
+     Futures.Energy.GASOLINE_EUROBOB_OXY_NWE_BARGES_ARGUS
+    
+    : Gasoline Euro-bob Oxy NWE Barges (Argus) Futures (NYMEX: B7H)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GroupThreeSuboctaneGasolinePlattsVsRBOB
+    
+    
+     Futures.Energy.GROUP_THREE_SUBOCTANE_GASOLINE_PLATTS_VS_RBOB
+    
+    : Group Three Sub-octane Gasoliine (Platts) vs. RBOB Futures (NYMEX: AA8)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GroupThreeULSDPlattsVsNYHarborULSD
+    
+    
+     Futures.Energy.GROUP_THREE_ULSD_PLATTS_VS_NY_HARBOR_ULSD
+    
+    : Group Three ULSD (Platts) vs. NY Harbor ULSD Futures (NYMEX: AA6)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GulfCoastCBOBGasolineA2PlattsVsRBOBGasoline
+    
+    
+     Futures.Energy.GULF_COAST_CBOB_GASOLINE_A_2_PLATTS_VS_RBOB_GASOLINE
+    
+    : Gulf Coast CBOB Gasoline A2 (Platts) vs. RBOB Gasoline Futures (NYMEX: CRB)
+   
      • 
+   
    • 
+    
+     Futures.Energy.GulfCoastHSFOPlattsVsEuropeanThreePointFivePercentFuelOilBargesFOBRdamPlatts
+    
+    
+     Futures.Energy.GULF_COAST_HSFO_PLATTS_VS_EUROPEAN_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
+    
+    : Gulf Coast HSFO (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: GCU)
+   
      • 
+   
    • 
+    
+     Futures.Energy.HeatingOil
+    
+    
+     Futures.Energy.HEATING_OIL
+    
+    : Heating Oil Futures (NYMEX: HO)
+   
      • 
+   
    • 
+    
+     Futures.Energy.LosAngelesCARBOBGasolineOPISvsRBOBGasoline
+    
+    
+     Futures.Energy.LOS_ANGELES_CARBOB_GASOLINE_OPI_SVS_RBOB_GASOLINE
+    
+    : Los Angeles CARBOB Gasoline (OPIS) vs. RBOB Gasoline Futures (NYMEX: AJL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.LosAngelesCARBDieselOPISvsNYHarborULSD
+    
+    
+     Futures.Energy.LOS_ANGELES_CARB_DIESEL_OPI_SVS_NY_HARBOR_ULSD
+    
+    : Los Angeles CARB Diesel (OPIS) vs. NY Harbor ULSD Futures (NYMEX: AKL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.LosAngelesJetOPISvsNYHarborULSD
+    
+    
+     Futures.Energy.LOS_ANGELES_JET_OPI_SVS_NY_HARBOR_ULSD
+    
+    : Los Angeles Jet (OPIS) vs. NY Harbor ULSD Futures (NYMEX: AJS)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MarsArgusVsWTIFinancial
+    
+    
+     Futures.Energy.MARS_ARGUS_VS_WTI_FINANCIAL
+    
+    : Mars (Argus) vs. WTI Financial Futures (NYMEX: AYX)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MarsArgusVsWTITradeMonth
+    
+    
+     Futures.Energy.MARS_ARGUS_VS_WTI_TRADE_MONTH
+    
+    : Mars (Argus) vs. WTI Trade Month Futures (NYMEX: AYV)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroCrudeOilWTI
+    
+    
+     Futures.Energy.MICRO_CRUDE_OIL_WTI
+    
+    : Micro WTI Crude Oil Futures (NYMEX: MCL)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroEuropeanFOBRdamMarineFuelZeroPointFivePercentBargesPlatts
+    
+    
+     Futures.Energy.MICRO_EUROPEAN_FOB_RDAM_MARINE_FUEL_ZERO_POINT_FIVE_PERCENT_BARGES_PLATTS
+    
+    : Micro European FOB Rdam Marine Fuel 0.5% Barges (Platts) Futures (NYMEX: R5O)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroEuropeanThreePointFivePercentOilBargesFOBRdamPlatts
+    
+    
+     Futures.Energy.MICRO_EUROPEAN_THREE_POINT_FIVE_PERCENT_OIL_BARGES_FOB_RDAM_PLATTS
+    
+    : Micro European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: MEF)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroGasoilZeroPointOnePercentBargesFOBARAPlatts
+    
+    
+     Futures.Energy.MICRO_GASOIL_ZERO_POINT_ONE_PERCENT_BARGES_FOBARA_PLATTS
+    
+    : Micro Gasoil 0.1% Barges FOB ARA (Platts) Futures (NYMEX: M1B)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroSingaporeFOBMarineFuelZeroPointFivePercetPlatts
+    
+    
+     Futures.Energy.MICRO_SINGAPORE_FOB_MARINE_FUEL_ZERO_POINT_FIVE_PERCET_PLATTS
+    
+    : Micro Singapore FOB Marine Fuel 0.5% (Platts) Futures (NYMEX: S5O)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MicroSingaporeFuelOil380CSTPlatts
+    
+    
+     Futures.Energy.MICRO_SINGAPORE_FUEL_OIL_380_CST_PLATTS
+    
+    : Micro Singapore Fuel Oil 380CST (Platts) Futures (NYMEX: MAF)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MiniEuropeanThreePointPercentFiveFuelOilBargesPlatts
+    
+    
+     Futures.Energy.MINI_EUROPEAN_THREE_POINT_PERCENT_FIVE_FUEL_OIL_BARGES_PLATTS
+    
+    : Mini European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: A0D)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MiniSingaporeFuelOil180CstPlatts
+    
+    
+     Futures.Energy.MINI_SINGAPORE_FUEL_OIL_180_CST_PLATTS
+    
+    : Mini Singapore Fuel Oil 180 cst (Platts) Futures (NYMEX: A0F)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MontBelvieuEthaneOPIS
+    
+    
+     Futures.Energy.MONT_BELVIEU_ETHANE_OPIS
+    
+    : Mont Belvieu Ethane (OPIS) Futures (NYMEX: AC0)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MontBelvieuLDHPropaneOPIS
+    
+    
+     Futures.Energy.MONT_BELVIEU_LDH_PROPANE_OPIS
+    
+    : Mont Belvieu LDH Propane (OPIS) Futures (NYMEX: B0)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MontBelvieuNaturalGasolineOPIS
+    
+    
+     Futures.Energy.MONT_BELVIEU_NATURAL_GASOLINE_OPIS
+    
+    : Mont Belvieu Natural Gasoline (OPIS) Futures (NYMEX: A7Q)
+   
      • 
+   
    • 
+    
+     Futures.Energy.MontBelvieuNormalButaneOPIS
+    
+    
+     Futures.Energy.MONT_BELVIEU_NORMAL_BUTANE_OPIS
+    
+    : Mont Belvieu Normal Butane (OPIS) Futures (NYMEX: AD0)
+   
      • 
+   
    • 
+    
+     Futures.Energy.NaturalGas
+    
+    
+     Futures.Energy.NATURAL_GAS
+    
+    : Natural Gas Futures (NYMEX: NG)
+   
      • 
+   
    • 
+    
+     Futures.Energy.NaturalGasHenryHubLastDayFinancial
+    
+    
+     Futures.Energy.NATURAL_GAS_HENRY_HUB_LAST_DAY_FINANCIAL
+    
+    : Natural Gas (Henry Hub) Last-day Financial Futures (NYMEX: HH)
+   
      • 
+   
    • 
+    
+     Futures.Energy.NaturalGasHenryHubPenultimateFinancial
+    
+    
+     Futures.Energy.NATURAL_GAS_HENRY_HUB_PENULTIMATE_FINANCIAL
+    
+    : Natural Gas (Henry Hub) Penultimate Financial Futures (NYMEX: HP)
+   
      • 
+   
    • 
+    
+     Futures.Energy.OnePercentFuelOilCargoesFOBNWEPlattsVsThreePointFivePercentFuelOilBargesFOBRdamPlatts
+    
+    
+     Futures.Energy.ONE_PERCENT_FUEL_OIL_CARGOES_FOBNWE_PLATTS_VS_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
+    
+    : 1% Fuel Oil Cargoes FOB NWE (Platts) vs. 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: FSS)
+   
      • 
+   
    • 
+    
+     Futures.Energy.PremiumUnleadedGasoline10ppmFOBMEDPlatts
+    
+    
+     Futures.Energy.PREMIUM_UNLEADED_GASOLINE_10_PPM_FOBMED_PLATTS
+    
+    : Premium Unleaded Gasoline 10 ppm FOB MED (Platts) Futures (NYMEX: A3G)
+   
      • 
+   
    • 
+    
+     Futures.Energy.PropaneNonLDHMontBelvieuOPIS
+    
+    
+     Futures.Energy.PROPANE_NON_LDH_MONT_BELVIEU_OPIS
+    
+    : Propane Non-LDH Mont Belvieu (OPIS) Futures (NYMEX: A1R)
+   
      • 
+   
    • 
+    
+     Futures.Energy.RBOBGasolineCrackSpread
+    
+    
+     Futures.Energy.RBOB_GASOLINE_CRACK_SPREAD
+    
+    : RBOB Gasoline Crack Spread Futures (NYMEX: ARE)
+   
      • 
+   
    • 
+    
+     Futures.Energy.RBOBGasolineVsEurobobOxyNWEBargesArgusThreeHundredFiftyThousandGallons
+    
+    
+     Futures.Energy.RBOB_GASOLINE_VS_EUROBOB_OXY_NWE_BARGES_ARGUS_THREE_HUNDRED_FIFTY_THOUSAND_GALLONS
+    
+    : RBOB Gasoline vs. Euro-bob Oxy NWE Barges (Argus) (350,000 gallons) Futures (NYMEX: EXR)
+   
      • 
+   
    • 
+    
+     Futures.Energy.SingaporeFuelOil380cstPlattsVsEuropeanThreePointFivePercentFuelOilBargesFOBRdamPlatts
+    
+    
+     Futures.Energy.SINGAPORE_FUEL_OIL_380_CST_PLATTS_VS_EUROPEAN_THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS
+    
+    : Singapore Fuel Oil 380 cst (Platts) vs. European 3.5% Fuel Oil Barges FOB Rdam (Platts) Futures (NYMEX: EVC)
+   
      • 
+   
    • 
+    
+     Futures.Energy.SingaporeGasoilPlattsVsLowSulphurGasoilFutures
+    
+    
+     Futures.Energy.SINGAPORE_GASOIL_PLATTS_VS_LOW_SULPHUR_GASOIL_FUTURES
+    
+    : Singapore Gasoil (Platts) vs. Low Sulphur Gasoil Futures (NYMEX: AGA)
+   
      • 
+   
    • 
+    
+     Futures.Energy.SingaporeMogas92UnleadedPlattsBrentCrackSpread
+    
+    
+     Futures.Energy.SINGAPORE_MOGAS_92_UNLEADED_PLATTS_BRENT_CRACK_SPREAD
+    
+    : Singapore Mogas 92 Unleaded (Platts) Brent Crack Spread Futures (NYMEX: D1N)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ThreePointFivePercentFuelOilBargesFOBRdamPlattsCrackSpread
+    
+    
+     Futures.Energy.THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS_CRACK_SPREAD
+    
+    : 3.5% Fuel Oil Barges FOB Rdam (Platts) Crack Spread Futures (NYMEX: FO)
+   
      • 
+   
    • 
+    
+     Futures.Energy.ThreePointFivePercentFuelOilBargesFOBRdamPlattsCrackSpread1000mt
+    
+    
+     Futures.Energy.THREE_POINT_FIVE_PERCENT_FUEL_OIL_BARGES_FOB_RDAM_PLATTS_CRACK_SPREAD_1000_MT
+    
+    : 3.5% Fuel Oil Barges FOB Rdam (Platts) Crack Spread (1000mt) Futures (NYMEX: BOO)
+   
      • 
+   
    • 
+    
+     Futures.Energy.WTIBrentFinancial
+    
+    
+     Futures.Energy.WTI_BRENT_FINANCIAL
+    
+    : WTI-Brent Financial Futures (NYMEX: BK)
+   
      • 
+   
    • 
+    
+     Futures.Energy.WTIFinancial
+    
+    
+     Futures.Energy.WTI_FINANCIAL
+    
+    : WTI Financial Futures (NYMEX: CSX)
+   
      • 
+   
    • 
+    
+     Futures.Energy.WTIHoustonArgusVsWTITradeMonth
+    
+    
+     Futures.Energy.WTI_HOUSTON_ARGUS_VS_WTI_TRADE_MONTH
+    
+    : WTI Houston (Argus) vs. WTI Trade Month Futures (NYMEX: HTT)
+   
      • 
+   
    • 
+    
+     Futures.Energy.WTIHoustonCrudeOil
+    
+    
+     Futures.Energy.WTI_HOUSTON_CRUDE_OIL
+    
+    : WTI Houston Crude Oil Futures (NYMEX: HCL)
+   
      • 
+   
    • 
+    
+     Futures.Financials.EuroDollar
+    
+    
+     Futures.Financials.EURO_DOLLAR
+    
+    : EuroDollar Futures (CME: GE)
+   
      • 
+   
    • 
+    
+     Futures.Financials.FiveYearUSDMACSwap
+    
+    
+     Futures.Financials.FIVE_YEAR_USDMAC_SWAP
+    
+    : 5-Year USD MAC Swap Futures (CBOT: F1U)
+   
      • 
+   
    • 
+    
+     Futures.Financials.MicroY10TreasuryNote
+    
+    
+     Futures.Financials.MICRO_Y_10_TREASURY_NOTE
+    
+    : Micro 10-Year Yield Futures (CBOT: 10Y)
+   
      • 
+   
    • 
+    
+     Futures.Financials.MicroY2TreasuryBond
+    
+    
+     Futures.Financials.MICRO_Y_2_TREASURY_BOND
+    
+    : Micro 2-Year Yield Futures (CBOT: 2YY)
+   
      • 
+   
    • 
+    
+     Futures.Financials.MicroY30TreasuryBond
+    
+    
+     Futures.Financials.MICRO_Y_30_TREASURY_BOND
+    
+    : Micro 30-Year Yield Futures (CBOT: 30Y)
+   
      • 
+   
    • 
+    
+     Futures.Financials.MicroY5TreasuryBond
+    
+    
+     Futures.Financials.MICRO_Y_5_TREASURY_BOND
+    
+    : Micro 5-Year Yield Futures (CBOT: 5YY)
+   
      • 
+   
    • 
+    
+     Futures.Financials.UltraTenYearUSTreasuryNote
+    
+    
+     Futures.Financials.ULTRA_TEN_YEAR_US_TREASURY_NOTE
+    
+    : Ultra 10-Year U.S. Treasury Note Futures (CBOT: TN)
+   
      • 
+   
    • 
+    
+     Futures.Financials.UltraUSTreasuryBond
+    
+    
+     Futures.Financials.ULTRA_US_TREASURY_BOND
+    
+    : Ultra U.S. Treasury Bond Futures (CBOT: UB)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y10TreasuryNote
+    
+    
+     Futures.Financials.Y_10_TREASURY_NOTE
+    
+    : 10Y U.S. Treasury Note Futures (CBOT: ZN)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y2TreasuryNote
+    
+    
+     Futures.Financials.Y_2_TREASURY_NOTE
+    
+    : 2Y U.S. Treasury Note Futures (CBOT: ZT)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y30TreasuryBond
+    
+    
+     Futures.Financials.Y_30_TREASURY_BOND
+    
+    : 30Y U.S. Treasury Bond Futures (CBOT: ZB)
+   
      • 
+   
    • 
+    
+     Futures.Financials.Y5TreasuryNote
+    
+    
+     Futures.Financials.Y_5_TREASURY_NOTE
+    
+    : 5Y U.S. Treasury Note Futures (CBOT: ZF)
+   
      • 
+   
    • 
+    
+     Futures.Forestry.Lumber
+    
+    
+     Futures.Forestry.LUMBER
+    
+    : Lumber Futures (CME: LBR)
+   
      • 
+   
    • 
+    
+     Futures.Forestry.RandomLengthLumber
+    
+    
+     Futures.Forestry.RANDOM_LENGTH_LUMBER
+    
+    : Random Length Lumber Futures (CME: LBS)
+   
      • 
+   
    • 
+    
+     Futures.Grains.BlackSeaCornFinanciallySettledPlatts
+    
+    
+     Futures.Grains.BLACK_SEA_CORN_FINANCIALLY_SETTLED_PLATTS
+    
+    : Black Sea Corn Financially Settled (Platts) Futures (CBOT: BCF)
+   
      • 
+   
    • 
+    
+     Futures.Grains.BlackSeaWheatFinanciallySettledPlatts
+    
+    
+     Futures.Grains.BLACK_SEA_WHEAT_FINANCIALLY_SETTLED_PLATTS
+    
+    : Black Sea Wheat Financially Settled (Platts) Futures (CBOT: BWF)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Corn
+    
+    
+     Futures.Grains.CORN
+    
+    : Corn Futures (CBOT: ZC)
+   
      • 
+   
    • 
+    
+     Futures.Grains.HRWWheat
+    
+    
+     Futures.Grains.HRW_WHEAT
+    
+    : KC HRW Wheat Futures (CBOT: KE)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Oats
+    
+    
+     Futures.Grains.OATS
+    
+    : Oats Futures (CBOT: ZO)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Soybeans
+    
+    
+     Futures.Grains.SOYBEANS
+    
+    : Soybeans Futures (CBOT: ZS)
+   
      • 
+   
    • 
+    
+     Futures.Grains.SoybeanMeal
+    
+    
+     Futures.Grains.SOYBEAN_MEAL
+    
+    : Soybean Meal Futures (CBOT: ZM)
+   
      • 
+   
    • 
+    
+     Futures.Grains.SoybeanOil
+    
+    
+     Futures.Grains.SOYBEAN_OIL
+    
+    : Soybean Oil Futures (CBOT: ZL)
+   
      • 
+   
    • 
+    
+     Futures.Grains.Wheat
+    
+    
+     Futures.Grains.WHEAT
+    
+    : Default wheat contract is SRWWheat (CBOT: ZW)
+   
      • 
+   
    • 
+    
+     Futures.Indices.BloombergCommodityIndex
+    
+    
+     Futures.Indices.BLOOMBERG_COMMODITY_INDEX
+    
+    : Bloomberg Commodity Index Futures (CBOT: AW)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Dow30EMini
+    
+    
+     Futures.Indices.DOW_30_E_MINI
+    
+    : E-mini Dow Indu 30 Futures (CBOT: YM)
+   
      • 
+   
    • 
+    
+     Futures.Indices.DowJonesRealEstate
+    
+    
+     Futures.Indices.DOW_JONES_REAL_ESTATE
+    
+    : Dow Jones Real Estate futures on CME (CME: RX)
+   
      • 
+   
    • 
+    
+     Futures.Indices.FTSEEmergingEmini
+    
+    
+     Futures.Indices.FTSE_EMERGING_EMINI
+    
+    : E-mini FTSE Emerging Index Futures (CME: EI)
+   
      • 
+   
    • 
+    
+     Futures.Indices.MicroDow30EMini
+    
+    
+     Futures.Indices.MICRO_DOW_30_E_MINI
+    
+    : Micro E-mini Dow Jones Industrial Average Index Futures (CBOT: MYM)
+   
      • 
+   
    • 
+    
+     Futures.Indices.MicroNASDAQ100EMini
+    
+    
+     Futures.Indices.MICRO_NASDAQ_100_E_MINI
+    
+    : Micro E-mini Nasdaq-100 Index Futures (CME: MNQ)
+   
      • 
+   
    • 
+    
+     Futures.Indices.MicroRussell2000EMini
+    
+    
+     Futures.Indices.MICRO_RUSSELL_2000_E_MINI
+    
+    : Micro E-mini Russell 2000 Index Futures (CME: M2K)
+   
      • 
+   
    • 
+    
+     Futures.Indices.MicroSP500EMini
+    
+    
+     Futures.Indices.MICRO_SP_500_E_MINI
+    
+    : Micro E-mini S&P 500 Index Futures (CME: MES)
+   
      • 
+   
    • 
+    
+     Futures.Indices.NASDAQ100BiotechnologyEMini
+    
+    
+     Futures.Indices.NASDAQ_100_BIOTECHNOLOGY_E_MINI
+    
+    : E-mini Nasdaq-100 Biotechnology Index Futures (CME: BIO)
+   
      • 
+   
    • 
+    
+     Futures.Indices.NASDAQ100EMini
+    
+    
+     Futures.Indices.NASDAQ_100_E_MINI
+    
+    : E-mini NASDAQ 100 Futures (CME: NQ)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Nikkei225Dollar
+    
+    
+     Futures.Indices.NIKKEI_225_DOLLAR
+    
+    : Nikkei-225 Dollar Futures (CME: NKD)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Nikkei225YenCME
+    
+    
+     Futures.Indices.NIKKEI_225_YEN_CME
+    
+    : Nikkei-225 Yen denominated Futures on CME (CME: NIY)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Russell1000EMini
+    
+    
+     Futures.Indices.RUSSELL_1000_E_MINI
+    
+    : E-mini Russell 1000 futures on CME (CME: RS1)
+   
      • 
+   
    • 
+    
+     Futures.Indices.Russell2000EMini
+    
+    
+     Futures.Indices.RUSSELL_2000_E_MINI
+    
+    : E-mini Russell 2000 Futures (CME: RTY)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SPGSCICommodity
+    
+    
+     Futures.Indices.SPGSCI_COMMODITY
+    
+    : S&P-GSCI Commodity Index Futures (CME: GD)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SP400MidCapEmini
+    
+    
+     Futures.Indices.SP_400_MID_CAP_EMINI
+    
+    : E-mini S&P MidCap 400 Futures (CME: EMD)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SP500AnnualDividendIndex
+    
+    
+     Futures.Indices.SP_500_ANNUAL_DIVIDEND_INDEX
+    
+    :  (CME: SDA)
+   
      • 
+   
    • 
+    
+     Futures.Indices.SP500EMini
+    
+    
+     Futures.Indices.SP_500_E_MINI
+    
+    : E-mini S&P 500 Futures (CME: ES)
+   
      • 
+   
    • 
+    
+     Futures.Indices.TOPIXYEN
+    
+    : YEN Denominated Topix Index Futures on CME (CME: TPY)
+   
      • 
+   
    • 
+    
+     Futures.Indices.USDDenominatedIbovespa
+    
+    
+     Futures.Indices.USD_DENOMINATED_IBOVESPA
+    
+    : USD-Denominated Ibovespa Index Futures (CME: IBV)
+   
      • 
+   
    • 
+    
+     Futures.Indices.VIX
+    
+    : CBOE Volatility Index Futures (CFE: VX)
+   
      • 
+   
    • 
+    
+     Futures.Meats.FeederCattle
+    
+    
+     Futures.Meats.FEEDER_CATTLE
+    
+    : Feeder Cattle Futures (CME: GF)
+   
      • 
+   
    • 
+    
+     Futures.Meats.LeanHogs
+    
+    
+     Futures.Meats.LEAN_HOGS
+    
+    : Lean Hogs Futures (CME: HE)
+   
      • 
+   
    • 
+    
+     Futures.Meats.LiveCattle
+    
+    
+     Futures.Meats.LIVE_CATTLE
+    
+    : Live Cattle Futures (CME: LE)
+   
      • 
+   
    • 
+    
+     Futures.Metals.AluminiumEuropeanPremiumDutyPaidMetalBulletin
+    
+    
+     Futures.Metals.ALUMINIUM_EUROPEAN_PREMIUM_DUTY_PAID_METAL_BULLETIN
+    
+    : Aluminium European Premium Duty-Paid (Metal Bulletin) Futures (COMEX: EDP)
+   
      • 
+   
    • 
+    
+     Futures.Metals.AluminumMWUSTransactionPremiumPlatts25MT
+    
+    
+     Futures.Metals.ALUMINUM_MWUS_TRANSACTION_PREMIUM_PLATTS_25_MT
+    
+    : Aluminum MW U.S. Transaction Premium Platts (25MT) Futures (COMEX: AUP)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Copper
+    
+    
+     Futures.Metals.COPPER
+    
+    : Copper Futures (COMEX: HG)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Gold
+    
+    
+     Futures.Metals.GOLD
+    
+    : Gold Futures (COMEX: GC)
+   
      • 
+   
    • 
+    
+     Futures.Metals.MicroGold
+    
+    
+     Futures.Metals.MICRO_GOLD
+    
+    : Micro Gold Futures (COMEX: MGC)
+   
      • 
+   
    • 
+    
+     Futures.Metals.MicroGoldTAS
+    
+    
+     Futures.Metals.MICRO_GOLD_TAS
+    
+    : Micro Gold TAS Futures (COMEX: MGT)
+   
      • 
+   
    • 
+    
+     Futures.Metals.MicroPalladium
+    
+    
+     Futures.Metals.MICRO_PALLADIUM
+    
+    : Micro Palladium Futures (NYMEX: PAM)
+   
      • 
+   
    • 
+    
+     Futures.Metals.MicroSilver
+    
+    
+     Futures.Metals.MICRO_SILVER
+    
+    : Micro Silver Futures (COMEX: SIL)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Palladium
+    
+    
+     Futures.Metals.PALLADIUM
+    
+    : Palladium Futures (NYMEX: PA)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Platinum
+    
+    
+     Futures.Metals.PLATINUM
+    
+    : Platinum Futures (NYMEX: PL)
+   
      • 
+   
    • 
+    
+     Futures.Metals.Silver
+    
+    
+     Futures.Metals.SILVER
+    
+    : Silver Futures (COMEX: SI)
+   
      • 
+   
    • 
+    
+     Futures.Metals.USMidwestDomesticHotRolledCoilSteelCRUIndex
+    
+    
+     Futures.Metals.US_MIDWEST_DOMESTIC_HOT_ROLLED_COIL_STEEL_CRU_INDEX
+    
+    : U.S. Midwest Domestic Hot-Rolled Coil Steel (CRU) Index Futures (NYMEX: HRC)
+   
      • 
+   
    • 
+    
+     Futures.Softs.Sugar11
+    
+    
+     Futures.Softs.SUGAR_11
+    
+    : Sugar #11 Futures ICE (ICE: SB)
+   
      • 
+   
    • 
+    
+     Futures.Softs.Sugar11CME
+    
+    
+     Futures.Softs.SUGAR_11_CME
+    
+    : Sugar #11 Futures CME (NYMEX: YO)
+   
      • 
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Futures dataset enables you to accurately design Futures strategies. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Buying the Futures contract with the most open interest to reduce slippage and market impact
+   
      • 
+   
    • 
+    Trading bull calendar spreads to reduce volatility and margin requirements
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm buys the Mini Gold Futures contract with the greatest open interest and sells the Micro Gold Futures contract with the greatest open interest. When the open interest of a different contract exceeds the open interest of a contract in the portfolio, the algorithm rebalances the portfolio.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class USFuturesDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(1000000)
+        # Setting the continuous contract mapping criteria for both Gold and Micro Gold contracts, since we want to order the highest liquidity contracts
+        self.settings.seed_initial_prices = True
+        self.gold = self.add_future(Futures.Metals.GOLD,
+            extended_market_hours=True,
+            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
+            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
+            contract_depth_offset=0)
+        self.micro_gold = self.add_future(Futures.Metals.MICRO_GOLD,
+            extended_market_hours=True,
+            data_mapping_mode=DataMappingMode.OPEN_INTEREST,
+            data_normalization_mode=DataNormalizationMode.BACKWARDS_RATIO,
+            contract_depth_offset=0)
+        # The contract multiplier is cached in the Security symbol_properties property from the symbol properties database
+        self.gold_multiplier = self.gold.symbol_properties.contract_multiplier
+        self.micro_gold_multiplier = self.micro_gold.symbol_properties.contract_multiplier
+
+    def on_data(self, slice: Slice) -> None:
+        # Make sure to calculate the order size by the most updated price data of both contracts
+        if not self.portfolio.invested and self.gold.symbol in slice.bars and self.micro_gold.symbol in slice.bars:
+            # Calculate the order size for $500k
+            # Get the quotient after dividing the contract multiplier since the order size must be whole number
+            gold_quantity = 500000 / slice.bars[self.gold.symbol].close // self.gold_multiplier
+            micro_gold_quantity = 500000 / slice.bars[self.micro_gold.symbol].close // self.micro_gold_multiplier
+
+            self.market_order(self.gold.mapped, gold_quantity)
+            self.market_order(self.micro_gold.mapped, micro_gold_quantity)
+                
+    def on_securities_changed(self, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            # Historical data
+            history = self.history(security.symbol, 10, Resolution.MINUTE)
+            self.debug(f"We got {len(history)} from our history request for {security.symbol}")
    
+   
    public class USFuturesDataAlgorithm : QCAlgorithm
+{
+    private Future _gold, _microGold;
+    private decimal _goldMulitplier, _microGoldMulitplier;
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(1000000);
+        // Setting the continuous contract mapping criteria for both Gold and Micro Gold contracts, since we want to order the highest liquidity contracts
+        Settings.SeedInitialPrices = true;
+        _gold = AddFuture(Futures.Metals.Gold,
+            extendedMarketHours: true,
+            dataMappingMode: DataMappingMode.OpenInterest,
+            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
+            contractDepthOffset: 0);
+        _microGold = AddFuture(Futures.Metals.MicroGold,
+            extendedMarketHours: true,
+            dataMappingMode: DataMappingMode.OpenInterest,
+            dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
+            contractDepthOffset: 0);
+        // The contract multiplier is cached in the Security SymbolProperties property from the symbol properties database
+        _goldMulitplier = _gold.SymbolProperties.ContractMultiplier;
+        _microGoldMulitplier = _microGold.SymbolProperties.ContractMultiplier;
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // Make sure to calculate the order size by the most updated price data of both contracts
+        if (!Portfolio.Invested && slice.Bars.ContainsKey(_gold.Symbol) && slice.Bars.ContainsKey(_microGold.Symbol))
+        {
+            // Calculate the order size for $500k
+            // Get the quotient after dividing the contract multiplier since the order size must be whole number
+            var goldQuantity = Math.Floor(500000m / slice.Bars[_gold.Symbol].Close / _goldMulitplier);
+            var microGoldQuantity = Math.Floor(500000m / slice.Bars[_microGold.Symbol].Close / _microGoldMulitplier);
+
+            MarketOrder(_gold.Mapped, goldQuantity);
+            MarketOrder(_microGold.Mapped, microGoldQuantity);
+        }
+    }
+    
+    public override void OnSecuritiesChanged(SecurityChanges changes)
+    {
+        foreach (var security in changes.AddedSecurities)
+        {
+            // Historical data
+            var history = History(security.Symbol, 100, Resolution.Minute);
+            Debug($"We got {history.Count()} from our history request for {security.Symbol}");
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm buys the front-month Mini Gold Futures contract and sells the front-month Micro Gold Futures contract. When the front-month contract changes, the algorithm rebalances the portfolio.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class USFuturesDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000)
+        self.universe_settings.asynchronous = True
+        # Set up an universe selection model that selects the front month contract
+        self.set_universe_selection(FrontMonthFutureUniverseSelectionModel())
+        self.add_alpha(ConstantFuturesAlphaModel())
+        # A portfolio construction model that only order a single share per insight signal
+        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
+        
+class FrontMonthFutureUniverseSelectionModel(FutureUniverseSelectionModel):
+    def __init__(self,) -> None:
+        # Daily updating with select_future_chain_symbols function
+        super().__init__(timedelta(1), self.select_future_chain_symbols)
+
+    def select_future_chain_symbols(self, utcTime: datetime) -> List[Symbol]:
+        # Select gold and micro gold contracts for the strategy need
+        future_pairs = [
+            (Futures.Metals.GOLD, Market.COMEX),
+            (Futures.Metals.MICRO_GOLD, Market.COMEX)
+        ]
+        return [Symbol.create(pair[0], SecurityType.FUTURE, pair[1]) for pair in future_pairs]
+
+    def filter(self, filter: FutureFilterUniverse) -> FutureFilterUniverse:
+        # Filter only front month contract for liquidity and most informed information
+        return filter.front_month().only_apply_filter_at_market_open()
+
+class ConstantFuturesAlphaModel(AlphaModel):
+    # Long gold and short micro gold in this strategy
+    long_symbol = Symbol.create(Futures.Metals.GOLD, SecurityType.FUTURE, Market.COMEX)
+    short_symbol = Symbol.create(Futures.Metals.MICRO_GOLD, SecurityType.FUTURE, Market.COMEX)
+        
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        if algorithm.portfolio.invested:
+            return []
+        
+        insights = []
+        # For both gold and micro gold, select the front month contract (only contract) in the chain
+        for symbol, contracts in slice.future_chains.items():
+            chain = [contract for contract in contracts]
+            contract = chain[0]
+            
+            # Long gold and short micro gold as planned
+            if symbol == self.long_symbol:
+                insights.append(Insight.price(contract.symbol, contract.expiry + timedelta(days=1), InsightDirection.UP))
+            elif symbol == self.short_symbol:
+                insights.append(Insight.price(contract.symbol, contract.expiry + timedelta(days=1), InsightDirection.DOWN))
+        
+        return insights
+        
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            # Historical data
+            history = algorithm.history(security.symbol, 10, Resolution.MINUTE)
+            algorithm.debug(f"We got {len(history)} from our history request for {security.symbol}")
+
+class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
+    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
+        targets = []
+        for insight in insights:
+            if algorithm.securities[insight.symbol].is_tradable:
+                # Single share only using integer portfolio target
+                targets.append(PortfolioTarget(insight.symbol, insight.direction))
+        return targets
    
+   
    public class USFuturesDataAlgorithm : QCAlgorithm
+{        
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(1000000);
+        UniverseSettings.Asynchronous = true;
+        // Set up an universe selection model that selects the front month contract
+        SetUniverseSelection(new FrontMonthFutureUniverseSelectionModel());
+        SetAlpha(new ConstantFuturesAlphaModel());
+        // A portfolio construction model that only order a single share per insight signal
+        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
+    }
+}
+    
+class FrontMonthFutureUniverseSelectionModel : FutureUniverseSelectionModel
+{
+    // Daily updating with select_future_chain_symbols function
+    public FrontMonthFutureUniverseSelectionModel()
+        : base(TimeSpan.FromDays(1), SelectFutureChainSymbols) {}
+
+    private static IEnumerable<Symbol> SelectFutureChainSymbols(DateTime utcTime)
+    {
+        //Select gold and micro gold contracts for the strategy need
+        return new List<Symbol> {
+            QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX),
+            QuantConnect.Symbol.Create(Futures.Metals.MicroGold, SecurityType.Future, Market.COMEX)
+        };
+    }
+
+    protected override FutureFilterUniverse Filter(FutureFilterUniverse filter)
+    {
+        // Filter only front month contract for liquidity and most informed information
+        return filter.FrontMonth().OnlyApplyFilterAtMarketOpen();
+    }
+}
+
+
+class ConstantFuturesAlphaModel : AlphaModel
+{
+    // Long gold and short micro gold in this strategy
+    private Symbol 
+        _longSymbol = QuantConnect.Symbol.Create(Futures.Metals.Gold, SecurityType.Future, Market.COMEX), 
+        _shortSymbol = QuantConnect.Symbol.Create(Futures.Metals.MicroGold, SecurityType.Future, Market.COMEX);
+    
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    {
+        var insights = new List<Insight>();
+
+        if (algorithm.Portfolio.Invested)
+        {
+            return insights;
+        }
+        
+        // For both gold and micro gold, select the front month contract (only contract) in the chain
+        foreach (var kvp in slice.FutureChains)
+        {
+            var symbol = kvp.Key;
+            var chain = kvp.Value;
+            var contract = chain.First();
+            
+            // Long gold and short micro gold as planned
+            if (symbol == _longSymbol)
+            {
+                insights.Add(Insight.Price(contract.Symbol, contract.Expiry + TimeSpan.FromDays(1), InsightDirection.Up));
+            }
+            else if (symbol == _shortSymbol)
+            {
+                insights.Add(Insight.Price(contract.Symbol, contract.Expiry + TimeSpan.FromDays(1), InsightDirection.Down));
+            }
+        }
+
+        return insights;
+    }
+    
+    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
+    {
+        foreach (var security in changes.AddedSecurities)
+        {
+            // Historical data
+            var history = algorithm.History(security.Symbol, 100, Resolution.Minute);
+            algorithm.Debug($"We got {history.Count()} from our history request for {security.Symbol}");
+        }
+    }
+}
+    
+    
+class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
+{
+    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
+    {
+        var targets = new List<PortfolioTarget>();
+        foreach (var insight in insights)
+        {
+            if (algorithm.Securities[insight.Symbol].IsTradable)
+            {
+                // Single share only using integer portfolio target
+                targets.Add(new PortfolioTarget(insight.Symbol, (int) insight.Direction));
+            }
+        }
+        return targets;
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Futures dataset provides
+   
+    FuturesChain
+   
+   ,
+   
+    Future
+   
+   , and
+   
+    OpenInterest
+   
+   objects. To configure the continuous Future settings, use the
+   
+    DataNormalizationMode
+   
+   and
+   
+    DataMappingMode
+   
+   enumerations.
+  
    
+  
    
+   DataNormalizationMode Values
+  
    
+  
    
+   The
+   
+    DataNormalizationMode
+   
+   enumeration has the following values:
+  
    
+  
    
+  
    
+  
    
+   DataMappingMode Values
+  
    
+  
    
+   The
+   
+    DataMappingMode
+   
+   enumeration has the following values:
+  
    
+  
    
+  
    
+  
    
+   Future Attributes
+  
    
+  
    
+   
+    Future
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   FuturesChain Attributes
+  
    
+  
    
+   
+    FuturesChain
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   OpenInterest Attributes
+  
    
+  
    
+   
+    OpenInterest
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > AlgoSeek > US Index Options
    
+
    
+    
    
+        
    AlgoSeek
    
+        
    US Index Options
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Index Options dataset by AlgoSeek provides Option data, including prices, strikes, expires, and open interest. The data covers European Option contracts for 3 US Indices: SPX, VIX, and NDX. It starts from January 2012 and is delivered on minute resolution. This dataset is created by monitoring the Options Price Reporting Authority (OPRA) data feed, which consolidates last sale and quotation information originating from the national securities exchanges that have been approved by the Securities and Exchange Commission.
+  
    
+  
    
+   The US Index Options dataset depends on the
+   
+    US Index Option Universe
+   
+   dataset because the US Index Options Universe dataset contains information on the available contracts, including their daily Greeks and implied volatility values.
+  
    
+  
    
+   For more information about the US Index Options dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   AlgoSeek is a leading historical intraday US market data provider offering the most comprehensive and detailed market data and analytics products in the financial industry covering Equities, Futures, Options, cash FOREX, and Cryptocurrencies. AlgoSeek data is built for quantitative trading and machine learning.  For more information about AlgoSeek, visit
+   
+    algoseek.com
+   
+   .
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the US Index Options dataset:
+  
    
+  
    
+   
    self.index_symbol = self.add_index('VIX').symbol
+option = self.add_index_option(self.index_symbol)
+option.set_filter(-2, 2, 0, 90)
+self.option_symbol = option.symbol
    
+   
    _indexSymbol = AddIndex("VIX").Symbol;
+var option = AddIndexOption(_indexSymbol);
+option.SetFilter(-2, 2, 0, 90);
+_optionSymbol = option.Symbol;
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 2012
+     
    
+      Asset Coverage
+     
+      7 Index Options
+     
    
+      Data Density
+     
+      Regular
+     
    
+      Resolution
+     
+      Minute, Hourly, & Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+      Market Hours
+     
+      
+       Regular Only
+      
+     
    
+ 
+
+
+
    Specification Over Time
    
+
+ 
+  
    
+   According to the SPX Options
+   
+    contract specification
+   
+   , some SPX contracts expire every month and SPXW contracts expires every day. Before 2021, you could only trade SPX contracts with the following expiration dates:
+  
    
+  
      
+   
    • 
+    Expires within the next 4 months
+   
      • 
+   
    • 
+    Expires in September within the next 14 months
+   
      • 
+   
    • 
+    Expires in January, March, or June within the next 2 years
+   
      • 
+   
    • 
+    Expires in December within the next 3 years
+   
      • 
+  
    
+  
    
+   During this time, SPXW didn't have 0DTE every day.
+  
    
+  
    
+   
+    Sources
+   
+   :
+   
    
+   -
+   
+    Cboe Options Exchange to List Three Long-Dated SPX Options Expirations, Beginning November 1, 2021
+   
+   
    
+   -
+   
+    S&P 500 Weekly Options Now Expire Five Days a Week
+   
+  
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add US Index Options data to your algorithm, call the
+   
+    AddIndexOption
+   
+   
+    add_index_option
+   
+   method. Save a reference to the Index Option
+   
+    Symbol
+   
+   so you can access the data later in your algorithm.
+  
    
+  
    
+   
    class IndexOptionsDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2021, 1, 1);
+        self.set_end_date(2021, 6, 1);
+        self.set_cash(1000000);
+        self.universe_settings.asynchronous = True
+        self.index_symbol = self.add_index('VIX').symbol
+        
+        standard_option = self.add_index_option(self.index_symbol)
+        standard_option.set_filter(-2, 2, 0, 90)
+        self.standard_option_symbol = standard_option.symbol
+
+        weekly_option = self.add_index_option(self.index_symbol, "VIXW")
+        weekly_option.set_filter(-2, 2, 0, 90)
+        self.weekly_option_symbol = weekly_option.symbol
    
+   
    public class IndexOptionsDataAlgorithm : QCAlgorithm
+{
+    private Symbol _indexSymbol, _standardOptionSymbol, _weeklyOptionSymbol;
+
+    public override void Initialize()
+    {
+        SetStartDate(2021, 1, 1);
+        SetEndDate(2021, 6, 1);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = true;
+        _indexSymbol = AddIndex("VIX").Symbol;
+
+        var standardOption = AddIndexOption(_indexSymbol);
+        standardOption.SetFilter(-2, 2, 0, 90);
+        _standardOptionSymbol = standardOption.Symbol;
+
+        var weeklyOption = AddIndexOption(_indexSymbol, "VIXW");
+        weeklyOption.SetFilter(-2, 2, 0, 90);
+        _weeklyOptionSymbol = weeklyOption.Symbol;
+    }
+}
    
+  
    
+  
    
+   The Index resolution must be less than or equal to the Index Option resolution. For example, if you set the Index resolution to minute, then you must set the Index Option resolution to minute, hour, or daily.
+  
    
+  
    
+   For more information about creating US Index Option subscriptions, see
+   
+    Requesting Data
+   
+   or
+   
+    Index Options Universes
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current US Index Options data, index the
+   
+    OptionChains
+   
+   
+    option_chains
+   
+   property of the current
+   
+    
+     Slice
+    
+   
+   with the canonical Index Option
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your Index Option at every time step.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    standard_chain = slice.option_chains.get(self.standard_option_symbol)
+    if standard_chain:
+        for contract in standard_chain:
+            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
+
+    weekly_chain = slice.option_chains.get(self.weekly_option_symbol)
+    if weekly_chain:
+        for contract in weekly_chain:
+            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
    
+   
    public override void OnData(Slice slice)
+{
+    if (slice.OptionChains.ContainsKey(_standardOptionSymbol))
+    {
+        var standardChain = slice.OptionChains[_standardOptionSymbol];
+        foreach (var contract in standardChain)
+        {
+            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
+        }
+    }
+
+    if (slice.OptionChains.ContainsKey(_weeklyOptionSymbol))
+    {
+        var weeklyChain = slice.OptionChains[_weeklyOptionSymbol];
+        foreach (var contract in weeklyChain)
+        {
+            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
+        }
+    }
+}
    
+  
    
+  
    
+   You can also iterate through all of the
+   
+    OptionChain
+   
+   objects in the current
+   
+    Slice
+   
+   .
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    for canonical_symbol, chain in slice.option_chains.items():
+        for contract in chain:
+            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
+
    
+   
    public override void OnData(Slice slice)
+{
+    foreach (var kvp in slice.OptionChains)
+    {
+        var canonicalSymbol = kvp.Key;
+        var chain = kvp.Value;
+        foreach (var contract in chain)
+        {
+            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
+        }
+    }
+}
    
+  
    
+  
    
+   For more information about accessing US Index Options data, see
+   
+    Handling Data
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical US Index Options data in an algorithm and the Research Environment.
+  
    
+  
    
+   Historical Data In Algorithms
+  
    
+  
    
+   To get historical US Index Options data in an algorithm, call the
+   
+    History
+   
+   
+    history
+   
+   method with the Index Option contract
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame of trade and quote data
+history_df = self.history(contract.symbol, 100, Resolution.MINUTE)
+
+# DataFrame of open interest data
+history_oi_df = self.history(OpenInterest, contract.symbol, 100, Resolution.MINUTE)
+
+# TradeBar objects
+history_trade_bars = self.history[TradeBar](contract.symbol, 100, Resolution.MINUTE)
+
+# QuoteBar objects
+history_quote_bars = self.history[QuoteBar](contract.symbol, 100, Resolution.MINUTE)
+
+# OpenInterest objects
+history_oi = self.history[OpenInterest](contract.symbol, 100, Resolution.MINUTE)
    
+   
    // TradeBar objects 
+var historyTradeBars = History(contract.Symbol, 100, Resolution.Minute);
+
+// QuoteBar objects 
+var historyQuoteBars = History<QuoteBar>(contract.Symbol, 100, Resolution.Minute);
+
+// OpenInterest objects
+var historyOpenInterest = History<OpenInterest>(contract.Symbol, 100, Resolution.Minute);
    
+  
    
+  
    
+   For more information about historical data in algorithms, see
+   
+    History Requests
+   
+   .
+  
    
+  
    
+   Historical Data In Research
+  
    
+  
    
+   To get historical US Index Options data in the Research Environment, call the
+   
+    History
+   
+   
+    history
+   
+   or
+   
+    OptionHistory
+   
+   
+    option_history
+   
+   method. The
+   
+    History
+   
+   
+    history
+   
+   method returns the price, volume, and open interest history for some given Option contract(s). The
+   
+    OptionHistory
+   
+   
+    option_history
+   
+   method returns the price and volume history for the contracts that pass your daily universe filter.
+  
    
+  
    
+   
    qb = QuantBook()
+index_symbol = qb.add_index('VIX').symbol
+option = qb.add_index_option(index_symbol) # or qb.add_index_option(index_symbol, "VIXW")
+option.set_filter(-2, 2, 0, 90)
+history = qb.option_history(option.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5))
+history_df = history.data_frame
+expiries = history.get_expiry_dates() 
+strikes = history.get_strikes()
    
+   
    var qb = new QuantBook();
+var indexSymbol = qb.AddIndex("VIX").Symbol;
+var option = qb.AddIndexOption(indexSymbol); // or qb.AddIndexOption(indexSymbol, "VIXW");
+option.SetFilter(-2, 2, 0, 90);
+var history = qb.OptionHistory(option.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 5));
+
+var contracts = history
+    .SelectMany(x => x.OptionChains.SelectMany(y => y.Value.Contracts.Keys))
+    .Distinct().ToList();
+var expiries = contracts.Select(x => x.ID.Date).Distinct().ToList();
+var strikes = contracts.Select(x => x.ID.StrikePrice).Distinct().ToList();
    
+  
    
+  
    
+   To get historical data for arbitrary US Index Option contracts instead of just the that pass your universe filter, call the
+   
+    History
+   
+   
+    history
+   
+   method like you would in an algorithm, but on the
+   
+    QuantBook
+   
+   object. For more information about historical data in the Research Environment, see
+   
+    Key Concepts
+   
+   .
+  
    
+  
    
+   Historical Greeks and IV Data
+  
    
+  
    
+   To get historical data for the Greeks and implied volatility of Index Options, see the
+   
+    US Index Option Universe
+   
+   dataset.
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   The following table shows the available Index Options:
+  
    
+  
+   
+    
+     
+     
+     
+     
+     
+     
+    
+   
+   
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+     
+    
+   
+  
    
+      Underlying Index
+     
+      Underlying Ticker
+     
+      Target Ticker
+     
+      Standard Contracts
+     
+      Weekly Contracts
+     
+      Tradable on Expiry Day
+     
    
+      NASDAQ-100
+     
+      NDX
+     
+     
+      green check
+     
+     
+     
    
+      NASDAQ-100
+     
+      NDX
+     
+      NDXP
+     
+      green check
+     
+     
+      green check
+     
    
+      NASDAQ-100
+     
+      NDX
+     
+      NQX
+     
+      green check
+     
+      green check
+     
+      green check
+     
    
+      Russell 2000
+     
+      RUT
+     
+     
+      green check
+     
+     
+     
    
+      Russell 2000
+     
+      RUT
+     
+      RUTW
+     
+     
+      green check
+     
+      green check
+     
    
+      S&P500
+     
+      SPX
+     
+     
+      green check
+     
+     
+     
    
+      S&P500
+     
+      SPX
+     
+      SPXW
+     
+     
+      green check
+     
+      green check
+     
    
+      S&P500
+     
+      VIX
+     
+     
+      green check
+     
+     
+     
    
+      S&P500
+     
+      VIX
+     
+      VIXW
+     
+     
+      green check
+     
+     
    
+  
    
+   For more information about each underlying Index, see
+   
+    Supported Indices
+   
+   .
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Index Options dataset enables you to accurately design strategies for Index Options. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Buying VIX call Options to hedge against upcoming volatility
+   
      • 
+   
    • 
+    Buying VIX put Options to capture the natural downward price movement in the VIX index
+   
      • 
+   
    • 
+    Buying SPX put Options to protect against downward price movement in the S&P 500
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm buys the VIX Index Option Bull Call Spread with the furthest expiration:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class IndexOptionsDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(200000)
+        # Asynchronous can use computational resources efficiently
+        self.universe_settings.asynchronous = True
+        # Filter to get ATM calls expiring in 180 days to form the Bull Call Spread
+        option = self.add_index_option("SPX")
+        option.set_filter(lambda u: u.calls_only().strikes(-2, +2).expiration(0, 180))
+        self.option_symbol = option.symbol
+
+    def on_data(self, slice: Slice) -> None:
+        if not self.portfolio.invested and self.is_market_open(self.option_symbol):
+            # Make sure getting the updated VIX option chain
+            chain = slice.option_chains.get(self.option_symbol)
+            if chain:
+                expiry = max([c.expiry for c in chain])
+                call_contracts = sorted([c for c in chain if c.expiry == expiry],
+                    key=lambda c: c.strike)
+                
+                # Need 2 contracts to form a call spread
+                if len(call_contracts) < 2:
+                    return
+                
+                # Obtain 2 call contracts with different strike price to form the call spread
+                longCall, shortCall = call_contracts[0:2]
+                
+                # Use all the buying power, but need to ensure the order size of the long and short call are the same
+                quantity = min([
+                    abs(self.calculate_order_quantity(shortCall.symbol, -1)),
+                    abs(self.calculate_order_quantity(longCall.symbol, 1))])
+                if not quantity:
+                    return
+
+                # Bull call spread consists of long a lower-strike call and short a higher-strike call
+                self.market_order(shortCall.symbol, -quantity)
+                self.market_order(longCall.symbol, quantity)
+                
+                expected_margin_usage = max((longCall.strike - shortCall.strike) * self.securities[longCall.symbol].symbol_properties.contract_multiplier * quantity, 0)
+                if expected_margin_usage != self.portfolio.total_margin_used:
+                    raise Exception("Unexpect margin used!")
+
+
+    def on_securities_changed(self, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            if security.type == SecurityType.INDEX_OPTION:
+                # Historical data
+                history = self.history(security.symbol, 10, Resolution.MINUTE)
    
+   
    public class IndexOptionsDataAlgorithm : QCAlgorithm
+{
+    private Symbol _optionSymbol;
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(200000);
+        // Asynchronous can use computational resources efficiently
+        UniverseSettings.Asynchronous = true;
+        // Filter to get ATM calls expiring in 180 days to form the Bull Call Spread
+        var option = AddIndexOption("SPX");
+        option.SetFilter((u) => u.CallsOnly().Strikes(-2, +2).Expiration(0, 180));
+        _optionSymbol = option.Symbol;
+    }
+    
+    public override void OnData(Slice slice)
+    {
+        if (!Portfolio.Invested && IsMarketOpen(_optionSymbol))
+        {
+            // Make sure getting the updated VIX option chain
+            if (slice.OptionChains.TryGetValue(_optionSymbol, out var chain))
+            {
+                var expiry = chain.Max(x => x.Expiry);
+                var callContracts = chain
+                    .Where(x => x.Expiry == expiry)
+                    .OrderBy(x => x.Strike)
+                    .ToList();
+
+                // Need 2 contracts to form a call spread
+                if (callContracts.Count < 2)
+                {
+                    return;
+                }
+
+                // Obtain 2 call contracts with different strike price to form the call spread
+                var longCall = callContracts.First();
+                var shortCall = callContracts.First(contract => contract.Strike > longCall.Strike);
+
+                // Use all the buying power, but need to ensure the order size of the long and short call are the same
+                var quantity = new[] {
+                    CalculateOrderQuantity(shortCall.Symbol, -1m),
+                    CalculateOrderQuantity(longCall.Symbol, 1m) }
+                    .Min(x=> Math.Abs(x));
+                if (quantity == 0) return;
+
+                // Bull call spread consists of long a lower-strike call and short a higher-strike call
+                MarketOrder(shortCall.Symbol, -quantity);
+                MarketOrder(longCall.Symbol, quantity);
+
+                var expectedMarginUsage = Math.Max((longCall.Strike - shortCall.Strike) * Securities[longCall.Symbol].SymbolProperties.ContractMultiplier * quantity, 0);
+                if (expectedMarginUsage != Portfolio.TotalMarginUsed)
+                {
+                    throw new Exception("Unexpect margin used!");
+                }
+            }
+        }
+    }
+
+    public override void OnSecuritiesChanged(SecurityChanges changes)
+    {
+        foreach (var security in changes.AddedSecurities)
+        {
+            if (security.Type == SecurityType.IndexOption)
+            {
+                // Historical data
+                var history = History(security.Symbol, 10, Resolution.Minute);
+            }
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm buys the VIX Index Option Bull Call Spread with the furthest expiration:
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class IndexOptionsDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(200000)
+        self.universe_settings.asynchronous = True
+        self.set_universe_selection(VIXIndexOptionUniverseSelectionModel())
+
+        self.set_alpha(BullCallSpreadAlphaModel())
+
+        self.set_portfolio_construction(SingleSharePortfolioConstructionModel())
+
+
+class VIXIndexOptionUniverseSelectionModel(OptionUniverseSelectionModel):
+
+    def __init__(self):
+        super().__init__(timedelta(1), self.option_chain_symbol_selector)
+    
+    def option_chain_symbol_selector(self, utc_time: datetime) -> List[Symbol]:
+        # Only interest in VIX options
+        return [Symbol.create("VIX", SecurityType.INDEX_OPTION, Market.USA)]
+
+    def filter(self, option_filter_universe: OptionFilterUniverse) -> OptionFilterUniverse:
+        # Filter to get ATM calls expiring in 180 days to form the Bull Call Spread
+        return option_filter_universe.calls_only().strikes(-2, +2).expiration(0, 180)
+
+
+class BullCallSpreadAlphaModel(AlphaModel):
+
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        if algorithm.portfolio.invested:
+            return []
+            
+        for canonical_symbol, chain in slice.option_chains.items():
+            if not chain:
+                return []
+            # Bull call spread legs are having the same expiry                  
+            expiry = max([c.expiry for c in chain])
+            call_contracts = sorted([c for c in chain if c.expiry == expiry],
+                key=lambda c: c.strike)
+
+            # Need 2 contracts to form a call spread
+            if len(call_contracts) < 2:
+                return []
+
+            # Obtain 2 call contracts with different strike price to form the call spread
+            long_call, short_call = call_contracts[0:2]
+            
+            # Bull call spread consists of long a lower-strike call and short a higher-strike call
+            return Insight.group(
+                [
+                    Insight.price(long_call.symbol, expiry + timedelta(1), InsightDirection.UP),
+                    Insight.price(short_call.symbol, expiry + timedelta(1), InsightDirection.DOWN)
+                ])
+        return []
+        
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            if security.type == SecurityType.INDEX_OPTION:
+                # Historical data
+                history = algorithm.history(security.symbol, 10, Resolution.MINUTE)
+
+
+class SingleSharePortfolioConstructionModel(PortfolioConstructionModel):
+
+    def create_targets(self, algorithm: QCAlgorithm, insights: List[Insight]) -> List[PortfolioTarget]:
+        if not insights:
+            return []
+        # Only a single contract toi ensure both legs size is equal
+        quantity = min([abs(algorithm.calculate_order_quantity(i.symbol, int(i.direction)))
+            for i in insights])
+    
+        return [PortfolioTarget(i.symbol, quantity * int(i.direction))
+            for i in insights]
    
+   
    public class IndexOptionsDataAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(200000);
+        UniverseSettings.Asynchronous = true;
+        SetUniverseSelection(new VIXIndexOptionUniverseSelectionModel());
+
+        SetAlpha(new BullCallSpreadAlphaModel());
+
+        SetPortfolioConstruction(new SingleSharePortfolioConstructionModel());
+    }
+}
+
+public class VIXIndexOptionUniverseSelectionModel : OptionUniverseSelectionModel
+{
+    // Only interest in VIX options
+    public VIXIndexOptionUniverseSelectionModel()
+        : base(TimeSpan.FromDays(1), 
+            _ => new[] {QuantConnect.Symbol.Create("VIX", SecurityType.IndexOption, Market.USA)})
+    {
+    }
+
+    protected override OptionFilterUniverse Filter(OptionFilterUniverse optionFilterUniverse)
+    {
+        // Filter to get ATM calls expiring in 180 days to form the Bull Call Spread
+        return optionFilterUniverse.CallsOnly().Strikes(-2, +2).Expiration(0, 180);
+    }
+}
+
+public class BullCallSpreadAlphaModel : AlphaModel
+{
+    public BullCallSpreadAlphaModel()
+    {
+    }
+
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    {
+        var insights = Enumerable.Empty<Insight>();
+        if (algorithm.Portfolio.Invested)
+        {
+            return insights;
+        }
+            
+        foreach (var kvp in slice.OptionChains)
+        {
+            var canoncialSymbol = kvp.Key;
+            var chain = kvp.Value;
+            if (chain.IsNullOrEmpty())
+            {
+                return insights;
+            }
+            // Bull call spread legs are having the same expiry
+            var expiry = chain.Max(contract => contract.Expiry);
+            var callContracts = chain
+                .Where(contract => contract.Expiry == expiry)
+                .OrderBy(x => x.Strike)
+                .ToList();
+
+            // Need 2 contracts to form a call spread
+            if (callContracts.Count < 2)
+            {
+                return insights;
+            }
+
+            // Obtain 2 call contracts with different strike price to form the call spread
+            var longCall = callContracts.First();
+            var shortCall = callContracts.First(contract => contract.Strike > longCall.Strike);
+
+            // Bull call spread consists of long a lower-strike call and short a higher-strike call
+            return Insight.Group(
+                Insight.Price(longCall.Symbol, expiry.AddDays(1), InsightDirection.Up),
+                Insight.Price(shortCall.Symbol, expiry.AddDays(1), InsightDirection.Down));
+        }
+        return insights;
+    }
+}
+    
+public class SingleSharePortfolioConstructionModel : PortfolioConstructionModel
+{
+    public override IEnumerable<PortfolioTarget> CreateTargets(QCAlgorithm algorithm, Insight[] insights)
+    {
+        if (insights.Count() == 0)
+        {
+            return Enumerable.Empty<PortfolioTarget>();
+        }
+        // Only a single contract toi ensure both legs size is equal
+        var quantity = insights
+            .Select(i => Math.Abs(algorithm.CalculateOrderQuantity(i.Symbol, (decimal)i.Direction)))
+            .Min();
+
+        return insights.Select(i => new PortfolioTarget(i.Symbol, quantity * (decimal)i.Direction));
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Index Options dataset provides
+   
+    TradeBar
+   
+   ,
+   
+    QuoteBar
+   
+   , and
+   
+    OpenInterest
+   
+   objects.
+  
    
+  
    
+   TradeBar Attributes
+  
    
+  
    
+   
+    TradeBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   QuoteBar Attributes
+  
    
+  
    
+   
+    QuoteBar
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   OpenInterest Attributes
+  
    
+  
    
+   
+    OpenInterest
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > Morningstar
    
+
    
+    
    
+        
    Datasets
    
+        
    Morningstar
    
+    
    
+
    
+
+ 
+  
    
+   
    
+    
    
+     Morningstar was founded by Joe Mansueto in 1984 with the goal of empowering investors by connecting people to the investing information and tools they need. Morningstar provides access extensive line of products and services for individual investors, financial advisors, asset managers, and retirement plan providers. Morningstar provides data on approximately 525,000 investment offerings including stocks, mutual funds, and similar vehicles, along with real-time global market data on nearly 18 million equities, indexes, futures, options, commodities, and precious metals, in addition to foreign exchange and Treasury markets. Morningstar also offers investment management services through its investment advisory subsidiaries, with $244 billion in assets under advisement or management as of 2021.
+    
    
+   
    
+   
    
+    
    
+     
+      
    
+       
    
+        
    
+         US Fundamental Data
+        
    
+       
    
+      
    
+         
+    
    
+   
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > Morningstar > US Fundamental Data
    
+
    
+    
    
+        
    Morningstar
    
+        
    US Fundamental Data
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Fundamental Data by Morningstar tracks US Equity fundamentals. The data covers 8,000 US Equities, starts in January 1998, and is delivered on a daily frequency. The data does not include ETFs, ADRs, or OTC equities. This dataset is created by using a combination of string matching, Regular Expressions, and Machine Learning to gather the fundamental data published by companies.
+  
    
+  
    
+   For older symbols, the file date is approximated 45 days after the as of date. When a filing date is present on the Morningstar data, it is used. As we are a quant platform, all the data is loaded using "As Original Reported" figures. If there was a mistake reporting the figure, this will not be fixed later. The market typically responds quickly to these initially reported figures. Data is available for multiple periods depending on the property. Periods available include: 1 mo, 2 mo, 3 mo, 6 mo, 9 mo, 12 mo, 1 Yr, 2 Yr, 3 Yr, and 5 Yr. Morningstar symbols cover the NYSE, NASDAQ, AMEX, and BATS exchanges.
+  
    
+  
    
+   In live trading, Morningstar data is delivered to your algorithm at approximately 6 am each day. The majority of the fundamental data update occurs once per month. This includes updates to all of the key information for each security Morningstar supports. On top of this monthly update, there are daily updates of the financial ratios.
+  
    
+  
    
+   As Morningstar data arrives, it updates the master copy and is passed into your algorithm, similar to how TradeBars are fill-forwarded in your data feed. If there have been no updates for a day, you'll receive the same fundamental data.
+  
    
+  
    
+   This dataset depends on the
+   
+    US Equity Security Master
+   
+   dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes.
+  
    
+  
    
+   QuantConnect/LEAN combines the data of this dataset with
+   
+    US Coarse Universe
+   
+   data in runtime.
+  
    
+  
    
+   For more information about the US Fundamental Data dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   Morningstar was founded by Joe Mansueto in 1984 with the goal of empowering investors by connecting people to the investing information and tools they need. Morningstar provides access extensive line of products and services for individual investors, financial advisors, asset managers, and retirement plan providers. Morningstar provides data on approximately 525,000 investment offerings including stocks, mutual funds, and similar vehicles, along with real-time global market data on nearly 18 million equities, indexes, futures, options, commodities, and precious metals, in addition to foreign exchange and Treasury markets. Morningstar also offers investment management services through its investment advisory subsidiaries, with $244 billion in assets under advisement or management as of 2021.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippets demonstrate how to request data from the US Fundamental dataset:
+  
    
+  
      
+   
    • 
+    Direct Access using the Security object
+   
      • 
+  
    
+  
    
+   
    equity = self.add_equity("IBM")
+ibm_fundamental = equity.fundamentals
    
+   
    var equity = AddEquity("IBM");
+var ibmFundamental = equity.Fundamentals;
    
+  
    
+  
      
+   
    • 
+    Direct Access using the Symbol object
+   
      • 
+  
    
+  
    
+   
    ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
+ibm_fundamental = self.fundamentals(ibm)
    
+   
    var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
+var ibmFundamental = Fundamentals(ibm);
    
+  
    
+  
      
+   
    • 
+    Universe Selection
+   
      • 
+  
    
+  
    
+   
    def initialize(self) -> None:
+    self._universe = self.add_universe(self.fundamental_filter_function)
+
+def fundamental_filter_function(self, fundamental: List[Fundamental]):
+    filtered = [f for f in fundamental if f.has_fundamental_data and f.price > 10 and not np.isnan(f.valuation_ratios.pe_ratio)]
+    sorted_by_pe_ratio = sorted(filtered, key=lambda f: f.valuation_ratios.pe_ratio)
+    return [f.symbol for f in sorted_by_pe_ratio[:10]]
    
+   
    public override void Initialize()
+{
+    _universe = AddUniverseSelection(new FundamentalUniverseSelectionModel(FundamentalFilterFunction));
+}
+
+public override List<Symbol> FundamentalFilterFunction(List<Fundamental> fundamental)
+{
+    return (from f in fundamental
+            where f.HasFundamentalData && f.Price > 10 && !Double.IsNaN(f.ValuationRatios.PERatio)
+            orderby f.ValuationRatios.PERatio
+            select f.Symbol).Take(10);
+}
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 1998
+     
    
+      Asset Coverage
+     
+      8,000 US Equities
+      
+       *
+      
+     
    
+      Corporate Indicators / Tracked Fields
+     
+      900 Fields
+     
    
+      Data Density
+     
+      Sparse
+     
    
+      Resolution
+     
+      Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+  
+   *
+  
+  The data does not include ETFs, ADRs, or OTC equities.
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   You don't need any special code to request US Fundamental Data. You can access the current and historical fundamental data for any of the US Equities that this dataset includes.
+  
    
+  
    
+   
    class MorningStarDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2021, 1, 1)
+        self.set_end_date(2021, 7, 1)
+        self.set_cash(100000) 
+        self.universe_settings.asynchronous = True
+
+        # Option 1: Subscribe to individual US Equity assets
+        self.add_equity("IBM")        
+
+        # Option 2A: Create a fundamental universe (classic version)
+        self._universe = self.add_universe(self.fundamental_function)
+
+        # Option 2B: Create a fundamental universe (framework version)
+        self.add_universe_selection(FundamentalUniverseSelectionModel(self.fundamental_function))
    
+   
    public class MorningStarDataAlgorithm : QCAlgorithm
+{
+    private Universe _universe;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2021, 1, 1);
+        SetEndDate(2021, 7, 1);
+        SetCash(100000);
+        UniverseSettings.Asynchronous = true;
+        
+        // Option 1: Subscribe to individual US Equity assets
+        AddEquity("IBM");
+
+        // Option 2A: Create a fundamental universe (classic version)
+        _universe = AddUniverse(FundamentalFilterFunction);
+
+        // Option 2B: Create a fundamental universe (framework version)
+        AddUniverseSelection(new FundamentalUniverseSelectionModel(FundamentalFilterFunction));
+    }
+}
    
+  
    
+  
    
+   For more information about universe settings, see
+   
+    Settings
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   If you add a
+   
+    fundamental universe
+   
+   to your algorithm, you can access fundamental data in the universe selection function.
+  
    
+  
    
+   
    def fundamental_function(self, fundamental: List[Fundamental]) -> List[Symbol]:
+    return [f.symbol for f in fundamental if f.price > 10 and f.valuation_ratios.pe_ratio > 30]
    
+   
    public IEnumerable<Symbol> FundamentalFunction(IEnumerable<Fundamental> fundamental)
+{
+    return fundamental.Where(f => f.Price > 10 && f.ValuationRatios.PERatios > 30).Select(f => f.Symbol);
+}
    
+  
    
+  
    
+   To get fundamental data for Equities in your algorithm, use the
+   
+    Fundamentals
+   
+   
+    fundamentals
+   
+   property of the
+   
+    Equity
+   
+   objects. The fundamental data represent the corporate fundamentals for the current algorithm time.
+  
    
+  
    
+   
    fundamentals = self.securities[symbol].fundamentals
    
+   
    var fundamentals = Securities[symbol].Fundamentals;
    
+  
    
+  
    
+   To get fundamental data for Equities, regardless of whether or not you have subscribed to them in your algorithm, call the
+   
+    Fundamentals
+   
+   
+    fundamentals
+   
+   method. If you pass one
+   
+    Symbol
+   
+   , the method returns a
+   
+    Fundamental
+   
+   object. If you pass a list of
+   
+    Symbol
+   
+   objects, the method returns a list of
+   
+    Fundamental
+   
+   objects.
+  
    
+  
    
+   
    // Single asset 
+var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
+var ibmFundamental = Fundamentals(ibm);
+
+// Multiple assets
+var nb = QuantConnect.Symbol.Create("NB", SecurityType.Equity, Market.USA);
+var fundamentals = Fundamentals(new List<Symbol>{ nb, ibm }).ToList();
    
+   
    # Single asset
+ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
+ibm_fundamental = self.fundamentals(ibm)
+
+# Multiple assets
+nb = Symbol.create("NB", SecurityType.EQUITY, Market.USA)
+fundamentals = self.fundamentals([ nb, ibm ])
    
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical fundamental data in an algorithm and in the Research Environment.
+  
    
+  
    
+   Historical Data in Algorithms
+  
    
+  
    
+   To get historical fundamental data in an algorithm, call the
+   
+    History
+   
+   
+    history
+   
+   method with
+   
+    Fundamental
+   
+   type and the Equity
+   
+    Symbol
+   
+   or the
+   
+    Universe
+   
+   object. If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    var ibm = QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA);
+
+// Fundamental objects
+var fundamentalHistory = History<Fundamental>(ibm, TimeSpan.FromDays(30));
+
+// Fundamentals objects for all US Equities (including delisted companies)
+var fundamentalsHistory = History<Fundamentals>(TimeSpan.FromDays(30));
+
+// Collection of Fundamental objects for all US Equities (including delisted companies)
+var collectionHistory = History(_universe, 30, Resolution.Daily);
+foreach (var fundamental in collectionHistory)
+{
+    // Cast to Fundamental is required
+    var highestMarketCap = fundamental.OfType<Fundamental>().OrderByDescending(x => x.MarketCap).Take(5);
+}
    
+   
    ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
+
+# DataFrame object where the columns are the Fundamental attributes
+asset_df_history = self.history(Fundamental, ibm, timedelta(30), flatten=True)
+
+# Fundamental objects
+fundamental_history= self.history[Fundamental](ibm, timedelta(30))
+
+# Fundamentals objects for all US Equities (including delisted companies)
+fundamentals_history = self.history[Fundamentals](timedelta(30))
+
+# Multi-index Series objects where the values are lists of Fundamental objects
+series_history = self.history(self.universe, 30, Resolution.DAILY)
+for (universe_symbol, time), fundamentals in series_history.items():
+    highest_market_cap = sorted(fundamentals, key=lambda x: x.market_cap)[-5:]
    
+  
    
+  
    
+   Historical Data in the Research Environment
+  
    
+  
    
+   To get historical data in the Research Environment, call any of the preceding methods or call the
+   
+    UniverseHistory
+   
+   
+    universe_history
+   
+   method with the
+   
+    Universe
+   
+   object, a start date, and an end date. This method returns the filtered universe. Alternatively, call the
+   
+    GetFundamental
+   
+   
+    get_fundamental
+   
+   method with the Equity
+   
+    Symbol
+   
+   , a
+   
+    Fundamental
+   
+   property, a start date, and an end date. If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame object where the columns are the Fundamental attributes
+universe_history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
+
+# Multi-index Series objects where the values are lists of Fundamental objects
+universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+for (universe_symbol, time), fundamentals in universe_history.items():
+    for fundamental in fundamentals:
+        print(f"{fundamental.symbol} market cap at {fundamental.end_time}: {fundamental.market_cap}")
+
+# DataFrame of a single Fundamental attribute
+history = qb.get_fundamental(symbol, "ValuationRatios.pe_ratios", datetime(2021, 1, 1), datetime(2021, 7, 1))
    
+   
    var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var fundamentals in universeHistory)
+{
+    foreach (Fundamental fundamental in fundamentals)
+    {
+        Console.WriteLine($"{fundamental.Symbol} market cap at {fundamental.EndTime}: {fundamental.MarketCap}");
+    }
+}
+
+var history = qb.GetFundamental(symbol, "ValuationRatios.PERatios", new DateTime(2021, 1, 1), new DateTime(2021, 7, 1));
    
+  
    
+  
    
+   For more information about historical US Equity fundamental data, see
+   
+    Equity Fundamental Data
+   
+   .
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The US Fundamentals dataset enables you to design strategies harnessing fundamental data points. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Ranking a universe of securities by a value factor like the
+    
+     Piotroski F-score
+    
+    and buying a subset of the universe with the best factor ranking
+   
      • 
+   
    • 
+    Using the Morningstar asset classification to target specific industries or to ensure your strategy is diversified across several sectors
+   
      • 
+   
    • 
+    Trading securities that recently performed an IPO
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm creates a dynamic universe of US Equities. The first stage filter selects the 100 most liquid US Equities and the second stage filter selects the 10 assets with the greatest PE ratio. The algorithm then forms an equal-weighted portfolio with the 10 assets in the universe.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class MorningStarDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000) 
+        # Seed the price of each asset with its last known price to avoid trading errors.
+        self.settings.seed_initial_prices = True
+        self.universe_settings.resolution = Resolution.DAILY
+        self.universe_size = 10
+        # Add universe selection to filter with fundamental data
+        self.add_universe(self.fundamental_selection_function)
+        
+    def fundamental_selection_function(self, fundamental: List[Fundamental]) -> List[Symbol]:
+        # Make sure fundamental data and PE ratio available 
+        # To avoid penny stocks with high volatility, set price above $1
+        selected = [f for f in fundamental if f.has_fundamental_data and f.price > 1 and not np.isnan(f.valuation_ratios.pe_ratio)]
+        # Filter the top 100 high dollar volume equities to ensure liquidity
+        sorted_by_dollar_volume = sorted(selected, key=lambda f: f.dollar_volume, reverse=True)[:100]
+        # Get the top 10 PE Ratio stocks to follow speculations of a large number of capital
+        sorted_by_pe_ratio = sorted(sorted_by_dollar_volume, key=lambda f: f.valuation_ratios.pe_ratio, reverse=True)[:self.universe_size]
+        return [ f.symbol for f in sorted_by_pe_ratio ]
+        
+
+    def on_data(self, slice: Slice) -> None:
+        # if we have no changes, do nothing
+        if self._changes is None or not slice.bars: return
+
+        # liquidate removed securities, as not being the most popular stocks anymore
+        for security in self._changes.removed_securities:
+            if security.invested:
+                self.liquidate(security.symbol)
+
+        # we want 1/N allocation in each security in our universe to evenly dissipate risk
+        for security in self._changes.added_securities:
+            self.set_holdings(security.symbol, 1 / self.universe_size)
+
+        self._changes = None
+           
+    def on_securities_changed(self, changes: SecurityChanges) -> None:
+        self._changes = changes
+
    
+   
    public class MorningStarDataAlgorithm : QCAlgorithm
+{
+    private int _universeSize = 10;
+    private SecurityChanges _changes = SecurityChanges.None;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+        // Seed the price of each asset with its last known price to avoid trading errors.
+        Settings.SeedInitialPrices = true;
+        // Requesting data
+        UniverseSettings.Resolution = Resolution.Daily;
+        // Add universe selection to filter with fundamental data
+        AddUniverse(FundamentalSelectionFunction);
+    }
+
+    public IEnumerable<Symbol> FundamentalSelectionFunction(IEnumerable<Fundamental> fundamental)
+    {
+        // Make sure fundamental data and PE ratio available 
+        // To avoid penny stocks with high volatility, set price above $1
+        // Filter the top 100 high dollar volume equities to ensure liquidity
+        // Get the top 10 PE Ratio stocks to follow speculations of a large number of capital
+        return fundamental
+            .Where(x => x.HasFundamentalData && x.Price > 1 && !Double.IsNaN(x.ValuationRatios.PERatio))
+            .OrderByDescending(x => x.DollarVolume)
+            .Take(100)
+            .OrderByDescending(x => x.ValuationRatios.PERatio)
+            .Take(_universeSize)
+            .Select(x => x.Symbol);
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // if we have no changes, do nothing
+        if (_changes == SecurityChanges.None || slice.Bars.Count == 0) return;
+
+        // liquidate removed securities, as not being the most popular stocks anymore
+        foreach (var security in _changes.RemovedSecurities)
+        {
+            if (security.Invested)
+            {
+                Liquidate(security.Symbol);
+            }
+        }
+
+        // we want 1/N allocation in each security in our universe to evenly dissipate risk
+        foreach (var security in _changes.AddedSecurities)
+        {
+            SetHoldings(security.Symbol, 1m / _universeSize);
+        }
+
+        _changes = SecurityChanges.None;
+    }
+    
+    public override void OnSecuritiesChanged(SecurityChanges changes)
+    {
+        _changes = changes;
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm creates a dynamic universe of US Equities. The first stage filter selects the 100 most liquid US Equities and the second stage filter selects the 10 assets with the greatest PE ratio. The algorithm then forms an equal-weighted portfolio with the 10 assets in the universe.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+class MorningStarDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100000) 
+
+        # Add universe selection to filter with fundamental data
+        self.universe_settings.resolution = Resolution.DAILY
+        self.set_universe_selection(CustomFundamentalUniverseSelectionModel())
+
+        self.set_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(1)))
+
+        # we want 1/N allocation in each security in our universe to evenly dissipate risk
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+
+        self.add_risk_management(NullRiskManagementModel())
+
+        self.set_execution(ImmediateExecutionModel())
+
+
+class CustomFundamentalUniverseSelectionModel(FundamentalUniverseSelectionModel):
+
+    def select(self, algorithm: QCAlgorithm, fundamental: List[Fundamental]) -> List[Symbol]:
+        # Make sure fundamental data and PE ratio available 
+        # To avoid penny stocks with high volatility, set price above $1
+        # Filter the top 100 high dollar volume equities to ensure liquidity
+        sorted_by_dollar_volume = sorted([x for x in fundamental if x.price > 1 and not np.isnan(x.valuation_ratios.pe_ratio)],
+            key=lambda x: x.dollar_volume, reverse=True)[:100]
+        # Get the top 10 PE Ratio stocks to follow speculations of a large number of capital
+        sorted_by_pe_ratio = sorted(sorted_by_dollar_volume, key=lambda x: x.valuation_ratios.pe_ratio, reverse=True)[:10]
+
+        # Take the top entries from our sorted collection
+        return [ x.symbol for x in sorted_by_pe_ratio ]
    
+   
    public class MorningStarDataAlgorithm : QCAlgorithm
+{        
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100000);
+        
+        // Add universe selection to filter with fundamental data
+        UniverseSettings.Resolution = Resolution.Daily;
+        AddUniverseSelection(new CustomFundamentalUniverseSelectionModel());
+        
+        SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
+
+        // we want 1/N allocation in each security in our universe to evenly dissipate risk
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+        
+        AddRiskManagement(new NullRiskManagementModel());
+        
+        SetExecution(new ImmediateExecutionModel());
+    }
+}
+
+public class CustomFundamentalUniverseSelectionModel : FundamentalUniverseSelectionModel
+{
+    public override IEnumerable<Symbol> Select(QCAlgorithm algorithm, IEnumerable<Fundamental> fundamental)
+    {
+        return fundamental
+            // Make sure fundamental data and PE ratio available 
+            // To avoid penny stocks with high volatility, set price above $1
+            .Where(x => x.HasFundamentalData && x.Price > 1 && !Double.IsNaN(x.ValuationRatios.PERatio))
+            // Filter the top 100 high dollar volume equities to ensure liquidity
+            .OrderByDescending(x => x.DollarVolume)
+            .Take(100)
+            // Get the top 10 PE Ratio stocks to follow speculations of a large number of capital
+            .OrderByDescending(x => x.ValuationRatios.PERatio)
+            .Take(10)
+            .Select(x => x.Symbol);
+    }
+}
    
+  
    
+  
    
+   Research Example
+  
    
+  
    
+   The following example selects the 100 most liquid US Equities and lists the 10 assets with the greatest PE ratio.:
+  
    
+  
    
+   
    var qb = new QuantBook();
+
+// Add Fundamental Universe Selection
+IEnumerable<Symbol> FundamentalSelectionFunction(IEnumerable<Fundamental> fundamentals)
+{
+    return fundamentals
+        .Where(x => !Double.IsNaN(x.ValuationRatios.PERatio))
+        .OrderByDescending(x => x.DollarVolume).Take(100)
+        .OrderByDescending(x => x.ValuationRatios.PERatio).Take(10)
+        .Select(x => x.Symbol);
+}
+
+var universe = qb.AddUniverse(FundamentalSelectionFunction); 
+
+// Historical Universe data
+var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
+foreach (var fundamentals in universeHistory)
+{
+    foreach (Fundamental fundamental in fundamentals)
+    {
+        Console.WriteLine($"{fundamental.Symbol} PE ratio at {fundamental.EndTime}: {fundamental.ValuationRatios.PERatio}");
+    }
+}
    
+   
    qb = QuantBook()
+
+# Add Fundamental Universe Selection
+def fundamental_selection_function(fundamentals):
+    selected = [f for f in fundamentals if not np.isnan(f.valuation_ratios.pe_ratio)]
+    sorted_by_dollar_volume = sorted(selected, key=lambda f: f.dollar_volume, reverse=True)[:100]
+
+    sorted_by_pe_ratio = sorted(sorted_by_dollar_volume,
+        key=lambda f: f.valuation_ratios.pe_ratio, reverse=True)[:10]
+    return [ f.symbol for f in sorted_by_pe_ratio ]
+
+universe = qb.add_universe(fundamental_selection_function)
+
+# Historical Universe data
+universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
+for (universe_symbol, time), fundamentals in universe_history.items():
+    for fundamental in fundamentals:
+        print(f"{fundamental.symbol} PE ratio volume at {fundamental.end_time}: {fundamental.valuation_ratios.pe_ratio}")
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The US Fundamentals dataset provides
+   
+    Fundamental
+   
+   objects. To filter
+   
+    Fundamental
+   
+   objects, you can use one of the enumeration helpers.
+  
    
+  
    
+   Fundamental Attributes
+  
    
+  
    
+   
+    Fundamental
+   
+   objects have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   MorningstarSectorCode Enumeration
+  
    
+  
    
+   Sectors are large super categories of data. To access the sector of an Equity, use the
+   
+    MorningstarSectorCode
+   
+   property.
+  
    
+  
    
+   
    filteredFundamentals = fundamental.Where(x =>
+    x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology);
    
+   
    filtered_fundamentals = [x for x in fundamental
+    if x.asset_classification.morningstar_sector_code == MorningstarSectorCode.TECHNOLOGY]
    
+  
    
+  
    
+   The
+   
+    MorningstarSectorCode
+   
+   enumeration has the following members:
+  
    
+  
    
+  
    
+  
    
+   MorningstarIndustryGroupCode Enumeration
+  
    
+  
    
+   Industry groups are clusters of related industries that tie together.  To access the industry group of an Equity, use the
+   
+    MorningstarIndustryGroupCode
+   
+   property.
+  
    
+  
    
+   
    filteredFundamentals = fundamental.Where(x =>
+    x.AssetClassification.MorningstarIndustryGroupCode == MorningstarIndustryGroupCode.ApplicationSoftware);
    
+   
    filtered_fundamentals = [x for x in fundamental
+    if x.asset_classification.morningstar_industry_group_code == MorningstarIndustryGroupCode.APPLICATION_SOFTWARE]
    
+  
    
+  
    
+   The
+   
+    MorningstarIndustryGroupCode
+   
+   enumeration has the following members:
+  
    
+  
    
+  
    
+  
    
+   MorningstarIndustryCode Enumeration
+  
    
+  
    
+   Industries are the finest classification level available and are the individual industries according to the Morningstar classification system.  To access the industry group of an Equity, use the
+   
+    MorningstarIndustryCode
+   
+   property.
+  
    
+  
    
+   
    filteredFundamentals = fundamental.Where(x =>
+    x.AssetClassification.MorningstarIndustryCode == MorningstarIndustryCode.SoftwareApplication);
    
+   
    filtered_fundamentals = [x for x in fundamental
+    if x.asset_classification.morningstar_industry_code == MorningstarIndustryCode.SOFTWARE_APPLICATION]
    
+  
    
+  
    
+   The
+   
+    MorningstarIndustryCode
+   
+   enumeration has the following members:
+  
    
+  
    
+  
    
+  
    
+   StockType Enumeration
+  
    
+  
    
+   The Stock Type classification groups companies according to the underlying fundamentals of their business. It answers the question: "If I buy this stock, what kind of company am I buying?" The emphasis is on income statement, balance sheet, and cash-flow data—not price data or valuation multiples. To access the stock type of an Equity, use the
+   
+    StockType
+   
+   property.
+  
    
+  
    
+   
    filteredFundamentals = fundamental.Where(x =>
+    x.AssetClassification.StockType == StockType.AggressiveGrowth);
    
+   
    filtered_fundamentals = [x for x in fundamental
+    if x.asset_classification.stock_type == StockType.AGGRESSIVE_GROWTH]
    
+  
    
+  
    
+   The
+   
+    StockType
+   
+   enumeration has the following members:
+  
    
+  
    
+  
    
+  
    
+   StyleBox Enumeration
+  
    
+  
    
+   The Morningstar Equity Style Box is a grid that provides a graphical representation of the investment style of stocks and portfolios. It classifies securities according to market capitalization (vertical axis) and value-growth scores (horizontal axis). For stocks, the central column represents the core style. To access the style box of an Equity, use the
+   
+    StyleBox
+   
+   property.
+  
    
+  
    
+   
    filteredFundamentals = fundamental.Where(x =>
+    x.AssetClassification.StyleBox == StyleBox.LargeValue);
    
+   
    filtered_fundamentals = [x for x in fundamental
+    if x.asset_classification.style_box == StyleBox.LARGE_VALUE]
    
+  
    
+  
    
+   The
+   
+    StyleBox
+   
+   enumeration has the following members:
+  
    
+  
    
+  
    
+  
    
+   MorningstarEconomySphereCode Enumeration
+  
    
+  
    
+   Sectors are consolidated into three major economic spheres or Super Sectors: Cyclical, Defensive, and Sensitive. To access the economy sphere (Super Sector) of an Equity, use the
+   
+    MorningstarEconomySphereCode
+   
+   property.
+  
    
+  
    
+   
    filteredFundamentals = fundamental.Where(x =>
+    x.AssetClassification.MorningstarEconomySphereCode == MorningstarEconomySphereCode.Cyclical);
    
+   
    filtered_fundamentals = [x for x in fundamental
+    if x.asset_classification.morningstar_economy_sphere_code == MorningstarEconomySphereCode.CYCLICAL]
    
+  
    
+  
    
+   The
+   
+    MorningstarEconomySphereCode
+   
+   enumeration has the following members:
+  
    
+  
    
+  
    
+  
    
+   Exchange ID Values
+  
    
+  
    
+   The exchange ID represents the exchange that lists the Equity. To access the exchange ID of an Equity, use the
+   
+    PrimaryExchangeID
+   
+   property.
+  
    
+  
    
+   
    filteredFundamentals = fundamental.Where(x =>
+    x.CompanyReference.PrimaryExchangeID == "NAS");
    
+   
    filtered_fundamentals = [x for x in fundamental
+    if x.company_reference.primary_exchange_id == "NAS"]
    
+  
    
+  
    
+   The exchanges are represented by the following string values:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      String Representation
+     
+      Exchange
+     
    
+      NYS
+     
+      New York Stock Exchange (NYSE)
+     
    
+      NAS
+     
+      NASDAQ
+     
    
+      ASE
+     
+      American Stock Exchange (AMEX)
+     
    
+      TSE
+     
+      Tokyo Stock Exchange
+     
    
+      AMS
+     
+      Amsterdam Internet Exchange
+     
    
+      SGO
+     
+      Santiago Stock Exchange
+     
    
+      XMAD
+     
+      Madrid Stock Exchange
+     
    
+      ASX
+     
+      Australian Securities Exchange
+     
    
+      BVMF
+     
+      B3 (stock exchange)
+     
    
+      LON
+     
+      London Stock Exchange
+     
    
+      TKS
+     
+      Istanbul Stock Exchange Settlement and Custody Bank
+     
    
+      SHG
+     
+      Shanghai Exchange
+     
    
+      LIM
+     
+      Lima Stock Exchange
+     
    
+      FRA
+     
+      Frankfurt Stock Exchange
+     
    
+      JSE
+     
+      Johannesburg Stock Exchange
+     
    
+      MIL
+     
+      Milan Stock Exchange
+     
    
+      TAE
+     
+      Tel Aviv Stock Exchange
+     
    
+      STO
+     
+      Stockholm Stock Exchange
+     
    
+      ETR
+     
+      Deutsche Boerse Xetra Core
+     
    
+      PAR
+     
+      Paris Stock Exchange
+     
    
+      BUE
+     
+      Buenos Aires Stock Exchange
+     
    
+      KRX
+     
+      Korea Exchange
+     
    
+      SWX
+     
+      SIX Swiss Exchange
+     
    
+      PINX
+     
+      Pink Sheets (OTC)
+     
    
+      CSE
+     
+      Canadian Securities Exchange
+     
    
+      PHS
+     
+      Philippine Stock Exchange
+     
    
+      MEX
+     
+      Mexican Stock Exchange
+     
    
+      TAI
+     
+      Taiwan Stock Exchange
+     
    
+      IDX
+     
+      Indonesia Stock Exchange
+     
    
+      OSL
+     
+      Oslo Stock Exchange
+     
    
+      BOG
+     
+      Colombia Stock Exchange
+     
    
+      NSE
+     
+      National Stock Exchange of India
+     
    
+      HEL
+     
+      Nasdaq Helsinki
+     
    
+      MISX
+     
+      Moscow Exchange
+     
    
+      HKG
+     
+      Hong Kong Stock Exchange
+     
    
+      IST
+     
+      Istanbul Stock Exchange
+     
    
+      BOM
+     
+      Bombay Stock Exchange
+     
    
+      TSX
+     
+      Toronto Stock Exchange
+     
    
+      BRU
+     
+      Brussels Stock Exchange
+     
    
+      BATS
+     
+      BATS Global Markets
+     
    
+      ARCX
+     
+      NYSE Arca
+     
    
+      GREY
+     
+      Grey Market (OTC)
+     
    
+      DUS
+     
+      Dusseldorf Stock Exchange
+     
    
+      BER
+     
+      Berlin Stock Exchange
+     
    
+      ROCO
+     
+      Taipei Exchange
+     
    
+      CNQ
+     
+      Canadian Trading and Quotation System Inc.
+     
    
+      BSP
+     
+      Bangko Sentral ng Pilipinas
+     
    
+      NEOE
+     
+      NEO Exchange
+     
    
+ 
+
+
+
     
    
+
    Datasets > TickData
    
+
    
+    
    
+        
    Datasets
    
+        
    TickData
    
+    
    
+
    
+
+ 
+  
    
+   
    
+    
    
+     
+      TickData
+     
+     was founded by a futures broker and a programmer in 1984 as the first company in the world to offer historical tick-by-tick prices on the futures and index markets. TickData provides access to comprehensive and detailed market data and analytics products in the financial industry covering Equities, Futures, Options, cash FOREX, and Cash Indices.
+    
    
+   
    
+   
    
+    
    
+     
+      
    
+       
    
+        
    
+         International Futures
+        
    
+       
    
+      
    
+         
+    
    
+   
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > TickData > International Futures
    
+
    
+    
    
+        
    TickData
    
+        
    International Futures
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The International Futures dataset by TickData provides Futures data, including price, volume, open interest, and expiry. The data covers the 4 contracts, DAX, FESX, HSI, and NKD, starting in July 1998 and is delivered on any frequency from tick to daily. This dataset is created by TickData, which negotiates directly with exchanges for their official archive and partners with real-time data providers with direct exchange connections and multiple redundant ticker plants.
+  
    
+  
    
+   This dataset also depends on the
+   
+    US Futures Security Master
+   
+   because the US Futures Security Master dataset contains information to construct continuous Futures.
+  
    
+  
    
+   For more information about the International Futures dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   
+    TickData
+   
+   was founded by a futures broker and a programmer in 1984 as the first company in the world to offer historical tick-by-tick prices on the futures and index markets. TickData provides access to comprehensive and detailed market data and analytics products in the financial industry covering Equities, Futures, Options, cash FOREX, and Cash Indices.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the International Futures dataset:
+  
    
+  
    
+   
    hsi = self.add_future(Futures.Indices.HANG_SENG, Resolution.MINUTE).symbol           # "HSI"
+dax = self.add_future(Futures.Indices.DAX, Resolution.MINUTE).symbol                 # "DAX"
+fesx = self.add_future(Futures.Indices.EURO_STOXX_50, Resolution.MINUTE).symbol      # "FESX"
+nkd = self.add_future(Futures.Indices.NIKKEI_225_DOLLAR, Resolution.MINUTE).symbol   # "NKD"
    
+   
    var hsi= AddFuture(Futures.Indices.HangSeng, Resolution.Minute).Symbol           // "HSI";
+var dax = AddFuture(Futures.Indices.Dax, Resolution.Minute).Symbol               // "DAX";
+var fesx = AddFuture(Futures.Indices.EuroStoxx50, Resolution.Minute).Symbol      // "FESX";
+var nkd = AddFuture(Futures.Indices.Nikkei225Dollar, Resolution.Minute).Symbol   // "NKD";
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      July 1998, see
+      
+       Supported Assets
+      
+      below for details
+     
    
+      Coverage
+     
+      4 Contracts
+     
    
+      Data Density
+     
+      Dense
+     
    
+      Resolution
+     
+      Minute, Hour, & Daily
+     
    
+      Timezone
+     
+      Various, refer to
+      
+       Supported Assets
+      
+      below
+     
    
+      Market Hours
+     
+      
+       Regular and Extended
+      
+     
    
+ 
+
+
+
    Volume Discrepancies
    
+
+ 
+  For Futures contracts trading in the US, for example, NKD, see
+  
+   US Futures
+  
+  .
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add International Futures data to your algorithm, call the
+   
+    AddFuture
+   
+   
+    add_future
+   
+   method. Save a reference to the Future so you can access the data later in your algorithm.
+  
    
+  
    
+   
    class InternationalFuturesDataAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2013, 12, 20) 
+        self.set_end_date(2014, 2, 20) 
+        self.set_cash(1000000) 
+        self.universe_settings.asynchronous = True
+        # Request Hang Seng Index Futures data
+        future = self.add_future(Futures.Indices.HANG_SENG) 
+        # Set filter to obtain the contracts expire within 90 days
+        future.set_filter(0, 90)
+        self.future_symbol = future.symbol
    
+   
    public class InternationalFuturesDataAlgorithm : QCAlgorithm
+{
+    private Symbol _futureSymbol;
+    
+    public override void Initialize()
+    {
+        SetStartDate(2013, 12, 20);
+        SetEndDate(2014, 2, 20);
+        SetCash(1000000);
+        UniverseSettings.Asynchronous = true;
+        // Request Hang Seng Index Futures data
+        var future = AddFuture(Futures.Indices.HangSeng);
+        // Set filter to obtain the contracts expire within 90 days
+        future.SetFilter(0, 90);
+        _futureSymbol = future.Symbol;
+    }
+}
    
+  
    
+  
    
+   For more information about creating Future subscriptions, see
+   
+    Requesting Data
+   
+   or
+   
+    Futures Universes
+   
+   .
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current International Futures data, index the
+   
+    FuturesChains
+   
+   
+    futures_chains
+   
+   property of the current
+   
+    
+     Slice
+    
+   
+   with the canonical Futures
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your Future at every time step.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Get future chain of the canonical symbol
+    chain = slice.futures_chains.get(self.future_symbol)
+    if chain:
+        # Iterate each contract in the future chain
+        for contract in chain:
+            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
+
    
+   
    public override void OnData(Slice slice)
+{
+    // Get future chain of the canonical symbol
+    if (slice.FuturesChains.TryGetValue(_futureSymbol, out var chain))
+    {
+        // Iterate each contract in the future chain
+        foreach (var contract in chain)
+        {
+            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
+        }
+    }
+}
    
+  
    
+  
    
+   You can also iterate through all of the
+   
+    FuturesChain
+   
+   objects in the current
+   
+    Slice
+   
+   .
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    # Iterate all future chains
+    for canonical_symbol, chain in slice.futures_chains.items():
+        # Iterate each contract in the future chain
+        for contract in chain:
+            self.log(f"{contract.symbol} price at {slice.time}: {contract.last_price}")
    
+   
    public override void OnData(Slice slice)
+{
+    // Iterate all future chains
+    foreach (var kvp in slice.FuturesChains)
+    {
+        var canonicalSymbol = kvp.Key;
+        var chain = kvp.Value;
+        // Iterate each contract in the future chain
+        foreach (var contract in chain)
+        {
+            Log($"{contract.Symbol} price at {slice.Time}: {contract.LastPrice}");
+        }
+    }
+}
    
+  
    
+  
    
+   For more information about accessing Futures data, see
+   
+    Handling Data
+   
+   .
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   You can get historical International Futures data in an algorithm and the Research Environment.
+  
    
+  
    
+   Historical Data In Algorithms
+  
    
+  
    
+   To get historical International Futures data in an algorithm, call the
+   
+    History
+   
+   
+    history
+   
+   method with the canonical Futures
+   
+    Symbol
+   
+   or a Futures contract
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame objects
+contract_history_df = self.history(contract.symbol, 100, Resolution.MINUTE)
+continuous_history_df = self.history(self.future_symbol,
+    start=self.time - timedelta(days=15), 
+    end=self.time, 
+    resolution=Resolution.MINUTE, 
+    fill_forward=False, 
+    extended_market_hours=False,
+    data_mapping_mode=DataMappingMode.OPEN_INTEREST, 
+    data_normalization_mode=DataNormalizationMode.RAW, 
+    contract_depth_offset=0)
+
+# TradeBar objects
+contract_history_trade_bars = self.history[TradeBar](contract.symbol, 100, Resolution.MINUTE)
+continous_history_trade_bars = self.history[TradeBar](self.future_symbol, 100, Resolution.MINUTE)
+
+# QuoteBar objects
+contract_history_quote_bars = self.history[QuoteBar](contract.symbol, 100, Resolution.MINUTE)
+continous_history_quote_bars = self.history[QuoteBar](self.future_symbol, 100, Resolution.MINUTE)
+
+# Tick objects
+contract_history_ticks = self.history[Tick](contract.symbol, timedelta(seconds=10), Resolution.TICK)
+continous_history_ticks = self.history[Tick](self.future_symbol, timedelta(seconds=10), Resolution.TICK)
    
+   
    // TradeBar objects
+var contractHistoryTradeBars = History(contract.Symbol, 100, Resolution.Minute);
+var continuousHistoryTradeBars = History(
+    symbols: new[] {_futureSymbol}, 
+    start: Time - TimeSpan.FromDays(15),
+    end: Time,
+    resolution: Resolution.Minute,
+    fillForward: false,
+    extendedMarketHours: false,
+    dataMappingMode: DataMappingMode.OpenInterest,
+    dataNormalizationMode: DataNormalizationMode.Raw,
+    contractDepthOffset: 0);
+
+// QuoteBar objects
+var contractHistoryQuoteBars = History<QuoteBar>(contract.Symbol, 100, Resolution.Minute);
+var continuousHistoryQuoteBars = History<QuoteBar>(_futureSymbol, 100, Resolution.Minute);
+
+// Tick objects
+var contractHistoryTicks = History<Tick>(contract.Symbol, TimeSpan.FromSeconds(10), Resolution.Tick);
+var continuousHistoryTicks = History<Tick>(_futureSymbol, TimeSpan.FromSeconds(10), Resolution.Tick);
    
+  
    
+  
    
+   For more information about historical data in algorithms, see
+   
+    History Requests
+   
+   . For more information about the price adjustments for continuous contracts, see
+   
+    Continous Contracts
+   
+   .
+  
    
+  
    
+   Historical Data In Research
+  
    
+  
    
+   To get historical International Futures data in the Research Environment for an entire Futures chain, call the
+   
+    FutureHistory
+   
+   
+    future_history
+   
+   method with the canonical Future
+   
+    Symbol
+   
+   .
+  
    
+  
    
+   
    qb = QuantBook()
+future = qb.add_future(Futures.Indices.HANG_SENG) 
+future.set_filter(0, 90)
+history = qb.future_history(future.symbol, datetime(2020, 6, 1), datetime(2020, 6, 5))
+history_df = history.data_frame
+all_history = history.get_all_data()
+expiries = history.get_expiry_dates()
    
+   
    var qb = new QuantBook();
+var future = qb.AddFuture(Futures.Indices.HangSeng);
+future.SetFilter(0, 90);
+var history = qb.FutureHistory(future.Symbol, new DateTime(2020, 6, 1), new DateTime(2020, 6, 5));
+    
+var contracts = history.SelectMany(x => x.OptionChains.SelectMany(y => y.Value.Contracts.Keys)).Distinct().ToList();
+var expiries = contracts.Select(x => x.ID.Date).Distinct().ToList();
    
+  
    
+  
    
+   To get historical data for a single International Futures contract or the continuous Futures contract, call the
+   
+    History
+   
+   
+    history
+   
+   method like you would in an algorithm but on the
+   
+    QuantBook
+   
+   object. For more information about historical data in the Research Environment, see
+   
+    Futures
+   
+   .
+  
    
+ 
+
+
+
    Supported Assets
    
+
+ 
+  
    
+   The following table shows the available Futures:
+  
    
+  
+   
+    
+     
+     
+     
+     
+     
+    
+   
+   
+    
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+     
+    
+   
+  
    
+      Ticker
+     
+      Future
+     
+      Start Date
+     
+      Time Zone
+     
+      Currency
+     
    
+      HSI
+     
+      Hang Seng Index Futures
+     
+      Jan 2010
+     
+      Asia/Hong Kong
+     
+      HKD
+     
    
+      DAX
+     
+      German Index Futures
+     
+      Jan 2010
+     
+      Europe/Berlin
+     
+      EUR
+     
    
+      FESX
+     
+      EURO STOXX 50 Index Futures
+     
+      Jul 1998
+     
+      Europe/Berlin
+     
+      EUR
+     
    
+      NKD
+     
+      Nikkei 225 Index Futures
+     
+      Jan 2007
+     
+      America/Chicago
+     
+      USD
+     
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The International Futures dataset enables you to design Futures strategies accurately. Examples include the following strategies:
   
    
   
      
    
    • 
-    Buy and hold
+    Buying the Futures contract with the most open interest to reduce slippage and market impact
    
      • 
    
    • 
-    Trading Cryptocurrency volatility and price action
+    Trade speculation on an International Index
    
      • 
    
    • 
-    Allocating a small portion of your portfolio to Cryptocurrencies to hedge against inflation
+    Trading bull calendar spreads to reduce volatility and margin requirements
    
      • 
   
    
   
    
-   Classic Algorithm Example
+   Classic Algorithm Example -- HSI Futures
   
    
   
    
-   The following example algorithm buys and holds Bitcoin through the Kraken exchange:
+   The following example algorithm uses
+   
+    ZigZag
+   
+   to determine the trend of Hang Seng Index. To trade the Indices, we make use of the HSI Futures.
   
    
   
    
    
    from AlgorithmImports import *
 
 
-class CoinAPIDataAlgorithm(QCAlgorithm):
-
+class InternationalFuturesDataAlgorithm(QCAlgorithm):
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        self.universe_settings.asynchronous = True
-        self.set_cash(100_000)
-        # Kraken accepts both Cash and Margin account types. Select the
-        # one you need for the best reality modeling.
-        self.set_brokerage_model(BrokerageName.KRAKEN, AccountType.MARGIN)
-        # Add BTCUSD data.
-        self._btc = self.add_crypto("BTCUSD", Resolution.MINUTE, Market.KRAKEN)
-        # Historical data
-        history = self.history(self._btc.symbol, 30, Resolution.DAILY)
-        # Add a Crypto universe that selects Crypto pairs on Kraken.
-        universe = self.add_universe(CryptoUniverse.kraken(self._select_assets))
-        # Historical Universe data
-        universe_history = self.history(universe, 30, Resolution.DAILY)
-        for (universe_symbol, time), universe_day in universe_history.items():
-            for universe_item in universe_day:
-                symbol = universe_item.symbol
-                price = universe_item.close
+        # Set the time zone to HKT to make it more comparable with the exchange.
+        self.set_time_zone(TimeZones.HONG_KONG)
+        # Set the account currency as HKD to trade HSI Futures.
+        self.set_account_currency("HKD", 1000000)
+        # Seed the last price of the contracts for filling.
+        self.settings.seed_initial_prices = True
 
-    def _select_assets(self, universe_day):
-        # Filter for materially traded Crypto pairs with significant 
-        # size and dollar volume, assuming higher capital flow in for 
-        # higher return.
-        return [universe_item.symbol for universe_item in universe_day
-                if universe_item.volume >= 100 
-                and universe_item.volume_in_usd > 10_000_000]
+        # Request HSI Futures to trade. 
+        # Note that we will trade the contract with the highest open interest for liquidity.
+        self.hsi_future = self.add_future(
+            Futures.Indices.HANG_SENG,
+            extended_market_hours=True,
+            data_mapping_mode=DataMappingMode.LAST_TRADING_DAY,
+            contract_depth_offset=0
+        )
+        # Request the corresponding underlying Index for feeding indicator for trade signal generation.
+        hsi_index = self.add_index("HSI").symbol
+
+        # Create a ZigZag indicator to trade Hang Seng Index price pivot points.
+        self._zz = self.zz(hsi_index, 0.15, 5, Resolution.DAILY)
+        # Warm up indicator for immediate readiness to trade.
+        self.warm_up_indicator(hsi_index, self._zz, Resolution.DAILY)
 
     def on_data(self, slice: Slice) -> None:
-        # Speculate-invest all available free cash on BTCUSD, repecting 
-        # the order quantity restrictions to avoid invalid orders.
-        if self.portfolio.cash_book['BTC'].amount != 0:
-            return
-        # Define a buffer so that we can scale down the order size,
-        # avoiding trading errors.
-        buffer = max(
-            KrakenFeeModel.TAKER_TIER_1_CRYPTO_FEE, 
-            self.settings.free_portfolio_value_percentage
-        )
-        quantity = (
-            self.portfolio.cash_book['USD'].amount
-            / self._btc.ask_price
-            * (1-buffer)
-        )
-        # Check if the quantity exceeds the minimum order size. On 
-        # Kraken, the minimum order size is measured in the base
-        # currency (BTC in this case).
-        if abs(quantity) < self._btc.symbol_properties.minimum_order_size:
-            return
-        self.market_order(self._btc, quantity)
    
-   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+        # Only place trade if the Future contracts is in market opening hours to avoid stale fills.
+        if self.is_market_open(self.hsi_future.symbol) and self._zz.is_ready:
+            pivot = self._zz.pivot_type
+            # If the last pivot point is a low point, the current trend is increasing after this low point.
+            if pivot == PivotPointType.LOW and not self.portfolio[self.hsi_future.symbol].is_long:
+                self.set_holdings(self.hsi_future.mapped, 0.2)
+            # If the last pivot point is a high point, the current trend is decreasing after this high point.
+            elif pivot == PivotPointType.HIGH and not self.portfolio[self.hsi_future.symbol].is_short:
+                self.set_holdings(self.hsi_future.mapped, -0.2)
+
+        # Handle rollover in case the current mapped contract changes.
+        for _, changed_event in slice.symbol_changed_events.items():
+            old_symbol = changed_event.old_symbol
+            new_symbol = self.add_future_contract(changed_event.new_symbol).symbol
+            quantity = self.portfolio[old_symbol].quantity
+            # Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
+            self.liquidate(old_symbol)
+            if quantity != 0:
+                self.market_order(new_symbol, quantity)
    
+   
    public class InternationalFuturesDataAlgorithm : QCAlgorithm
 {
-    private Crypto _btc;
-    
+    private Future _hsiFuture;
+    private ZigZag _zz;
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        UniverseSettings.Asynchronous = true;
-        SetCash(100000);
-        // Kraken accepts both Cash and Margin account types. Select the
-        // one you need for the best reality modeling.
-        SetBrokerageModel(BrokerageName.Kraken, AccountType.Margin);
-        // Add BTCUSD data.
-        _btc = AddCrypto("BTCUSD", Resolution.Minute, Market.Kraken);   
-        // Historical data
-        var history = History(_btc.Symbol, 30, Resolution.Daily);
-        // Add a Crypto universe that selects trading pairs on Kraken.
-        var universe = AddUniverse(CryptoUniverse.Kraken(SelectAssets));
-        // Historical Universe data
-        var universeHistory = History(universe, 30, Resolution.Daily);
-        foreach (var universeDay in universeHistory)
-        {
-            foreach (CryptoUniverse universeItem in universeDay)
-            {
-                var symbol = universeItem.Symbol;
-                var price = universeItem.Close;
-            }
-        }
-    }
+        // Set the time zone to HKT to make it more comparable with the exchange.
+        SetTimeZone(TimeZones.HongKong);
+        // Set the account currency as HKD to trade HSI Futures.
+        SetAccountCurrency("HKD", 1000000);
 
-    private IEnumerable<Symbol> SelectAssets(IEnumerable<CryptoUniverse> universeDay)
-    {
-        // Filter for materially traded Crypto pairs with significant 
-        // size and dollar volume, assuming higher capital flow in for 
-        // higher return.
-        return from universeItem in universeDay
-                where universeItem.Volume >= 100m 
-                && universeItem.VolumeInUsd > 10000000m
-                select universeItem.Symbol;
+        // Seed the last price of the contracts for filling.
+        Settings.SeedInitialPrices = true;
+
+        // Request HSI Futures to trade. 
+        // Note that we will trade the contract with the highest open interest for liquidity.
+        _hsiFuture = AddFuture(
+            Futures.Indices.HangSeng,
+            extendedMarketHours: true,
+            dataMappingMode: DataMappingMode.LastTradingDay,
+            contractDepthOffset: 0
+        );
+        // Request the corresponding underlying index for feeding indicators for trade signal generation.
+        var hsiIndex = AddIndex("HSI").Symbol;
+
+        // Create a ZigZag indicator to trade Hang Seng Index price pivot points.
+        _zz = ZZ(hsiIndex, 0.15m, 5, Resolution.Daily);
+        // Warm up indicator for immediate readiness to trade.
+        WarmUpIndicator(hsiIndex, _zz, Resolution.Daily);
     }
 
     public override void OnData(Slice slice)
     {
-        // Speculate-invest all available free cash on BTCUSD, repecting 
-        // the order quantity restrictions to avoid invalid orders.
-        if (Portfolio.CashBook["BTC"].Amount != 0)
+        // Only place trade if the Future contracts is in market opening hours to avoid stale fills.
+        if (IsMarketOpen(_hsiFuture.Symbol) && _zz.IsReady)
         {
-            return;
+            var pivot = _zz.PivotType;
+            // If the last pivot point is low, the current trend is increasing after this low point.
+            if (pivot == PivotPointType.Low && !Portfolio[_hsiFuture.Symbol].IsLong)
+            {
+                SetHoldings(_hsiFuture.Mapped, 0.2m);
+            }
+            // If the last pivot point is high, the current trend decreases after this high point.
+            else if (pivot == PivotPointType.High && !Portfolio[_hsiFuture.Symbol].IsShort)
+            {
+                SetHoldings(_hsiFuture.Mapped, -0.2m);
+            }
         }
-        // Define a buffer so that we can scale down the order size,
-        // avoiding trading errors.
-        var buffer = Math.Max(KrakenFeeModel.TakerTier1CryptoFee, Settings.FreePortfolioValuePercentage);
-        var quantity = Portfolio.CashBook["USD"].Amount / _btc.AskPrice * (1-buffer);
-        // Check if the quantity exceeds the minimum order size. On 
-        // Kraken, the minimum order size is measured in the base 
-        // currency (BTC in this case).
-        if (Math.Abs(quantity) < _btc.SymbolProperties.MinimumOrderSize)
+
+        // Handle rollover in case the current mapped contract changes.
+        foreach (var (_, changedEvent) in slice.SymbolChangedEvents)
         {
-            return;
+            var oldSymbol = changedEvent.OldSymbol;
+            var newSymbol = AddFutureContract(changedEvent.NewSymbol).Symbol;
+            var quantity = Portfolio[oldSymbol].Quantity;
+            // Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
+            Liquidate(oldSymbol);
+            if (quantity != 0)
+            {
+                MarketOrder(newSymbol, quantity);
+            }
         }
-        MarketOrder(_btc, quantity);
     }
 }
    
   
    
   
    
-   Framework Algorithm Example
+   Classic Algorithm Example -- FESX Futures
   
    
   
    
-   The following example algorithm creates a dynamic universe of Crypto pairs on the Kraken exchange and then forms a equal-weighted portfolio of all the pairs in the universe:
+   The following example algorithm uses the
+   
+    MACD
+   
+   indicator to determine the trend of the EuroStoxx50 Index.  To trade the Index, we make use of the FESX Futures.
   
    
   
    
    
    from AlgorithmImports import *
 
 
-class CoinAPIDataAlgorithm(QCAlgorithm):
-
+class InternationalFuturesDataAlgorithm(QCAlgorithm):
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        self.settings.free_portfolio_value_percentage = 0.05
+        # Set the time zone to Berlin to make it more comparable with the exchange.
+        self.set_time_zone(TimeZones.BERLIN)
+        # Set the account currency as EUR to trade FSX Futures.
+        self.set_account_currency("EUR", 1000000)
+        # Seed the last price of the contracts for filling.
+        self.settings.seed_initial_prices = True
 
-        # Kraken accepts both Cash and Margin type account, select the one you need for the best reality modeling.
-        self.set_brokerage_model(BrokerageName.KRAKEN, AccountType.MARGIN)
+        # Request FESX Futures to trade. 
+        # Note that we will trade the contract with the highest open interest for liquidity.
+        self.fesx_future = self.add_future(
+            Futures.Indices.EURO_STOXX_50,
+            extended_market_hours=True,
+            data_mapping_mode=DataMappingMode.LAST_TRADING_DAY,
+            contract_depth_offset=0
+        )
+        # Request the corresponding underlying Index for feeding indicator for trade signal generation.
+        fesx_index = self.add_index("SX5E").symbol
 
-        # Warm up the security with the last known price to avoid conversion error
-        self.settings.seed_initial_prices = True
-        
-        # Get the pairs on Kraken that have USD as the quote currency.
-        self._market_pairs = [
-            x.key.symbol 
-            for x in self.symbol_properties_database.get_symbol_properties_list(Market.KRAKEN) 
-            if x.value.quote_currency == self.account_currency
-        ]
-        # Add Crypto Universe Selection that select crypto pairs in Kraken exchange
-        self.add_universe(CryptoUniverse.kraken(self.universe_selection_filter))
-        
-        self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(hours=4), 0.025, None))
-        # Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+        # Create a MACD indicator to trade EuroStoxx trend changes on the fast-slow term trend convergence.
+        self._macd = self.macd(fesx_index, 12, 26, 9, resolution=Resolution.DAILY)
+        # Warm up indicator for immediate readiness to trade.
+        self.warm_up_indicator(fesx_index, self._macd, Resolution.DAILY)
 
-    def universe_selection_filter(self, universe_day: List[CryptoUniverse]) -> List[Symbol]:
-        # Select pairs where the quote currency is USD.
-        pairs = [c for c in universe_day if c.symbol.value in self._market_pairs]
-        # Filter for materially traded crypto pairs with significant dollar volume, assuming higher capital flow in for higher return.
-        pairs = sorted(pairs, key=lambda c: c.volume_in_usd)[-3:]      
-        return [c.symbol for c in pairs]
+    def on_data(self, slice: Slice) -> None:
+        # Only place trade if the Future contracts is in market opening hours to avoid stale fills.
+        if self.is_market_open(self.fesx_future.symbol) and self._macd.is_ready:
+            # To identify a sensitive change in trend, use the change in the histogram value of the MACD.
+            histogram = self._macd.histogram
+            histogram_chg = histogram.current.value - histogram.previous.value
+            # Positive histogram value increasing means the fast trend is rising faster than the slow trend, indicating a strong uptrend.
+            if histogram_chg > 0 and histogram.current.value > 0 and not self.portfolio[self.fesx_future.symbol].is_long:
+                self.set_holdings(self.fesx_future.mapped, 0.2)
+            # Negative histogram value decreasing means the slow trend is dropping faster than the fast trend, indicating a strong downtrend.
+            elif histogram_chg < 0 and histogram.current.value < 0 and not self.portfolio[self.fesx_future.symbol].is_short:
+                self.set_holdings(self.fesx_future.mapped, -0.2)
 
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            # Historical data
-            history = self.history(security.symbol, 30, Resolution.DAILY)
-            self.debug(f"We got {len(history)} items from our history request")
    
-   
    public class CoinAPIDataAlgorithm : QCAlgorithm
+        # Handle rollover in case the current mapped contract changes.
+        for _, changed_event in slice.symbol_changed_events.items():
+            old_symbol = changed_event.old_symbol
+            new_symbol = self.add_future_contract(changed_event.new_symbol).symbol
+            quantity = self.portfolio[old_symbol].quantity
+            # Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
+            self.liquidate(old_symbol)
+            if quantity != 0:
+                self.market_order(new_symbol, quantity)
    
+   
    public class InternationalFuturesDataAlgorithm : QCAlgorithm
 {
-    private List<string> _marketPairs;
-    
+    private Future _fesxFuture;
+    private MovingAverageConvergenceDivergence _macd;
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        Settings.FreePortfolioValuePercentage = 0.05m;
-
-        // Kraken accepts both Cash and Margin type account, select the one you need for the best reality modeling.            
-        SetBrokerageModel(BrokerageName.Kraken, AccountType.Margin);
-
-        // Warm up the security with the last known price to avoid conversion error
+        // Set the time zone to Berlin to make it more comparable with the exchange.
+        SetTimeZone(TimeZones.Berlin);
+        // Set the account currency as EUR to trade FSX Futures.
+        SetAccountCurrency("EUR", 1000000);
+        // Seed the last price of the contracts for filling.
         Settings.SeedInitialPrices = true;
-        
-        // Get the pairs on Kraken that have USD as the quote currency.
-        _marketPairs = SymbolPropertiesDatabase.GetSymbolPropertiesList(Market.Kraken)
-            .Where(x => x.Value.QuoteCurrency == AccountCurrency)
-            .Select(x => x.Key.Symbol)
-            .ToList();
-        // Add Crypto Universe Selection that select crypto pairs in Kraken exchange
-        AddUniverse(CryptoUniverse.Kraken(UniverseSelectionFilter));
-    
-        AddAlpha( new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromHours(4), 0.025, null) );
-        // Equally invest to evenly dissipate the capital concentration risk of inidividual crypto pair
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
-    }
 
-    private IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
-    {
-        // Select pairs where the quote currency is USD.
-        return universeDay.Where(c => _marketPairs.Contains(c.Symbol.Value))
-            // Filter for materially traded crypto pairs with significant size and dollar volume, assuming higher capital flow in for higher return
-            .OrderBy(c => c.VolumeInUsd)
-            .TakeLast(3)
-            .Select(c => c.Symbol);
+        // Request FESX Futures to trade. 
+        // Note that we will trade the contract with the highest open interest for liquidity.
+        _fesxFuture = AddFuture(
+            Futures.Indices.EuroStoxx50,
+            extendedMarketHours: true,
+            dataMappingMode: DataMappingMode.LastTradingDay,
+            contractDepthOffset: 0
+        );
+        // Request the corresponding underlying Index for feeding indicator for trade signal generation.
+        var fesxIndex = AddIndex("SX5E").Symbol;
+
+        // Create a MACD indicator to trade EuroStoxx trend changes on the fast-slow term trend convergence.
+        _macd = MACD(fesxIndex, 12, 26, 9, resolution: Resolution.Daily);
+        // Warm up indicator for immediate readiness to trade.
+        WarmUpIndicator(fesxIndex, _macd, Resolution.Daily);
     }
 
-    public override void OnSecuritiesChanged(SecurityChanges changes)
+    public override void OnData(Slice slice)
     {
-        foreach(var security in changes.AddedSecurities)
+        // Only place trade if the Future contracts is in market opening hours to avoid stale fills.
+        if (IsMarketOpen(_fesxFuture.Symbol) && _macd.IsReady)
         {
-            // Historical data
-            var history = History(security.Symbol, 30, Resolution.Daily);
-            Debug($"We got {history.Count()} items from our history request");
+            // To identify a sensitive change in trend, use the change in the histogram value of the MACD.
+            var histogram = _macd.Histogram;
+            var histogramChg = histogram - histogram.Previous;
+            // Positive histogram value increasing means the fast trend is rising faster than the slow trend, indicating a strong uptrend.
+            if (histogramChg > 0 && histogram > 0 && !Portfolio[_fesxFuture.Symbol].IsLong)
+            {
+                SetHoldings(_fesxFuture.Mapped, 0.2m);
+            }
+            // Negative histogram value decreasing means the slow trend is dropping faster than the fast trend, indicating a strong downtrend.
+            else if (histogramChg < 0 && histogram < 0 && !Portfolio[_fesxFuture.Symbol].IsShort)
+            {
+                SetHoldings(_fesxFuture.Mapped, -0.2m);
+            }
         }
-    }
-}
    
-  
    
-  
    
-   Research Example
-  
    
-  
    
-   The following example lists crypto-currency pairs with the greatest dollar volume in the Kraken exchange:
-  
    
-  
    
-   
    var qb = new QuantBook();
-
-// Add Cryptocurrency pair
-var symbol = qb.AddCrypto("BTCUSDT", market:Market.Kraken).Symbol;
-
-// Historical data
-var history = qb.History(symbol, 30, Resolution.Daily);
-foreach (var bar in history)
-{
-    Console.WriteLine($"{bar.EndTime} {bar}");
-}
-
-// Add Crypto Universe Selection
-IEnumerable<Symbol> UniverseSelectionFilter(IEnumerable<CryptoUniverse> universeDay)
-{
-    return universeDay
-        .Where(x => x.VolumeInUsd != null && x.VolumeInUsd > 10000m)
-        .OrderByDescending(x => x.VolumeInUsd)
-        .Take(5)
-        .Select(x => x.Symbol);
-}
-var universe = qb.AddUniverse(CryptoUniverse.Kraken(UniverseSelectionFilter));
 
-// Historical Universe data
-var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-foreach (var universeDay in universeHistory)
-{
-    Console.WriteLine($"=== {universeDay.First().EndTime} ===");
-    foreach (CryptoUniverse universeItem in universeDay.OrderByDescending(x => x.VolumeInUsd))
-    {
-        Console.WriteLine($"{universeItem.Symbol}: {universeItem.VolumeInUsd}");
+        // Handle rollover in case the current mapped contract changes.
+        foreach (var (_, changedEvent) in slice.SymbolChangedEvents)
+        {
+            var oldSymbol = changedEvent.OldSymbol;
+            var newSymbol = AddFutureContract(changedEvent.NewSymbol).Symbol;
+            var quantity = Portfolio[oldSymbol].Quantity;
+            // Rolling over: to liquidate any position of the old mapped contract and switch to the newly mapped contract
+            Liquidate(oldSymbol);
+            if (quantity != 0)
+            {
+                MarketOrder(newSymbol, quantity);
+            }
+        }
     }
 }
    
-   
    qb = QuantBook()
-
-# Add Cryptocurrency pair
-symbol = qb.add_crypto("BTCUSDT", market=Market.KRAKEN).symbol
-
-# Historical data
-history = qb.history(symbol, 30, Resolution.DAILY)
-for (symbol, time), row in history.iterrows():
-    print(f'{time} {symbol} {row.close}')
-
-# Add Crypto Universe Selection
-def universe_selection_filter(universe_day):
-    selected = sorted([x for x in universe_day
-        if x.volume_in_usd and x.volume_in_usd > 10000],
-        key=lambda x: x.volume_in_usd, reverse=True)[:5]
-    return [x.symbol for x in selected]
-
-universe = qb.add_universe(CryptoUniverse.kraken(universe_selection_filter))
-
-# Historical Universe data
-history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (univere_symbol, time), universe_day in history.items():
-    print(f'=== {time} ===')
-    for universe_item in sorted(universe_day, key=lambda x: x.volume_in_usd, reverse=True):
-        print(f"{universe_item.symbol}: {universe_item.volume_in_usd}")
    
   
    
  
 
@@ -237288,67 +239225,84 @@ 
    Data Point Attributes
    
 
  
   
    
-   The Kraken Crypto Price dataset provides
+   The International Futures dataset provides
    
-    TradeBar
+    Future
    
    ,
    
-    QuoteBar
+    FuturesChain
    
-   ,
+   , and
    
-    Tick
+    OpenInterest
    
-   , and
+   objects. To configure the continuous Future settings, use the
    
-    CryptoUniverse
+    DataNormalizationMode
    
-   objects.
+   and
+   
+    DataMappingMode
+   
+   enumerations.
   
    
   
    
-   TradeBar Attributes
+   Future Attributes
   
    
   
    
    
-    TradeBar
+    Future
    
    objects have the following attributes:
   
    
-  
    
+  
    
   
    
   
    
-   QuoteBar Attributes
+   FuturesChain Attributes
   
    
   
    
    
-    QuoteBar
+    FuturesChain
    
    objects have the following attributes:
   
    
-  
    
+  
    
   
    
   
    
-   Tick Attributes
+   OpenInterest Attributes
   
    
   
    
    
-    Tick
+    OpenInterest
    
    objects have the following attributes:
   
    
-  
    
+  
    
   
    
   
    
-   CryptoUniverse Attributes
+   DataNormalizationMode Values
   
    
   
    
+   The
    
-    CryptoUniverse
+    DataNormalizationMode
    
-   objects have the following attributes:
+   enumeration has the following values:
   
    
-  
    
+  
    
+  
    
+  
    
+   DataMappingMode Values
+  
    
+  
    
+   The
+   
+    DataMappingMode
+   
+   enumeration has the following values:
+  
    
+  
    
   
    
  
 
@@ -237356,7 +239310,7 @@ 
    
 
     
    
 
    Datasets > Benzinga
    
 
    
-    
    
+    
    
         
    Datasets
    
         
    Benzinga
    
     
    
@@ -237372,7 +239326,7 @@ 
    Benzinga
    
    
    
     
    
      
-      
    
+      
    
        
    
         
    
          Benzinga News Feed
@@ -237389,7 +239343,7 @@ 
    
 
     
    
 
    Datasets > Benzinga > Benzinga News Feed
    
 
    
-    
    
+    
    
         
    Benzinga
    
         
    Benzinga News Feed
    
     
    
@@ -237817,7 +239771,7 @@ 
    
 
 class BenzingaNewsDataAlgorithm(QCAlgorithm):
 
-    def initialize(self) -> None:
+    def initialize(self) ->  None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
         self.set_cash(100000)
@@ -237835,181 +239789,152 @@ 
    
         
             
 class BenzingaNewsAlphaModel(AlphaModel):
-    
-    symbol_data_by_symbol = {}
+
+    _securities = []
     # A custom word-score map for calculating the total sentiment score
     word_scores = {'good': 1, 'great': 1, 'best': 1, 'growth': 1,
                    'bad': -1, 'terrible': -1, 'worst': -1, 'loss': -1}
     
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+    def update(self, algorithm: QCAlgorithm, slice: Slice) ->  List[Insight]:
         insights = []
         
-        for symbol, symbol_data in self.symbol_data_by_symbol.items():
-            if slice.contains_key(symbol_data.benzinga_symbol):
+        for security in self._securities:
+            if slice.contains_key(security.benzinga_data):
                 # Assign a sentiment score to the news article by specific word appearance scoring
-                content_words = slice[symbol_data.benzinga_symbol].contents.lower()
+                content_words = slice[security.benzinga_data].contents.lower()
                 score = 0
                 for word, word_score in self.word_scores.items():
                     score += (content_words.count(word) * word_score)
-                symbol_data.target_direction = InsightDirection.UP if score > 0 else InsightDirection.FLAT
+                security.target_direction = InsightDirection.UP if score >  0 else InsightDirection.FLAT
         
             # Ensure we have security data in the current Slice to avoid stale filling
-            if not (slice.contains_key(symbol) and slice[symbol] is not None and not slice[symbol].is_fill_forward):
+            if not (slice.contains_key(security) and slice[security] is not None and not slice[security].is_fill_forward):
                 continue
         
             # Buy or sell if the sentiment has changed from our current holdings
-            if symbol_data.current_direction != symbol_data.target_direction:
-                symbol_data.current_direction = symbol_data.target_direction
-                insights.append(Insight.price(symbol, timedelta(days=14), symbol_data.target_direction))
+            if security.current_direction != security.target_direction:
+                security.current_direction = security.target_direction
+                insights.append(Insight.price(security.symbol, timedelta(14), security.target_direction))
         
         return insights
         
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) ->  None:
         for security in changes.added_securities:
-            symbol = security.symbol
-            self.symbol_data_by_symbol[symbol] = SymbolData(algorithm, symbol)
-        
-        for security in changes.removed_securities:
-            symbol_data = self.symbol_data_by_symbol.pop(security.symbol, None)
-            if symbol_data:
-                symbol_data.dispose()
             
-class SymbolData:
-    
-    current_direction = InsightDirection.FLAT
-    target_direction = InsightDirection.FLAT
-    
-    def __init__(self, algorithm: QCAlgorithm, symbol: Symbol) -> None:
-        self.algorithm = algorithm
-        
-        # Requesting data to obtain the updated news for sentiment score calculation
-        self.benzinga_symbol = algorithm.add_data(BenzingaNews, symbol).symbol
-        
-        # Historical data
-        history = algorithm.history(self.benzinga_symbol, 14, Resolution.DAILY)
-        algorithm.debug(f"We got {len(history)} items from our history request")
+            # Requesting data to obtain the updated news for sentiment score calculation
+            security.benzinga_data = algorithm.add_data(BenzingaNews, security)
+            security.current_direction = InsightDirection.FLAT
+            security.target_direction = InsightDirection.FLAT
+
+            # Historical data
+            history = algorithm.history(security.benzinga_data, 14, Resolution.DAILY)
+            algorithm.debug(f"We got {len(history)} items from our history request")
+
+            self._securities.append(security)
         
-    def dispose(self) -> None:
-        # Unsubscribe from Benzinga news feed for this security to release computational resources
-        self.algorithm.remove_security(self.benzinga_symbol)
    
+        for security in changes.removed_securities:
+            if security in self._securities:
+                # Unsubscribe from Benzinga news feed for this security to release computational resources
+                algorithm.remove_security(security.benzinga_data)
+                self._securities.remove(security)
    using System.Text.RegularExpressions;
 
-public class BenzingaNewsDataAlgorithm : QCAlgorithm
+
+namespace QuantConnect.Algorithm.CSharp
 {
-    public override void Initialize()
+    public class BenzingaNewsDataAlgorithm : QCAlgorithm
     {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        
-        AddUniverseSelection(
-            new ManualUniverseSelectionModel(
-                QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA)
-        ));
-        
-        AddAlpha(new BenzingaAlphaModel());
-        
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
-        
-        AddRiskManagement(new NullRiskManagementModel());
-        
-        SetExecution(new ImmediateExecutionModel());
+        public override void Initialize()
+        {
+            SetStartDate(2024, 9, 1);
+            SetEndDate(2024, 12, 31);
+            SetCash(100000);
+            
+            AddUniverseSelection(
+                new ManualUniverseSelectionModel(
+                    QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA)
+            ));
+            
+            AddAlpha(new BenzingaAlphaModel());
+            
+            SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
+            
+            AddRiskManagement(new NullRiskManagementModel());
+            
+            SetExecution(new ImmediateExecutionModel());
+        }
     }
-}
-
-public class BenzingaAlphaModel : AlphaModel
-{
-    private Dictionary<Symbol, SymbolData> _symbolDataBySymbol = new Dictionary<Symbol, SymbolData>();
-    // A custom word-score map for calculating the total sentiment score
-    private Dictionary<string, int> _wordScores = new Dictionary<string, int>(){
-        {"good", 1}, {"great", 1}, {"best", 1}, {"growth", 1},
-        {"bad", -1}, {"terrible", -1}, {"worst", -1}, {"loss", -1}
-    };
 
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    public class BenzingaAlphaModel : AlphaModel
     {
-        var insights = new List<Insight>();
-        
-        foreach (var kvp in _symbolDataBySymbol)
+        private List< Security>  _securities = [];
+        // A custom word-score map for calculating the total sentiment score
+        private readonly Dictionary< string, int>  _wordScores = new(){
+            {"good", 1}, {"great", 1}, {"best", 1}, {"growth", 1},
+            {"bad", -1}, {"terrible", -1}, {"worst", -1}, {"loss", -1}
+        };
+
+        public override IEnumerable< Insight>  Update(QCAlgorithm algorithm, Slice slice)
         {
-            var symbol = kvp.Key;
-            var symbolData = kvp.Value;
-            
-            if (slice.ContainsKey(symbolData.benzingaSymbol))
-            {
-                // Assign a sentiment score to the news article by specific word appearance scoring
-                var contentWords = slice[symbolData.benzingaSymbol].Contents.ToLower();
+            var insights = new List< Insight> ();
+
+            foreach (var (benzingaSymbol, newsItem) in slice.Get< BenzingaNews> ())
+            {               
+                var security = algorithm.Securities[benzingaSymbol.Underlying];
+                var contentWords = newsItem.Contents.ToLower();
                 var score = 0;
+
                 foreach (var entry in _wordScores)
                 {
-                    score += (Regex.Matches(contentWords, entry.Key).Count * entry.Value);
+                    score += Regex.Matches(contentWords, entry.Key).Count * entry.Value;
                 }
-                symbolData.targetDirection = score > 0 ? InsightDirection.Up : InsightDirection.Flat;
-            }
-            
-            
-            // Ensure we have security data in the current Slice to avoid stale filling
-            if (!(slice.ContainsKey(symbol) && slice[symbol] != null && !slice[symbol].IsFillForward))
-            {
-                continue;
+                var targetDirection = score >  0 ? InsightDirection.Up : InsightDirection.Flat;
+                security.Set("TargetDirection", targetDirection);
             }
             
-            // Buy or sell if the sentiment has changed from our current holdings
-            if (symbolData.currentDirection != symbolData.targetDirection)
+            // Generate insights based on direction changes
+            foreach (var security in _securities)
             {
-                insights.Add(Insight.Price(symbol, TimeSpan.FromDays(14), symbolData.targetDirection));
-                symbolData.currentDirection = symbolData.targetDirection;
+                if (!(slice.ContainsKey(security)  &&  slice[security] != null  &&  !slice[security].IsFillForward)) continue;
+                
+                var targetDirection = security.Get< InsightDirection> ("TargetDirection");
+                
+                // Buy or sell if the sentiment has changed from our current holdings
+                if (security.Get< InsightDirection> ("CurrentDirection") != targetDirection)
+                {
+                    insights.Add(Insight.Price(security, TimeSpan.FromDays(14), targetDirection));
+                    security.Set("CurrentDirection", targetDirection);
+                }
             }
+            return insights;
         }
-        return insights;
-    }
 
-    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-    {
-        foreach (var security in changes.AddedSecurities)
+        public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
         {
-            var symbol = security.Symbol;
-            _symbolDataBySymbol.Add(symbol, new SymbolData(algorithm, symbol));
-        }
+            foreach (var security in changes.AddedSecurities)
+            {
+                // Requesting data to obtain the updated news for sentiment score calculation
+                var benzingaSymbol = algorithm.AddData< BenzingaNews> (security.Symbol).Symbol;
+                security.Set("CurrentDirection", InsightDirection.Flat);
+                security.Set("TargetDirection", InsightDirection.Flat);
 
-        foreach (var security in changes.RemovedSecurities)
-        {
-            var symbol = security.Symbol;
-            if (_symbolDataBySymbol.ContainsKey(symbol))
+                // Historical data
+                var history = algorithm.History< BenzingaNews> (benzingaSymbol, 14, Resolution.Daily);
+                algorithm.Debug($"We got {history.Count()} items from our history request");
+                _securities.Add(security);
+                security.Set("BenzingaSymbol", benzingaSymbol);
+            }
+
+            foreach (var security in changes.RemovedSecurities)
             {
-                _symbolDataBySymbol[symbol].dispose();
-                _symbolDataBySymbol.Remove(symbol);
+                if (_securities.Contains(security))
+                {
+                    algorithm.RemoveSecurity(security.Get< Symbol> ("BenzingaSymbol"));
+                    _securities.Remove(security);
+                }
             }
         }
     }
-}
-
-
-public class SymbolData
-{
-    private QCAlgorithm _algorithm;
-    public Symbol benzingaSymbol;
-    public InsightDirection currentDirection = InsightDirection.Flat;
-    public InsightDirection targetDirection = InsightDirection.Flat;
-    
-    
-    public SymbolData(QCAlgorithm algorithm, Symbol symbol)
-    {
-        _algorithm = algorithm;
-        
-        // Requesting data to obtain the updated news for sentiment score calculation
-        benzingaSymbol = algorithm.AddData<BenzingaNews>(symbol).Symbol;
-        
-        // Historical data
-        var history = algorithm.History<BenzingaNews>(benzingaSymbol, 14, Resolution.Daily);
-        algorithm.Debug($"We got {history.Count()} items from our history request");
-    }
-    
-    public void dispose()
-    {
-        // Unsubscribe from the Benzinga feed for this security to release computational resources
-        _algorithm.RemoveSecurity(benzingaSymbol);
-    }
 }
    @@ -238033,7 +239958,7 @@

    Data Point Attributes

     

    Datasets > Blockchain

    -
    +

    Datasets

    Blockchain

    @@ -238052,7 +239977,7 @@

    Blockchain

    -
    +

    Bitcoin Metadata @@ -238069,7 +239994,7 @@

     

    Datasets > Blockchain > Bitcoin Metadata

    -
    +

    Blockchain

    Bitcoin Metadata

    @@ -238506,7 +240431,7 @@

    insights = [] # Trade only based on updated Bitcoin Metadata - data = slice.Get(BitcoinMetadata) + data = slice.get(BitcoinMetadata) for symbol, bitcoin_metadata_symbol in self.bitcoin_metadata_symbol_by_symbol.items(): if data.contains_key(bitcoin_metadata_symbol) and data[bitcoin_metadata_symbol] != None and data[bitcoin_metadata_symbol].hash_rate: @@ -238629,7 +240554,7 @@

    Data Point Attributes

     

    Datasets > Brain

    -
    +

    Datasets

    Brain

    @@ -238648,7 +240573,7 @@

    Brain

    -
    +

    Brain Language Metrics on Company Filings @@ -238659,7 +240584,7 @@

    -
    +

    Brain ML Stock Ranking @@ -238670,7 +240595,7 @@

    -
    +

    Brain Sentiment Indicator @@ -238681,7 +240606,7 @@

    -
    +

    Brain Wikipedia Page Views @@ -238698,7 +240623,7 @@

     

    Datasets > Brain > Brain Language Metrics on Company Filings

    -
    +

    Brain

    Brain Language Metrics on Company Filings

    @@ -239251,32 +241176,32 @@

    class BrainCompanyFilingNLPDataAlgorithm(QCAlgorithm): - def initialize(self): + def initialize(self) -> None: self.set_start_date(2024, 9, 1) self.set_end_date(2024, 12, 31) - self.set_cash(100000) + self.set_cash(100000) # Seed the price of each asset with its last known price to avoid trading errors. self.settings.seed_initial_prices = True - # Requesting data -- we aim to obtain a sentiment score from the company filings - # Combining both fundamental and sentiment factor, as well as past performance and future provision - self._symbol = self.add_equity("AAPL", Resolution.DAILY).symbol - self.dataset_symbol = self.add_data(BrainCompanyFilingLanguageMetrics10K , self._symbol).symbol - + # Request company filing data to obtain sentiment scores from company reports. + # Combine sentiment with fundamental context, past performance, and forward-looking provisions. + self._equity = self.add_equity("AAPL", Resolution.DAILY) + self._filing_data = self.add_data(BrainCompanyFilingLanguageMetrics10K, self._equity) # Historical data - history = self.history(self.dataset_symbol, 365, Resolution.DAILY) - self.debug(f"We got {len(history)} items from our history request for {self.dataset_symbol}") - - def on_data(self, data): - # Trade base on the updated report sentiment - if data.contains_key(self.dataset_symbol): - sentiment = data[self.dataset_symbol].report_sentiment.sentiment - # Buy for a positive sentiment score for the positive return projection - self.set_holdings(self._symbol, int(sentiment > 0)) + history = self.history(self._filing_data, 365, Resolution.DAILY) + self.debug(f"We got {len(history)} items from our history request for {self._filing_data}") + + def on_data(self, data: Slice) -> None: + # Trade based on the updated report sentiment. + if self._filing_data not in data: + return + sentiment = data[self._filing_data].report_sentiment.sentiment + # Buy when sentiment is positive to express the expected positive return projection. + self.set_holdings(self._equity, int(sentiment > 0)) 
    public class BrainCompanyFilingNLPDataAlgorithm : QCAlgorithm
 {
-    private Symbol _symbol;
-    private Symbol _datasetSymbol;
-    
+    private Equity _equity;
+    private Symbol _filingData;
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
@@ -239284,24 +241209,23 @@ 
    
         SetCash(100000);
         // Seed the price of each asset with its last known price to avoid trading errors.
         Settings.SeedInitialPrices = true;
-        // Requesting data -- we aim to obtain a sentiment score from the company filings
-        // Combining both fundamental and sentiment factor, as well as past performance and future provision
-        _symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
-        _datasetSymbol = AddData<BrainCompanyFilingLanguageMetrics10K>(_symbol).Symbol;
-        
+        // Request company filing data to obtain sentiment scores from company reports.
+        // Combine sentiment with fundamental context, past performance, and forward-looking provisions.
+        _equity = AddEquity("AAPL", Resolution.Daily);
+        _filingData = AddData<BrainCompanyFilingLanguageMetrics10K>(_equity.Symbol).Symbol;
         // Historical data
-        var history = History<BrainCompanyFilingLanguageMetrics10K>(_datasetSymbol, 365, Resolution.Daily);
-        Debug($"We got {history.Count()} items from our history request for {_datasetSymbol}");
+        var history = History<BrainCompanyFilingLanguageMetrics10K>(_filingData, 365, Resolution.Daily);
+        Debug($"We got {history.Count()} items from our history request for {_filingData}");
     }
 
     public override void OnData(Slice slice)
     {
-        // Trade base on the updated report sentiment
-        if (slice.ContainsKey(_datasetSymbol))
+        // Trade based on the updated report sentiment.
+        if (slice.ContainsKey(_filingData))
         {
-            // Buy for a positive sentiment score for the positive return projection
-            var sentiment = slice[_datasetSymbol].ReportSentiment.Sentiment;
-            SetHoldings(_symbol, sentiment > 0 ? 1 : 0);
+            var sentiment = slice[_filingData].ReportSentiment.Sentiment;
+            // Buy when sentiment is positive to express the expected positive return projection.
+            SetHoldings(_equity.Symbol, sentiment > 0 ? 1 : 0);
         }
     }
 }
    @@ -239466,26 +241390,26 @@ 

    qb = QuantBook()
 
 # Requesting Data
-aapl = qb.AddEquity("AAPL", Resolution.Daily).Symbol
-symbol = qb.AddData(BrainCompanyFilingLanguageMetrics10K, aapl).Symbol
+aapl = qb.add_equity("AAPL", Resolution.DAILY).symbol
+symbol = qb.add_data(BrainCompanyFilingLanguageMetrics10K, aapl).symbol
 
 # Historical data
-history = qb.History(BrainCompanyFilingLanguageMetrics10K, symbol, 180, Resolution.Daily)
+history = qb.history(BrainCompanyFilingLanguageMetrics10K, symbol, 180, Resolution.DAILY)
 for (symbol, time), row in history.iterrows():
-    print(f"{symbol} sentiment at {time}: {row['reportsentiment'].Sentiment}")
+    print(f"{symbol} sentiment at {time}: {row['reportsentimentsentiment']}")
 
 # Add Universe Selection
-def UniverseSelection(alt_coarse: List[BrainCompanyFilingLanguageMetricsUniverse10K]) -> List[Symbol]:
-    return [d.Symbol for d in sorted([x for x in alt_coarse if x.ReportSentiment.Sentiment],
-        key=lambda x: x.ReportSentiment.Sentiment, reverse=True)[:10]]
+def universe_selection(alt_coarse: List[BrainCompanyFilingLanguageMetricsUniverse10K]) -> List[Symbol]:
+    return [d.symbol for d in sorted([x for x in alt_coarse if x.report_sentiment.sentiment],
+        key=lambda x: x.report_sentiment.sentiment, reverse=True)[:10]]
 
-universe = qb.AddUniverse(BrainCompanyFilingLanguageMetricsUniverse10K, UniverseSelection)
+universe = qb.add_universe(BrainCompanyFilingLanguageMetricsUniverse10K, universe_selection)
 
 # Historical Universe data
-universe_history = qb.UniverseHistory(universe, qb.Time-timedelta(10), qb.Time)
+universe_history = qb.universe_history(universe, qb.time-timedelta(10), qb.time)
 for (_, time), universeDay in universe_history.items():
     for language_metrics in universeDay:
-        print(f"{language_metrics.Symbol} sentiment at {language_metrics.EndTime}: {language_metrics.ReportSentiment.Sentiment}")
    + print(f"{language_metrics.symbol} sentiment at {language_metrics.end_time}: {language_metrics.report_sentiment.sentiment}")

    @@ -239532,7 +241456,7 @@

     

    Datasets > Brain > Brain ML Stock Ranking

    -
    +

    Brain

    Brain ML Stock Ranking

    @@ -240132,103 +242056,91 @@

    class BrainMLRankingDataAlgorithm(QCAlgorithm): + def initialize(self) -> None: self.set_start_date(2024, 9, 1) self.set_end_date(2024, 12, 31) - self.set_cash(100000) - # Seed the price of each asset with its last known price to avoid trading errors. + self.set_cash(100000) + # Seed initial prices to avoid trading errors before fresh market data arrives. self.settings.seed_initial_prices = True - # We cherry picked 5 largest stocks, high trading volume provides better information and credibility for ML ranking + # Select liquid large-cap equities to support credible ranking comparisons. tickers = ["AAPL", "TSLA", "MSFT", "F", "KO"] - self.symbol_by_dataset_symbol = {} + self.equity_by_dataset_symbol = {} for ticker in tickers: - # Requesting data to get 2 days estimated relative ranking - symbol = self.add_equity(ticker, Resolution.DAILY).symbol - dataset_symbol = self.add_data(BrainStockRanking2Day, symbol).symbol - self.symbol_by_dataset_symbol[dataset_symbol] = symbol - - # Historical data + # Subscribe to each equity and its two-day estimated relative ranking data. + equity = self.add_equity(ticker, Resolution.DAILY) + dataset_symbol = self.add_data(BrainStockRanking2Day, equity).symbol + self.equity_by_dataset_symbol[dataset_symbol] = equity + # Request historical ranking data to inspect subscription coverage. history = self.history(dataset_symbol, 365, Resolution.DAILY) - self.debug(f"We got {len(history)} items from our history request for {symbol}") - - def on_data(self, slice: Slice) -> None: - # Collect rankings for all selected symbols for ranking them - points = slice.Get(BrainStockRanking2Day) - if points is None: + self.debug(f"We got {len(history)} items from our history request for {equity}") + + def on_data(self, data: Slice) -> None: + # Collect ranking points for all selected equities before assigning weights. + points = data.get(BrainStockRanking2Day) + if not points: return - symbols = [] - ranks = [] - for point in points.Values: - symbols.append(self.symbol_by_dataset_symbol[point.symbol]) - ranks.append(point.rank) - - # Rank each symbol's Brain ML ranking relative to each other for positional sizing - if len(ranks) == 0: + rank_by_equity = {} + for dataset_symbol, point in points.items(): + rank_by_equity[self.equity_by_dataset_symbol[dataset_symbol]] = point.rank + if not rank_by_equity: return - ranks = [sorted(ranks).index(rank) + 1 for rank in ranks] - - # Place orders according to the ML ranking, the better the rank, the higher the estimated return and hence weight - for i, rank in enumerate(ranks): - weight = rank / sum(ranks) - self.set_holdings(symbols[i], weight) + # Convert raw ranking values into comparable position sizes. + sorted_ranks = sorted(rank_by_equity.values()) + ranked = {equity: sorted_ranks.index(rank) + 1 for equity, rank in rank_by_equity.items()} + # Allocate more capital to equities with stronger estimated return rankings. + total = sum(ranked.values()) + for equity, rank in ranked.items(): + self.set_holdings(equity.symbol, rank / total) 
    public class BrainMLRankingDataAlgorithm : QCAlgorithm
 {
-    private Dictionary<Symbol, Symbol> _symbolByDatasetSymbol = new Dictionary<Symbol, Symbol>();
-    
+    private readonly Dictionary<Symbol, Equity> _equityByDatasetSymbol = [];
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
         SetCash(100000);
-        // Seed the price of each asset with its last known price to avoid trading errors.
+        // Seed initial prices to avoid trading errors before fresh market data arrives.
         Settings.SeedInitialPrices = true;
-        // We cherry picked 5 largest stocks, high trading volume provides better information and credibility for ML ranking
-        var tickers = new List<string>() {"AAPL", "TSLA", "MSFT", "F", "KO"};
+        // Select liquid large-cap equities to support credible ranking comparisons.
+        var tickers = new List<string> {"AAPL", "TSLA", "MSFT", "F", "KO"};
         foreach (var ticker in tickers)
         {
-            // Requesting data to get 2 days estimated relative ranking
-            var symbol = AddEquity(ticker, Resolution.Daily).Symbol;
-            var datasetSymbol = AddData<BrainStockRanking2Day>(symbol).Symbol;
-            _symbolByDatasetSymbol.Add(datasetSymbol, symbol);
-            
-            // Historical data
+            // Subscribe to each equity and its two-day estimated relative ranking data.
+            var equity = AddEquity(ticker, Resolution.Daily);
+            var datasetSymbol = AddData<BrainStockRanking2Day>(equity.Symbol).Symbol;
+            _equityByDatasetSymbol.Add(datasetSymbol, equity);
+            // Request historical ranking data to inspect subscription coverage.
             var history = History<BrainStockRanking2Day>(datasetSymbol, 365, Resolution.Daily);
-            Debug($"We got {history.Count()} items from our history request for {symbol}");
+            Debug($"We got {history.Count()} items from our history request for {equity}");
         }
     }
 
     public override void OnData(Slice slice)
     {
-        // Collect rankings for all symbols for ranking them
+        // Collect ranking points for all selected equities before assigning weights.
         var points = slice.Get<BrainStockRanking2Day>();
-        if (points == null)
+        if (points == null || points.Count == 0)
         {
             return;
         }
-        var symbols = new List<Symbol>();
-        var ranks = new List<decimal>();
-        foreach (var point in points.Values)
+        var rankByEquity = new Dictionary<Equity, decimal>();
+        foreach (var (datasetSymbol, point) in points)
         {
-            symbols.Add(_symbolByDatasetSymbol[point.Symbol]);
-            ranks.Add(point.Rank);
+            rankByEquity[_equityByDatasetSymbol[datasetSymbol]] = point.Rank;
         }
-        
-        // Rank each symbol's Brain ML ranking relative to the other symbols for positional sizing
-        if (ranks.Count() == 0) return;
-        var sortedRanksTemp = new List<decimal>(ranks); 
-        sortedRanksTemp.Sort();
-        var sortedRanks = new List<decimal>();
-        for (var i = 0; i < symbols.Count(); i++)
-        {
-            sortedRanks.Add(sortedRanksTemp.IndexOf(ranks[i]) + 1);
-        }
-        
-        // Place orders, the better the rank, the higher the estimated return and hence weight
-        for (var i = 0; i < symbols.Count(); i++)
+        // Convert raw ranking values into comparable position sizes.
+        var sortedRanks = rankByEquity.Values.OrderBy(r => r).ToList();
+        var ranked = rankByEquity.ToDictionary(
+            kvp => kvp.Key,
+            kvp => (decimal)(sortedRanks.IndexOf(kvp.Value) + 1)
+        );
+        // Allocate more capital to equities with stronger estimated return rankings.
+        var total = ranked.Values.Sum();
+        foreach (var (equity, rank) in ranked)
         {
-            var rank = sortedRanks[i];
-            var weight = rank / sortedRanks.Sum();
-            SetHoldings(symbols[i], weight);
+            SetHoldings(equity.Symbol, rank / total);
         }
     }
 }
    @@ -240247,88 +242159,56 @@

    def initialize(self) -> None: self.set_start_date(2024, 9, 1) self.set_end_date(2024, 12, 31) - self.set_cash(100000) - + self.set_cash(100000) + self.settings.seed_initial_prices = True self.universe_settings.resolution = Resolution.DAILY - # Select based on ML ranking data + # Select securities based on ML ranking data. self.add_universe(BrainStockRankingUniverse, self.universe_selection) - self.add_alpha(BrainMLRankingAlphaModel()) - self.set_portfolio_construction(InsightWeightingPortfolioConstructionModel()) - self.add_risk_management(NullRiskManagementModel()) - self.set_execution(ImmediateExecutionModel()) def universe_selection(self, alt_coarse: List[BrainStockRankingUniverse]) -> List[Symbol]: - # Select the ones that expected to out-perform the median of the whole market in 2-3 days - return [d.symbol for d in alt_coarse \ - if d.Rank2Days > 0.2 \ - and d.Rank3Days > 0.2] + # Select the securities expected to outperform the median market return in two to three days. + filtered = [d for d in alt_coarse if d.rank_2_days > 0 and d.rank_3_days > 0] + filtered = sorted(filtered, key=lambda d: d.rank_2_days)[-2:] + return [d.symbol for d in filtered] + class BrainMLRankingAlphaModel(AlphaModel): - - symbol_data_by_symbol = {} - symbol_by_dataset_symbol = {} - + _securities = [] + def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]: - insights = [] - - # Collect rankings for all selected symbols for ranking them - points = slice.Get(BrainStockRanking2Day) - if points is None: - return [] - symbols = [] - ranks = [] - for point in points.Values: - symbols.append(self.symbol_by_dataset_symbol[point.symbol]) - ranks.append(point.rank) - - # Rank each symbol's Brain ML ranking relative to each other for positional sizing - if len(ranks) == 0: + # Collect rankings for all selected securities. + points = slice.get(BrainStockRanking2Day) + if not points: return [] - ranks = [sorted(ranks).index(rank) + 1 for rank in ranks] - - # Place orders according to the ML ranking, the better the rank, the higher the estimated return and hence weight - for i, rank in enumerate(ranks): - weight = rank / sum(ranks) - insights.append(Insight.price(symbols[i], timedelta(days=7), InsightDirection.UP, None, None, None, weight)) - - return insights - + rank_by_security = {} + for dataset_symbol, point in points.items(): + security = algorithm.securities[dataset_symbol.underlying] + if not security.price: + continue + rank_by_security[security] = point.rank + total_rank = sum(rank_by_security.values()) + return [ + Insight.price(security, timedelta(1), InsightDirection.UP, weight=rank / total_rank) + for security, rank in rank_by_security.items() + ] + def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None: for security in changes.added_securities: - symbol = security.symbol - symbol_data = SymbolData(algorithm, symbol) - self.symbol_data_by_symbol[symbol] = symbol_data - self.symbol_by_dataset_symbol[symbol_data.dataset_symbol] = symbol - + self._securities.append(security) + # Request data for the two-day estimated relative ranking. + security.brain_stock_ranking_2_day = algorithm.add_data(BrainStockRanking2Day, security, Resolution.DAILY) + # Historical data. + history = algorithm.history(security.brain_stock_ranking_2_day, 365, Resolution.DAILY) + algorithm.debug(f"We got {len(history)} items from our history request for {security.brain_stock_ranking_2_day.symbol}") for security in changes.removed_securities: - symbol_data = self.symbol_data_by_symbol.pop(security.symbol, None) - if symbol_data: - symbol_data.dispose() - - for dataset_symbol, symbol in self.symbol_by_dataset_symbol.items(): - if symbol == security.symbol: - self.symbol_by_dataset_symbol.pop(dataset_symbol) - break - - -class SymbolData: - def __init__(self, algorithm, symbol): - self.algorithm = algorithm - - # Requesting data to get 2 days estimated relative ranking - self.dataset_symbol = algorithm.add_data(BrainStockRanking2Day, symbol).symbol - - # Historical data - history = algorithm.history(self.dataset_symbol, 365, Resolution.DAILY) - algorithm.debug(f"We got {len(history)} items from our history request for {symbol}") - - def dispose(self): - # Unsubscribe from the Brain ML Ranking feed for this security to release computation resources - self.algorithm.remove_security(self.dataset_symbol) + # Unsubscribe from the Brain ML Ranking feed for this security to release resources. + if security in self._securities: + algorithm.remove_security(security.brain_stock_ranking_2_day) + self._securities.remove(security) 
    public class BrainMLRankingDataAlgorithm : QCAlgorithm
 {
     public override void Initialize()
@@ -240336,127 +242216,80 @@ 
    
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
         SetCash(100000);
-        
-        // Select based on ML ranking data
-        AddUniverse<BrainStockRankingUniverse>(altCoarse =>
-        {
-            // Select the ones that expected to out-perform the median of the whole market in 2-3 days
-            return from d in altCoarse.OfType<BrainStockRankingUniverse>() 
-                where d.Rank2Days > 0.2m && d.Rank3Days > 0.2m
-                select d.Symbol;
-        });
-        
+        Settings.SeedInitialPrices = true;
+        UniverseSettings.Resolution = Resolution.Daily;
+        // Select securities based on ML ranking data.
+        AddUniverse<BrainStockRankingUniverse>(
+            altCoarse => altCoarse
+                .Select(dataPoint => dataPoint as BrainStockRankingUniverse)
+                .Where(dataPoint => dataPoint.Rank2Days > 0m && dataPoint.Rank3Days > 0m)
+                .OrderBy(dataPoint => dataPoint.Rank2Days)
+                .TakeLast(2)
+                .Select(dataPoint => dataPoint.Symbol)
+        );
         AddAlpha(new BrainMLRankingAlphaModel());
-        
         SetPortfolioConstruction(new InsightWeightingPortfolioConstructionModel());
-        
         AddRiskManagement(new NullRiskManagementModel());
-        
         SetExecution(new ImmediateExecutionModel());
     }
 }
-    
-    
+
+
 public class BrainMLRankingAlphaModel : AlphaModel
 {
-    private Dictionary<Symbol, SymbolData> _symbolDataBySymbol = new Dictionary<Symbol, SymbolData>();
-    private Dictionary<Symbol, Symbol> _symbolByDatasetSymbol = new Dictionary<Symbol, Symbol>();
+    private readonly List<Security> _securities = new List<Security>();
 
     public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
     {
-        var insights = new List<Insight>();
-        
-        // Collect rankings for all selected symbols for ranking them
+        // Collect rankings for all selected securities.
         var points = slice.Get<BrainStockRanking2Day>();
-        if (points == null)
-        {
-            return insights;
-        }
-        var symbols = new List<Symbol>();
-        var ranks = new List<decimal>();
-        foreach (var point in points.Values)
+        if (points == null || points.Count == 0)
         {
-            symbols.Add(_symbolByDatasetSymbol[point.Symbol]);
-            ranks.Add(point.Rank);
-        }
-        
-        // Rank each symbol's Brain ML ranking relative to the other symbols for positional sizing
-        if (ranks.Count() == 0) 
-        {
-            return insights; 
-        }
-        var sortedRanksTemp = new List<decimal>(ranks); 
-        sortedRanksTemp.Sort();
-        var sortedRanks = new List<decimal>();
-        for (var i = 0; i < symbols.Count(); i++)
-        {
-            sortedRanks.Add(sortedRanksTemp.IndexOf(ranks[i]) + 1);
+            return Enumerable.Empty<Insight>();
         }
-        
-        // Place orders according to the ML ranking, the better the rank, the higher the estimated return and hence weight
-        for (var i = 0; i < symbols.Count(); i++)
+
+        var rankBySecurity = new Dictionary<Security, decimal>();
+        foreach (var (datasetSymbol, point) in points)
         {
-            var rank = sortedRanks[i];
-            var weight = rank / sortedRanks.Sum();
-            insights.Add(Insight.Price(symbols[i], TimeSpan.FromDays(7), InsightDirection.Up, null, null, null, (double)weight));
+            var security = algorithm.Securities[datasetSymbol.Underlying];
+            if (security.Price == 0)
+            {
+                continue;
+            }
+            rankBySecurity[security] = point.Rank;
         }
-        
-        return insights;
+
+        var totalRank = rankBySecurity.Values.Sum();
+        return rankBySecurity.Select(kvp =>
+            Insight.Price(
+                kvp.Key.Symbol, TimeSpan.FromDays(1), InsightDirection.Up,
+                weight: (double)(kvp.Value / totalRank)
+            )
+        );
     }
 
     public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
     {
         foreach (var security in changes.AddedSecurities)
         {
-            var symbol = security.Symbol;
-            var symbolData = new SymbolData(algorithm, symbol);
-            _symbolDataBySymbol.Add(symbol, symbolData);
-            _symbolByDatasetSymbol.Add(symbolData.datasetSymbol, symbol);
+            _securities.Add(security);
+            // Request data for the two-day estimated relative ranking.
+            var brainStockRanking2Day = algorithm.AddData<BrainStockRanking2Day>(security.Symbol, Resolution.Daily);
+            security.Set("BrainStockRanking2Day", brainStockRanking2Day);
+            // Historical data.
+            var history = algorithm.History<BrainStockRanking2Day>(brainStockRanking2Day.Symbol, 365, Resolution.Daily);
+            algorithm.Debug($"We got {history.Count()} items from our history request for {brainStockRanking2Day}");
         }
-
         foreach (var security in changes.RemovedSecurities)
         {
-            var symbol = security.Symbol;
-            if (_symbolDataBySymbol.ContainsKey(symbol))
-            {
-                _symbolDataBySymbol[symbol].dispose();
-                _symbolDataBySymbol.Remove(symbol);
-            }
-            
-            foreach (var entry in _symbolByDatasetSymbol)
+            // Unsubscribe from the Brain ML Ranking feed for this security to release resources.
+            if (_securities.Contains(security))
             {
-                if (entry.Value == symbol)
-                {
-                    _symbolByDatasetSymbol.Remove(entry.Key);
-                }
+                algorithm.RemoveSecurity(security.Get<Security>("BrainStockRanking2Day").Symbol);
+                _securities.Remove(security);
             }
         }
     }
-}
-
-
-public class SymbolData
-{
-    private QCAlgorithm _algorithm;
-    public Symbol datasetSymbol;
-
-    public SymbolData(QCAlgorithm algorithm, Symbol symbol)
-    {
-        _algorithm = algorithm;
-        
-        // Requesting data to get 2 days estimated relative ranking
-        datasetSymbol = algorithm.AddData<BrainStockRanking2Day>(symbol).Symbol;
-        
-        // Historical data
-        var history = algorithm.History<BrainStockRanking2Day>(datasetSymbol, 365, Resolution.Daily);
-        algorithm.Debug($"We got {history.Count()} items from our history request for {symbol}");
-    }
-    
-    public void dispose()
-    {
-        // Unsubscribe from the Brain ML Ranking feed for this security to release computation resources
-        _algorithm.RemoveSecurity(datasetSymbol);
-    }
 }

    @@ -240512,7 +242345,7 @@

    # Add Universe Selection def universe_selection(alt_coarse: List[BrainStockRankingUniverse]) -> List[Symbol]: - return [d.symbol for d in sorted(alt_coarse, key=lambda x: x.Rank2Days, reverse=True)[:10]] + return [d.symbol for d in sorted(alt_coarse, key=lambda x: x.rank_2_days, reverse=True)[:10]] universe = qb.add_universe(BrainStockRankingUniverse, universe_selection) @@ -240520,7 +242353,7 @@

    universe_history = qb.universe_history(universe, qb.time-timedelta(5), qb.time) for (_, time), ranks in universe_history.items(): for rank in ranks: - print(f"{rank.symbol} 2-day rank at {rank.end_time}: {rank.Rank2Days}") + print(f"{rank.symbol} 2-day rank at {rank.end_time}: {rank.rank_2_days}")

    @@ -240567,7 +242400,7 @@

     

    Datasets > Brain > Brain Sentiment Indicator

    -
    +

    Brain

    Brain Sentiment Indicator

    @@ -241075,71 +242908,60 @@

    class BrainSentimentDataAlgorithm(QCAlgorithm): - latest_sentiment_value = None target_holdings = 0 - + def initialize(self) -> None: self.set_start_date(2024, 9, 1) self.set_end_date(2024, 12, 31) - self.set_cash(100000) - - # Requesting the processed longer term (30-day) sentiment score data for sentiment trading - self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol + self.set_cash(100000) + # Request the processed longer term sentiment score data for sentiment trading. + self.aapl = self.add_equity("AAPL", Resolution.DAILY) self.dataset_symbol = self.add_data(BrainSentimentIndicator30Day, self.aapl).symbol - - # Historical data history = self.history(self.dataset_symbol, 100, Resolution.DAILY) self.debug(f"We got {len(history)} items from our history request for {self.dataset_symbol}") if history.empty: return - - # Warm up historical sentiment values, cache for comparing last sentiment score to trade, making it immediately tradable signal + # Warm up historical sentiment values so the signal is immediately tradable. previous_sentiment_values = history.loc[self.dataset_symbol].sentiment.values for sentiment in previous_sentiment_values: self.update(sentiment) - + def update(self, sentiment: float) -> None: - # Comparing the last sentiment score and decide to buy if the sentiment increases to ride the popularity + # Compare the last sentiment score to buy when sentiment increases. if self.latest_sentiment_value is not None: self.target_holdings = int(sentiment > self.latest_sentiment_value) self.latest_sentiment_value = sentiment - - def on_data(self, slice: Slice) -> None: - # Update trade direction based on updated data - if slice.contains_key(self.dataset_symbol): - sentiment = slice[self.dataset_symbol].sentiment + + def on_data(self, data: Slice) -> None: + # Update trade direction from the latest sentiment data. + if self.dataset_symbol in data: + sentiment = data[self.dataset_symbol].sentiment self.update(sentiment) - - # Ensure we have security data in the current slice to avoid stale fill - if not (slice.contains_key(self.aapl) and slice[self.aapl] is not None): + # Require current equity data to avoid stale fills. + if self.aapl not in data or data[self.aapl] is None: return - - # Buy if sentiment increase, liquidate otherwise to ride on the popularity of the equity + # Buy when sentiment increases, otherwise liquidate the equity. if self.target_holdings != self.portfolio.invested: self.set_holdings(self.aapl, self.target_holdings) 
    public class BrainSentimentDataAlgorithm : QCAlgorithm
 {
-    private Symbol _symbol;
+    private Equity _aapl;
     private Symbol _datasetSymbol;
     private decimal? _latestSentimentValue = null;
     private int _targetHoldings = 0;
-    
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
         SetCash(100000);
-        
-        // Requesting the processed longer term (30-day) sentiment score data for sentiment trading
-        _symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
-        _datasetSymbol = AddData<BrainSentimentIndicator30Day>(_symbol).Symbol;
-        
-        // Historical data
+        // Request the processed longer term sentiment score data for sentiment trading.
+        _aapl = AddEquity("AAPL", Resolution.Daily);
+        _datasetSymbol = AddData<BrainSentimentIndicator30Day>(_aapl.Symbol).Symbol;
         var history = History<BrainSentimentIndicator30Day>(_datasetSymbol, 100, Resolution.Daily);
         Debug($"We got {history.Count()} items from our history request for {_datasetSymbol}");
-        
-        // Warm up historical sentiment values, cache for comparing last sentiment score to trade, making it immediately tradable signal
+        // Warm up historical sentiment values so the signal is immediately tradable.
         var previousSentimentValues = history.Select(x => x.Sentiment);
         foreach (var sentiment in previousSentimentValues)
         {
@@ -241147,31 +242969,33 @@ 
    
         }
     }
 
-    
     public void Update(decimal sentiment)
     {
-        // Comparing the last sentiment score and decide to buy if the sentiment increases to ride the popularity
+        // Compare the last sentiment score to buy when sentiment increases.
         if (_latestSentimentValue != null)
         {
             _targetHoldings = sentiment > _latestSentimentValue ? 1 : 0;
         }
         _latestSentimentValue = sentiment;
     }
-    
-    public override void OnData(Slice slice)
+
+    public override void OnData(Slice data)
     {
-        // Update trade direction based on updated data
-        if (slice.ContainsKey(_datasetSymbol))
+        // Update trade direction from the latest sentiment data.
+        if (data.ContainsKey(_datasetSymbol))
         {
-            var sentiment = slice[_datasetSymbol].Sentiment;
+            var sentiment = data[_datasetSymbol].Sentiment;
             Update(sentiment);
         }
-
-        // Ensure we have security data in the current slice to avoid stale fill
-        // Buy if sentiment increase, liquidate otherwise to ride on the popularity of the equity
-        if (slice.Bars.ContainsKey(_symbol) && _targetHoldings == 1 != Portfolio.Invested)
+        // Require current equity data to avoid stale fills.
+        if (!data.Bars.ContainsKey(_aapl.Symbol))
         {
-            SetHoldings(_symbol, _targetHoldings);
+            return;
+        }
+        // Buy when sentiment increases, otherwise liquidate the equity.
+        if (_targetHoldings == 1 != Portfolio.Invested)
+        {
+            SetHoldings(_aapl.Symbol, _targetHoldings);
         }
     }
 }
    @@ -241189,157 +243013,109 @@

    class BrainSentimentDataAlgorithm(QCAlgorithm): def initialize(self) -> None: self.set_start_date(2024, 9, 1) - self.set_end_date(2024, 9, 10) - self.set_cash(100000) - - self.settings.minimum_order_margin_portfolio_percentage = 0 - self.universe_settings.resolution = Resolution.DAILY + self.set_end_date(2024, 12, 31) + self.set_cash(100000) + self.settings.seed_initial_prices = True + self.universe_settings.resolution = Resolution.HOUR # Filter base on sentiment data - self.add_universe(BrainSentimentIndicatorUniverse, self.universe_selection) - - self.add_alpha(BrainSentimentAlphaModel()) - - self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel()) - - self.add_risk_management(NullRiskManagementModel()) - + self.add_universe(BrainSentimentIndicatorUniverse, self.universe_selection) + self.add_alpha(BrainSentimentAlphaModel()) + self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel()) + self.add_risk_management(NullRiskManagementModel()) self.set_execution(ImmediateExecutionModel()) def universe_selection(self, alt_coarse: List[BrainSentimentIndicatorUniverse]) -> List[Symbol]: - # Filter for any sentiment on last 7 days to trade on sentiment news - return [d.symbol for d in alt_coarse \ - if d.SentimentalArticleMentions7Days is not None and d.SentimentalArticleMentions7Days > 0] + # Select the stocks with the most sentiment news in the last 7 days + filtered = [d for d in alt_coarse if d.sentimental_article_mentions_7_days] + filtered = sorted(filtered, key=lambda d: d.sentimental_article_mentions_7_days)[-50:] + return [d.symbol for d in filtered] + - class BrainSentimentAlphaModel(AlphaModel): - - symbol_data_by_symbol = {} + _securities = [] def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]: insights = [] - - for symbol, symbol_data in self.symbol_data_by_symbol.items(): - # Update trade direction based on updated data - if slice.contains_key(symbol_data.dataset_symbol) and slice[symbol_data.dataset_symbol] is not None: - sentiment = slice[symbol_data.dataset_symbol].sentiment - symbol_data.update(sentiment) - - # Ensure we have security data in the current slice to avoid stale fill - if not (slice.contains_key(symbol) and slice[symbol] is not None): - continue - + for dataset_symbol, data_point in slice.get(BrainSentimentIndicator30Day).items(): + security = algorithm.securities[dataset_symbol.underlying] + target_direction = InsightDirection.FLAT + if security.latest_sentiment_value and data_point.sentiment > security.latest_sentiment_value: + target_direction = InsightDirection.UP + security.latest_sentiment_value = data_point.sentiment # Buy if sentiment increase, liquidate otherwise to ride on the popularity of the equity - if symbol_data.target_direction == InsightDirection.UP != algorithm.portfolio[symbol].invested: - insights.append(Insight.price(symbol, timedelta(days=100), symbol_data.target_direction)) + if security.price and target_direction == InsightDirection.UP and not security.invested: + insights.append(Insight.price(security, timedelta(100), target_direction)) return insights - + def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None: for security in changes.added_securities: - symbol = security.symbol - self.symbol_data_by_symbol[symbol] = SymbolData(algorithm, symbol) - + # Subscribe to custom data and store dataset symbol + dataset = algorithm.add_data(BrainSentimentIndicator30Day, security, Resolution.DAILY) + security.brain_sentiment_data = dataset.symbol + security.latest_sentiment_value = None + # Historical data + history = algorithm.history(dataset, 100, Resolution.DAILY) + if not history.empty: + # Warm up historical sentiment values, cache for comparing last sentiment score to trade + previous_sentiment_values = history.loc[security.brain_sentiment_data].sentiment.values + for sentiment in previous_sentiment_values: + security.latest_sentiment_value = sentiment + self._securities.append(security) for security in changes.removed_securities: - symbol_data = self.symbol_data_by_symbol.pop(security.symbol, None) - if symbol_data: - symbol_data.dispose() - - -class SymbolData: - - target_direction = InsightDirection.FLAT - _latest_sentiment_value = None - - def __init__(self, algorithm: QCAlgorithm, symbol: Symbol) -> None: - self.algorithm = algorithm - - # Requesting the processed longer term (30-day) sentiment score data for sentiment trading - self.dataset_symbol = algorithm.add_data(BrainSentimentIndicator30Day, symbol).symbol - - # Historical data - history = algorithm.history(self.dataset_symbol, 100, Resolution.DAILY) - if history.empty or 'sentiment' not in history.columns: - return - - # Warm up historical sentiment values, cache for comparing last sentiment score to trade, making it immediately tradable signal - previous_sentiment_values = history.loc[self.dataset_symbol].sentiment.values - for sentiment in previous_sentiment_values: - self.update(sentiment) - - def dispose(self) -> None: - # Unsubscribe from the Brain Sentiment feed for this security to release computational resources - self.algorithm.remove_security(self.dataset_symbol) - - def update(self, sentiment: float) -> None: - # Comparing the last sentiment score and decide to buy if the sentiment increases to ride the popularity - if self._latest_sentiment_value is not None: - if sentiment > self._latest_sentiment_value: - self.target_direction = InsightDirection.UP - else: - self.target_direction = InsightDirection.FLAT - self._latest_sentiment_value = sentiment + # Unsubscribe from the Brain Sentiment feed + algorithm.remove_security(security.brain_sentiment_data) + # Remove from tracking + if security in self._securities: + self._securities.remove(security) 
    public class BrainSentimentDataAlgorithm : QCAlgorithm
 {
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 9, 10);
+        SetEndDate(2024, 12, 31);
         SetCash(100000);
-        
-        Settings.MinimumOrderMarginPortfolioPercentage = 0;
-        UniverseSettings.Resolution = Resolution.Daily;
-        // Filter by sentiment data
-        AddUniverse<BrainSentimentIndicatorUniverse>(altCoarse =>
-        {
-            // Filter for any sentiment on last 7 days to trade on sentiment news
-            return from d in altCoarse.OfType<BrainSentimentIndicatorUniverse>()
-                where d.SentimentalArticleMentions7Days > 0m
-                select d.Symbol;
-        });
-        
+        Settings.SeedInitialPrices = true;
+        UniverseSettings.Resolution = Resolution.Hour;
+        // Filter base on sentiment data
+        AddUniverse<BrainSentimentIndicatorUniverse>(
+            altCoarse => altCoarse
+            .Select(d => d as BrainSentimentIndicatorUniverse)
+            .Where(d => d.SentimentalArticleMentions7Days != 0m)
+            .OrderBy(d => d.SentimentalArticleMentions7Days)
+            .TakeLast(50)
+            .Select(d => d.Symbol)
+        );
         AddAlpha(new BrainSentimentAlphaModel());
-        
         SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
-        
         AddRiskManagement(new NullRiskManagementModel());
-        
         SetExecution(new ImmediateExecutionModel());
     }
 }
-    
-    
+
+
 public class BrainSentimentAlphaModel : AlphaModel
 {
-    private Dictionary<Symbol, SymbolData> _symbolDataBySymbol = new Dictionary<Symbol, SymbolData>();
+    private readonly List<Security> _securities = [];
 
     public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
     {
         var insights = new List<Insight>();
-        
-        foreach (var entry in _symbolDataBySymbol)
+        foreach (var (datasetSymbol, dataPoint) in slice.Get<BrainSentimentIndicator30Day>())
         {
-            var symbol = entry.Key;
-            var symbolData = entry.Value;
-
-            // Update trade direction based on updated data
-            if (slice.ContainsKey(symbolData.datasetSymbol)   && slice[symbolData.datasetSymbol] != null)
-            {
-                var sentiment = slice[symbolData.datasetSymbol].Sentiment;
-                symbolData.Update(sentiment);
-            }
-            
-            // Ensure we have security data in the current slice to avoid stale fill
-            if (!(slice.ContainsKey(symbol)  && slice[symbol] != null))
+            var security = algorithm.Securities[datasetSymbol.Underlying];
+            var targetDirection = InsightDirection.Flat;
+            var latestSentimentValue = security.Get<decimal?>("LatestSentimentValue");
+            if (latestSentimentValue.HasValue && dataPoint.Sentiment > latestSentimentValue.Value)
             {
-                continue;
+                targetDirection = InsightDirection.Up;
             }
-
+            security.Set("LatestSentimentValue", dataPoint.Sentiment);
             // Buy if sentiment increase, liquidate otherwise to ride on the popularity of the equity
-            if (symbolData.targetDirection == InsightDirection.Up  && !algorithm.Portfolio[symbol].Invested)
+            if (security.Price != 0 && targetDirection == InsightDirection.Up && !security.Holdings.Invested)
             {
-                insights.Add(Insight.Price(symbol, TimeSpan.FromDays(100), symbolData.targetDirection));
+                insights.Add(Insight.Price(security.Symbol, TimeSpan.FromDays(100), targetDirection));
             }
         }
-        
         return insights;
     }
 
@@ -241347,67 +243123,34 @@ 
    
     {
         foreach (var security in changes.AddedSecurities)
         {
-            var symbol = security.Symbol;
-            _symbolDataBySymbol.Add(symbol, new SymbolData(algorithm, symbol));
+            // Subscribe to custom data and store dataset symbol
+            var dataset = algorithm.AddData<BrainSentimentIndicator30Day>(security.Symbol, Resolution.Daily);
+            security.Set("BrainSentimentData", dataset.Symbol);
+            security.Set("LatestSentimentValue", null);
+            // Historical data
+            var history = algorithm.History<BrainSentimentIndicator30Day>(dataset.Symbol, 100, Resolution.Daily).ToList();
+            if (history.Count() != 0)
+            {
+                // Warm up historical sentiment values, cache for comparing last sentiment score to trade
+                foreach (var sentiment in history.Select(dataPoint => dataPoint.Sentiment))
+                {
+                    security.Set("LatestSentimentValue", sentiment);
+                }
+            }
+            _securities.Add(security);
         }
 
         foreach (var security in changes.RemovedSecurities)
         {
-            var symbol = security.Symbol;
-            if (_symbolDataBySymbol.ContainsKey(symbol))
+            // Unsubscribe from the Brain Sentiment feed
+            algorithm.RemoveSecurity(security.Get<Symbol>("BrainSentimentData"));
+            // Remove from tracking
+            if (_securities.Contains(security))
             {
-                _symbolDataBySymbol[symbol].dispose();
-                _symbolDataBySymbol.Remove(symbol);
+                _securities.Remove(security);
             }
         }
     }
-}
-
-
-public class SymbolData
-{
-    public Symbol datasetSymbol;
-    public InsightDirection targetDirection = InsightDirection.Flat;
-    private QCAlgorithm _algorithm;
-    private decimal? _latestSentimentValue = null;
-
-    public SymbolData(QCAlgorithm algorithm, Symbol symbol)
-    {
-        _algorithm = algorithm;
-        
-        // Requesting the processed longer term (30-day) sentiment score data for sentiment trading
-        datasetSymbol = algorithm.AddData<BrainSentimentIndicator30Day>(symbol).Symbol;
-        
-        // Historical data
-        var history = algorithm.History<BrainSentimentIndicator30Day>(datasetSymbol, 100, Resolution.Daily);
-        if (history.Count() == 0)
-        {
-            return;
-        }
-        
-        // Warm up historical sentiment values, cache for comparing last sentiment score to trade, making it immediately tradable signal
-        var previousSentimentValues = history.Select(x => x.Sentiment);
-        foreach (var sentiment in previousSentimentValues)
-        {
-            Update(sentiment);
-        }
-    }
-    
-    public void dispose()
-    {
-        // Unsubscribe from the Brain Sentiment feed for this security to release computational resources
-        _algorithm.RemoveSecurity(datasetSymbol);
-    }
-    
-    public void Update(decimal sentiment)
-    {
-        // Comparing the last sentiment score and decide to buy if the sentiment increases to ride the popularity
-        if (_latestSentimentValue != null)
-        {
-            targetDirection = sentiment > _latestSentimentValue ? InsightDirection.Up : InsightDirection.Flat;
-        }
-        _latestSentimentValue = sentiment;
-    }
 }

    @@ -241463,8 +243206,8 @@

    # Add Universe Selection def universe_selection(alt_coarse: List[BrainSentimentIndicatorUniverse]) -> List[Symbol]: - return [d.symbol for d in sorted([x for x in alt_coarse if x.SentimentalArticleMentions7Days], - key=lambda x: x.SentimentalArticleMentions7Days, reverse=True)[:10]] + return [d.symbol for d in sorted([x for x in alt_coarse if x.sentimental_article_mentions_7_days], + key=lambda x: x.sentimental_article_mentions_7_days, reverse=True)[:10]] universe = qb.add_universe(BrainSentimentIndicatorUniverse, universe_selection) @@ -241472,7 +243215,7 @@

    universe_history = qb.universe_history(universe, qb.time-timedelta(5), qb.time) for (_, time), sentiments in universe_history.items(): for sentiment in sentiments: - print(f"{sentiment.symbol} 7-day sentiment at {sentiment.end_time}: {sentiment.SentimentalArticleMentions7Days}") + print(f"{sentiment.symbol} 7-day sentiment at {sentiment.end_time}: {sentiment.sentimental_article_mentions_7_days}")

    @@ -241519,7 +243262,7 @@

     

    Datasets > Brain > Brain Wikipedia Page Views

    -
    +

    Brain

    Brain Wikipedia Page Views

    @@ -241957,12 +243700,13 @@ 

    from AlgorithmImports import *
 import math
 
+
 class BrainWikipediaPageViewsFrameworkRankingAlgorithm(QCAlgorithm):
     def initialize(self) -> None:
-        self.set_start_date(2025, 1, 1)
-        self.set_end_date(2025, 11, 23)
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
         self.set_cash(100000)
-
+        self.settings.seed_initial_prices = True
         self.universe_settings.resolution = Resolution.DAILY
 
         # Universe: pick liquid names
@@ -241973,7 +243717,7 @@ 
    
         self.schedule.on(
             self.date_rules.every_day(),
             self.time_rules.at(9, 0),
-            alpha.emit_insights
+            lambda: alpha.emit_insights(self)
         )
 
         self.set_portfolio_construction(InsightWeightingPortfolioConstructionModel())
@@ -241984,7 +243728,7 @@ 
    
 
     def _select(self, fundamental: List[Fundamental]) -> List[Symbol]:
         # Liquid universe
-        filtered = [c for c in fundamental if c.market_cap and c.price > 10]
+        filtered = [c for c in fundamental if c.has_fundamental_data and c.price > 10]
         filtered.sort(key=lambda c: c.dollar_volume, reverse=True)
 
         # Keep it manageable
@@ -241993,316 +243737,182 @@ 
    
 
 class BrainWikipediaPageViewsRankingAlpha(AlphaModel):
     def __init__(self):
-        self.symbol_data_by_symbol = {}
-        self.symbol_by_dataset_symbol = {}
-
-        # Cache latest score per equity symbol
-        self.latest_scores = {}     # Symbol -> float
-        self.last_score_date = None # date of the latest cache refresh (algorithm.time.date())
-
-        # Store algorithm reference for scheduled emission
-        self.algorithm = None
+        self._securities = []  
 
     def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        self.algorithm = algorithm
-
         points = slice.get(BrainWikipediaPageViews)
-        if points is None:
+        if not points:
             return []
 
-        for point in points.values():
-            # Map dataset symbol -> equity symbol
-            if point.symbol not in self.symbol_by_dataset_symbol:
-                continue
-
-            sym = self.symbol_by_dataset_symbol[point.symbol]
+        for brain_symbol, point in points.items():
+            security = algorithm.securities[brain_symbol.underlying]
 
-            number_of_views = point.NumberViews1 if point.NumberViews1 else 0
-            buzz1day = point.Buzz1 if point.Buzz1 else 0
-
-            score = buzz1day + math.sqrt(number_of_views)
-            self.latest_scores[sym] = score
+            number_of_views = point.number_views_1 if point.number_views_1 else 0
+            buzz_1_day = point.buzz_1 if point.buzz_1 else 0
 
-        self.last_score_date = algorithm.time.date()
+            score = buzz_1_day + math.sqrt(number_of_views)
+            security.score = score
 
         return []
 
-    def emit_insights(self) -> None:
-        algorithm = self.algorithm
-        if not algorithm:
-            return
-
-        if self.last_score_date != algorithm.time.date():
-            return
-
-        paired = []
-        for sym, sc in self.latest_scores.items():
-            sec = algorithm.securities.get(sym)
-            if sec is None or not sec.has_data or sec.price is None or sec.price <= 0:
-                continue
-            if sc == 0:
-                continue
-            paired.append((sym, sc))
-
-        if not paired:
+    def emit_insights(self, algorithm: QCAlgorithm) -> None:
+        # Collect securities with a non-zero score
+        securities = [security for security in self._securities if security.score]
+        if not securities:
             return
 
-        # Rank (higher is better)
-        paired.sort(key=lambda x: x[1], reverse=True)
+        # Rank by score and emit weighted bullish insights
+        securities.sort(key=lambda security: security.score, reverse=True)
 
-        n = len(paired)
-        ranks = list(range(n, 0, -1))  # best gets largest rank
-        denom = sum(ranks)
+        n = len(securities)
+        denom = n * (n + 1) / 2.0
 
         insights = []
-        for i, (sym, sc) in enumerate(paired):
-            weight = ranks[i] / denom
-            insights.append(
-                Insight.price(sym, timedelta(days=7), InsightDirection.UP, None, None, None, weight)
-            )
+        for i, security in enumerate(securities):
+            rank = n - i
+            insights.append(Insight.price(security, timedelta(7), InsightDirection.UP, weight=rank / denom))
 
-        # Emit insights into the framework
         algorithm.emit_insights(insights)
 
     def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
         for security in changes.added_securities:
-            symbol = security.symbol
-            if symbol in self.symbol_data_by_symbol:
-                continue
-
-            sd = SymbolData(algorithm, symbol)
-            self.symbol_data_by_symbol[symbol] = sd
-            self.symbol_by_dataset_symbol[sd.dataset_symbol] = symbol
-
-        for security in changes.removed_securities:
-            symbol = security.symbol
-
-            sd = self.symbol_data_by_symbol.pop(symbol, None)
-            if sd is not None:
-                sd.dispose()
+            # Subscribe to custom data and store dataset symbol via duck typing
+            dataset = algorithm.add_data(BrainWikipediaPageViews, security, Resolution.DAILY)
+            security.wiki_data = dataset
+            security.score = 0.0
 
-            to_remove = None
-            for ds_sym, eq_sym in self.symbol_by_dataset_symbol.items():
-                if eq_sym == symbol:
-                    to_remove = ds_sym
-                    break
-            if to_remove is not None:
-                self.symbol_by_dataset_symbol.pop(to_remove, None)
+            # Fetch historical data
+            history = algorithm.history(dataset, 365, Resolution.DAILY)
+            algorithm.debug(f"We got {len(history)} items from our history request")
 
-            self.latest_scores.pop(symbol, None)
-
-
-class SymbolData:
-    def __init__(self, algorithm: QCAlgorithm, symbol: Symbol):
-        self.algorithm = algorithm
-
-        # Subscribe to Brain Wikipedia Page Views metrics for this equity symbol
-        self.dataset_symbol = algorithm.add_data(BrainWikipediaPageViews, symbol, Resolution.DAILY).symbol
+            # Track this security
+            self._securities.append(security)
 
-        history = algorithm.history(self.dataset_symbol, 365, Resolution.DAILY)
-        algorithm.debug(f"We got {len(history)} items from our history request for {symbol}")
+        for security in changes.removed_securities:
+            # Unsubscribe from custom data
+            algorithm.remove_security(security.wiki_data)
 
-    def dispose(self):
-        # Unsubscribe custom data subscription
-        self.algorithm.remove_security(self.dataset_symbol)
    + # Remove from tracking + if security in self._securities: + self._securities.remove(security) 
    public class BrainWikipediaPageViewsFrameworkRankingAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2025, 1, 1);
-        SetEndDate(2025, 11, 23);
-        SetCash(100000);
-
-        UniverseSettings.Resolution = Resolution.Daily;
-
-        AddUniverse(f => f
-            .Where(c => c.HasFundamentalData && c.Price > 10)
-            .OrderByDescending(c => c.DollarVolume)
-            .Take(50)
-            .Select(c => c.Symbol));
-
-        var alpha = new BrainWikipediaPageViewsRankingAlpha();
-        AddAlpha(alpha);
-
-        Schedule.On(
-            DateRules.EveryDay(),
-            TimeRules.At(9, 0),
-            alpha.EmitInsights
-        );
+ {
+        public override void Initialize()
+        {
+            SetStartDate(2024, 9, 1);
+            SetEndDate(2024, 12, 31);
+            SetCash(100000);
+            Settings.SeedInitialPrices = true;
+            UniverseSettings.Resolution = Resolution.Daily;
 
-        SetPortfolioConstruction(new InsightWeightingPortfolioConstructionModel());
-        AddRiskManagement(new NullRiskManagementModel());
-        SetExecution(new ImmediateExecutionModel());
+            AddUniverse(f => f
+                .Where(c => c.HasFundamentalData && c.Price > 10)
+                .OrderByDescending(c => c.DollarVolume)
+                .Take(50)
+                .Select(c => c.Symbol));
 
-        SetWarmUp(30, Resolution.Daily);
-    }
-}
+            var alpha = new BrainWikipediaPageViewsRankingAlpha();
+            AddAlpha(alpha);
 
-public class BrainWikipediaPageViewsRankingAlpha : AlphaModel
-{
-    private readonly Dictionary<Symbol, SymbolData> _symbolDataBySymbol = new();
-    private readonly Dictionary<Symbol, Symbol> _symbolByDatasetSymbol = new();
+            Schedule.On(
+                DateRules.EveryDay(),
+                TimeRules.At(9, 0),
+                () => alpha.EmitInsights(this)
+            );
 
-    private readonly Dictionary<Symbol, double> _latestScores = new();
-    private DateTime? _lastScoreDate;
+            SetPortfolioConstruction(new InsightWeightingPortfolioConstructionModel());
+            AddRiskManagement(new NullRiskManagementModel());
+            SetExecution(new ImmediateExecutionModel());
 
-    private QCAlgorithm _algorithm;
+            SetWarmUp(30, Resolution.Daily);
+        }
+  }
 
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    public class BrainWikipediaPageViewsRankingAlpha : AlphaModel
     {
-        _algorithm = algorithm;
+        private readonly List<Security> _securities = [];
 
-        var points = slice.Get<BrainWikipediaPageViews>();
-        if (points == null)
-        {
-            return Enumerable.Empty<Insight>();
-        }
-
-        foreach (var kvp in points)
+        public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
         {
-            var dsSymbol = kvp.Key;
-            var point = kvp.Value;
-
-            if (!_symbolByDatasetSymbol.TryGetValue(dsSymbol, out var sym))
+            var points = slice.Get<BrainWikipediaPageViews>();
+            if (points == null)
             {
-                continue;
+                return [];
             }
 
-            var numberOfViews = point.NumberViews1.HasValue ? (double)point.NumberViews1.Value : 0.0;
-            var buzz1day = point.Buzz1.HasValue ? (double)point.Buzz1.Value : 0.0;
-
-            var score = buzz1day + Math.Sqrt(numberOfViews);
-            _latestScores[sym] = score;
-        }
-
-        _lastScoreDate = algorithm.Time.Date;
+            foreach (var (brainSymbol, point) in points)
+            {
+                var security = algorithm.Securities[brainSymbol.Underlying];
 
-        return Enumerable.Empty<Insight>();
-    }
+                var numberOfViews = (double)(point.NumberViews1 ?? 0);
+                var buzz1day = (double)(point.Buzz1 ?? 0);
+                var score = buzz1day + Math.Sqrt(numberOfViews);
 
-    public void EmitInsights()
-    {
-        var algorithm = _algorithm;
-        if (algorithm == null)
-        {
-            return;
-        }
+                security.Set("score", score);
+            }
 
-        if (!_lastScoreDate.HasValue || _lastScoreDate.Value != algorithm.Time.Date)
-        {
-            return;
+            return [];
         }
 
-        var paired = new List<(Symbol Symbol, double Score)>();
-        foreach (var kvp in _latestScores)
+        public void EmitInsights(QCAlgorithm algorithm)
         {
-            var sym = kvp.Key;
-            var sc = kvp.Value;
-
-            if (!algorithm.Securities.TryGetValue(sym, out var sec) || sec == null || !sec.HasData || sec.Price <= 0)
+            // Collect securities with a non-zero score
+            var securities = _securities.Where(security => security.Get<double>("score") != 0);
+            if (securities.IsNullOrEmpty())
             {
-                continue;
-            }
-
-            if (sc == 0)
-            {
-                continue;
+                return;
             }
 
-            paired.Add((sym, sc));
-        }
+            // Rank by score and emit weighted bullish insights
+            securities = securities.OrderByDescending(c => c.Get<double>("score"));
 
-        if (paired.Count == 0)
-        {
-            return;
-        }
+            var number = securities.Count();
+            var denom = number * (number + 1) / 2.0;
 
-        paired.Sort((a, b) => b.Score.CompareTo(a.Score));
-
-        var n = paired.Count;
-        var denom = n * (n + 1) / 2.0;
+            var insights = new List<Insight>(number);
+            for (var i = 0; i < number; i++)
+            {
+                var security = securities.ElementAt(i);
+                var rank = number - i;
+                var weight = rank / denom;
 
-        var insights = new List<Insight>(n);
-        for (int i = 0; i < n; i++)
-        {
-            var sym = paired[i].Symbol;
-            var rank = n - i;
-            var weight = rank / denom;
+                insights.Add(Insight.Price(security, TimeSpan.FromDays(7), InsightDirection.Up, weight: weight));
+            }
 
-            insights.Add(Insight.Price(sym, TimeSpan.FromDays(7), InsightDirection.Up, null, null, null, weight));
+            algorithm.EmitInsights(insights.ToArray());
         }
 
-        algorithm.EmitInsights(insights.ToArray());
-
-    }
-
-    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-    {
-        foreach (var security in changes.AddedSecurities)
+        public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
         {
-            var symbol = security.Symbol;
-            if (_symbolDataBySymbol.ContainsKey(symbol))
+            foreach (var security in changes.AddedSecurities)
             {
-                continue;
-            }
-
-            var sd = new SymbolData(algorithm, symbol);
-            _symbolDataBySymbol[symbol] = sd;
-            _symbolByDatasetSymbol[sd.DatasetSymbol] = symbol;
-        }
+                // Subscribe to custom data and store dataset symbol via duck typing
+                var dataset = algorithm.AddData<BrainWikipediaPageViews>(security, Resolution.Daily);
+                security.Set("wiki_data", dataset.Symbol);
+                security.Set("score", 0.0);
 
-        foreach (var security in changes.RemovedSecurities)
-        {
-            var symbol = security.Symbol;
+                // Fetch historical data
+                var history = algorithm.History<BrainWikipediaPageViews>(dataset, 365, Resolution.Daily);
+                algorithm.Debug($"We got {history.Count()} items from our history request for {security}");
 
-            if (_symbolDataBySymbol.TryGetValue(symbol, out var sd))
-            {
-                _symbolDataBySymbol.Remove(symbol);
-                sd.Dispose();
+                // Track this security
+                _securities.Add(security);
             }
 
-            Symbol toRemove = default;
-            var found = false;
-            foreach (var kvp in _symbolByDatasetSymbol)
+            foreach (var security in changes.RemovedSecurities)
             {
-                if (kvp.Value == symbol)
+                // Unsubscribe from custom data
+                var wikiData = security.Get<Symbol>("wiki_data");
+                algorithm.RemoveSecurity(wikiData);
+
+                // Remove from tracking
+                if (_securities.Contains(security))
                 {
-                    toRemove = kvp.Key;
-                    found = true;
-                    break;
+                    _securities.Remove(security);
                 }
             }
-
-            if (found)
-            {
-                _symbolByDatasetSymbol.Remove(toRemove);
-            }
-
-            _latestScores.Remove(symbol);
         }
-    }
-}
-
-public class SymbolData
-{
-    private readonly QCAlgorithm _algorithm;
-    public Symbol DatasetSymbol { get; }
-
-    public SymbolData(QCAlgorithm algorithm, Symbol symbol)
-    {
-        _algorithm = algorithm;
-
-        DatasetSymbol = algorithm.AddData<BrainWikipediaPageViews>(symbol, Resolution.Daily).Symbol;
-
-        var history = algorithm.History(DatasetSymbol, 365, Resolution.Daily);
-        algorithm.Debug($"We got {history.Count()} items from our history request for {symbol}");
-    }
-
-    public void Dispose()
-    {
-        _algorithm.RemoveSecurity(DatasetSymbol);
-    }
-}
    + }

    Research Example @@ -242313,11 +243923,11 @@ 

    qb = QuantBook() 
 # Requesting Data 
-aapl = qb.AddEquity("AAPL", Resolution.Daily).Symbol 
-symbol = qb.AddData(BrainWikipediaPageViews, aapl).Symbol 
+aapl = qb.add_equity("AAPL", Resolution.DAILY).symbol 
+symbol = qb.add_data(BrainWikipediaPageViews, aapl).symbol 
 
 # Historical data 
-history = qb.History(BrainWikipediaPageViews, symbol, 180, Resolution.Daily) 
+history = qb.history(BrainWikipediaPageViews, symbol, 180, Resolution.DAILY) 
 for (symbol, time), row in history.iterrows(): 
     print(f"{symbol} views at {time}: {row['numberviews1']}")
    #r "../QuantConnect.DataSource.BrainSentiment.dll"
@@ -242357,6 +243967,706 @@ 
    Data Point Attributes
    
  
 
 
+
     
    
+
    Datasets > Bureau of Labor Statistics
    
+
    
+    
    
+        
    Datasets
    
+        
    Bureau of Labor Statistics
    
+    
    
+
    
+
+ 
+  
    
+   
    
+    
    
+     The BLS was established in 1884 as a division of the Department of the Interior, with the goal of collecting and disseminating essential economic data about the U.S. labor market and price changes. BLS provides access to employment, inflation, compensation, and productivity data for researchers, policymakers, and financial professionals.
+    
    
+   
    
+   
    
+    
    
+     
+      
    
+       
    
+        
    
+         US Bureau of Labor Statistics (BLS)
+        
    
+       
    
+      
    
+         
+    
    
+   
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > Bureau of Labor Statistics > US Bureau of Labor Statistics (BLS)
    
+
    
+    
    
+        
    Bureau of Labor Statistics
    
+        
    US Bureau of Labor Statistics (BLS)
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The US Bureau of Labor Statistics (BLS) tracks key U.S. macroeconomic indicators across four major surveys: Consumer Price Index (CPI), Current Employment Statistics (CES), Producer Price Index (PPI), and Job Openings and Labor Turnover Survey (JOLTS). The data covers 50 non-seasonally-adjusted series, starting in January 2000, and is delivered on a daily frequency (monthly releases with exact publication timestamps). This dataset is created by analyzing BLS release schedules for accurate publication dates and fetching series data from the
+   
+    Public Data API
+   
+   .
+  
    
+  
    
+   For more information about the US Bureau of Labor Statistics (BLS) dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   The BLS was established in 1884 as a division of the Department of the Interior, with the goal of collecting and disseminating essential economic data about the U.S. labor market and price changes. BLS provides access to employment, inflation, compensation, and productivity data for researchers, policymakers, and financial professionals.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the BLS dataset:
+  
    
+  
    
+   
    self.add_data(BLSEconomicSurveysCpi, "CPI")
+self.add_data(BLSEconomicSurveysCes, "CES")
+self.add_data(BLSEconomicSurveysPpi, "PPI")
+self.add_data(BLSEconomicSurveysJolts, "JOLTS")
    
+   
    AddData<BLSEconomicSurveysCpi>("CPI");
+AddData<BLSEconomicSurveysCes>("CES");
+AddData<BLSEconomicSurveysPpi>("PPI");
+AddData<BLSEconomicSurveysJolts>("JOLTS");
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 2000 (CPI/CES/PPI), November 2007 (JOLTS)
+     
    
+      Data Density
+     
+      Sparse
+     
    
+      Survey Coverage
+     
+      50 non-seasonally-adjusted series across 4 surveys
+     
    
+      Resolution
+     
+      Daily
+     
    
+      Timezone
+     
+      New York
+     
    
+ 
+
+
+
    Requesting Data
    
+
+ 
+  
    
+   To add BLS data to your algorithm, call the
+   
+    AddData
+   
+   method. Save a reference to the dataset
+   
+    Symbol
+   
+   so you can access the data later in your algorithm.
+  
    
+  
    
+   
    class BLSDataAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2020, 1, 1)
+        self.set_end_date(2024, 1, 1)
+        self.set_cash(100000)
+        self._cpi = self.add_data(BLSEconomicSurveysCpi, "CPI").symbol
+        self._ces = self.add_data(BLSEconomicSurveysCes, "CES").symbol
+        self._ppi = self.add_data(BLSEconomicSurveysPpi, "PPI").symbol
+        self._jolts = self.add_data(BLSEconomicSurveysJolts, "JOLTS").symbol
    
+   
    public class BLSDataAlgorithm : QCAlgorithm
+{
+    private Symbol _cpi, _ces, _ppi, _jolts;
+
+    public override void Initialize()
+    {
+        SetStartDate(2020, 1, 1);
+        SetEndDate(2024, 1, 1);
+        SetCash(100000);
+        _cpi = AddData<BLSEconomicSurveysCpi>("CPI").Symbol;
+        _ces = AddData<BLSEconomicSurveysCes>("CES").Symbol;
+        _ppi = AddData<BLSEconomicSurveysPpi>("PPI").Symbol;
+        _jolts = AddData<BLSEconomicSurveysJolts>("JOLTS").Symbol;
+    }
+}
    
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current BLS data, index the current
+   
+    
+     Slice
+    
+   
+   with the dataset
+   
+    Symbol
+   
+   .
+   
+    Slice
+   
+   objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your dataset at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, data: Slice) -> None:
+    if self._cpi in data:
+        cpi = data[self._cpi]
+        all_items = cpi.all_items
+        core = cpi.core_cpi
+        energy = cpi.energy
    
+   
    public override void OnData(Slice data)
+{
+    if (data.ContainsKey(_cpi))
+    {
+        var cpi = data.Get<BLSEconomicSurveysCpi>(_cpi);
+        var allItems = cpi.AllItems;
+        var core = cpi.CoreCpi;
+        var energy = cpi.Energy;
+    }
+}
+
    
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   To get historical BLS data, call the
+   
+    History
+   
+   method with the dataset
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    # DataFrame
+history_df = self.history(self._cpi, 100, Resolution.DAILY)
+
+# Dataset objects
+history_bars = self.history[BLSEconomicSurveysCpi](self._cpi, 100, Resolution.DAILY)
    
+   
    var history = History<BLSEconomicSurveysCpi>(_cpi, 100, Resolution.Daily);
    
+  
    
+  
    
+   For more information about historical data, see
+   
+    History Requests
+   
+   .
+  
    
+ 
+
+
+
    Remove Subscriptions
    
+
+ 
+  
    
+   To remove your subscription to BLS data, call the
+   
+    RemoveSecurity
+   
+   method.
+  
    
+  
    
+   
    self.remove_security(self._cpi)
    
+   
    RemoveSecurity(_cpi);
    
+  
    
+ 
+
+
+
    Supported Surveys
    
+
+ 
+  
    
+   The BLS database contains over 77 million series from over 60 different surveys, but not all of them are integrated into QuantConnect. The following table show the integrated surveys:
+  
    
+  
+   
+    
+     
+     
+     
+     
+    
+   
+   
+    
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+    
+   
+  
    
+      Ticker
+     
+      Survey
+     
+      Release Time (ET)
+     
+      Start Date
+     
    
+      CPI
+     
+      Consumer Price Index
+     
+      08:30 AM
+     
+      Jan 2000
+     
    
+      CES
+     
+      Current Employment Statistics
+     
+      08:30 AM
+     
+      Jan 2000
+     
    
+      PPI
+     
+      Producer Price Index
+     
+      08:30 AM
+     
+      Jan 2000
+     
    
+      JOLTS
+     
+      Job Openings and Labor Turnover
+     
+      10:00 AM
+     
+      Nov 2007
+     
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The BLS dataset provides unique alternative data that the  US government releases. Example applications of this dataset include the following strategies:
+  
    
+  
      
+   
    • 
+    Monitoring month-over-month changes in nonfarm payrolls and sector employment to identify economic regime shifts.
+   
      • 
+   
    • 
+    Tracking the inflation rate of gasoline to forecast the performance of electric vehicle (EV) manufacturers on the premise that rising gas prices will increase the revenue and stock prices of EV companies.
+   
      • 
+   
    • 
+    Combining CPI, PPI, and CES data to build composite economic health indicators.
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm rotates to an energy ETF when energy prices are rising faster than core CPI. Otherwise, it holds the market benchmark.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class BLSEconomicSurveysAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2023, 1, 1)
+        self.set_end_date(2026, 3, 1)
+        self.set_cash(100000)
+        # Add some Equities to trade.
+        self._market = self.add_equity('SPY')
+        self._energy = self.add_equity('XLE')
+        # Add the BLS CPI survey data.
+        self._cpi = self.add_data(BLSEconomicSurveysCpi, "CPI").symbol
+        # Add a warm-up period so we have the most recent CPI survey on deployment.
+        self._target = None
+        self.set_warm_up(timedelta(60))
+
+    def on_data(self, data: Slice):
+        # Check for CPI data.
+        if data.contains_key(self._cpi):
+            cpi = data.get(BLSEconomicSurveysCpi, self._cpi)
+            # Simple signal: if energy CPI is rising faster than core, rotate to the energy ETF.
+            if cpi.energy is not None and cpi.core_cpi is not None and cpi.energy > cpi.core_cpi * 1.5:
+                self._target = self._energy
+            else:
+                self._target = self._market
+        # Rebalance whenever the target changes.
+        if not self.is_warming_up and self._target and not self._target.invested:
+            self.set_holdings(self._target, 1, True)
+
    
+   
    public class BLSEconomicSurveysAlgorithm: QCAlgorithm
+{
+    private Equity _market, _energy, _target;
+    private Symbol _cpi;
+
+    public override void Initialize()
+    {
+        SetStartDate(2023, 1, 1);
+        SetEndDate(2026, 3, 1);
+        SetCash(100000);
+        // Add some Equities to trade.
+        _market = AddEquity("SPY");
+        _energy = AddEquity("XLE");
+        // Add the BLS CPI survey data.
+        _cpi = AddData<BLSEconomicSurveysCpi>("CPI").Symbol;
+        // Add a warm-up period so we have the most recent CPI survey on deployment.
+        SetWarmUp(TimeSpan.FromDays(60));
+    }
+
+    public override void OnData(Slice data)
+    {
+        // Check for CPI data.
+        if (data.ContainsKey(_cpi))
+        {
+            var cpi = data.Get<BLSEconomicSurveysCpi>(_cpi);
+            // Simple signal: if energy CPI is rising faster than core, rotate to the energy ETF.
+            if (cpi.Energy.HasValue && cpi.CoreCpi.HasValue && 
+                cpi.Energy.Value > cpi.CoreCpi.Value * 1.5m)
+            {
+                _target = _energy;
+            }
+            else
+            {
+                _target = _market;
+            }
+        }
+        // Rebalance whenever the target changes.
+        if (!IsWarmingUp && !_target.Invested)
+        {
+            SetHoldings(_target, 1.0m, true);
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm rotates to energy stocks in the OEF ETF when energy prices are rising faster than core CPI. Otherwise, it holds an equal-weighted portfolio of the ETF's constituents.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class BLSEconomicSurveysAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2023, 1, 1)
+        self.set_end_date(2026, 3, 1)
+        self.set_cash(100000)
+        # Add the framework models.
+        self.add_universe_selection(ETFConstituentsUniverseSelectionModel('OEF'))
+        self.add_alpha(CpiAlphaModel(self))
+        self.set_portfolio_construction(InsightWeightingPortfolioConstructionModel())
+
+
+class MyAlphaModel(AlphaModel):
+
+    def __init__(self, algorithm):
+        # Set up a member to track the universe.
+        self._universe = []
+        # Add the BLS CPI survey data.
+        self._cpi = algorithm.add_data(BLSEconomicSurveysCpi, "CPI").symbol
+
+    def update(self, algorithm: QCAlgorithm, data: Slice) -> List[Insight]:
+        # Get the latest CPI survey.
+        if data.contains_key(self._cpi):
+            cpi = data.get(BLSEconomicSurveysCpi, self._cpi)
+            # Simple signal: if energy CPI is rising faster than core, rotate to the energy stocks.
+            target_energy = cpi.energy is not None and cpi.core_cpi is not None and cpi.energy > cpi.core_cpi * 1.5:
+            securities = []
+            for security in self._universe:
+                if target_energy:
+                    if security.fundamentals.asset_classification.morningstar_sector_code == MorningstarSectorCode.ENERGY:
+                        securities.append(security)
+                else: 
+                    securities.append(security)
+            # Emit monthly insights for the selected stocks.
+            return [Insight.price(s, timedelta(35), InsightDirection.UP, weight=1/len(securities)) for s in securities]
+        return []
+
+    # As assets enter and leave the universe, update our self._universe member.
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
+        for security in changes.added_securities:
+            self._universe.append(security)
+        for security in changes.removed_securities:
+            self._universe.remove(security)
+
+
+
    
+   
    public class BLSEconomicSurveysAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetStartDate(2023, 1, 1);
+        SetEndDate(2026, 3, 1);
+        SetCash(100000);
+        // Add the framework models.
+        AddUniverseSelection(new ETFConstituentsUniverseSelectionModel("OEF"));
+        AddAlpha(new CpiAlphaModel(this));
+        SetPortfolioConstruction(new InsightWeightingPortfolioConstructionModel());
+    }
+}
+
+public class CpiAlphaModel : AlphaModel
+{
+    private readonly Symbol _cpi;
+    private readonly List<Security> _universe = new List<Security>();
+
+    public CpiAlphaModel(QCAlgorithm algorithm)
+    {
+        // Add the BLS CPI survey data.
+        _cpiSymbol = algorithm.AddData<BLSEconomicSurveysCpi>("CPI").Symbol;
+    }
+
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data)
+    {
+        // Get the latest CPI survey.
+        if (data.ContainsKey(_cpi))
+        {
+            var cpi = data.Get<BLSEconomicSurveysCpi>(_cpi);
+            // Simple signal: if energy CPI is rising faster than core (energy > 1.5 * core)
+            var targetEnergy = cpi.Energy.HasValue 
+                             && cpi.CoreCpi.HasValue 
+                             && cpi.Energy.Value > cpi.CoreCpi.Value * 1.5;
+            var securities = new List<Security>();
+            foreach (var security in _universe)
+            {
+                if (targetEnergy)
+                {
+                    if (security.Fundamentals.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Energy)
+                    {
+                        securities.Add(security);
+                    }
+                }
+                else
+                {
+                    securities.Add(security);
+                }
+            }
+
+            if (securities.Count > 0)
+            {
+                // Emit monthly insights for the selected stocks.
+                return securities.Select(s => 
+                    Insight.Price(s, TimeSpan.FromDays(35), InsightDirection.Up, weight: 1.0m / securities.Count)
+                );
+            }
+        }
+        return Enumerable.Empty<Insight>();
+    }
+
+    // As assets enter and leave the universe, update our _universe member.
+    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
+    {
+        foreach (var security in changes.AddedSecurities)
+        {
+            _universe.Add(security);
+        }
+        foreach (var security in changes.RemovedSecurities)
+        {
+            _universe.Remove(security);
+        }
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The BLS database contains over 77 million series from over 60 different surveys, but not all of them are integrated into QuantConnect. The following table show the integrated surveys:
+  
    
+  
+   
+    
+     
+     
+     
+     
+    
+   
+   
+    
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+    
+    
+     
+     
+     
+     
+    
+   
+  
    
+      Ticker
+     
+      Survey
+     
+      Release Time (ET)
+     
+      Start Date
+     
    
+      CPI
+     
+      Consumer Price Index
+     
+      08:30 AM
+     
+      Jan 2000
+     
    
+      CES
+     
+      Current Employment Statistics
+     
+      08:30 AM
+     
+      Jan 2000
+     
    
+      PPI
+     
+      Producer Price Index
+     
+      08:30 AM
+     
+      Jan 2000
+     
    
+      JOLTS
+     
+      Job Openings and Labor Turnover
+     
+      10:00 AM
+     
+      Nov 2007
+     
    
+ 
+
+
 
     
    
 
    Datasets > CoinGecko
    
 
    
@@ -244976,74 +247286,73 @@ 
    
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
+        # Request BTCUSD crypto data for trading.
+        self._crypto = self.add_crypto("BTCUSD")
+        # Request CoinGecko market cap data of BTC for trade signal generation.
+        self._custom_data_symbol = self.add_data(CoinGecko, "BTC").symbol
+        # Save the last two market cap values for capital flow analysis.
+        self._window = RollingWindow(2)
 
-        # Request BTCUSD crypto data for trading
-        self.crypto_symbol = self.add_crypto("BTCUSD").symbol
-        # Request CoinGecko Market Cap data of BTC for trade signal generation
-        self.custom_data_symbol = self.add_data(CoinGecko, "BTC").symbol
-        # Use RollingWindow to save the last 2 market cap data for capital flow analysis
-        self.window = RollingWindow(2)
-
-    def on_data(self, slice: Slice) -> None:
-        # Trade based on updated market cap data
-        data = slice.get(CoinGecko)
-        if data and self.custom_data_symbol in data:
-            # Update RollingWindow for updated comparison
-            self.window.add(data[self.custom_data_symbol])
-            if not self.window.is_ready:
-                return
-
-            # Buy BTCUSD if the market cap of BTC is increasing, which suggests the capital flow towards BTC market and drive up the demand
-            if self.window[0].market_cap > self.window[1].market_cap:
-                self.set_holdings(self.crypto_symbol, 1)
-            # Sell otherwise, since the capital is flowing out and the demand of BTC lowered
-            else:
-                self.set_holdings(self.crypto_symbol, -0.5)
+    def on_data(self, data: Slice) -> None:
+        # Trade based on updated market cap data.
+        market_cap_data = data.get(CoinGecko)
+        if not market_cap_data or self._custom_data_symbol not in market_cap_data:
+            return
+        # Update the window only when fresh CoinGecko data arrives.
+        self._window.add(market_cap_data[self._custom_data_symbol])
+        if not self._window.is_ready:
+            return
+        # Buy BTCUSD when BTC market cap is increasing and capital is flowing in.
+        if self._window[0].market_cap > self._window[1].market_cap:
+            self.set_holdings(self._crypto, 1)
+        # Sell when capital is flowing out of BTC.
+        else:
+            self.set_holdings(self._crypto, -0.5)
 
-    def on_order_event(self, orderEvent: OrderEvent) -> None:
-        if orderEvent.status == OrderStatus.FILLED:
-            self.debug(f'Purchased Crypto: {orderEvent.symbol}')
    + def on_order_event(self, order_event: OrderEvent) -> None: + if order_event.status == OrderStatus.FILLED: + self.debug(f"Purchased Crypto: {order_event.symbol}") 
    public class CoinGeckoAlgorithm : QCAlgorithm
 {
-    private Symbol _cryptoSymbol, _customDataSymbol;
+    private Crypto _crypto;
+    private Symbol _customDataSymbol;
     private RollingWindow<CoinGecko> _window;
 
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-
-        // Request BTCUSD crypto data for trading
-        _cryptoSymbol = AddCrypto("BTCUSD").Symbol;
-        // Request CoinGecko Market Cap data of BTC for trade signal generation
+        // Request BTCUSD crypto data for trading.
+        _crypto = AddCrypto("BTCUSD");
+        // Request CoinGecko market cap data of BTC for trade signal generation.
         _customDataSymbol = AddData<CoinGecko>("BTC").Symbol;
-        // Use RollingWindow to save the last 2 market cap data for capital flow analysis
+        // Save the last two market cap values for capital flow analysis.
         _window = new RollingWindow<CoinGecko>(2);
     }
 
-    public override void OnData(Slice slice)
+    public override void OnData(Slice data)
     {
-        // Trade based on updated market cap data
-        var data = slice.Get<CoinGecko>();
-        if (!data.IsNullOrEmpty() && data.ContainsKey(_customDataSymbol))
+        // Trade based on updated market cap data.
+        var marketCapData = data.Get<CoinGecko>();
+        if (marketCapData.IsNullOrEmpty() || !marketCapData.ContainsKey(_customDataSymbol))
         {
-            // Update RollingWindow for updated comparison
-            _window.Add(data[_customDataSymbol]);
-            if (!_window.IsReady)
-            {
-                return;
-            }
-
-            // Buy BTCUSD if the market cap of BTC is increasing, which suggests the capital flow towards BTC market and drive up the demand
-            if (_window[0].MarketCap > _window[1].MarketCap)
-            {
-                SetHoldings(_cryptoSymbol, 1);
-            }
-            // Sell otherwise, since the capital is flowing out and the demand of BTC lowered
-            else
-            {
-                SetHoldings(_cryptoSymbol, -0.5m);
-            }
+            return;
+        }
+        // Update the window only when fresh CoinGecko data arrives.
+        _window.Add(marketCapData[_customDataSymbol]);
+        if (!_window.IsReady)
+        {
+            return;
+        }
+        // Buy BTCUSD when BTC market cap is increasing and capital is flowing in.
+        if (_window[0].MarketCap > _window[1].MarketCap)
+        {
+            SetHoldings(_crypto.Symbol, 1);
+        }
+        // Sell when capital is flowing out of BTC.
+        else
+        {
+            SetHoldings(_crypto.Symbol, -0.5m);
         }
     }
 
@@ -245096,7 +247405,7 @@ 
    
         insights = []
 
         # Trade based on updated market cap data
-        data = slice.Get(CoinGecko)
+        data = slice.get(CoinGecko)
         for dataset_symbol, crypto_symbol in self.symbol_dict.items():
             if not data.contains_key(dataset_symbol): 
                 continue
@@ -245487,532 +247796,541 @@ 
    Data Summary
    
  
 
 
-
    Requesting Data
    
-
- 
-  
    
-   To add Economic Events data to your algorithm, call the
-   
-    AddData<EODHDUpcomingIPOs>
-   
-   
-    add_data
-   
-   method.
-  
    
-  
    
-   
    class EconomicEventsDataAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2019, 1, 1)
-        self.set_end_date(2020, 6, 1)
-        self.set_cash(100000)
-
-        ticker = EODHD.Events.UnitedStates.CPI
-        # ticker = Country.UNITED_STATES
-        self.dataset_symbol = self.add_data(EODHDEconomicEvents, ticker).symbol
    
-   
    public class EconomicEventsDataAlgorithm : QCAlgorithm
-{
-    private Symbol _datasetSymbol;
-
-    public override void Initialize()
-    {
-        SetStartDate(2019, 1, 1);
-        SetEndDate(2020, 6, 1);
-        SetCash(100000);
-
-        var ticker = EODHD.Events.UnitedStates.Cpi;
-        // var ticker = Country.UnitedStates;
-        _datasetSymbol = AddData<EODHDEconomicEvents>(ticker).Symbol;
-    }
-}
    
-  
    
- 
-
-
-
    Accessing Data
    
-
- 
-  
    
-   To get the current Economic Events data, index the current
-   
-    
-     Slice
-    
-   
-   with the dataset
-   
-    Symbol
-   
-   . Slice objects deliver unique events to your algorithm as they happen, but the
-   
-    Slice
-   
-   may not contain data for your dataset at every time step. To avoid issues, check if the
-   
-    Slice
-   
-   contains the data you want before you index it.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    events = slice.get(EODHDEconomicEvents).get(self.dataset_symbol)
-    for event in events:
-        self.log(f"{event.event_type} of {event.country} over {event.event_period} with be held at {event.event_time} with estimated value {event.estimate}")
    
-   
    public override void OnData(Slice slice)
-{
-    if (slice.Get<EODHDEconomicEvents>().TryGetValue(_datasetSymbol, out var economicEvents))
-    {
-        foreach (EODHDEconomicEvent economicEvent in economicEvents)
-        {
-           Log($"{economicEvent.EventType} of {economicEvent.Country} over {economicEvent.EventPeriod} with be held at {economicEvent.EventTime} with estimated value {economicEvent.Estimate}");
-        }
-    }
-}
    
-  
    
-  
    
-   To iterate through all of the dataset objects in the current
-   
-    Slice
-   
-   , call the
-   
-    Get
-   
-   
-    get
-   
-   method.
-  
    
-  
    
-   
    def on_data(self, slice: Slice) -> None:
-    for symbol, events in slice.get(EODHDEconomicEvents).items():
-        for event in events:
-            self.log(f"{event.event_type} of {event.country} over {event.event_period} with be held at {event.event_time} with estimated value {event.estimate}")
-
    
-   
    public override void OnData(Slice slice)
-{
-    foreach (var (symbol, economicEvents) in slice.Get<EODHDEconomicEvents>())
-    {
-        foreach (EODHDEconomicEvent economicEvent in economicEvents)
-        {
-           Log($"{economicEvent.EventType} of {economicEvent.Country} over {economicEvent.EventPeriod} with be held at {economicEvent.EventTime} with estimated value {economicEvent.Estimate}");
-        }
-    }
-}
    
-  
    
- 
-
-
-
    Historical Data
    
-
- 
-  
    
-   To get historical Economic Events data, call the
-   
-    History
-   
-   
-    history
-   
-   method with the type
-   
-    EODHDEconomicEvents
-   
-   cast and the underlying
-   
-    Symbol
-   
-   . If there is no data in the period you request, the history result is empty.
-  
    
-  
    
-   
    history_bars = self.history[EODHDEconomicEvents](self.dataset_symbol, 100, Resolution.DAILY)
    
-   
    var history = History<EODHDEconomicEvents>(_datasetSymbol, 100, Resolution.Daily);
    
-  
    
-  
    
-   For more information about historical data, see
-   
-    History Requests
-   
-   .
-  
    
- 
-
-
-
    Remove Subscriptions
    
-
- 
-  
    
-   To remove a subscription, call the
-   
-    RemoveSecurity
-   
-   
-    remove_security
-   
-   method.
-  
    
-  
    
-   
    self.remove_security(self.dataset_symbol)
    
-   
    RemoveSecurity(_datasetSymbol);
    
-  
    
- 
-
-
-
    Example Applications
    
-
- 
-  
    
-   The Economic Events dataset provides timely notifications about upcoming economic events, allowing traders to trade on that volatility. Examples include the following strategies:
-  
    
-  
      
-   
    • 
-    Long straddle to trade the volatility of the economic events.
-   
      • 
-   
    • 
-    Trade market representative ETFs based on estimated figures for the upcoming events.
-   
      • 
-   
    • 
-    Statistical arbitrage on two or more correlated global markets based on economic events of different locations.
-   
      • 
-  
    
-  
    
-   Classic Algorithm Example
-  
    
-  
    
-   The following example algorithm trade Industrial sector ETF based on US PMI estimated change direction. If the estimated PMI of the upcoming announcement is above the previous PMI, buy the Industrial ETF, and sell it otherwise.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class EODHDEconomicEventsAlgorithm(QCAlgorithm):
-    def initialize(self):
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        # Use industrial sector ETF as a vehicle to trade.
-        self.equity_symbol = self.add_equity("XLI").symbol
-        # Request US PMI economic event data to generate trade signals.
-        ticker = EODHD.Events.UnitedStates.MARKIT_MANUFACTURING_PURCHASING_MANAGERS_INDEX
-        self.dataset_symbol = self.add_data(EODHDEconomicEvents, ticker).symbol
-
-    def on_data(self, slice):
-        # Trade based on the updated economic events.
-        if self.dataset_symbol in slice.get(EODHDEconomicEvents):
-            # Use the Manufacturing Index to generate trade signals on manufacturing industry vehicles.
-            # Make sure previous and estimate are available to estimate the direction of the industry.
-            event = slice[self.dataset_symbol].data[0]
-            if event.previous and event.estimate:
-                # If the estimated PMI is higher than the previous PMI, the manufacturing ETF price is expected to rise.
-                if event.previous > event.estimate:
-                    self.set_holdings(self.equity_symbol, 1)
-                # Otherwise, it is expected manufacturing ETF prices will drop.
-                else:
-                    self.set_holdings(self.equity_symbol, -1)
    
-   
    public class EODHDEconomicEventsAlgorithm : QCAlgorithm
-{
-    private Symbol _equitySymbol, _datasetSymbol;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        // Use industrial sector ETF as a vehicle to trade.
-        _equitySymbol = AddEquity("XLI").Symbol;
-        // Request US PMI economic event data to generate trade signals.
-        var ticker = EODHD.Events.UnitedStates.MarkitManufacturingPurchasingManagersIndex;
-        _datasetSymbol = AddData<EODHDEconomicEvents>(ticker).Symbol;
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // Trade based on the updated economic events.
-        if (slice.Get<EODHDEconomicEvents>().TryGetValue(_datasetSymbol, out var economicEvents))
-        {
-            var economicEvent = economicEvents.FirstOrDefault() as EODHDEconomicEvent;         
-            // Use the Manufacturing Index to generate trade signals on manufacturing industry vehicles.
-            // Make sure previous and estimate are available to estimate the direction of the industry.
-            if (economicEvent.Previous.HasValue && economicEvent.Estimate.HasValue)
-            {
-                //If the estimated PMI is higher than the previous PMI, the manufacturing ETF price is expected to rise.
-                if (economicEvent.Previous.Value > economicEvent.Estimate.Value)
-                {
-                    SetHoldings(_equitySymbol, 1);
-                }
-                // Otherwise, it is expected manufacturing ETF price will drop.
-                else
-                {
-                    SetHoldings(_equitySymbol, -1);
-                }
-            }
-        }
-    }
-}
    
-  
    
-  
    
-   Framework Algorithm Example
-  
    
-  
    
-   The following example algorithm trade Industrial sector ETF based on US PMI estimated change direction using algorithm framework. If the estimated PMI of the upcoming announcement is above the previous PMI, buy the Industrial ETF, sell it otherwise.
-  
    
-  
    
-   
    from AlgorithmImports import *
-
-
-class EODHDEconomicEventsAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        # Use industrial sector ETF as a vehicle to trade.
-        symbol = Symbol.create("XLI", SecurityType.EQUITY, Market.USA)
-        self.add_universe_selection(ManualUniverseSelectionModel(symbol))
-        # Custom alpha model to emit insights according to economic events.
-        self.add_alpha(EconomicEventAlphaModel(self))
-        # Equal weighting position sizing to dissipate capital risk evenly.
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel(Expiry.END_OF_MONTH))
-
-class EconomicEventAlphaModel(AlphaModel):
-    def __init__(self, algorithm: QCAlgorithm) -> None:
-        # Request US PMI economic event data to generate trade signals.
-        ticker = EODHD.Events.UnitedStates.MARKIT_MANUFACTURING_PURCHASING_MANAGERS_INDEX
-        self.dataset_symbol = algorithm.add_data(EODHDEconomicEvents, ticker).symbol
-        self._symbol = None
-
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        # Trade based on the updated economic events.
-        if self.dataset_symbol not in slice.get(EODHDEconomicEvents):
-            return []
-        # Use the US Manufacturing Index to generate trade signals on manufacturing industry vehicles.
-        # Make sure previous and estimate are available to estimate the direction of the industry.
-        event = slice[self.dataset_symbol].data[0]
-        # If the estimated PMI is higher than the previous PMI, the manufacturing ETF price is expected to rise.
-        # Otherwise, it is expected manufacturing ETF prices will drop.
-        direction = InsightDirection.UP if event.previous > event.estimate else InsightDirection.DOWN
-        return [Insight.price(self._symbol, timedelta(5), direction)]
-
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        for added in changes.added_securities:
-            self._symbol = added.symbol
    
-   
    public class EODHDEconomicEventsAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        // Use industrial sector ETF as a vehicle to trade.
-        var symbol = QuantConnect.Symbol.Create("XLI", SecurityType.Equity, Market.USA);
-        AddUniverseSelection(new ManualUniverseSelectionModel(new[] {symbol}));
-        // Custom alpha model to emit insights according to economic events.
-        AddAlpha(new EconomicEventAlphaModel(this));
-        // Equal weighting position sizing to dissipate capital risk evenly.
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(Expiry.EndOfMonth));
-    }
-}
-
-public class EconomicEventAlphaModel : AlphaModel
-{
-    private Symbol _symbol, _datasetSymbol;
-
-    public EconomicEventAlphaModel(QCAlgorithm algorithm)
-    {
-        // Request US PMI economic event data to generate trade signals.
-        var ticker = EODHD.Events.UnitedStates.MarkitManufacturingPurchasingManagersIndex;
-        _datasetSymbol = algorithm.AddData<EODHDEconomicEvents>(ticker).Symbol;
-    }
-
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        // Trade based on the updated economic events.
-        if (slice.Get<EODHDEconomicEvents>().TryGetValue(_datasetSymbol, out var economicEvents))
-        {
-            // Use the US Manufacturing Index to generate trade signals on manufacturing industry vehicles.
-            // Make sure previous and estimate are available to estimate the direction of the industry.
-            var economicEvent = economicEvents.FirstOrDefault() as EODHDEconomicEvent;
-            if (economicEvent.Previous.HasValue && economicEvent.Estimate.HasValue)
-            {
-                // If the estimated PMI is higher than the previous PMI, the manufacturing ETF price is expected to rise.
-                // Otherwise, it is expected manufacturing ETF prices will drop.
-                var direction = economicEvent.Previous.Value > economicEvent.Estimate.Value
-                    ? InsightDirection.Up
-                    : InsightDirection.Down;
-                yield return Insight.Price(_symbol, TimeSpan.FromDays(5), direction);
-            }
-        }
-    }
-
-    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-    {
-        foreach (var added in changes.AddedSecurities)
-        {
-            _symbol = added.Symbol;
-        }
-    }
-}
    
-  
    
- 
-
-
-
    Data Point Attributes
    
-
- 
-  
    
-   The EODHD Economic Events dataset provides
-   
-    EODHDEconomicEvent
-   
-   objects, which have the following attributes:
-  
    
-  
    
-  
    
-  
    
-   The
-   
-    EODHDEconomicEvents
-   
-   objects represent a collection of
-   
-    EODHDEconomicEvent
-   
-   object
-  
    
-  
    
-  
    
-  
    
-   Country Enumeration
-  
    
-  
    
-   To filter the countries of the economic events, you can make use of the
-   
-    Country
-   
-   enumeration. The
-   
-    Country
-   
-   enumeration has the following members:
-  
    
-  
    
-  
    
-  
    
-   Events Enumeration
-  
    
-  
    
-   To filter the event types of the economic events, you can make use of the
-   
-    Events
-   
-   enumeration. The
-   
-    Events
-   
-   enumeration has the following members:
-  
    
-  
    
-  
    
- 
-
-
-
     
    
-
    Datasets > EOD Historical Data > Macroeconomics Indicators
    
-
    
-    
    
-        
    EOD Historical Data
    
-        
    Macroeconomics Indicators
    
-    
    
-
    
-
    Introduction
    
-
- 
-  
    
-   The Macroeconomic Indicators dataset, provided by EODHD, provides data on 39 major macroeconomic indicators of 249 countries and regions. The data starts in January 1998, and is delivered on indicators update.
-  
    
-  
    
-   For more information about the Macroeconomics Indicators dataset, including CLI commands and pricing, see the
-   
-    dataset listing
-   
-   .
-  
    
-  
    
-  
    
- 
-
-
-
    About the Provider
    
-
- 
-  
    
-   EODHD was a France financial data provider founded in April 2015. They focus on providing clean financial data, including stock prices, splits, dividends, fundamentals, macroeconomy indicators, technical indicators, and alternative data sources, through 24/7 API seamlessly.
-  
    
- 
-
-
-
    Getting Started
    
-
- 
-  
    
-   The following snippet demonstrates how to request data from the Macroeconomic Indicators dataset:
-  
    
-  
    
-   
    self.add_data(EODHDMacroIndicators, Country.UNITED_STATES)
-self.add_data(EODHDMacroIndicators, EODHD.MacroIndicators.UnitedStates.GDP_CURRENT_USD) 
    
-   
    AddData<EODHDMacroIndicators>(Country.UnitedStates);
-AddData<EODHDMacroIndicators>(EODHD.MacroIndicators.UnitedStates.GdpCurrentUsd);
    
-  
    
- 
-
-
-
    Data Summary
    
+
    Requesting Data
    
+
+ 
+  
    
+   To add Economic Events data to your algorithm, call the
+   
+    AddData<EODHDUpcomingIPOs>
+   
+   
+    add_data
+   
+   method.
+  
    
+  
    
+   
    class EconomicEventsDataAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2019, 1, 1)
+        self.set_end_date(2020, 6, 1)
+        self.set_cash(100000)
+
+        ticker = EODHD.Events.UnitedStates.CPI
+        # ticker = Country.UNITED_STATES
+        self.dataset_symbol = self.add_data(EODHDEconomicEvents, ticker).symbol
    
+   
    public class EconomicEventsDataAlgorithm : QCAlgorithm
+{
+    private Symbol _datasetSymbol;
+
+    public override void Initialize()
+    {
+        SetStartDate(2019, 1, 1);
+        SetEndDate(2020, 6, 1);
+        SetCash(100000);
+
+        var ticker = EODHD.Events.UnitedStates.Cpi;
+        // var ticker = Country.UnitedStates;
+        _datasetSymbol = AddData<EODHDEconomicEvents>(ticker).Symbol;
+    }
+}
    
+  
    
+ 
+
+
+
    Accessing Data
    
+
+ 
+  
    
+   To get the current Economic Events data, index the current
+   
+    
+     Slice
+    
+   
+   with the dataset
+   
+    Symbol
+   
+   . Slice objects deliver unique events to your algorithm as they happen, but the
+   
+    Slice
+   
+   may not contain data for your dataset at every time step. To avoid issues, check if the
+   
+    Slice
+   
+   contains the data you want before you index it.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    events = slice.get(EODHDEconomicEvents).get(self.dataset_symbol)
+    for event in events:
+        self.log(f"{event.event_type} of {event.country} over {event.event_period} with be held at {event.event_time} with estimated value {event.estimate}")
    
+   
    public override void OnData(Slice slice)
+{
+    if (slice.Get<EODHDEconomicEvents>().TryGetValue(_datasetSymbol, out var economicEvents))
+    {
+        foreach (EODHDEconomicEvent economicEvent in economicEvents)
+        {
+           Log($"{economicEvent.EventType} of {economicEvent.Country} over {economicEvent.EventPeriod} with be held at {economicEvent.EventTime} with estimated value {economicEvent.Estimate}");
+        }
+    }
+}
    
+  
    
+  
    
+   To iterate through all of the dataset objects in the current
+   
+    Slice
+   
+   , call the
+   
+    Get
+   
+   
+    get
+   
+   method.
+  
    
+  
    
+   
    def on_data(self, slice: Slice) -> None:
+    for symbol, events in slice.get(EODHDEconomicEvents).items():
+        for event in events:
+            self.log(f"{event.event_type} of {event.country} over {event.event_period} with be held at {event.event_time} with estimated value {event.estimate}")
+
    
+   
    public override void OnData(Slice slice)
+{
+    foreach (var (symbol, economicEvents) in slice.Get<EODHDEconomicEvents>())
+    {
+        foreach (EODHDEconomicEvent economicEvent in economicEvents)
+        {
+           Log($"{economicEvent.EventType} of {economicEvent.Country} over {economicEvent.EventPeriod} with be held at {economicEvent.EventTime} with estimated value {economicEvent.Estimate}");
+        }
+    }
+}
    
+  
    
+ 
+
+
+
    Historical Data
    
+
+ 
+  
    
+   To get historical Economic Events data, call the
+   
+    History
+   
+   
+    history
+   
+   method with the type
+   
+    EODHDEconomicEvents
+   
+   cast and the underlying
+   
+    Symbol
+   
+   . If there is no data in the period you request, the history result is empty.
+  
    
+  
    
+   
    history_bars = self.history[EODHDEconomicEvents](self.dataset_symbol, 100, Resolution.DAILY)
    
+   
    var history = History<EODHDEconomicEvents>(_datasetSymbol, 100, Resolution.Daily);
    
+  
    
+  
    
+   For more information about historical data, see
+   
+    History Requests
+   
+   .
+  
    
+ 
+
+
+
    Remove Subscriptions
    
+
+ 
+  
    
+   To remove a subscription, call the
+   
+    RemoveSecurity
+   
+   
+    remove_security
+   
+   method.
+  
    
+  
    
+   
    self.remove_security(self.dataset_symbol)
    
+   
    RemoveSecurity(_datasetSymbol);
    
+  
    
+ 
+
+
+
    Example Applications
    
+
+ 
+  
    
+   The Economic Events dataset provides timely notifications about upcoming economic events, allowing traders to trade on that volatility. Examples include the following strategies:
+  
    
+  
      
+   
    • 
+    Long straddle to trade the volatility of the economic events.
+   
      • 
+   
    • 
+    Trade market representative ETFs based on estimated figures for the upcoming events.
+   
      • 
+   
    • 
+    Statistical arbitrage on two or more correlated global markets based on economic events of different locations.
+   
      • 
+  
    
+  
    
+   Classic Algorithm Example
+  
    
+  
    
+   The following example algorithm trade Industrial sector ETF based on US PMI estimated change direction. If the estimated PMI of the upcoming announcement is above the previous PMI, buy the Industrial ETF, and sell it otherwise.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class EODHDEconomicEventsAlgorithm(QCAlgorithm):
+    def initialize(self):
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        # Use industrial sector ETF as a vehicle to trade.
+        self.equity_symbol = self.add_equity("XLI").symbol
+        # Request US PMI economic event data to generate trade signals.
+        ticker = EODHD.Events.UnitedStates.MARKIT_MANUFACTURING_PURCHASING_MANAGERS_INDEX
+        self.dataset_symbol = self.add_data(EODHDEconomicEvents, ticker).symbol
+
+    def on_data(self, slice):
+        # Trade based on the updated economic events.
+        if self.dataset_symbol in slice.get(EODHDEconomicEvents):
+            # Use the Manufacturing Index to generate trade signals on manufacturing industry vehicles.
+            # Make sure previous and estimate are available to estimate the direction of the industry.
+            event = slice[self.dataset_symbol].data[0]
+            if event.previous and event.estimate:
+                # If the estimated PMI is higher than the previous PMI, the manufacturing ETF price is expected to rise.
+                if event.previous > event.estimate:
+                    self.set_holdings(self.equity_symbol, 1)
+                # Otherwise, it is expected manufacturing ETF prices will drop.
+                else:
+                    self.set_holdings(self.equity_symbol, -1)
    
+   
    public class EODHDEconomicEventsAlgorithm : QCAlgorithm
+{
+    private Symbol _equitySymbol, _datasetSymbol;
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        // Use industrial sector ETF as a vehicle to trade.
+        _equitySymbol = AddEquity("XLI").Symbol;
+        // Request US PMI economic event data to generate trade signals.
+        var ticker = EODHD.Events.UnitedStates.MarkitManufacturingPurchasingManagersIndex;
+        _datasetSymbol = AddData<EODHDEconomicEvents>(ticker).Symbol;
+    }
+
+    public override void OnData(Slice slice)
+    {
+        // Trade based on the updated economic events.
+        if (slice.Get<EODHDEconomicEvents>().TryGetValue(_datasetSymbol, out var economicEvents))
+        {
+            var economicEvent = economicEvents.FirstOrDefault() as EODHDEconomicEvent;         
+            // Use the Manufacturing Index to generate trade signals on manufacturing industry vehicles.
+            // Make sure previous and estimate are available to estimate the direction of the industry.
+            if (economicEvent.Previous.HasValue && economicEvent.Estimate.HasValue)
+            {
+                //If the estimated PMI is higher than the previous PMI, the manufacturing ETF price is expected to rise.
+                if (economicEvent.Previous.Value > economicEvent.Estimate.Value)
+                {
+                    SetHoldings(_equitySymbol, 1);
+                }
+                // Otherwise, it is expected manufacturing ETF price will drop.
+                else
+                {
+                    SetHoldings(_equitySymbol, -1);
+                }
+            }
+        }
+    }
+}
    
+  
    
+  
    
+   Framework Algorithm Example
+  
    
+  
    
+   The following example algorithm trade Industrial sector ETF based on US PMI estimated change direction using algorithm framework. If the estimated PMI of the upcoming announcement is above the previous PMI, buy the Industrial ETF, sell it otherwise.
+  
    
+  
    
+   
    from AlgorithmImports import *
+
+
+class EODHDEconomicEventsAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        # Use industrial sector ETF as a vehicle to trade.
+        symbol = Symbol.create("XLI", SecurityType.EQUITY, Market.USA)
+        self.add_universe_selection(ManualUniverseSelectionModel(symbol))
+        # Custom alpha model to emit insights according to economic events.
+        self.add_alpha(EconomicEventAlphaModel(self))
+        # Equal weighting position sizing to dissipate capital risk evenly.
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel(Expiry.END_OF_MONTH))
+
+class EconomicEventAlphaModel(AlphaModel):
+    def __init__(self, algorithm: QCAlgorithm) -> None:
+        # Request US PMI economic event data to generate trade signals.
+        ticker = EODHD.Events.UnitedStates.MARKIT_MANUFACTURING_PURCHASING_MANAGERS_INDEX
+        self.dataset_symbol = algorithm.add_data(EODHDEconomicEvents, ticker).symbol
+        self._symbol = None
+
+    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
+        # Trade based on the updated economic events.
+        if self.dataset_symbol not in slice.get(EODHDEconomicEvents):
+            return []
+        # Use the US Manufacturing Index to generate trade signals on manufacturing industry vehicles.
+        # Make sure previous and estimate are available to estimate the direction of the industry.
+        event = slice[self.dataset_symbol].data[0]
+        # If the estimated PMI is higher than the previous PMI, the manufacturing ETF price is expected to rise.
+        # Otherwise, it is expected manufacturing ETF prices will drop.
+        direction = InsightDirection.UP if event.previous > event.estimate else InsightDirection.DOWN
+        return [Insight.price(self._symbol, timedelta(5), direction)]
+
+    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
+        for added in changes.added_securities:
+            self._symbol = added.symbol
    
+   
    public class EODHDEconomicEventsAlgorithm : QCAlgorithm
+{
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        // Use industrial sector ETF as a vehicle to trade.
+        var symbol = QuantConnect.Symbol.Create("XLI", SecurityType.Equity, Market.USA);
+        AddUniverseSelection(new ManualUniverseSelectionModel(new[] {symbol}));
+        // Custom alpha model to emit insights according to economic events.
+        AddAlpha(new EconomicEventAlphaModel(this));
+        // Equal weighting position sizing to dissipate capital risk evenly.
+        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(Expiry.EndOfMonth));
+    }
+}
+
+public class EconomicEventAlphaModel : AlphaModel
+{
+    private Symbol _symbol, _datasetSymbol;
+
+    public EconomicEventAlphaModel(QCAlgorithm algorithm)
+    {
+        // Request US PMI economic event data to generate trade signals.
+        var ticker = EODHD.Events.UnitedStates.MarkitManufacturingPurchasingManagersIndex;
+        _datasetSymbol = algorithm.AddData<EODHDEconomicEvents>(ticker).Symbol;
+    }
+
+    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
+    {
+        // Trade based on the updated economic events.
+        if (slice.Get<EODHDEconomicEvents>().TryGetValue(_datasetSymbol, out var economicEvents))
+        {
+            // Use the US Manufacturing Index to generate trade signals on manufacturing industry vehicles.
+            // Make sure previous and estimate are available to estimate the direction of the industry.
+            var economicEvent = economicEvents.FirstOrDefault() as EODHDEconomicEvent;
+            if (economicEvent.Previous.HasValue && economicEvent.Estimate.HasValue)
+            {
+                // If the estimated PMI is higher than the previous PMI, the manufacturing ETF price is expected to rise.
+                // Otherwise, it is expected manufacturing ETF prices will drop.
+                var direction = economicEvent.Previous.Value > economicEvent.Estimate.Value
+                    ? InsightDirection.Up
+                    : InsightDirection.Down;
+                yield return Insight.Price(_symbol, TimeSpan.FromDays(5), direction);
+            }
+        }
+    }
+
+    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
+    {
+        foreach (var added in changes.AddedSecurities)
+        {
+            _symbol = added.Symbol;
+        }
+    }
+}
    
+  
    
+ 
+
+
+
    Data Point Attributes
    
+
+ 
+  
    
+   The EODHD Economic Events dataset provides
+   
+    EODHDEconomicEvent
+   
+   objects, which have the following attributes:
+  
    
+  
    
+  
    
+  
    
+   The
+   
+    EODHDEconomicEvents
+   
+   objects represent a collection of
+   
+    EODHDEconomicEvent
+   
+   object
+  
    
+  
    
+  
    
+  
    
+   Country Enumeration
+  
    
+  
    
+   To filter the countries of the economic events, you can make use of the
+   
+    Country
+   
+   enumeration. The
+   
+    Country
+   
+   enumeration has the following members:
+  
    
+  
    
+  
    
+  
    
+   Events Enumeration
+  
    
+  
    
+   To filter the event types of the economic events, you can make use of the
+   
+    Events
+   
+   enumeration. The
+   
+    Events
+   
+   enumeration has the following members:
+  
    
+  
    
+  
    
+ 
+
+
+
     
    
+
    Datasets > EOD Historical Data > Macroeconomics Indicators
    
+
    
+    
    
+        
    EOD Historical Data
    
+        
    Macroeconomics Indicators
    
+    
    
+
    
+
    Introduction
    
+
+ 
+  
    
+   The Macroeconomic Indicators dataset, provided by EODHD, provides data on 39 major macroeconomic indicators of 249 countries and regions. The data starts in January 1998, and is delivered on indicators update.
+  
    
+  
    
+   For more information about the Macroeconomics Indicators dataset, including CLI commands and pricing, see the
+   
+    dataset listing
+   
+   .
+  
    
+  
    
+  
    
+ 
+
+
+
    About the Provider
    
+
+ 
+  
    
+   EODHD was a France financial data provider founded in April 2015. They focus on providing clean financial data, including stock prices, splits, dividends, fundamentals, macroeconomy indicators, technical indicators, and alternative data sources, through 24/7 API seamlessly.
+  
    
+ 
+
+
+
    Getting Started
    
+
+ 
+  
    
+   The following snippet demonstrates how to request data from the Macroeconomic Indicators dataset:
+  
    
+  
    
+   
    self.add_data(EODHDMacroIndicators, Country.UNITED_STATES)
+self.add_data(EODHDMacroIndicators, EODHD.MacroIndicators.UnitedStates.GDP_CURRENT_USD) 
    
+   
    AddData<EODHDMacroIndicators>(Country.UnitedStates);
+AddData<EODHDMacroIndicators>(EODHD.MacroIndicators.UnitedStates.GdpCurrentUsd);
    
+  
    
+ 
+
+
+
    Data Summary
    
+
+ 
+  
    
+   The following table describes the dataset properties:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Property
+     
+      Value
+     
    
+      Start Date
+     
+      January 1998
+     
    
+      Data Density
+     
+      Sparse
+     
    
+      Resolution
+     
+      Daily
+     
    
+      Timezone
+     
+      UTC
+     
    
+ 
+
+
+
    About Title Example
    
 
  
-  
    
-   The following table describes the dataset properties:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Property
-     
-      Value
-     
    
-      Start Date
-     
-      January 1998
-     
    
-      Data Density
-     
-      Sparse
-     
    
-      Resolution
-     
-      Daily
-     
    
-      Timezone
-     
-      UTC
-     
    
+  
+   Template content example
+  
  
 
 
@@ -246217,16 +248535,16 @@ 
    
     def initialize(self):
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        self.equity_symbol = self.add_equity("SPY", Resolution.Daily).symbol
+        self.equity_symbol = self.add_equity("SPY", Resolution.DAILY).symbol
 
         ticker = EODHD.MacroIndicators.UnitedStates.GDP_GROWTH_ANNUAL
-        self.dataset_symbol = self.add_data(EODHDMacroIndicators, ticker).Symbol
+        self.dataset_symbol = self.add_data(EODHDMacroIndicators, ticker).symbol
 
     def on_data(self, slice):
         indicators = slice.get(EODHDMacroIndicators).get(self.dataset_symbol)
         if indicators:
             gdp = indicators.data[0].value
-            self.SetHoldings(self.equity_symbol, 1 if gdp > 0 else -1)
    + self.set_holdings(self.equity_symbol, 1 if gdp > 0 else -1) 
    public class EODHDMacroIndicatorsAlgorithm : QCAlgorithm
 {
     private Symbol _equitySymbol, _datasetSymbol;
@@ -246756,8 +249074,7 @@ 
    
         self.settings.seed_initial_prices = True
         # Universe consists of equities with upcoming dividend events.
         self._universe = self.add_universe(EODHDUpcomingDividends, self.selection)
-        # Add a Scheduled Event to rebalance the portfolio every morning
-        # based on upcoming dividend signals.
+        # Add a Scheduled Event to rebalance the portfolio every morning based on upcoming dividend signals.
         spy = Symbol.create('SPY', SecurityType.EQUITY, Market.USA)
         self.schedule.on(
             self.date_rules.every_day(spy), 
@@ -246770,11 +249087,11 @@ 
    
         return [x.symbol for x in dividends if x.dividend_date < self.time + timedelta(1) and x.dividend > 0.5]
     
     def _rebalance(self) -> None:
-        # Equally invest in each member of the universe to evenly dissipate the capital risk.
+        # Equally invest in each member of the universe.
         symbols = [s for s in self._universe.selected if self.securities[s].price]
         total_count = len(symbols)
-        targets = [PortfolioTarget(symbol, -1. / total_count) for symbol in symbols]
-        self.set_holdings(targets, liquidate_existing_holdings=True)
    + targets = [PortfolioTarget(symbol, -1 / total_count) for symbol in symbols] + self.set_holdings(targets, True) 
    public class UpcomingDividendsExampleAlgorithm : QCAlgorithm
 {
     private Universe _universe;
@@ -246787,27 +249104,27 @@ 
    
         // Seed the price of each asset with its last known price to avoid trading errors.
         Settings.SeedInitialPrices = true;
         // Universe consists of equities with upcoming dividend events.
-        _universe = AddUniverse<EODHDUpcomingDividends>((dividends) => {
-            // Select the stocks with upcoming dividend record date, with a sufficient dividend size.
-            return from EODHDUpcomingDividends d in dividends
-                    where d.DividendDate < Time.AddDays(1) && d.Dividend > 0.5m
-                    select d.Symbol;
-        });
-       // Add a Scheduled Event to rebalance the portfolio every morning
-        // based on upcoming dividend signals.
+        // Select the stocks with upcoming dividend record date, with a sufficient dividend size.
+        _universe = AddUniverse<EODHDUpcomingDividends>(
+            dividends => dividends
+                .OfType<EODHDUpcomingDividends>()
+                .Where(dividend => dividend.DividendDate < Time.AddDays(1) && dividend.Dividend > 0.5m)
+                .Select(dividend => dividend.Symbol)
+        );
+        // Add a Scheduled Event to rebalance the portfolio every morning based on upcoming dividend signals.
         var spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
         Schedule.On(DateRules.EveryDay(spy), TimeRules.AfterMarketOpen(spy, 1), Rebalance);
     }
 
     public void Rebalance()
     {
-        // Equally invest in each member of the universe to evenly dissipate the capital risk.
+        // Equally invest in each member of the universe.
         var symbols = _universe.Selected.Where(s => Securities[s].Price != 0);
         var totalCount = symbols.Count();
         var targets = symbols
             .Select(symbol => new PortfolioTarget(symbol, -1m / totalCount))
             .ToList();
-        SetHoldings(targets, liquidateExistingHoldings: true);
+        SetHoldings(targets, true);
     }
 }
    @@ -248056,30 +250373,30 @@

    self.set_start_date(2024, 9, 1) self.set_end_date(2024, 12, 31) self.set_cash(100000) - # Filter for new stocks to trade their hype using EODHDUpcomingIPOs. + # Filter for new stocks with EODHDUpcomingIPOs to trade first-day hype. self._universe = self.add_universe(EODHDUpcomingIPOs, self.selection) def selection(self, ipos: List[EODHDUpcomingIPOs]) -> List[Symbol]: - # Select the stocks that IPO starts today and traded in Nasdaq + # Select stocks with IPO dates by tomorrow on Nasdaq. return [ - x.symbol for x in ipos - if (x.ipo_date and - x.ipo_date <= self.time + timedelta(1) and + x.symbol for x in ipos + if (x.ipo_date and + x.ipo_date <= self.time + timedelta(1) and x.exchange == Exchange.NASDAQ) ] - def on_data(self, slice: Slice) -> None: - # Invest in new stocks and trade on their first day for their hype. - # Equally invest in each new stocks to evenly dissipate the capital risk. + def on_data(self, data: Slice) -> None: + # Invest in new stocks to trade first-day hype. + # Equally invest in each new stock to distribute capital risk. for symbol in self._universe.selected: security = self.securities[symbol] if not security.holdings.invested and security.price: - self.set_holdings(symbol, 0.1/len(self._universe.selected)) + self.set_holdings(symbol, 0.1 / len(self._universe.selected)) def on_securities_changed(self, changes: SecurityChanges) -> None: - # Liquidate the new stocks traded today/yesterday to capitalize the first-day hype. - for removed in changes.removed_securities: - self.liquidate(removed.symbol) + # Liquidate new stocks traded today or yesterday to capitalize on first-day hype. + for security in changes.removed_securities: + self.liquidate(security) 
    public class UpcomingIPOsExampleAlgorithm : QCAlgorithm
 {
     private Universe _universe;
@@ -248089,35 +250406,34 @@ 
    
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
         SetCash(100000);
-        
-        // Filter for new stocks to trade their hype using EODHDUpcomingIPOs.
-        _universe = AddUniverse<EODHDUpcomingIPOs>(ipos => 
-            ipos.Select(x => x as EODHDUpcomingIPOs)
+        // Filter for new stocks with EODHDUpcomingIPOs to trade first-day hype.
+        _universe = AddUniverse<EODHDUpcomingIPOs>(ipos =>
+            ipos.OfType<EODHDUpcomingIPOs>()
                 .Where(x => x.IpoDate <= Time.AddDays(1) && x.Exchange == Exchange.NASDAQ)
                 .Select(x => x.Symbol)
         );
     }
 
-    public override void OnData(Slice slice)
+    public override void OnData(Slice data)
     {
-        // Invest in new stocks and trade on their first day for their hype.
-        // Equally invest in each new stocks to evenly dissipate the capital risk.
+        // Invest in new stocks to trade first-day hype.
+        // Equally invest in each new stock to distribute capital risk.
         foreach (var symbol in _universe.Selected)
         {
             var security = Securities[symbol];
             if (!security.Holdings.Invested && security.Price != 0)
             {
-                SetHoldings(symbol, 0.1m/_universe.Selected.Count);
+                SetHoldings(symbol, 0.1m / _universe.Selected.Count);
             }
         }
     }
 
     public override void OnSecuritiesChanged(SecurityChanges changes)
     {
-        // Liquidate the new stocks traded today/yesterday to capitalize the first-day hype.
-        foreach (var removed in changes.RemovedSecurities)
+        // Liquidate new stocks traded today or yesterday to capitalize on first-day hype.
+        foreach (var security in changes.RemovedSecurities)
         {
-            Liquidate(removed.Symbol);
+            Liquidate(security.Symbol);
         }
     }
 }
    @@ -248640,38 +250956,33 @@

    class UpcomingSplitsExampleAlgorithm(QCAlgorithm): - + def initialize(self) -> None: self.set_start_date(2024, 9, 1) self.set_end_date(2024, 12, 31) self.set_cash(100000) # Seed the price of each asset with its last known price to avoid trading errors. self.settings.seed_initial_prices = True - # Universe consists of equities with upcoming splits events. + # Select equities with upcoming split events. self._universe = self.add_universe(EODHDUpcomingSplits, self.selection) - # Add a Scheduled Event to rebalance the portfolio every morning - # based on upcoming splits signals. + # Add a Scheduled Event to rebalance the portfolio every morning based on upcoming split signals. spy = Symbol.create('SPY', SecurityType.EQUITY, Market.USA) self.schedule.on( - self.date_rules.every_day(spy), - self.time_rules.after_market_open(spy, 1), + self.date_rules.every_day(spy), + self.time_rules.after_market_open(spy, 1), self._rebalance ) - + def selection(self, splits: List[EODHDUpcomingSplits]) -> List[Symbol]: - # Split (more shares with lower price) will make the stock more affordable and drive up the demand. - # Hence, include all stocks that will have a split within the next 7 days. - # Note that spliting up the stock means the split factor > 1. + # Select splits that increase share count and make each share more affordable. return [x.symbol for x in splits if x.split_factor > 1] - + def _rebalance(self) -> None: # Equally invest in each member of the universe to evenly dissipate the capital risk. symbols = [s for s in self._universe.selected if self.securities[s].price] total_count = len(symbols) targets = [PortfolioTarget(symbol, 1. / total_count) for symbol in symbols] - self.set_holdings(targets, liquidate_existing_holdings=True) - - + self.set_holdings(targets, True) 
    public class UpcomingSplitsExampleAlgorithm : QCAlgorithm
 {
     private Universe _universe;
@@ -248683,17 +250994,14 @@ 
    
         SetCash(100000);
         // Seed the price of each asset with its last known price to avoid trading errors.
         Settings.SeedInitialPrices = true;
-        // Universe consists of equities with upcoming earnings events.
-        _universe = AddUniverse<EODHDUpcomingSplits>((splits) => {
-            return splits
-                // Split (more shares with lower price) will make the stock more affordable and drive up the demand.
-                // Hence, include all stocks that will have a split within the next 7 days.
-                // Note that spliting up the stock means the split factor > 1.
-                .Where(datum => (datum as EODHDUpcomingSplits).SplitFactor > 1)
-                .Select(datum => datum.Symbol);
-        });
-        // Add a Scheduled Event to rebalance the portfolio every morning
-        // based on upcoming splits signals.
+        // Select splits that increase share count and make each share more affordable.
+        _universe = AddUniverse<EODHDUpcomingSplits>(
+            splits => splits
+                .OfType<EODHDUpcomingSplits>()
+                .Where(split => split.SplitFactor > 1)
+                .Select(split => split.Symbol)
+        );
+        // Add a Scheduled Event to rebalance the portfolio every morning based on upcoming split signals.
         var spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
         Schedule.On(DateRules.EveryDay(spy), TimeRules.AfterMarketOpen(spy, 1), Rebalance);
     }
@@ -248706,7 +251014,7 @@ 
    
         var targets = symbols
             .Select(symbol => new PortfolioTarget(symbol, 1m / totalCount))
             .ToList();
-        SetHoldings(targets, liquidateExistingHoldings: true);
+        SetHoldings(targets, true);
     }
 }

    @@ -261802,7 +264110,7 @@

    def on_data(self, slice: Slice) -> None: # Trade with updated FED peak-to-trough indicator if slice.contains_key(self.fred_peak_to_trough) and slice.contains_key(self.spy): - peak_to_trough = slice.Get(Fred, self.fred_peak_to_trough).value + peak_to_trough = slice.get(Fred, self.fred_peak_to_trough).value # Buy SPY if peak to trough value is 0, which is the expansionary period if peak_to_trough == 0 and not self.portfolio.invested: @@ -261902,7 +264210,7 @@

    # Trade with updated FED peak-to-trough indicator if slice.contains_key(self.fred_peak_to_trough): - self.peak_to_trough_value = slice.Get(Fred, self.fred_peak_to_trough).value + self.peak_to_trough_value = slice.get(Fred, self.fred_peak_to_trough).value # Ensure we have a FRED peak to trough value if self.peak_to_trough_value is None: @@ -263721,7 +266029,7 @@

    insights = [] # Trade only based on updated NASDAQ data link data - for dataset_symbol, data_point in slice.Get(NasdaqCustomColumns).items(): + for dataset_symbol, data_point in slice.get(NasdaqCustomColumns).items(): if dataset_symbol != self.bitfinex_exchange_rate_symbol or algorithm.portfolio[dataset_symbol].invested: continue insights.append(Insight.price(dataset_symbol, timedelta(days=6*22), InsightDirection.UP)) @@ -263878,17 +266186,6 @@

    -
    - -
    -
    -

    - WallStreetBets -

    -
    -
    -     -
    @@ -264389,65 +266686,64 @@

    def initialize(self) -> None: self.set_start_date(2024, 9, 1) self.set_end_date(2024, 12, 31) - self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol - # Subscribe to CNBC data for AAPL to generate trade signal - self.dataset_symbol = self.add_data(QuiverCNBCs, self.aapl).symbol - # Define a mapping from OrderDirection to int. + self._equity = self.add_equity("AAPL", Resolution.DAILY) + # Subscribe to CNBC data for AAPL to generate the trade signal. + self._dataset_symbol = self.add_data(QuiverCNBCs, self._equity).symbol + # Normalize order directions into numeric signals. self._int_by_direction = { OrderDirection.BUY: 1, OrderDirection.HOLD: 0, OrderDirection.SELL: -1 } + # Historical data + history = self.history(self._dataset_symbol, 10, Resolution.DAILY) + self.debug(f"We got {len(history)} items from historical data request of {self._dataset_symbol}.") - # history request - history = self.history(self.dataset_symbol, 10, Resolution.DAILY) - self.debug(f"We got {len(history)} items from historical data request of {self.dataset_symbol}.") - - def on_data(self, slice: Slice) -> None: - for cnbcs in slice.Get(QuiverCNBCs).values(): - # Using mean prediction from CNBC analysts to be the trade signal - # If the average CNBC insight is upward movement, invest AAPL + def on_data(self, data: Slice) -> None: + for cnbcs in data.get(QuiverCNBCs).values(): + # Use the mean CNBC analyst prediction as the trade signal. + # Invest in AAPL when the average CNBC insight is positive. if np.mean([self._int_by_direction[cnbc.direction] for cnbc in cnbcs]) > 0: - self.set_holdings(self.aapl, 1) + self.set_holdings(self._equity, 1) else: - self.set_holdings(self.aapl, 0) + self.set_holdings(self._equity, 0) 
    public class QuiverCNBCsAlgorithm : QCAlgorithm
 {
-    private Symbol _symbol, _datasetSymbol;
+    private Equity _equity;
+    private Symbol _datasetSymbol;
     private Dictionary<OrderDirection, int> _intByOrderDirection;
 
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        _symbol = AddEquity("AAPL").Symbol;
-        // Subscribe to CNBC data for AAPL to generate trade signal
-        _datasetSymbol = AddData<QuiverCNBCs>(_symbol).Symbol;
-        // Define a mapping from OrderDirection to int.
+        _equity = AddEquity("AAPL", Resolution.Daily);
+        // Subscribe to CNBC data for AAPL to generate the trade signal.
+        _datasetSymbol = AddData<QuiverCNBCs>(_equity.Symbol).Symbol;
+        // Normalize order directions into numeric signals.
         _intByOrderDirection = new () {
             {OrderDirection.Buy, 1},
             {OrderDirection.Hold, 0},
             {OrderDirection.Sell, -1}
         };
-
-        // history request
-        var history = History<QuiverCNBCs>(new[] {_datasetSymbol}, 10, Resolution.Daily);
+        // Historical data
+        var history = History<QuiverCNBCs>([_datasetSymbol], 10, Resolution.Daily);
         Debug($"We got {history.Count()} items from historical data request of {_datasetSymbol}.");
     }
 
-    public override void OnData(Slice slice)
+    public override void OnData(Slice data)
     {
-        foreach (var kvp in slice.Get<QuiverCNBCs>())
+        foreach (var cnbcs in data.Get<QuiverCNBCs>().Values)
         {
-            // Using mean prediction from CNBC analysts to be the trade signal
-            // If the average CNBC insight is upward movement, invest AAPL
-            if (kvp.Value.Average(x => _intByOrderDirection[(x as QuiverCNBC).Direction]) > 0)
+            // Use the mean CNBC analyst prediction as the trade signal.
+            // Invest in AAPL when the average CNBC insight is positive.
+            if (cnbcs.Average(cnbc => _intByOrderDirection[(cnbc as QuiverCNBC).Direction]) > 0)
             {
-                SetHoldings(_symbol, 1);
+                SetHoldings(_equity.Symbol, 1);
             }
             else
             {
-                SetHoldings(_symbol, 0);
+                SetHoldings(_equity.Symbol, 0);
             }
         }
     }
@@ -265191,55 +267487,55 @@ 
    
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        self._symbol = self.add_equity("AAPL", Resolution.DAILY).symbol
-        # Subscribe to lobbying data for AAPL to generate trade signal
-        self._dataset_symbol = self.add_data(QuiverLobbyings, self._symbol).symbol
-
-        # history request
+        self._equity = self.add_equity("AAPL", Resolution.DAILY)
+        self.settings.seed_initial_prices = True
+        # Subscribe to lobbying data for AAPL to generate the trade signal.
+        self._dataset_symbol = self.add_data(QuiverLobbyings, self._equity).symbol
+        # Request recent history to confirm the custom data subscription.
         history = self.history(self._dataset_symbol, 10, Resolution.DAILY)
         self.debug(f"We got {len(history)} items from historical data request of {self._dataset_symbol}.")
 
-    def on_data(self, slice: Slice) -> None:
-        # Trade only base on lobbying data
-        for lobbyings in slice.get(QuiverLobbyings).values():
-            # Buy if over 50000 lobbying amount, suggesting a favored political prospect and sentiment
+    def on_data(self, data: Slice) -> None:
+        # Trade only based on lobbying data.
+        for lobbyings in data.get(QuiverLobbyings).values():
+            # Buy when lobbying amount is over 50000, suggesting favorable political sentiment.
             if any([lobbying.amount > 50000 for lobbying in lobbyings]):
-                self.set_holdings(self._symbol, 1)
-            # Sell if below 10000 lobbying amount, suggesting a less favorable political prospect and sentiment
+                self.set_holdings(self._equity, 1)
+            # Sell when lobbying amount is below 10000, suggesting less favorable political sentiment.
             elif any([lobbying.amount < 10000 for lobbying in lobbyings]):
-                self.set_holdings(self._symbol, -1)
    + self.set_holdings(self._equity, -1) 
    public class QuiverLobbyingAlgorithm : QCAlgorithm
 {
-    private Symbol _symbol, _datasetSymbol;
+    private Equity _equity;
+    private Symbol _datasetSymbol;
 
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        _symbol = AddEquity("AAPL").Symbol;
-        // Subscribe to lobbying data for AAPL to generate trade signal
-        _datasetSymbol = AddData<QuiverLobbyings>(_symbol).Symbol;
-
-        // history request
-        var history = History<QuiverLobbyings>(new[] {_datasetSymbol}, 10, Resolution.Daily);
+        _equity = AddEquity("AAPL", Resolution.Daily);
+        // Subscribe to lobbying data for AAPL to generate the trade signal.
+        _datasetSymbol = AddData<QuiverLobbyings>(_equity.Symbol).Symbol;
+        // Request recent history to confirm the custom data subscription.
+        var history = History<QuiverLobbyings>([_datasetSymbol], 10, Resolution.Daily);
         Debug($"We got {history.Count()} items from historical data request of {_datasetSymbol}.");
     }
 
-    public override void OnData(Slice slice)
+    public override void OnData(Slice data)
     {
-        // Trade only base on lobbying data
-        foreach (var kvp in slice.Get<QuiverLobbyings>())
+        // Trade only based on lobbying data.
+        foreach (var kvp in data.Get<QuiverLobbyings>())
         {
             var lobbyings = kvp.Value;
-            // Buy if over 50000 lobbying amount, suggesting a favored political prospect and sentiment
-            if (lobbyings.Any(lobbying => ((QuiverLobbying) lobbying).Amount >= 50000m))
+            // Buy when lobbying amount is over 50000, suggesting favorable political sentiment.
+            if (lobbyings.Any(lobbying => ((QuiverLobbying) lobbying).Amount > 50000m))
             {
-                SetHoldings(_symbol, 1);
+                SetHoldings(_equity.Symbol, 1);
             }
-            // Sell if below 10000 lobbying amount, suggesting a less favorable political prospect and sentiment
-            else if (lobbyings.Any(lobbying => ((QuiverLobbying) lobbying).Amount <= 10000m))
+            // Sell when lobbying amount is below 10000, suggesting less favorable political sentiment.
+            else if (lobbyings.Any(lobbying => ((QuiverLobbying) lobbying).Amount < 10000m))
             {
-                SetHoldings(_symbol, -1);
+                SetHoldings(_equity.Symbol, -1);
             }
         }
     }
@@ -265575,7 +267871,7 @@ 
    Data Summary
    
       Start Date
      
      
-      25 April 2014
+      April 25 2014*
      
     
     
@@ -265612,6 +267908,16 @@ 
    Data Summary
    
     
    
   
+  
    
+   Before May 14 2021, this dataset included legacy data without all the information. Only the following are valid:
+   
+    price_per_share, shares, and shares_owned_following
+   
+   
+    PricePerShare, Shares, and SharesOwnedFollowing
+   
+   .
+  
    
  
 
 
@@ -266011,61 +268317,60 @@ 
    
 
 
 class QuiverInsiderTradingAlgorithm(QCAlgorithm):
-    
-    def initialize(self):
+
+    def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        self._symbol = self.add_equity("AAPL", Resolution.DAILY).symbol
-        # Subscribe to insider trade data for AAPL to generate trade signal
-        self.dataset_symbol = self.add_data(QuiverInsiderTrading, self._symbol).symbol
-
-        # history request
-        history = self.history(self.dataset_symbol, 10, Resolution.DAILY)
-        self.debug(f"We got {len(history)} items from historical data request of {self.dataset_symbol}.")
+        self._equity = self.add_equity("AAPL", Resolution.DAILY)
+        # Subscribe to insider trade data for AAPL to generate trade signals.
+        self._dataset_symbol = self.add_data(QuiverInsiderTrading, self._equity).symbol
+        # Warm up the custom data subscription with recent history.
+        history = self.history(self._dataset_symbol, 10, Resolution.DAILY)
+        self.debug(f"We got {len(history)} items from historical data request of {self._dataset_symbol}.")
 
-    def on_data(self, slice: Slice) -> None:
-        # Trade only base on insider trade data
-        for insider_trades in slice.Get(QuiverInsiderTrading).values():
+    def on_data(self, data: Slice) -> None:
+        # Trade only from insider trade data.
+        for insider_trades in data.get(QuiverInsiderTrading).values():
             for insider_trade in insider_trades:
-                # Any buy insider trade will result in buying, assuming insider have confidence in stock price with more informed information and projection
+                # Buy when insiders purchase shares because it may signal confidence in future prices.
                 if insider_trade.shares > 0:
-                    self.set_holdings(self._symbol, 1)
-                # Any sell insider trade will result in liquidation, assuming insiders believe the stock price has reached maximum or poor future confidence
+                    self.set_holdings(self._equity, 1)
+                # Liquidate when insiders sell shares because it may signal weaker future confidence.
                 else:
-                    self.liquidate(self._symbol)
    + self.liquidate(self._equity) 
    public class QuiverInsiderTradingAlgorithm : QCAlgorithm
 {
-    private Symbol _symbol, _datasetSymbol;
+    private Equity _equity;
+    private Symbol _datasetSymbol;
 
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        _symbol = AddEquity("AAPL").Symbol;
-        // Subscribe to insider trade data for AAPL to generate trade signal
-        _datasetSymbol = AddData<QuiverInsiderTrading>(_symbol).Symbol;
-
-        // history request
-        var history = History<QuiverInsiderTrading>(new[] {_datasetSymbol}, 10, Resolution.Daily);
+        _equity = AddEquity("AAPL", Resolution.Daily);
+        // Subscribe to insider trade data for AAPL to generate trade signals.
+        _datasetSymbol = AddData<QuiverInsiderTrading>(_equity.Symbol).Symbol;
+        // Warm up the custom data subscription with recent history.
+        var history = History<QuiverInsiderTrading>([_datasetSymbol], 10, Resolution.Daily);
         Debug($"We got {history.Count()} items from historical data request of {_datasetSymbol}.");
     }
 
-    public override void OnData(Slice slice)
+    public override void OnData(Slice data)
     {
-        // Trade only base on insider trade data
-        foreach (var kvp in slice.Get<QuiverInsiderTrading>())
+        // Trade only from insider trade data.
+        foreach (var kvp in data.Get<QuiverInsiderTrading>())
         {
             foreach (QuiverInsiderTrading insiderTrade in kvp.Value)
             {
-                // Any buy insider trade will result in buying, assuming insider have confidence in stock price with more informed information and projection
+                // Buy when insiders purchase shares because it may signal confidence in future prices.
                 if (insiderTrade.Shares > 0)
                 {
-                    SetHoldings(_symbol, 1);
+                    SetHoldings(_equity.Symbol, 1);
                 }
-                // Any sell insider trade will result in liquidation, assuming insiders believe the stock price has reached maximum or poor future confidence
+                // Liquidate when insiders sell shares because it may signal weaker future confidence.
                 else
                 {
-                    Liquidate(_symbol);
+                    Liquidate(_equity.Symbol);
                 }
             }
         }
@@ -266766,57 +269071,50 @@ 
    
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
         self.set_cash(100000)
-
-        # Requesting data per underlying data to subscribe to updated congress members' trade information
-        aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
-        quiver_congress_symbol = self.add_data(QuiverCongress, aapl).symbol
-
-        # Historical data
+        # Subscribe to Congress trading updates for the underlying equity.
+        equity = self.add_equity("AAPL", Resolution.DAILY)
+        quiver_congress_symbol = self.add_data(QuiverCongress, equity).symbol
+        # Warm up the custom dataset with recent Congress trading history.
         history = self.history(QuiverCongress, quiver_congress_symbol, 60, Resolution.DAILY)
-        self.debug(f"We got {len(history)} items from our history request");
-
-    def on_data(self, slice: Slice) -> None:
-        congress_by_symbol = slice.Get(QuiverCongress)
+        self.debug(f"We got {len(history)} items from our history request")
 
-        # Determine net direction of Congress trades for each security to estimate the sentiment direction due to political factor
+    def on_data(self, data: Slice) -> None:
+        congress_by_symbol = data.get(QuiverCongress)
+        # Estimate sentiment for each security from the net direction of Congress trades.
         net_quantity_by_symbol = {}
         for symbol, points in congress_by_symbol.items():
             symbol = symbol.underlying
             if symbol not in net_quantity_by_symbol:
                 net_quantity_by_symbol[symbol] = 0
-            # Voting weight is by order size
+            # Weight each vote by order size.
             for point in points:
                 net_quantity_by_symbol[symbol] += (1 if point.transaction == OrderDirection.BUY else -1) * point.amount
-            
         for symbol, net_quantity in net_quantity_by_symbol.items():
             if net_quantity == 0:
                 self.liquidate(symbol)
                 continue
-            # Buy when Congress members have bought, short otherwise
+            # Follow net Congress buying direction and short net selling direction.
             self.set_holdings(symbol, 1 if net_quantity > 0 else -1)
    public class QuiverCongressDataAlgorithm : QCAlgorithm
 {
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
         SetCash(100000);
-        
-        // Requesting data per underlying data to subscribe to updated congress members' trade information
-        var aapl = AddEquity("AAPL", Resolution.Daily).Symbol;
-        var quiverCongressSymbol = AddData<QuiverCongress>(aapl).Symbol;
-
-        // Historical data
+        // Subscribe to Congress trading updates for the underlying equity.
+        var equity = AddEquity("AAPL", Resolution.Daily);
+        var quiverCongressSymbol = AddData<QuiverCongress>(equity).Symbol;
+        // Warm up the custom dataset with recent Congress trading history.
         var history = History<QuiverCongress>(quiverCongressSymbol, 60, Resolution.Daily);
         Debug($"We got {history.Count()} items from our history request");
     }
 
-    
-    public override void OnData(Slice slice)
+    public override void OnData(Slice data)
     {
-        var congressBySymbol = slice.Get<QuiverCongress>();
-        
-        // Determine net direction of Congress trades for each security to estimate the sentiment direction due to political factor
+        var congressBySymbol = data.Get<QuiverCongress>();
+        // Estimate sentiment for each security from the net direction of Congress trades.
         var netQuantityBySymbol = new Dictionary<Symbol, decimal>();
         foreach (var (s, points) in congressBySymbol)
         {
@@ -266825,13 +269123,12 @@ 
    
             {
                 netQuantityBySymbol[symbol] = 0m;
             }
-            // Voting weight is by order size
-            foreach(QuiverCongressDataPoint point in points)
+            // Weight each vote by order size.
+            foreach (QuiverCongressDataPoint point in points)
             {
                 netQuantityBySymbol[symbol] += (point.Transaction == OrderDirection.Buy ? 1 : -1) * (point.Amount ?? 0m);
             }
         }
-        
         foreach (var (symbol, netQuantity) in netQuantityBySymbol)
         {
             if (netQuantity == 0)
@@ -266839,7 +269136,7 @@ 
    
                 Liquidate(symbol);
                 continue;
             }
-            // Buy when Congress members have bought, short otherwise
+            // Follow net Congress buying direction and short net selling direction.
             SetHoldings(symbol, netQuantity > 0 ? 1 : -1);
         }
     }
@@ -266861,7 +269158,9 @@ 
    
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
         self.set_cash(100000)
-        # Filter according to QuiverCongress data
+        self.settings.seed_initial_prices = True
+        self.universe_settings.minimum_time_in_universe = timedelta(days=7)
+        # Add the Quiver Congress universe.
         self.add_universe(QuiverQuantCongressUniverse, "QuiverQuantCongresssUniverse", Resolution.DAILY, self.universe_selection)
         self.add_alpha(CongressAlphaModel())
         self.set_portfolio_construction(InsightWeightingPortfolioConstructionModel(lambda time: None))
@@ -266869,71 +269168,56 @@ 
    
         self.set_execution(ImmediateExecutionModel())
 
     def universe_selection(self, alt_coarse: List[QuiverQuantCongressUniverse]) -> List[Symbol]:
-        # Only include the ones with large size buy, since they are estimated to be materially confident to go up
-        return [d.symbol for d in alt_coarse if d.amount > 10_000 and d.transaction == OrderDirection.BUY]
-        
+        # Return all symbols from the Quiver Congress universe.
+        return [d.symbol for d in alt_coarse]
+
 
 class CongressAlphaModel(AlphaModel):
-    
-    symbol_data_by_symbol = {}
-    insight_by_symbol = {}
+    _securities = []
+    _insight_by_asset = {}
 
     def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        congress_by_symbol = slice.get(QuiverCongress)
-        if congress_by_symbol:
-            self.insight_by_symbol.clear()
-            # Determine net direction of Congress trades for each security to estimate the sentiment direction due to political factor
-            net_quantity_by_symbol = {}
-            for symbol, points in congress_by_symbol.items():
-                symbol = symbol.underlying
-                if symbol not in net_quantity_by_symbol:
-                    net_quantity_by_symbol[symbol] = 0
-                # Voting weight is by order size
-                for point in points:
-                    net_quantity_by_symbol[symbol] += (1 if point.transaction == OrderDirection.BUY else -1) * point.amount
-
-            for symbol, net_quantity in net_quantity_by_symbol.items():
-                # Buy when Congress members have bought, as they may have advance information to be confident to the return direction
-                if net_quantity > 0 and not algorithm.portfolio[symbol].is_long:
+        congress_data_by_asset = slice.get(QuiverCongress)
+        if congress_data_by_asset:
+            self._insight_by_asset.clear()
+            net_quantity_by_asset = {}
+            for congress_data_symbol, congress_data in congress_data_by_asset.items():
+                security = algorithm.securities[congress_data_symbol.underlying]
+                if security not in net_quantity_by_asset:
+                    net_quantity_by_asset[security] = 0
+                for point in congress_data:
+                    net_quantity_by_asset[security] += (1 if point.transaction == OrderDirection.BUY else -1) * point.amount
+            for security, net_quantity in net_quantity_by_asset.items():
+                # Emit an up insight when Congress members are net buyers.
+                if net_quantity > 0 and not security.holdings.is_long:
                     direction = InsightDirection.UP
-                # Short sell when Congress members have sold
-                elif net_quantity < 0 and not algorithm.portfolio[symbol].is_short:
+                # Emit a down insight when Congress members are net sellers.
+                elif net_quantity < 0 and not security.holdings.is_short:
                     direction = InsightDirection.DOWN
                 else:
                     continue
-                self.insight_by_symbol[symbol] = Insight.price(symbol, timedelta(7), direction, weight=0.5)
+                self._insight_by_asset[security] = Insight.price(security, timedelta(7), direction, weight=0.5)
         # To avoid error messages, emit the insights when the algorithm has a price for the asset.
         return [
-            self.insight_by_symbol.pop(symbol) 
-            for symbol, i in list(self.insight_by_symbol.items()) 
-            if algorithm.securities[symbol].price
+                self._insight_by_asset.pop(security) 
+                for security in list(self._insight_by_asset.keys())
+                if security.price
         ]
 
     def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
         for security in changes.added_securities:
-            symbol = security.symbol
-            self.symbol_data_by_symbol[symbol] = SymbolData(algorithm, symbol)
-        
+            self._securities.append(security)
+            # Add the Quiver Congress subscription for the security.
+            security.congress_data = algorithm.add_data(QuiverCongress, security, Resolution.DAILY)
+            history = algorithm.history(QuiverCongress, security.congress_data, 14, Resolution.DAILY)
+            algorithm.debug(f"We got {len(history)} items from our history request for {security}")
         for security in changes.removed_securities:
-            symbol_data = self.symbol_data_by_symbol.pop(security.symbol, None)
-            if symbol_data:
-                symbol_data.dispose()
-
-
-class SymbolData:
-
-    def __init__(self, algorithm: QCAlgorithm, symbol: Symbol):
-        self.algorithm = algorithm
-        
-        # Requesting data per underlying data to subscribe to updated congress members' trade information
-        self.quiver_congress_symbol = algorithm.add_data(QuiverCongress, symbol).symbol
-        
-        # Historical data
-        history = algorithm.history(self.quiver_congress_symbol, 14, Resolution.DAILY)
-        
-    def dispose(self) -> None:
-        # Unsubscribe from Quiver Congress feed for this security to release computational resources
-        self.algorithm.remove_security(self.quiver_congress_symbol)
    + if security in self._securities: + if security in self._insight_by_asset: + self._insight_by_asset.pop(security) + # Remove the Quiver Congress subscription for the security. + algorithm.remove_security(security.congress_data) + self._securities.remove(security) 
    public class QuiverCongressDataAlgorithm : QCAlgorithm
 {
     public override void Initialize()
@@ -266941,14 +269225,10 @@ 
    
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
         SetCash(100000);
-        // Filter according to QuiverCongress data
-        AddUniverse<QuiverQuantCongressUniverse>("QuiverQuantCongresssUniverse", Resolution.Daily, altCoarse =>
-        {
-            // Only include the ones with large size buy, since they are estimated to be materially confident to go up
-            return from d in altCoarse.OfType<QuiverCongressDataPoint>() 
-                where d.Amount > 10000 && d.Transaction == OrderDirection.Buy 
-                select d.Symbol;
-        });
+        Settings.SeedInitialPrices = true;
+        UniverseSettings.MinimumTimeInUniverse = TimeSpan.FromDays(7);
+        // Add the Quiver Congress universe.
+        AddUniverse<QuiverQuantCongressUniverse>(altCoarse => altCoarse.Select(d => d.Symbol));
         AddAlpha(new CongressAlphaModel());
         SetPortfolioConstruction(new InsightWeightingPortfolioConstructionModel(time => null));
         AddRiskManagement(new NullRiskManagementModel());
@@ -266958,41 +269238,41 @@ 
    
 
 public class CongressAlphaModel : AlphaModel
 {
-    private Dictionary<Symbol, SymbolData> _symbolDataBySymbol = new ();
-    private Dictionary<Symbol, Insight> _insightBySymbol = new ();
+    private readonly List<Security> _securities = [];
+    private readonly Dictionary<Security, Insight> _insightByAsset = [];
 
     public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
     {
-        var congressBySymbol = slice.Get<QuiverCongress>();
-        if (congressBySymbol.Count > 0)
+        var congressDataByAsset = slice.Get<QuiverCongress>();
+        if (congressDataByAsset.Count > 0)
         {
-            _insightBySymbol.Clear();
-            // Determine net direction of Congress trades for each security to estimate the sentiment direction due to political factor
-            var netQuantityBySymbol = new Dictionary<Symbol, decimal>();
-            foreach (var (s, points) in congressBySymbol)
+            _insightByAsset.Clear();
+            // Aggregate the net Congress trade quantity for each security.
+            var netQuantityByAsset = new Dictionary<Security, decimal>();
+            foreach (var (congressDataSymbol, congressData) in congressDataByAsset)
             {
-                var symbol = s.Underlying;
-                if (!netQuantityBySymbol.ContainsKey(symbol))
+                var security = algorithm.Securities[congressDataSymbol.Underlying];
+                if (!netQuantityByAsset.ContainsKey(security))
                 {
-                    netQuantityBySymbol[symbol] = 0m;
+                    netQuantityByAsset[security] = 0m;
                 }
-                // Voting weight is by order size
-                foreach(QuiverCongressDataPoint point in points)
+                // Weight each transaction by its reported amount.
+                foreach (QuiverCongressDataPoint point in congressData)
                 {
-                    netQuantityBySymbol[symbol] += (point.Transaction == OrderDirection.Buy ? 1 : -1) * (point.Amount ?? 0m);
+                    netQuantityByAsset[security] += (point.Transaction == OrderDirection.Buy ? 1 : -1) * (point.Amount ?? 0m);
                 }
             }
             
-            foreach (var (symbol, netQuantity) in netQuantityBySymbol)
+            foreach (var (security, netQuantity) in netQuantityByAsset)
             {
-                // Buy when Congress members have bought, as they may have advance information to be confident to the return direction
+                // Emit an up insight when Congress members are net buyers.
                 InsightDirection direction;
-                if (netQuantity > 0 && !algorithm.Portfolio[symbol].IsLong)
+                if (netQuantity > 0 && !security.Holdings.IsLong)
                 {
                     direction = InsightDirection.Up;
                 }
-                // Short sell when Congress members have sold
-                else if (netQuantity < 0 && !algorithm.Portfolio[symbol].IsShort)
+                // Emit a down insight when Congress members are net sellers.
+                else if (netQuantity < 0 && !security.Holdings.IsShort)
                 {
                     direction = InsightDirection.Down;
                 }
@@ -267000,16 +269280,17 @@ 
    
                 {
                     continue;
                 }
-                _insightBySymbol[symbol] = Insight.Price(symbol, TimeSpan.FromDays(7), direction, weight: 0.5);
+                _insightByAsset[security] = Insight.Price(security.Symbol, TimeSpan.FromDays(7), direction, weight: 0.5);
             }
         }
-        return _insightBySymbol.Keys
-            .ToList() // snapshot keys
-            .Where(symbol => algorithm.Securities[symbol].Price != 0m)
-            .Select(symbol => {
-                var val = _insightBySymbol[symbol]; // capture value
-                _insightBySymbol.Remove(symbol);    // remove
-                return val;
+        // To avoid error messages, emit the insights when the algorithm has a price for the asset.
+        return _insightByAsset.Keys
+            .ToList()  // snapshot keys
+            .Where(security => security.Price != 0m)
+            .Select(security => {
+                var insight = _insightByAsset[security];  // capture value
+                _insightByAsset.Remove(security);  // remove
+                return insight;
             })
             .ToList();
     }
@@ -267018,44 +269299,26 @@ 
    
     {
         foreach (var security in changes.AddedSecurities)
         {
-            var symbol = security.Symbol;
-            _symbolDataBySymbol.Add(symbol, new SymbolData(algorithm, symbol));
+            _securities.Add(security);
+            // Add the Quiver Congress subscription for the security.
+            var congressData = algorithm.AddData<QuiverCongress>(security.Symbol, Resolution.Daily);
+            security.Set("CongressData", congressData);
+            var history = algorithm.History<QuiverCongress>(congressData.Symbol, 14, Resolution.Daily);
+            algorithm.Debug($"We got {history.Count()} items from our history request for {security}");
         }
 
         foreach (var security in changes.RemovedSecurities)
         {
-            var symbol = security.Symbol;
-            if (_symbolDataBySymbol.ContainsKey(symbol))
+            if (_securities.Contains(security))
             {
-                _symbolDataBySymbol[symbol].dispose();
-                _symbolDataBySymbol.Remove(symbol);
+                _insightByAsset.Remove(security);
+                // Remove the Quiver Congress subscription for the security.
+                var congressData = security.Get<Security>("CongressData");
+                algorithm.RemoveSecurity(congressData.Symbol);
+                _securities.Remove(security);
             }
         }
     }
-}
-
-public class SymbolData
-{
-    private Symbol _quiverCongressSymbol;
-    private QCAlgorithm _algorithm;
-    
-    public SymbolData(QCAlgorithm algorithm, Symbol symbol)
-    {
-        _algorithm = algorithm;
-        
-        // Requesting data per underlying data to subscribe to updated congress members' trade information
-        _quiverCongressSymbol = algorithm.AddData<QuiverCongress>(symbol).Symbol;
-        
-        // Historical data
-        var history = algorithm.History<QuiverCongress>(_quiverCongressSymbol, 60, Resolution.Daily);
-        algorithm.Debug($"We got {history.Count()} items from our history request for {symbol} Quiver Congress data");
-    }
-    
-    public void dispose()
-    {
-        // Unsubscribe from Quiver Congress feed for this security to release computational resources
-        _algorithm.RemoveSecurity(_quiverCongressSymbol);
-    }
 }

    @@ -267635,847 +269898,7 @@

    Remove Subscriptions

    RemoveSecurity(_datasetSymbol);

    - If you subscribe to US Government Contracts data for assets in a dynamic universe, remove the dataset subscription when the asset leaves your universe. To view a common design pattern, see - - Track Security Changes - - . -

    - - - -

    Example Applications

    - - -

    - The Quiver Quantitative US Government Contracts dataset enables you to create strategies using the latest information on government contracts activity. Examples include the following strategies: -

    -
      -
    • - Buying securities that have received the most new government contract awards over the last month -
      • -
    • - Trading securities that have had the biggest change in government contracts awards over the last year -
      • -
    -

    - Classic Algorithm Example -

    -

    - The following example algorithm buys Apple stock when they receive a new government contract worth over $50K. If they receive a new contract worth under $10K, the algorithm short sells Apple. -

    -
    - 
    from AlgorithmImports import *
-
-
-class QuiverGovernmentContractAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
-        # Subscribe to government contract data for AAPL to generate trade signal
-        self.dataset_symbol = self.add_data(QuiverGovernmentContract, self.aapl).symbol
-
-        # history request
-        history = self.history(self.dataset_symbol, 10, Resolution.DAILY)
-        self.debug(f"We got {len(history)} items from historical data request of {self.dataset_symbol}.")
-
-    def on_data(self, slice: Slice) -> None:
-        # Trade only base on government contract data
-        for gov_contracts in slice.Get(QuiverGovernmentContract).values():
-            # Buy if over 50000 government contract amount, suggesting a large income
-            if any([gov_contract.amount > 50000 for gov_contract in gov_contracts]):
-                self.set_holdings(self.aapl, 1)
-            # Sell if below 10000 government contract amount, suggesting a smaller than usual income
-            elif any([gov_contract.amount < 10000 for gov_contract in gov_contracts]):
-                self.set_holdings(self.aapl, -1)
    - 
    public class QuiverGovernmentContractAlgorithm : QCAlgorithm
-{
-    private Symbol _symbol, _datasetSymbol;
-
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        _symbol = AddEquity("AAPL").Symbol;
-        // Subscribe to government contract data for AAPL to generate trade signal
-        _datasetSymbol = AddData<QuiverGovernmentContract>(_symbol).Symbol;
-
-        // history request
-        var history = History<QuiverGovernmentContract>(new[] {_datasetSymbol}, 10, Resolution.Daily);
-        Debug($"We got {history.Count()} items from historical data request of {_datasetSymbol}.");
-    }
-
-    public override void OnData(Slice slice)
-    {
-        // Trade only base on government contract data
-        foreach (var kvp in slice.Get<QuiverGovernmentContract>())
-        {
-            // Buy if over 50000 government contract amount, suggesting a large income
-            if (kvp.Value.Any(x => (int) (x as QuiverGovernmentContract).Amount > 50000m))
-            {
-                SetHoldings(_symbol, 1);
-            }
-            // Sell if below 10000 government contract amount, suggesting a smaller than usual income
-            else if (kvp.Value.Any(x => (int) (x as QuiverGovernmentContract).Amount < 10000m))
-            {
-                SetHoldings(_symbol, -1);
-            }
-        }
-    }
-}
    -
    -

    - Framework Algorithm Example -

    -

    - The following example algorithm creates a dynamic universe of US Equities that have just received a government contract worth at least $5K. Each day, it then forms an equal-weighted dollar-neutral portfolio with the 10 companies that received the largest contracts and the 10 companies that received the smallest contracts. -

    -
    - 
    from AlgorithmImports import *
-
-
-class QuiverGovernmentContractDataAlgorithm(QCAlgorithm):
-
-    def initialize(self) -> None:
-        self.set_start_date(2024, 9, 1)
-        self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        # Seed the price of each asset with its last known price to avoid trading errors.
-        self.settings.seed_initial_prices = True
-        # Filter universe using government contract data
-        self.add_universe(QuiverGovernmentContractUniverse, self.universe_selection)
-
-        # Custom alpha model that emit insights based on updated government contract data
-        self.add_alpha(QuiverGovernmentContractAlphaModel())
-        
-        # Invest equally to evenly dissipate the capital concentration risk
-        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
-        
-        self.set_execution(ImmediateExecutionModel())
-        
-    def universe_selection(self, data: List[QuiverGovernmentContractUniverse]) -> List[Symbol]:
-        gov_contract_data_by_symbol = {}
-
-        for datum in data:
-            symbol = datum.symbol
-            
-            if symbol not in gov_contract_data_by_symbol:
-                gov_contract_data_by_symbol[symbol] = []
-            gov_contract_data_by_symbol[symbol].append(datum)
-        
-        # Only select the stocks with over 5000 government contracts, which is considered material information
-        return [symbol for symbol, d in gov_contract_data_by_symbol.items()
-                if sum([x.amount for x in d]) > 5000]
-        
-class QuiverGovernmentContractAlphaModel(AlphaModel):
-    
-    def __init__(self) -> None:
-        # A variable to control the rebalancing time
-        self.last_time = datetime.min
-        # To hold the government contract dataset symbol for managing subscription
-        self.dataset_symbol_by_symbol = {}
-        
-    def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        if self.last_time > algorithm.time: return []
-        
-        # Trade signal only based on government contract data
-        data_points = slice.Get(QuiverGovernmentContract)
-        
-        if not data_points: return []
-        
-        gov_contracts = {}
-        # To aggregate all data per symbol for analysis
-        for data_point in data_points:
-            if not algorithm.securities[data_point.key.underlying].price:
-                continue
-            if data_point.Key not in gov_contracts:
-                gov_contracts[data_point.Key] = 0
-
-            for gov_contract in data_point.Value:
-                gov_contracts[data_point.Key] += gov_contract.amount
-        
-        # Long the top 10 highest government contract amount, predicting a higher expected income and return
-        # Short the lowest 10 government contract amount, predicting a lower expected income and return
-        sorted_by_gov_contracts = sorted(gov_contracts.items(), key=lambda x: x[1])
-        long_symbols = [x[0].underlying for x in sorted_by_gov_contracts[-10:]]
-        short_symbols = [x[0].underlying for x in sorted_by_gov_contracts[:10]]
-        
-        insights = []
-        for symbol in long_symbols:
-            insights.append(Insight.price(symbol, Expiry.END_OF_DAY, InsightDirection.UP))        
-        for symbol in short_symbols:
-            insights.append(Insight.price(symbol, Expiry.END_OF_DAY, InsightDirection.DOWN))
-        
-        self.last_time = Expiry.END_OF_DAY(algorithm.Time)
-        
-        return insights
-        
-    def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        for security in changes.added_securities:
-            # Requesting government contract data for trade signal generation
-            symbol = security.symbol
-            dataset_symbol = algorithm.add_data(QuiverGovernmentContract, symbol).symbol
-            self.dataset_symbol_by_symbol[symbol] = dataset_symbol
-            # Historical Data
-            history = algorithm.history(dataset_symbol, 10, Resolution.DAILY)
-
-        for security in changes.removed_securities:
-            dataset_symbol = self.dataset_symbol_by_symbol.pop(security.symbol, None)
-            if dataset_symbol:
-                # Remove government contract data subscription to release computation resources
-                algorithm.remove_security(dataset_symbol)
    - 
    public class QuiverGovernmentContractFrameworkAlgorithm : QCAlgorithm
-{
-    public override void Initialize()
-    {
-        SetStartDate(2024, 9, 1);
-        SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        // Seed the price of each asset with its last known price to avoid trading errors.
-        Settings.SeedInitialPrices = true;
-        // Filter universe using government contract data
-        AddUniverse<QuiverGovernmentContractUniverse>(data =>
-        {
-            var govContractDataBySymbol = new Dictionary<Symbol, List<QuiverGovernmentContractUniverse>>();
-
-            foreach (var datum in data.OfType<QuiverGovernmentContractUniverse>())
-            {
-                var symbol = datum.Symbol;
-
-                if (!govContractDataBySymbol.ContainsKey(symbol))
-                {
-                    govContractDataBySymbol.Add(symbol, new List<QuiverGovernmentContractUniverse>());
-                }
-                govContractDataBySymbol[symbol].Add(datum);
-            }
-
-            // Only select the stocks with over 5000 government contracts, which is considered material information
-            return from kvp in govContractDataBySymbol
-                where kvp.Value.Sum(x => x.Amount) > 5000m
-                select kvp.Key;
-        });
-
-        // Custom alpha model that emit insights based on updated government contract data
-        AddAlpha(new QuiverGovernmentContractAlphaModel());
-
-        // Invest equally to evenly dissipate the capital concentration risk
-        SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
-        
-        SetExecution(new ImmediateExecutionModel());
-    }
-}
-
-public class QuiverGovernmentContractAlphaModel: AlphaModel
-{
-    // A variable to control the rebalancing time
-    private DateTime _time;
-    // To hold the government contract dataset symbol for managing subscription
-    private Dictionary<Symbol, Symbol> _datasetSymbolBySymbol = new();
-    
-    public QuiverGovernmentContractAlphaModel()
-    {
-        _time = DateTime.MinValue;
-    }
-    
-    public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
-    {
-        if (_time > algorithm.Time) return new List<Insight>();
-        
-        // Trade signal only based on government contract data
-        var dataPoints = slice.Get<QuiverGovernmentContract>();
-        
-        if (dataPoints.IsNullOrEmpty()) return new List<Insight>();
-
-        // To aggregate all data per symbol for analysis
-        var govContracts = dataPoints
-            .Where(kvp => algorithm.Securities[kvp.Key.Underlying].Price != 0)
-            .ToDictionary(kvp => kvp.Key, kvp => kvp.Value.Sum(x => ((QuiverGovernmentContract)x).Amount));
-        
-        // Long the top 10 highest government contract amount, predicting a higher expected income and return
-        // Short the lowest 10 government contract amount, predicting a lower expected income and return
-        var sortedByGovContract = from kvp in govContracts
-                        orderby kvp.Value descending
-                        select kvp.Key.Underlying;
-        var longSymbols = sortedByGovContract.Take(10).ToList();
-        var shortSymbols = sortedByGovContract.TakeLast(10).ToList();
-        
-        var insights = new List<Insight>();
-        insights.AddRange(longSymbols.Select(symbol => 
-            new Insight(symbol, Expiry.EndOfDay, InsightType.Price, InsightDirection.Up)));
-        insights.AddRange(shortSymbols.Select(symbol => 
-            new Insight(symbol, Expiry.EndOfDay, InsightType.Price, InsightDirection.Down)));
-        
-        _time = Expiry.EndOfDay(algorithm.Time);
-        
-        return insights;
-    }
-    
-    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
-    {
-        foreach (var security in changes.AddedSecurities)
-        {
-            // Requesting government contract data for trade signal generation
-            var symbol = security.Symbol;
-            var datasetSymbol = algorithm.AddData<QuiverGovernmentContract>(symbol).Symbol;
-            _datasetSymbolBySymbol.Add(symbol, datasetSymbol);
-            // History request
-            var history = algorithm.History<QuiverGovernmentContract>(datasetSymbol, 10, Resolution.Daily);
-        }
-        
-        foreach (var security in changes.RemovedSecurities)
-        {
-            var symbol = security.Symbol;
-            if (_datasetSymbolBySymbol.ContainsKey(symbol))
-            {
-                // Remove government contract data subscription to release computation resources
-                _datasetSymbolBySymbol.Remove(symbol, out var datasetSymbol);
-                algorithm.RemoveSecurity(datasetSymbol);
-            }
-        }
-    }
-}
    -
    -

    - Research Example -

    -

    - The following example lists all US Equities with Government contracts in the past year. -

    -
    - 
    #r "../QuantConnect.DataSource.QuiverGovernmentContracts.dll"
-using QuantConnect.DataSource;
-
-// Requesting data
-var aapl = qb.AddEquity("AAPL", Resolution.Daily).Symbol;
-var symbol = qb.AddData<QuiverGovernmentContract>(aapl).Symbol;
-
-// Historical data
-var history = qb.History<QuiverGovernmentContract>(symbol, 360, Resolution.Daily);
-foreach (var contracts in history)
-{
-    foreach (QuiverGovernmentContract contract in contracts)
-    {
-        Console.WriteLine($"{contract.Symbol} amount at {contract.EndTime}: {contract.Amount}");
-    }
-}
-
-// Add Universe Selection
-IEnumerable<Symbol> UniverseSelection(IEnumerable<BaseData> altCoarse)
-{
-    return from d in altCoarse.OfType<QuiverGovernmentContractUniverse>()
-        select d.Symbol;
-}
-var universe = qb.AddUniverse<QuiverGovernmentContractUniverse<(UniverseSelection);
-
-// Historical Universe data
-var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-360), qb.Time);
-foreach (var contracts in universeHistory)
-{
-    foreach (QuiverGovernmentContractUniverse contract in contracts)
-    {
-        Console.WriteLine($"{contract.Symbol} amount at {contract.EndTime}: {contract.Amount}");
-    }
-}
    - 
    qb = QuantBook()
-
-# Requesting Data
-aapl = qb.add_equity("AAPL", Resolution.DAILY).symbol
-symbol = qb.add_data(QuiverGovernmentContract, aapl).symbol
-
-# Historical data
-history = qb.history(QuiverGovernmentContract, symbol, 360, Resolution.DAILY)
-for (symbol, time), contracts in history.items():
-    for contract in contracts:
-        print(f"{contract.symbol} amount at {contract.end_time}: {contract.amount}")
-
-# Add Universe Selection
-def universe_selection(alt_coarse: List[QuiverGovernmentContractUniverse]) -> List[Symbol]:
-    return [d.symbol for d in alt_coarse]
-
-universe = qb.add_universe(QuiverGovernmentContractUniverse, universe_selection)
-        
-# Historical Universe data
-universe_history = qb.universe_history(universe, qb.time-timedelta(360), qb.time)
-for (_, time), contracts in universe_history.items():
-    for contract in contracts:
-        print(f"{contract.symbol} amount at {contract.end_time}: {contract.amount}")
    -
    - - - -

    Data Point Attributes

    - - -

    - The US Government Contracts dataset provides - - QuiverGovernmentContract - - and - - QuiverGovernmentContractUniverse - - objects. -

    -

    - QuiverGovernmentContract -

    -

    - - QuiverGovernmentContract - - objects have the following attributes: -

    -
    -
    -

    - QuiverGovernmentContractUniverse -

    -

    - - QuiverGovernmentContractUniverse - - objects have the following attributes: -

    -
    -
    - - - -

     

    -

    Datasets > Quiver Quantitative > WallStreetBets

    -
    -
    -

    Quiver Quantitative

    -

    WallStreetBets

    -
    -
    -

    Introduction

    - - -

    - The WallStreetBets dataset by Quiver Quantitative tracks daily mentions of different equities on Reddit’s popular WallStreetBets forum. The data covers 6,000 Equities, starts in August 2018, and is delivered on a daily frequency. The dataset is created by scraping the daily discussion threads on r/WallStreetBets and parsing the comments for ticker mentions. -

    -

    - This dataset depends on the - - US Equity Security Master - - dataset because the US Equity Security Master dataset contains information on splits, dividends, and symbol changes. -

    -

    - For more information about the WallStreetBets dataset, including CLI commands and pricing, see the - - dataset listing - - . -

    -

    -

    - - - -

    About the Provider

    - - -

    - - Quiver Quantitative - - was founded by two college students in February 2020 with the goal of bridging the information gap between Wall Street and non-professional investors. Quiver allows retail investors to tap into the power of big data and have access to actionable, easy to interpret data that hasn’t already been dissected by Wall Street. -

    - - - -

    Getting Started

    - - -

    - The following snippet demonstrates how to request data from the WallStreetBets dataset: -

    -
    - 
    self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
-self.dataset_symbol = self.add_data(QuiverWallStreetBets, self.aapl).symbol
-
-self._universe = self.add_universe(QuiverWallStreetBetsUniverse, self.universe_selection)
    - 
    _symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
-_datasetSymbol = AddData<QuiverWallStreetBets>(_symbol).Symbol;
-
-_universe = AddUniverse<QuiverWallStreetBetsUniverse>(UniverseSelection);
    -
    - - - -

    Data Summary

    - - -

    - The following table describes the dataset properties: -

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - Property - - Value -
    - Start Date - - August 2018 -
    - Asset Coverage - - 6,000 US Equities -
    - Data Density - - Sparse -
    - Resolution - - Daily -
    - Timezone - - UTC -
    - - - -

    Requesting Data

    - - -

    - To add WallStreetBets data to your algorithm, call the - - AddData - - - add_data - - method. Save a reference to the dataset - - Symbol - - so you can access the data later in your algorithm. -

    -
    - 
    class QuiverWallStreetBetsDataAlgorithm(QCAlgorithm):
-    def initialize(self) -> None:
-        self.set_start_date(2019, 1, 1)
-        self.set_end_date(2020, 6, 1)
-        self.set_cash(100000)
-
-        self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
-        self.dataset_symbol = self.add_data(QuiverWallStreetBets, self.aapl).symbol
    - 
    public class QuiverWallStreetBetsDataAlgorithm : QCAlgorithm
-{
-    private Symbol _symbol, _datasetSymbol;
-
-    public override void Initialize()
-    {
-        SetStartDate(2019, 1, 1);
-        SetEndDate(2020, 6, 1);
-        SetCash(100000);
-        _symbol = AddEquity("AAPL", Resolution.Daily).Symbol;
-        _datasetSymbol = AddData<QuiverWallStreetBets>(_symbol).Symbol;
-    }
-}
    -
    - - - -

    Accessing Data

    - - -

    - To get the current WallStreetBets data, index the current - - - Slice - - - with the dataset - - Symbol - - . Slice objects deliver unique events to your algorithm as they happen, but the - - Slice - - may not contain data for your dataset at every time step. To avoid issues, check if the - - Slice - - contains the data you want before you index it. -

    -
    - 
    def on_data(self, slice: Slice) -> None:
-    if slice.contains_key(self.dataset_symbol):
-        data_points = slice[self.dataset_symbol]
-        for data_point in data_points:
-            self.log(f"{self.dataset_symbol} mentions at {slice.time}: {data_point.mentions}")
    - 
    public override void OnData(Slice slice)
-{
-    if (slice.ContainsKey(_datasetSymbol))
-    {
-        var dataPoints = slice[_datasetSymbol];
-        foreach (var dataPoint in dataPoints)
-        {
-            Log($"{_datasetSymbol} mentions at {slice.Time}: {dataPoint.Mentions}");
-        }
-    }
-}
    -
    -

    - To iterate through all of the dataset objects in the current - - Slice - - , call the - - Get - - - get - - method. -

    -
    - 
    def on_data(self, slice: Slice) -> None:
-    for dataset_symbol, data_points in slice.get(QuiverWallStreetBets).items():
-        for data_point in data_points:
-            self.log(f"{dataset_symbol} mentions at {slice.time}: {data_point.mentions}")
    - 
    public override void OnData(Slice slice)
-{
-    foreach (var kvp in slice.Get<QuiverWallStreetBets>())
-    {
-        var datasetSymbol = kvp.Key;
-        var dataPoints = kvp.Value;
-        foreach (var dataPoint in dataPoints)
-        {
-            Log($"{datasetSymbol} mentions at {slice.Time}: {dataPoint.Mentions}");
-        }
-    }
-}
    -
    - - - -

    Historical Data

    - - -

    - To get historical WallStreetBets data, call the - - History - - - history - - method with the dataset - - Symbol - - . If there is no data in the period you request, the history result is empty. -

    -
    - 
    # DataFrame
-history_df = self.history(self.dataset_symbol, 100, Resolution.DAILY)
-
-# Dataset objects
-history_bars = self.history[QuiverWallStreetBets](self.dataset_symbol, 100, Resolution.DAILY)
    - 
    var history = History<QuiverWallStreetBets>(_datasetSymbol, 100, Resolution.Daily);
    -
    -

    - For more information about historical data, see - - History Requests - - . -

    - - - -

    Universe Selection

    - - -

    - To select a dynamic universe of US Equities based on WallStreetBets data, call the - - AddUniverse - - - add_universe - - method with the - - QuiverWallStreetBetsUniverse - - class and a selection function. -

    -
    - 
    def initialize(self) -> None:
-    self.universe = self.add_universe(QuiverWallStreetBetsUniverse, self.universe_selection)
-        
-def universe_selection(self, alt_coarse: List[QuiverWallStreetBetsUniverse]) -> List[Symbol]:
-    return [d.symbol for d in alt_coarse if d.mentions > 100  and d.rank < 100]
    - 
    private Universe _universe;
-public override void Initialize()
-{
-    _universe = AddUniverse<QuiverWallStreetBetsUniverse>(altCoarse =>
-    {
-        return from d in altCoarse.OfType<QuiverWallStreetBetsUniverse>()
-            where d.Mentions > 10 && d.Rank > 10 select d.Symbol;
-    });
-}
    -
    -

    - For more information about dynamic universes, see - - Universes - - . -

    - - - -

    Universe History

    - - -

    - You can get historical universe data in an algorithm and in the Research Environment. -

    -

    - Historical Universe Data in Algorithms -

    -

    - To get historical universe data in an algorithm, call the - - History - - - history - - method with the - - Universe - - object and the lookback period. If there is no data in the period you request, the history result is empty. -

    -
    - 
    var universeHistory = History(_universe, 30, Resolution.Daily);
-foreach (var bets in universeHistory)
-{
-    foreach (QuiverWallStreetBetsUniverse bet in bets)
-    {
-        Log($"{bet.Symbol} mentions at {bet.EndTime}: {bet.Mentions}");
-    }
-}
    - 
    # DataFrame example where the columns are the QuiverWallStreetBetsUniverse attributes: 
-history_df = self.history(self._universe, 30, Resolution.DAILY, flatten=True)
-
-# Series example where the values are lists of QuiverWallStreetBetsUniverse objects: 
-universe_history = self.history(self._universe, 30, Resolution.DAILY)
-for (univere_symbol, time), bets in universe_history.items():
-    for bet in bets:
-        self.log(f"{bet.symbol} mentions at {bet.end_time}: {bet.mentions}")
    -
    -

    - Historical Universe Data in Research -

    -

    - To get historical universe data in research, call the - - UniverseHistory - - - universe_history - - method with the - - Universe - - object, a start date, and an end date. This method returns the filtered universe. If there is no data in the period you request, the history result is empty. -

    -
    - 
    var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-30), qb.Time);
-{
-    foreach (QuiverWallStreetBetsUniverse bet in bets)
-    {
-        Log($"{bet.Symbol} rank at {bet.EndTime}: {bet.Rank}");
-    }
-}
    - 
    # DataFrame example where the columns are the QuiverWallStreetBetsUniverse attributes: 
-history_df = qb.universe_history(universe, qb.time-timedelta(30), qb.time, flatten=True)
-
-# Series example where the values are lists of QuiverWallStreetBetsUniverse objects: 
-universe_history = qb.universe_history(universe, qb.time-timedelta(30), qb.time)
-for (univere_symbol, time), bets in universe_history.items():
-    for bet in bets:
-        print(f"{bet.symbol} rank at {bet.end_time}: {bet.rank}")
    -
    -

    - You can call the - - History - - - history - - method in Research. -

    - - - -

    Remove Subscriptions

    - - -

    - To remove a subscription, call the - - RemoveSecurity - - - remove_security - - method. -

    -
    - 
    self.remove_security(self.dataset_symbol)
    - 
    RemoveSecurity(_datasetSymbol);
    -
    -

    - If you subscribe to WallStreetBets data for assets in a dynamic universe, remove the dataset subscription when the asset leaves your universe. To view a common design pattern, see + If you subscribe to US Government Contracts data for assets in a dynamic universe, remove the dataset subscription when the asset leaves your universe. To view a common design pattern, see Track Security Changes @@ -268488,393 +269911,359 @@

    Example Applications

    - The WallStreetBets dataset enables you to create strategies using the latest activity on the WallStreetBets daily discussion thread. Examples include the following strategies: + The Quiver Quantitative US Government Contracts dataset enables you to create strategies using the latest information on government contracts activity. Examples include the following strategies:

    • - Trading any security that is being mentioned -
      • -
    • - Trading securities that are receiving more/less mentions than they were previously + Buying securities that have received the most new government contract awards over the last month
    • - Trading the security that is being mentioned the most/least for the day + Trading securities that have had the biggest change in government contracts awards over the last year

    Classic Algorithm Example

    - The following example algorithm creates a dynamic universe of US Equities based on daily WallStreetBets data. When a security is mentioned on r/WallStreetBets more than five times in a day, the algorithm buys the security. When a security is mentioned five time in a day or less, the algorithm short sells the security. + The following example algorithm buys Apple stock when they receive a new government contract worth over $50K. If they receive a new contract worth under $10K, the algorithm short sells Apple. 

    from AlgorithmImports import *
 
 
-class QuiverWallStreetBetsDataAlgorithm(QCAlgorithm):
+class QuiverGovernmentContractAlgorithm(QCAlgorithm):
+
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
-        self.set_cash(100000)
-        # Seed the price of each asset with its last known price to avoid trading errors.
-        self.settings.seed_initial_prices = True
-        self.universe_settings.resolution = Resolution.DAILY
-        # Filter using wall street bet insights
-        self._universe = self.add_universe(QuiverWallStreetBetsUniverse, self.universe_selection)
-
-    def on_data(self, slice: Slice) -> None:
-        points = slice.Get(QuiverWallStreetBets)
-        for point in points.Values:
-            symbol = point.symbol.underlying
-            security = self.securities[symbol]
-            if not security.is_tradable:
-                continue
-
-            # Buy if the stock was mentioned more than 5 times in the WallStreetBets daily discussion, which translate into high popularity of rise
-            if point.mentions > 5 and not security.holdings.is_long:
-                self.market_order(symbol, 1)
-                
-            # Otherwise, short sell
-            elif point.mentions <= 5 and not security.holdings.is_short:
-                self.market_order(symbol, -1)
-
-    def on_securities_changed(self, changes: SecurityChanges) -> None:
-        for added in changes.added_securities:
-            # Requesting wall street bet data to obtain the trader's insights
-            quiver_wsb_symbol = self.add_data(QuiverWallStreetBets, added.symbol).symbol
+        self.aapl = self.add_equity("AAPL", Resolution.DAILY).symbol
+        # Subscribe to government contract data for AAPL to generate trade signal
+        self.dataset_symbol = self.add_data(QuiverGovernmentContract, self.aapl).symbol
 
-            # Historical data
-            history = self.history(QuiverWallStreetBets, quiver_wsb_symbol, 60, Resolution.DAILY)
+        # history request
+        history = self.history(self.dataset_symbol, 10, Resolution.DAILY)
+        self.debug(f"We got {len(history)} items from historical data request of {self.dataset_symbol}.")
 
-    def universe_selection(self, alt_coarse: List[QuiverWallStreetBetsUniverse]) -> List[Symbol]:       
-        # Select the ones with popularity (mentions) of better-than-others performance (rank)
-        return [d.symbol for d in alt_coarse \
-                    if d.mentions > 10 \
-                    and d.rank < 100]
    - 
    public class QuiverWallStreetBetsDataAlgorithm : QCAlgorithm
+    def on_data(self, slice: Slice) -> None:
+        # Trade only base on government contract data
+        for gov_contracts in slice.get(QuiverGovernmentContract).values():
+            # Buy if over 50000 government contract amount, suggesting a large income
+            if any([gov_contract.amount > 50000 for gov_contract in gov_contracts]):
+                self.set_holdings(self.aapl, 1)
+            # Sell if below 10000 government contract amount, suggesting a smaller than usual income
+            elif any([gov_contract.amount < 10000 for gov_contract in gov_contracts]):
+                self.set_holdings(self.aapl, -1)
    + 
    public class QuiverGovernmentContractAlgorithm : QCAlgorithm
 {
-    private Universe _universe;
+    private Symbol _symbol, _datasetSymbol;
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
-        SetCash(100000);
-        // Seed the price of each asset with its last known price to avoid trading errors.
-        Settings.SeedInitialPrices = true;
-        UniverseSettings.Resolution = Resolution.Daily;
-        // Filter using wall street bet insights
-        _universe = AddUniverse<QuiverWallStreetBetsUniverse>(altCoarse =>
-        {
-            // Select the ones with popularity (mentions) of better-than-others performance (rank)
-            return from d in altCoarse.OfType<QuiverWallStreetBetsUniverse>()
-                    where d.Mentions > 10 && d.Rank < 100
-                    select d.Symbol;
-        });
+        _symbol = AddEquity("AAPL").Symbol;
+        // Subscribe to government contract data for AAPL to generate trade signal
+        _datasetSymbol = AddData<QuiverGovernmentContract>(_symbol).Symbol;
+
+        // history request
+        var history = History<QuiverGovernmentContract>(new[] {_datasetSymbol}, 10, Resolution.Daily);
+        Debug($"We got {history.Count()} items from historical data request of {_datasetSymbol}.");
     }
 
     public override void OnData(Slice slice)
     {
-        var points = slice.Get<QuiverWallStreetBets>();
-        foreach (var point in points.Values)
+        // Trade only base on government contract data
+        foreach (var kvp in slice.Get<QuiverGovernmentContract>())
         {
-            var symbol = point.Symbol.Underlying;
-            var security = Securities[symbol];
-            if (!security.IsTradable)
-            {
-                continue;
-            }
-            
-            // Buy if the stock was mentioned more than 5 times in the WallStreetBets daily discussion, which translate into high popularity of rise
-            if (point.Mentions > 5 && !security.Holdings.IsLong)
+            // Buy if over 50000 government contract amount, suggesting a large income
+            if (kvp.Value.Any(x => (int) (x as QuiverGovernmentContract).Amount > 50000m))
             {
-                MarketOrder(symbol, 1);
+                SetHoldings(_symbol, 1);
             }
-            // Otherwise, short sell
-            else if (point.Mentions <= 5 && !security.Holdings.IsShort)
+            // Sell if below 10000 government contract amount, suggesting a smaller than usual income
+            else if (kvp.Value.Any(x => (int) (x as QuiverGovernmentContract).Amount < 10000m))
             {
-                MarketOrder(symbol, -1);
+                SetHoldings(_symbol, -1);
             }
         }
     }
-
-    public override void OnSecuritiesChanged(SecurityChanges changes)
-    {
-        foreach(var added in changes.AddedSecurities)
-        {
-            // Requesting wall street bet data to obtain the trader's insights
-            var quiverWSBSymbol = AddData<QuiverWallStreetBets>(added.Symbol).Symbol;
-            // Historical data
-            var history = History<QuiverWallStreetBets>(quiverWSBSymbol, 60, Resolution.Daily);
-        }
-    }
 }

    Framework Algorithm Example

    - The following example algorithm creates a dynamic universe of US Equities based on daily WallStreetBets data. When a security is mentioned on r/WallStreetBets more than five times in a day, the algorithm buys the security. When a security is mentioned five time in a day or less, the algorithm short sells the security. + The following example algorithm creates a dynamic universe of US Equities that have just received a government contract worth at least $5K. Each day, it then forms an equal-weighted dollar-neutral portfolio with the 10 companies that received the largest contracts and the 10 companies that received the smallest contracts. 

    from AlgorithmImports import *
 
 
-class QuiverWallStreetBetsDataAlgorithm(QCAlgorithm):
+class QuiverGovernmentContractDataAlgorithm(QCAlgorithm):
+
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
         self.set_cash(100000)
+        # Seed the price of each asset with its last known price to avoid trading errors.
+        self.settings.seed_initial_prices = True
+        # Filter universe using government contract data
+        self.add_universe(QuiverGovernmentContractUniverse, self.universe_selection)
 
-        self.universe_settings.resolution = Resolution.DAILY
-        # Filter using wall street bet insights
-        self._universe = self.add_universe(QuiverWallStreetBetsUniverse, self.universe_selection)
-        
-        self.add_alpha(WallStreamBetsAlphaModel())
+        # Custom alpha model that emit insights based on updated government contract data
+        self.add_alpha(QuiverGovernmentContractAlphaModel())
         
+        # Invest equally to evenly dissipate the capital concentration risk
         self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
         
-        self.add_risk_management(NullRiskManagementModel())
-        
         self.set_execution(ImmediateExecutionModel())
+        
+    def universe_selection(self, data: List[QuiverGovernmentContractUniverse]) -> List[Symbol]:
+        gov_contract_data_by_symbol = {}
 
-    def universe_selection(self, alt_coarse: List[QuiverWallStreetBetsUniverse]) -> List[Symbol]:        
-        # Select the ones with popularity (mentions) of better-than-others performance (rank)
-        return [d.symbol for d in alt_coarse
-                    if d.mentions > 10 and d.rank < 100]
-
-class WallStreamBetsAlphaModel(AlphaModel):
-    
-    symbol_data_by_symbol = {}
-    
-    def __init__(self, mentions_threshold: int = 5) -> None:
-        self.mentions_threshold = mentions_threshold
+        for datum in data:
+            symbol = datum.symbol
+            
+            if symbol not in gov_contract_data_by_symbol:
+                gov_contract_data_by_symbol[symbol] = []
+            gov_contract_data_by_symbol[symbol].append(datum)
+        
+        # Only select the stocks with over 5000 government contracts, which is considered material information
+        return [symbol for symbol, d in gov_contract_data_by_symbol.items()
+                if sum([x.amount for x in d]) > 5000]
+        
+class QuiverGovernmentContractAlphaModel(AlphaModel):
     
+    def __init__(self) -> None:
+        # A variable to control the rebalancing time
+        self.last_time = datetime.min
+        # To hold the government contract dataset symbol for managing subscription
+        self.dataset_symbol_by_symbol = {}
+        
     def update(self, algorithm: QCAlgorithm, slice: Slice) -> List[Insight]:
-        insights = []
+        if self.last_time > algorithm.time: return []
         
-        points = slice.Get(QuiverWallStreetBets)
-        for point in points.Values:
-            # Buy if the stock was mentioned more than 5 times in the WallStreetBets daily discussion, which translate into high popularity of rise
-            # Otherwise short sell
-            target_direction = InsightDirection.UP if point.mentions > self.mentions_threshold else InsightDirection.DOWN
-            self.symbol_data_by_symbol[point.symbol.underlying].target_direction = target_direction
-            
-        for symbol, symbol_data in self.symbol_data_by_symbol.items():
-            # Ensure we have security data for the current Slice to avoid stale fill
-            if not (slice.contains_key(symbol) and slice[symbol] is not None):
+        # Trade signal only based on government contract data
+        data_points = slice.get(QuiverGovernmentContract)
+        
+        if not data_points: return []
+        
+        gov_contracts = {}
+        # To aggregate all data per symbol for analysis
+        for data_point in data_points:
+            if not algorithm.securities[data_point.key.underlying].price:
                 continue
-            
-            if symbol_data.target_direction is not None:
-                insights += [Insight.price(symbol, timedelta(1), symbol_data.target_direction)]
-                symbol_data.target_direction = None
+            if data_point.key not in gov_contracts:
+                gov_contracts[data_point.key] = 0
 
-        return insights
+            for gov_contract in data_point.value:
+                gov_contracts[data_point.key] += gov_contract.amount
         
+        # Long the top 10 highest government contract amount, predicting a higher expected income and return
+        # Short the lowest 10 government contract amount, predicting a lower expected income and return
+        sorted_by_gov_contracts = sorted(gov_contracts.items(), key=lambda x: x[1])
+        long_symbols = [x[0].underlying for x in sorted_by_gov_contracts[-10:]]
+        short_symbols = [x[0].underlying for x in sorted_by_gov_contracts[:10]]
+        
+        insights = []
+        for symbol in long_symbols:
+            insights.append(Insight.price(symbol, Expiry.END_OF_DAY, InsightDirection.UP))        
+        for symbol in short_symbols:
+            insights.append(Insight.price(symbol, Expiry.END_OF_DAY, InsightDirection.DOWN))
+        
+        self.last_time = Expiry.END_OF_DAY(algorithm.time)
+        
+        return insights
         
     def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
         for security in changes.added_securities:
+            # Requesting government contract data for trade signal generation
             symbol = security.symbol
-            self.symbol_data_by_symbol[symbol] = SymbolData(algorithm, symbol)
-        
+            dataset_symbol = algorithm.add_data(QuiverGovernmentContract, symbol).symbol
+            self.dataset_symbol_by_symbol[symbol] = dataset_symbol
+            # Historical Data
+            history = algorithm.history(dataset_symbol, 10, Resolution.DAILY)
+
         for security in changes.removed_securities:
-            symbol_data = self.symbol_data_by_symbol.pop(security.symbol, None)
-            if symbol_data:
-                symbol_data.dispose()
-                
-                
-class SymbolData:
-    target_direction = None
-    
-    def __init__(self, algorithm: QCAlgorithm, symbol: Symbol) -> None:
-        self.algorithm = algorithm
-        
-        # Requesting wall street bet data to obtain the trader's insights
-        self.quiver_wsb_symbol = algorithm.add_data(QuiverWallStreetBets, symbol).symbol
-        
-        # Historical data
-        history = algorithm.history(self.quiver_wsb_symbol, 60, Resolution.DAILY)
-        
-    def dispose(self) -> None:
-        # Unsubscribe from the Quiver WallStreetBets feed for this security to release computationa resources
-        self.algorithm.remove_security(self.quiver_wsb_symbol)
    - 
    public class QuiverWallStreetBetsDataAlgorithm : QCAlgorithm
+            dataset_symbol = self.dataset_symbol_by_symbol.pop(security.symbol, None)
+            if dataset_symbol:
+                # Remove government contract data subscription to release computation resources
+                algorithm.remove_security(dataset_symbol)
    + 
    public class QuiverGovernmentContractFrameworkAlgorithm : QCAlgorithm
 {
-    private Universe _universe;
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
         SetCash(100000);
-
-        UniverseSettings.Resolution = Resolution.Daily;
-        // Filter using wall street bet insights
-        _universe = AddUniverse<QuiverWallStreetBetsUniverse>(altCoarse =>
+        // Seed the price of each asset with its last known price to avoid trading errors.
+        Settings.SeedInitialPrices = true;
+        // Filter universe using government contract data
+        AddUniverse<QuiverGovernmentContractUniverse>(data =>
         {
-            // Select the ones with popularity (mentions) of better-than-others performance (rank)
-            return from d in altCoarse.OfType<QuiverWallStreetBetsUniverse>()
-                    where d.Mentions > 10 && d.Rank < 100
-                    select d.Symbol;
+            var govContractDataBySymbol = new Dictionary<Symbol, List<QuiverGovernmentContractUniverse>>();
+
+            foreach (var datum in data.OfType<QuiverGovernmentContractUniverse>())
+            {
+                var symbol = datum.Symbol;
+
+                if (!govContractDataBySymbol.ContainsKey(symbol))
+                {
+                    govContractDataBySymbol.Add(symbol, new List<QuiverGovernmentContractUniverse>());
+                }
+                govContractDataBySymbol[symbol].Add(datum);
+            }
+
+            // Only select the stocks with over 5000 government contracts, which is considered material information
+            return from kvp in govContractDataBySymbol
+                where kvp.Value.Sum(x => x.Amount) > 5000m
+                select kvp.Key;
         });
 
-        AddAlpha(new WallStreamBetsAlphaModel());
-        
+        // Custom alpha model that emit insights based on updated government contract data
+        AddAlpha(new QuiverGovernmentContractAlphaModel());
+
+        // Invest equally to evenly dissipate the capital concentration risk
         SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
         
-        AddRiskManagement(new NullRiskManagementModel());
-        
         SetExecution(new ImmediateExecutionModel());
     }
 }
 
-public class WallStreamBetsAlphaModel : AlphaModel
+public class QuiverGovernmentContractAlphaModel: AlphaModel
 {
-    private Dictionary<Symbol, SymbolData> _symbolDataBySymbol = new Dictionary<Symbol, SymbolData>();
-    private int _mentionsThreshold;
+    // A variable to control the rebalancing time
+    private DateTime _time;
+    // To hold the government contract dataset symbol for managing subscription
+    private Dictionary<Symbol, Symbol> _datasetSymbolBySymbol = new();
     
-    public WallStreamBetsAlphaModel(int mentionsThreshold=5)
+    public QuiverGovernmentContractAlphaModel()
     {
-        _mentionsThreshold = mentionsThreshold;
+        _time = DateTime.MinValue;
     }
-
+    
     public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice slice)
     {
+        if (_time > algorithm.Time) return new List<Insight>();
+        
+        // Trade signal only based on government contract data
+        var dataPoints = slice.Get<QuiverGovernmentContract>();
+        
+        if (dataPoints.IsNullOrEmpty()) return new List<Insight>();
+
+        // To aggregate all data per symbol for analysis
+        var govContracts = dataPoints
+            .Where(kvp => algorithm.Securities[kvp.Key.Underlying].Price != 0)
+            .ToDictionary(kvp => kvp.Key, kvp => kvp.Value.Sum(x => ((QuiverGovernmentContract)x).Amount));
+        
+        // Long the top 10 highest government contract amount, predicting a higher expected income and return
+        // Short the lowest 10 government contract amount, predicting a lower expected income and return
+        var sortedByGovContract = from kvp in govContracts
+                        orderby kvp.Value descending
+                        select kvp.Key.Underlying;
+        var longSymbols = sortedByGovContract.Take(10).ToList();
+        var shortSymbols = sortedByGovContract.TakeLast(10).ToList();
+        
         var insights = new List<Insight>();
+        insights.AddRange(longSymbols.Select(symbol => 
+            new Insight(symbol, Expiry.EndOfDay, InsightType.Price, InsightDirection.Up)));
+        insights.AddRange(shortSymbols.Select(symbol => 
+            new Insight(symbol, Expiry.EndOfDay, InsightType.Price, InsightDirection.Down)));
         
-        var points = slice.Get<QuiverWallStreetBets>();
-        foreach (var point in points.Values)
-        {
-            // Buy if the stock was mentioned more than 5 times in the WallStreetBets daily discussion, which translate into high popularity of rise
-            // Otherwise short sell
-            var targetDirection = point.Mentions > _mentionsThreshold ? InsightDirection.Up : InsightDirection.Down;
-            _symbolDataBySymbol[point.Symbol.Underlying].targetDirection = targetDirection;
-        }
+        _time = Expiry.EndOfDay(algorithm.Time);
         
-        foreach (var kvp in _symbolDataBySymbol)
-        {
-            var symbol = kvp.Key;
-            var symbolData = kvp.Value;
-            
-            // Ensure we have security data for the current Slice to avoid stale fill
-            if (!(slice.ContainsKey(symbol) && slice[symbol] != null))
-            {
-                continue;
-            }
-            
-            if (symbolData.targetDirection != null)
-            {
-                insights.Add(Insight.Price(symbol, TimeSpan.FromDays(1), (InsightDirection)symbolData.targetDirection));
-                symbolData.targetDirection = null;
-            }
-        }
         return insights;
     }
-
+    
     public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
     {
         foreach (var security in changes.AddedSecurities)
         {
+            // Requesting government contract data for trade signal generation
             var symbol = security.Symbol;
-            _symbolDataBySymbol.Add(symbol, new SymbolData(algorithm, symbol));
+            var datasetSymbol = algorithm.AddData<QuiverGovernmentContract>(symbol).Symbol;
+            _datasetSymbolBySymbol.Add(symbol, datasetSymbol);
+            // History request
+            var history = algorithm.History<QuiverGovernmentContract>(datasetSymbol, 10, Resolution.Daily);
         }
-
+        
         foreach (var security in changes.RemovedSecurities)
         {
             var symbol = security.Symbol;
-            if (_symbolDataBySymbol.ContainsKey(symbol))
+            if (_datasetSymbolBySymbol.ContainsKey(symbol))
             {
-                _symbolDataBySymbol[symbol].dispose();
-                _symbolDataBySymbol.Remove(symbol);
+                // Remove government contract data subscription to release computation resources
+                _datasetSymbolBySymbol.Remove(symbol, out var datasetSymbol);
+                algorithm.RemoveSecurity(datasetSymbol);
             }
         }
     }
-}
-
-public class SymbolData
-{
-    private Symbol _quiverWSBSymbol;
-    private QCAlgorithm _algorithm;
-    public InsightDirection? targetDirection = null;
-    
-    public SymbolData(QCAlgorithm algorithm, Symbol symbol)
-    {
-        _algorithm = algorithm;
-        
-        // Requesting wall street bet data to obtain the trader's insights
-        _quiverWSBSymbol = algorithm.AddData<QuiverWallStreetBets>(symbol).Symbol;
-        
-        // Historical data
-        var history = algorithm.History<QuiverWallStreetBets>(_quiverWSBSymbol, 60, Resolution.Daily);
-    }
-    
-    public void dispose()
-    {
-        // Unsubscribe from the Quiver WallStreetBets feed for this security to release computationa resources
-        _algorithm.RemoveSecurity(_quiverWSBSymbol);
-    }
 }

    Research Example

    - The following example lists low-ranking US Equities that are mentioned more than ten times on r/WallStreetBets. + The following example lists all US Equities with Government contracts in the past year.

    - 
    #r "../QuantConnect.DataSource.QuiverWallStreetBets.dll"
+   
    #r "../QuantConnect.DataSource.QuiverGovernmentContracts.dll"
 using QuantConnect.DataSource;
 
-var qb = new QuantBook();
-
 // Requesting data
 var aapl = qb.AddEquity("AAPL", Resolution.Daily).Symbol;
-var symbol = qb.AddData<QuiverWallStreetBets>(aapl).Symbol;
+var symbol = qb.AddData<QuiverGovernmentContract>(aapl).Symbol;
 
 // Historical data
-var history = qb.History<QuiverWallStreetBets>(symbol, 60, Resolution.Daily);
-foreach (var bet in history.OfType<QuiverWallStreetBets>())
+var history = qb.History<QuiverGovernmentContract>(symbol, 360, Resolution.Daily);
+foreach (var contracts in history)
 {
-    Console.WriteLine($"{bet.Symbol} rank at {bet.EndTime}: {bet.Rank}");
+    foreach (QuiverGovernmentContract contract in contracts)
+    {
+        Console.WriteLine($"{contract.Symbol} amount at {contract.EndTime}: {contract.Amount}");
+    }
 }
 
 // Add Universe Selection
 IEnumerable<Symbol> UniverseSelection(IEnumerable<BaseData> altCoarse)
 {
-    return from d in altCoarse.OfType<QuiverWallStreetBetsUniverse>()
-        where d.Mentions > 10 && d.Rank < 100 select d.Symbol;
+    return from d in altCoarse.OfType<QuiverGovernmentContractUniverse>()
+        select d.Symbol;
 }
-var universe = qb.AddUniverse<QuiverWallStreetBetsUniverse>(UniverseSelection);
+var universe = qb.AddUniverse<QuiverGovernmentContractUniverse<(UniverseSelection);
 
 // Historical Universe data
-var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-60), qb.Time);
-foreach (var bets in universeHistory)
+var universeHistory = qb.UniverseHistory(universe, qb.Time.AddDays(-360), qb.Time);
+foreach (var contracts in universeHistory)
 {
-    foreach (QuiverWallStreetBetsUniverse bet in bets)
+    foreach (QuiverGovernmentContractUniverse contract in contracts)
     {
-        Console.WriteLine($"{bet.Symbol} rank at {bet.EndTime}: {bet.Rank}");
+        Console.WriteLine($"{contract.Symbol} amount at {contract.EndTime}: {contract.Amount}");
     }
 }
    
    
    qb = QuantBook()
 
-# Requesting data
+# Requesting Data
 aapl = qb.add_equity("AAPL", Resolution.DAILY).symbol
-symbol = qb.add_data(QuiverWallStreetBets, aapl).symbol
+symbol = qb.add_data(QuiverGovernmentContract, aapl).symbol
 
 # Historical data
-history = qb.history(QuiverWallStreetBets, symbol, 60, Resolution.DAILY)
-for (symbol, time), bet in history.iterrows():
-    print(f"{symbol} rank at {time}: {bet['rank']}")
+history = qb.history(QuiverGovernmentContract, symbol, 360, Resolution.DAILY)
+for (symbol, time), contracts in history.items():
+    for contract in contracts:
+        print(f"{contract.symbol} amount at {contract.end_time}: {contract.amount}")
 
 # Add Universe Selection
-def universe_selection(alt_coarse: List[QuiverWallStreetBetsUniverse]) -> List[Symbol]:
-    return [d.symbol for d in alt_coarse if d.mentions > 10 and d.rank < 100]
+def universe_selection(alt_coarse: List[QuiverGovernmentContractUniverse]) -> List[Symbol]:
+    return [d.symbol for d in alt_coarse]
 
-universe = qb.add_universe(QuiverWallStreetBetsUniverse, universe_selection)
+universe = qb.add_universe(QuiverGovernmentContractUniverse, universe_selection)
         
 # Historical Universe data
-universe_history = qb.universe_history(universe, qb.time-timedelta(60), qb.time)
-for (univere_symbol, time), bets in universe_history.items():
-    for bet in bets:
-        print(f"{bet.symbol} rank at {bet.end_time}: {bet.rank}")
    
+universe_history = qb.universe_history(universe, qb.time-timedelta(360), qb.time)
+for (_, time), contracts in universe_history.items():
+    for contract in contracts:
+        print(f"{contract.symbol} amount at {contract.end_time}: {contract.amount}")
    @@ -268883,37 +270272,37 @@

    Data Point Attributes

    - The WallStreetBets dataset provides + The US Government Contracts dataset provides - QuiverWallStreetBets + QuiverGovernmentContract and - QuiverWallStreetBetsUniverse + QuiverGovernmentContractUniverse objects.

    - QuiverWallStreetBets Attributes + QuiverGovernmentContract

    - QuiverWallStreetBets + QuiverGovernmentContract objects have the following attributes:

    -
    +

    - QuiverWallStreetBetsUniverse Attributes + QuiverGovernmentContractUniverse

    - QuiverWallStreetBetsUniverse + QuiverGovernmentContractUniverse objects have the following attributes:

    -
    +
    @@ -269840,9 +271229,9 @@

    Accessing Data

    data_point = slice[self.report_10k_symbol] self.log(f"{self.report_10k_symbol} report count at {slice.time}: {len(data_point.report.documents)}") - if slice.ContainsKey(self.report_10q_symbol): + if slice.contains_key(self.report_10q_symbol): data_point = slice[self.report_10q_symbol] - self.log(f"{self.report_10q_symbol} report count at {slice.Time}: {len(data_point.report.documents)}") + self.log(f"{self.report_10q_symbol} report count at {slice.time}: {len(data_point.report.documents)}") 
    public override void OnData(Slice slice)
 {
     if (slice.ContainsKey(_report8KSymbol))
@@ -270048,7 +271437,7 @@ 
    
 
     def on_data(self, slice: Slice) -> None:
         # Trade from SEC data 
-        for report in slice.Get(SECReport8K).Values:
+        for report in slice.get(SECReport8K).values():
             underlying_symbol = report.symbol.underlying
             # Skip the Symbol if it's no longer in the universe (insuffucient popularity to reach market efficiency of fundamental factor)
             if underlying_symbol not in self.dataset_symbol_by_symbol:
@@ -271022,70 +272411,58 @@ 
    
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
         self.set_cash(100000)
- 
-        self.aapl = self.add_equity("AAPL", Resolution.MINUTE).symbol
-        
-        # Requesting insider trade intention news and actual trades to estimate the return, since insiders may have better information of the future confidence
-        self.smart_insider_intention = self.add_data(SmartInsiderIntention, self.aapl).symbol
-        self.smart_insider_transaction = self.add_data(SmartInsiderTransaction, self.aapl).symbol
-        
-        # Historical data
-        history = self.history(self.smart_insider_intention, 365, Resolution.DAILY)
+        self._equity = self.add_equity("AAPL", Resolution.DAILY)
+        # Request insider trade intention news and actual trades because insiders may have better information about future prospects.
+        self._smart_insider_intention_data = self.add_data(SmartInsiderIntention, self._equity).symbol
+        self._smart_insider_transaction_data = self.add_data(SmartInsiderTransaction, self._equity).symbol
+        # Warm up custom data subscriptions with historical data.
+        history = self.history(self._smart_insider_intention_data, 365, Resolution.DAILY)
         self.debug(f"We got {len(history)} items from our history request for intentions")
-        
-        history = self.history(self.smart_insider_transaction, 365, Resolution.DAILY)
+        history = self.history(self._smart_insider_transaction_data, 365, Resolution.DAILY)
         self.debug(f"We got {len(history)} items from our history request for transactions")
 
-    def on_data(self, slice: Slice) -> None:
-        # Buy Apple whenever we receive a buyback intention or transaction notification, given the insiders may have confidence in the future to buy more
-        # This news may stimulate market popularity
-        if slice.contains_key(self.smart_insider_intention) or slice.contains_key(self.smart_insider_transaction):
-            self.set_holdings(self.aapl, 1)
-            self.entry_time = self.time
-        
-        # Liquidate holdings 3 days after the latest entry
-        # The market popularity and possible overbrought is cooled
-        if self.portfolio.invested and self.time >= self.entry_time + timedelta(days=3):
+    def on_data(self, data: Slice) -> None:
+        # Buy Apple when insider buyback events suggest stronger sentiment and potential market attention.
+        if self._smart_insider_intention_data in data or self._smart_insider_transaction_data in data:
+            self.set_holdings(self._equity, 1)
+            self._entry_time = self.time
+        # Liquidate after three days so the position exits once the event reaction has cooled.
+        if self.portfolio.invested and self.time >= self._entry_time + timedelta(days=3):
             self.liquidate()
    public class CorporateBuybacksDataAlgorithm : QCAlgorithm
 {
-    private Symbol _aapl;
-    private Symbol _smartInsiderIntention;
-    private Symbol _smartInsiderTransaction;
+    private Equity _equity;
+    private Symbol _smartInsiderIntentionData;
+    private Symbol _smartInsiderTransactionData;
     private DateTime _entryTime;
-    
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 12, 31);
         SetCash(100000);
-        
-        _aapl = AddEquity("AAPL", Resolution.Minute).Symbol;
-        
-        // Requesting insider trade intention news and actual trades to estimate the return, since insiders may have better information of the future confidence
-        _smartInsiderIntention = AddData<SmartInsiderIntention>(_aapl).Symbol;
-        _smartInsiderTransaction = AddData<SmartInsiderTransaction>(_aapl).Symbol;
-        
-        // Historical data
-        var intentionHistory = History<SmartInsiderIntention>(_smartInsiderIntention, 365, Resolution.Daily);
-        Debug($"We got {intentionHistory.Count()} items from our history request for intentions");
-        
-        var transactionHistory = History<SmartInsiderTransaction>(_smartInsiderTransaction, 365, Resolution.Daily);
-        Debug($"We got {transactionHistory.Count()} items from our history request for transactions");
+        _equity = AddEquity("AAPL", Resolution.Daily);
+        // Request insider trade intention news and actual trades because insiders may have better information about future prospects.
+        _smartInsiderIntentionData = AddData<SmartInsiderIntention>(_equity.Symbol).Symbol;
+        _smartInsiderTransactionData = AddData<SmartInsiderTransaction>(_equity.Symbol).Symbol;
+        // Warm up custom data subscriptions with historical data.
+        var intentionHistory = History<SmartInsiderIntention>(_smartInsiderIntentionData, TimeSpan.FromDays(365), Resolution.Daily);
+        var intentionCount = intentionHistory.Count();
+        Debug($"We got {intentionCount} items from our history request for intentions");
+        var transactionHistory = History<SmartInsiderTransaction>(_smartInsiderTransactionData, TimeSpan.FromDays(365), Resolution.Daily);
+        var transactionCount = transactionHistory.Count();
+        Debug($"We got {transactionCount} items from our history request for transactions");
     }
 
-    public override void OnData(Slice slice)
+    public override void OnData(Slice data)
     {
-        // Buy Apple whenever we receive a buyback intention or transaction notification, given the insiders may have confidence in the future to buy more
-        // This news may stimulate market popularity
-        if (slice.ContainsKey(_smartInsiderIntention) || slice.ContainsKey(_smartInsiderTransaction))
+        // Buy Apple when insider buyback events suggest stronger sentiment and potential market attention.
+        if (data.ContainsKey(_smartInsiderIntentionData) || data.ContainsKey(_smartInsiderTransactionData))
         {
-            SetHoldings(_aapl, 1);
+            SetHoldings(_equity.Symbol, 1);
             _entryTime = Time;
         }
-
-        // Liquidate holdings 3 days after the latest entry
-        // The market popularity and possible overbrought is cooled
+        // Liquidate after three days so the position exits once the event reaction has cooled.
         if (Portfolio.Invested && Time >= _entryTime + TimeSpan.FromDays(3))
         {
             Liquidate();
@@ -271268,46 +272645,46 @@ 
    
 qb = QuantBook()
 
 # Requesting Data
-symbol = qb.AddEquity("AAPL").Symbol
-intention_symbol = qb.AddData(SmartInsiderIntention, symbol).Symbol
-transaction_symbol = qb.AddData(SmartInsiderTransaction, symbol).Symbol
+symbol = qb.add_equity("AAPL").symbol
+intention_symbol = qb.add_data(SmartInsiderIntention, symbol).symbol
+transaction_symbol = qb.add_data(SmartInsiderTransaction, symbol).symbol
 
 # Historical data
-intention_history = qb.History(SmartInsiderIntention, intention_symbol, 300, Resolution.Daily)
+intention_history = qb.history(SmartInsiderIntention, intention_symbol, 300, Resolution.DAILY)
 for (symbol, time), row in intention_history.iterrows():
     if isnan(row['amountvalue']): continue
     print(f"{symbol} amount value at {time}: {row['amountvalue']}")
 
-transaction_history = qb.History(SmartInsiderTransaction, transaction_symbol, 300, Resolution.Daily)
+transaction_history = qb.history(SmartInsiderTransaction, transaction_symbol, 300, Resolution.DAILY)
 for (symbol, time), row in transaction_history.iterrows():
     if isnan(row['amount']): continue
     print(f"{symbol} amount at {time}: {row['amount']}")
 
 # Add Universe Selection for SmartInsiderIntention
-def IntentionSelection(alt_coarse: List[SmartInsiderIntentionUniverse]) -> List[Symbol]:
-    return [d.Symbol for d in sorted([x for x in alt_coarse if x.AmountValue],
-        key=lambda x: x.AmountValue, reverse=True)[:10]]
+def intention_selection(alt_coarse: List[SmartInsiderIntentionUniverse]) -> List[Symbol]:
+    return [d.symbol for d in sorted([x for x in alt_coarse if x.amount_value],
+        key=lambda x: x.amount_value, reverse=True)[:10]]
 
-intention_universe  = qb.AddUniverse(SmartInsiderIntentionUniverse, IntentionSelection)
+intention_universe  = qb.add_universe(SmartInsiderIntentionUniverse, intention_selection)
 
 # Historical Universe data
-intention_universe_history = qb.UniverseHistory(intention_universe, qb.Time-timedelta(10), qb.Time)
+intention_universe_history = qb.universe_history(intention_universe, qb.time-timedelta(10), qb.time)
 for (_, time), intentions in intention_universe_history.items():
     for intention in intentions:
-        print(f"{intention.Symbol.Value} amount value at {intention.EndTime}: {intention.AmountValue}")
+        print(f"{intention.symbol.value} amount value at {intention.end_time}: {intention.amount_value}")
 
 # Add Universe Selection for SmartInsiderTransaction
-def IntentionSelection(alt_coarse: List[SmartInsiderTransactionUniverse]) -> List[Symbol]:
-    return [d.Symbol for d in sorted([x for x in alt_coarse if x.Amount],
-        key=lambda x: x.Amount, reverse=True)[:10]]
+def intention_selection(alt_coarse: List[SmartInsiderTransactionUniverse]) -> List[Symbol]:
+    return [d.symbol for d in sorted([x for x in alt_coarse if x.amount],
+        key=lambda x: x.amount, reverse=True)[:10]]
 
-transaction_universe  = qb.AddUniverse(SmartInsiderTransactionUniverse, IntentionSelection)
+transaction_universe  = qb.add_universe(SmartInsiderTransactionUniverse, intention_selection)
 
 # Historical Universe data
-transaction_universe_history = qb.UniverseHistory(transaction_universe, qb.Time-timedelta(10), qb.Time)
+transaction_universe_history = qb.universe_history(transaction_universe, qb.time-timedelta(10), qb.time)
 for (_, time), transactions in transaction_universe_history.items():
     for transaction in transactions:
-        print(f"{transaction.Symbol.Value} amount at {transaction.EndTime}: {transaction.Amount}")
    + print(f"{transaction.symbol.value} amount at {transaction.end_time}: {transaction.amount}")
    @@ -275469,7 +276846,7 @@

    Debugging

    class MyCustomDataTypeAlgorithm(QCAlgorithm):
     def initialize(self):
         MyCustomDataType.ALGORITHM = self
-        self._symbol = self.add_data(MyCustomDataType, "<name>", Resolution.DAILY).Symbol
+        self._symbol = self.add_data(MyCustomDataType, "<name>", Resolution.DAILY).symbol
 
 class MyCustomDataType(PythonData):
     def reader(self, config: SubscriptionDataConfig, line: str, date: datetime, is_live_mode: bool) -> BaseData:
@@ -275478,7 +276855,7 @@ 
    Debugging
    
         custom.symbol = config.symbol
         if not line[0].isdigit():
             # Display the line with the header
-            MyCustomDataType.ALGORITHM.Debug(f"HEADER: {line}")
+            MyCustomDataType.ALGORITHM.debug(f"HEADER: {line}")
             return custom
         custom.end_time = datetime.strptime(data[0], '%Y%m%d') + timedelta(1)
         custom.value = float(data[1])
@@ -277927,7 +279304,69 @@ 
    Transport Binary Data
    
 
  
   
    
-   Follow these steps to transport binary files:
+   Follow these steps to transport binary files with
+   
+    joblib
+   
+   :
+  
    
+  
      
+   
    1. 
+    Add the following imports to your local program:
+   
      2. 
+   
      
+    
      import joblib
+from io import BytesIO
+from base64 import b64encode, b64decode
      
+   
      
+   
    2. 
+    Serialize your object and save it to a file. This Base64 serialization is necessary because the default
+    
+     joblib.dump
+    
+    serialization produces a binary file that the
+    
+     download
+    
+    method can't read without data corruption.
+   
      3. 
+   
      
+    
      buffer = BytesIO()
+joblib.dump(my_object, buffer)
+base64_str = b64encode(buffer.getvalue()).decode('ascii')
+with open("my_model.b64", "w") as f:
+    f.write(base64_str)
      
+   
      
+   
    3. 
+    Save the file to one of the
+    
+     supported sources
+    
+    . For example, save the file to Dropbox.
+   
      4. 
+   
    4. 
+    
+     Download the remote file
+    
+    into your project.
+   
      5. 
+   
      
+    
      base64_str = self.download("<fileURL>")
      
+   
      
+   
    5. 
+    Restore the object.
+   
      6. 
+   
      
+    
      model_bytes = b64decode(base64_str.encode('ascii'))
+restored_model = joblib.load(BytesIO(model_bytes))
      
+   
      
+  
    
+  
    
+   Follow these steps to transport binary files with
+   
+    pickle
+   
+   :
   
    
   
      
    
    1. 
@@ -277935,21 +279374,23 @@ 
      Transport Binary Data
      
    
      2. 
    
      
     
      import pickle
-import base64
      
+from base64 import b64encode, b64decode
  • - Serialize your object. + Serialize your object and save it to a file. 
    • pickle_bytes = pickle.dumps(my_object)
-base64_str = base64.b64encode(pickle_bytes).decode('ascii')
    +base64_str = b64encode(pickle_bytes).decode('ascii') +with open("my_model.b64", "w") as f: + f.write(base64_str)
  • - Save the string representation of your object to one of the + Save the file to one of the supported sources - . + . For example, save the file to Dropbox.
  • @@ -277964,9 +279405,8 @@

    Transport Binary Data

    Restore the object.
    • - 
    base64_bytes = base64_str.encode('ascii')
-model = base64.b64decode(base64_bytes)
-restored_model = pickle.loads(model)
    + 
    model_bytes = b64decode(base64_str.encode('ascii'))
+restored_model = pickle.loads(model_bytes)
    @@ -277997,9 +279437,11 @@

    method.

    - 
    from sklearn.svm import SVR
+   
    import joblib
+from io import BytesIO
+from base64 import b64decode, b64encode
+from sklearn.svm import SVR
 from sklearn.model_selection import GridSearchCV
-import joblib
 
 class BulkDownloadExampleAlgorithm(QCAlgorithm):
     def initialize(self) -> None:
@@ -278007,23 +279449,25 @@ 
    
         self.set_end_date(2024, 12, 31)
         self.set_cash(100000)
         # Request SPY data for model training, prediction, and trading.
-        self.symbol = self.add_equity("SPY", Resolution.DAILY).symbol
+        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
 
         # 2-year data to train the model.
         training_length = 252*2
         self.training_data = RollingWindow(training_length)
         # Warm up the training dataset to train the model immediately.
-        history = self.history[TradeBar](self.symbol, training_length, Resolution.DAILY)
+        history = self.history[TradeBar](self._symbol, training_length, Resolution.DAILY)
         for trade_bar in history:
             self.training_data.add(trade_bar)
 
         # Retrieve the already trained model from the object store for immediate use.
         if self.object_store.contains_key("sklearn_model"):
-            file = self.object_store.get_file_path("sklearn_model")
-        # Otherwise, bulk-download the model from an external source (Dropbox in this example).
+            self.model = joblib.load(self.object_store.get_file_path("sklearn_model"))
+        # Otherwise, bulk-download the base64-encoded model from an external source (Dropbox in this example).
         else:
-            file = self.download("https://www.dropbox.com/scl/fi/nhz2zxq3pr2bweia4av0o/sklearn_model?rlkey=loy09wbh69k9j6umlru9icsaj&st=6vdazyp4&dl=1")
-        self.model = joblib.load(file)
+            url = "https://www.dropbox.com/scl/fi/tj7wmpv1u2ysvejb0skpm/sklearn_model_b64?rlkey=pvcnuvqrp78oz1t73rytgnvdx&dl=1"
+            base64_str = self.download(url)
+            model_bytes = b64decode(base64_str.encode('ascii'))
+            self.model = joblib.load(BytesIO(model_bytes))
 
         # Train the model to use the prediction right away.
         self.train(self.my_training_method)
@@ -278056,8 +279500,9 @@ 
    
             self.model = self.model.fit(features, labels)
 
     def on_data(self, slice: Slice) -> None:
-        if self.symbol in slice.bars:
-            self.training_data.add(slice.bars[self.symbol])
+        bar = slice.bars.get(self._symbol)
+        if bar:
+            self.training_data.add(bar)
 
         # Get predictions by the updated features.
         features, _ = self.get_features_and_labels()
@@ -278066,16 +279511,28 @@ 
    
 
         # If the predicted direction is going upward, buy SPY.
         if prediction > 0:
-            self.set_holdings(self.symbol, 1)
+            self.set_holdings(self._symbol, 1)
         # If the predicted direction is going downward, sell SPY.
         elif prediction < 0:            
-            self.set_holdings(self.symbol, -1)
+            self.set_holdings(self._symbol, -1)
 
     def on_end_of_algorithm(self) -> None:
         # Store the model in the object store to retrieve it in other instances if the algorithm stops.
         model_key = "sklearn_model"
-        file_name = self.object_store.get_file_path(model_key)
-        joblib.dump(self.model, file_name)
    
+        joblib.dump(self.model, self.object_store.get_file_path(model_key))
+        self._save_base64_encoded_version(model_key)
+
+    def _save_base64_encoded_version(self, model_key):
+        # Save a base64-encoded version of the model to upload to an external source (e.g., Dropbox).
+        buffer = BytesIO()
+        joblib.dump(self.model, buffer)
+        content = b64encode(buffer.getvalue()).decode('ascii')
+        path = self.object_store.get_file_path(f"{model_key}_b64")
+        self.object_store.save(path, content)
+
+        # Locally, you may want to use the open method
+        #with open(path, "w") as f:
+        #    f.write(content)

    Other Examples @@ -279740,9 +281197,392 @@

    Consolidate Trade Bars

    You can also use the - + + Consolidate + + + consolidate + + helper method to create period consolidators and register them for automatic updates. With just one line of code, you can create data in any time period based on a + + timedelta + + + TimeSpan + + or + + Resolution + + object: +

    +
      +
    • + + TimeSpan + + Periods +
      • +
    • + + timedelta + + Periods +
      • +
      + 
      _consolidator = Consolidate(_symbol, TimeSpan.FromDays(1), ConsolidationHandler);
      + 
      self._consolidator = self.consolidate(self._symbol, timedelta(days=1), self._consolidation_handler)
      +
      +
    • + + Resolution + + Periods +
      • +
      + 
      _consolidator = Consolidate(_symbol, Resolution.Daily, ConsolidationHandler);
      + 
      self._consolidator = self.consolidate(self._symbol, Resolution.DAILY, self._consolidation_handler)
      +
      +
    +

    + If you use the + + Consolidate + + + consolidate + + helper method, the consolidation handler doesn't receive an + + object + + argument. +

    +
    + 
    void ConsolidationHandler(TradeBar consolidatedBar)
+{
+
+}
    + 
    def _consolidation_handler(self, consolidated_bar: TradeBar) -> None:
+    pass
    +
    + + + +

    Consolidate Quote Bars

    + + +

    + + QuoteBar + + consolidators aggregate + + QuoteBar + + objects into + + QuoteBar + + objects of the same size or larger. Follow these steps to create and manage a + + QuoteBar + + consolidator based on a period of time: +

    +
      +
    1. + Create the consolidator. +
      2. +

      + To set the time period for the consolidator, you can use either a + + timedelta + + + TimeSpan + + or + + Resolution + + object. The consolidator time period must be greater than or equal to the resolution of the security subscription. For instance, you can aggregate minute bars into 10-minute bars, but you can't aggregate hour bars into 10-minute bars. +

      +       +

      + If the security subscription in your algorithm provides + + TradeBar + + and + + QuoteBar + + data, + + ResolveConsolidator + + + resolve_consolidator + + returns a + + TradeBarConsolidator + + . +

      +

      + If you consolidate on an hourly basis, the consolidator ends at the top of the hour, not every hour after the market open. For US Equities, that's 10 AM Eastern Time (ET), not 10:30 AM ET. To change this behavior, set the + + startTime + + + start_time + + parameter of the QuoteBarConsolidator type constructor to + + TimeSpan(9, 30, 0) + + + timedelta(hours=9, minutes=30) + +

      +
    2. + Add an event handler to the consolidator. +
      3. +
      + 
      _consolidator.DataConsolidated += ConsolidationHandler;
      + 
      self._consolidator.data_consolidated += self._consolidation_handler
      +
      +

      + LEAN passes consolidated bars to the consolidator event handler in your algorithm. The most common error when creating consolidators is to put parenthesis + + () + + at the end of your method name when setting the event handler of the consolidator. If you use parenthesis, the method executes and the result is passed as the event handler instead of the method itself. Remember to pass the name of your method to the event system. Specifically, it should be + + ConsolidationHandler + + + self._consolidation_handler + + , not + + ConsolidationHandler() + + + self._consolidation_handler() + + . +

      +
    3. + Define the consolidation handler. +
      4. +
      + 
      void ConsolidationHandler(object sender, QuoteBar consolidatedBar)
+{
+
+}
      + 
      def _consolidation_handler(self, sender: object, consolidated_bar: QuoteBar) -> None:
+    pass
      +
      +

      + The consolidation event handler receives bars when the consolidated bar closes based on the + + exchange time zone + + . If you subscribe to minute resolution data for Bitcoin and create an hourly consolidator, you receive consolidated bars at the top of each hour. However, if you subscribe to minute resolution data for the regular trading hours of US Equities and create a daily consolidator, you receive consolidated bars at market close or midnight, depending on your + + DailyPreciseEndTime + + + daily_precise_end_time + + + setting + + . +

      +
    4. + Update the consolidator. +
      5. +

      + You can automatically or manually update the consolidator. +

      +
        +
      • + Automatic Updates +
        • +

        + To automatically update a consolidator with data from the security subscription, call the + + AddConsolidator + + + add_consolidator + + method of the Subscription Manager. +

        +
        + 
        self.subscription_manager.add_consolidator(self._symbol, self._consolidator)
        + 
        SubscriptionManager.AddConsolidator(_symbol, _consolidator);
        +
        +
      • + Manual Updates +
        • +

        + Manual updates let you control when the consolidator updates and what data you use to update it. If you need to warm up a consolidator with data outside of the + + warm-up period + + , you can manually update the consolidator. To manually update a consolidator, call its + + Update + + + update + + method with a + + QuoteBar + + object. You can update the consolidator with data from the + + Slice + + object in the + + OnData + + + on_data + + method or with data from a + + history request + + . +

        +
        + 
        # Example 1: Update the consolidator with data from the Slice object
+def on_data(self, slice: Slice) -> None:
+    quote_bar = slice.quote_bars[self._symbol]
+    self._consolidator.update(quote_bar)
+
+# Example 2: Update the consolidator with data from a history request
+history = self.history[QuoteBar](self._symbol, 30, Resolution.MINUTE)
+for quote_bar in history:
+    self._consolidator.update(quote_bar)
        + 
        // Example 1: Update the consolidator with data from the Slice object
+public override void OnData(Slice slice)
+{
+    var quoteBar = slice.QuoteBars[_symbol];
+    _consolidator.Update(quoteBar);
+}
+
+// Example 2: Update the consolidator with data from a history request
+var history = History<QuoteBar>(_symbol, 30, Resolution.Minute);
+foreach (var quoteBar in history)
+{
+    _consolidator.Update(quoteBar);
+}
        +
        +
      +
    5. + If you create consolidators for securities in a dynamic universe and register them for automatic updates, remove the consolidator when the security leaves the universe. +
      6. +
      + 
      SubscriptionManager.RemoveConsolidator(_symbol, _consolidator);
      + 
      self.subscription_manager.remove_consolidator(self._symbol, self._consolidator)
      +
      +

      + If you have a dynamic universe and don't remove consolidators, they compound internally, causing your algorithm to slow down and eventually die once it runs out of RAM. For an example of removing consolidators from universe subscriptions, see the + + GasAndCrudeOilEnergyCorrelationAlpha + + + GasAndCrudeOilEnergyCorrelationAlpha + + in the LEAN GitHub repository. +

      +
    +

    + You can also use the + Consolidate + + consolidate + helper method to create period consolidators and register them for automatic updates. With just one line of code, you can create data in any time period based on a timedelta @@ -279770,8 +281610,8 @@

    Consolidate Trade Bars

    Periods
    - 
    _consolidator = Consolidate(_symbol, TimeSpan.FromDays(1), ConsolidationHandler);
    - 
    self._consolidator = self.consolidate(self._symbol, timedelta(days=1), self._consolidation_handler)
    + 
    _consolidator = Consolidate<QuoteBar>(_symbol, TimeSpan.FromDays(1), TickType.Quote, ConsolidationHandler);
    + 
    self._consolidator = self.consolidate(self._symbol, timedelta(days=1), TickType.Quote, self._consolidation_handler)
  • @@ -279780,8 +281620,8 @@

    Consolidate Trade Bars

    Periods
    • - 
    _consolidator = Consolidate(_symbol, Resolution.Daily, ConsolidationHandler);
    - 
    self._consolidator = self.consolidate(self._symbol, Resolution.DAILY, self._consolidation_handler)
    + 
    _consolidator = Consolidate<QuoteBar>(_symbol, Resolution.Daily, TickType.Quote, ConsolidationHandler);
    + 
    self._consolidator = self.consolidate(self._symbol, Resolution.DAILY, TickType.Quote, self._consolidation_handler)

    @@ -279799,34 +281639,34 @@

    Consolidate Trade Bars

    argument.

    - 
    void ConsolidationHandler(TradeBar consolidatedBar)
+   
    void ConsolidationHandler(QuoteBar consolidatedBar)
 {
 
 }
    
-   
    def _consolidation_handler(self, consolidated_bar: TradeBar) -> None:
+   
    def _consolidation_handler(self, consolidated_bar: QuoteBar) -> None:
     pass
    -

    Consolidate Quote Bars

    +

    Consolidate Trade Ticks

    - QuoteBar + Tick consolidators aggregate - QuoteBar + Tick objects into - QuoteBar + TradeBar - objects of the same size or larger. Follow these steps to create and manage a + objects. Follow these steps to create and manage a - QuoteBar + Tick consolidator based on a period of time:

    @@ -279862,14 +281702,14 @@

    Consolidate Quote Bars

    Periods
    - 
    _consolidator = new QuoteBarConsolidator(TimeSpan.FromDays(1));
+     
    _consolidator = new TickConsolidator(TimeSpan.FromMilliseconds(100));
 // Aliases:
-// _consolidator = CreateConsolidator(TimeSpan.FromDays(1), typeof(QuoteBar));
-// _consolidator = ResolveConsolidator(_symbol, TimeSpan.FromDays(1));
    
-     
    self._consolidator = QuoteBarConsolidator(timedelta(days=1))
+// _consolidator = CreateConsolidator(TimeSpan.FromMilliseconds(100), typeof(Tick));
+// _consolidator = ResolveConsolidator(_symbol, TimeSpan.FromMilliseconds(100));
    
+     
    self._consolidator = TickConsolidator(timedelta(milliseconds=100))
 # Aliases:
-# self._consolidator = self.create_consolidator(timedelta(days=1), QuoteBar)
-# self._consolidator = self.resolve_consolidator(self._symbol, timedelta(days=1))
    
+# self._consolidator = self.create_consolidator(timedelta(milliseconds=100), Tick)
+# self._consolidator = self.resolve_consolidator(self._symbol, timedelta(milliseconds=100))

    The time period is relative to the @@ -279890,12 +281730,12 @@

    Consolidate Quote Bars

    start_time - parameter of the QuoteBarConsolidator type constructor to define a custom start time. + parameter of the TickConsolidator type constructor to define a custom start time.

    - 
    _consolidator = new QuoteBarConsolidator(TimeSpan.FromDays(1),
+     
    _consolidator = new TickConsolidator(TimeSpan.FromMilliseconds(100),
     startTime: new TimeSpan(9, 30, 0));
    
-     
    self._consolidator = QuoteBarConsolidator(timedelta(days=1),
+     
    self._consolidator = TickConsolidator(timedelta(milliseconds=100),
     start_time=timedelta(hours=9, minutes=30))
  • @@ -279914,32 +281754,10 @@

    Consolidate Quote Bars

    - 
    _consolidator = ResolveConsolidator(_symbol, Resolution.Daily);
    - 
    self._consolidator = self.resolve_consolidator(self._symbol, Resolution.DAILY)
    + 
    _consolidator = ResolveConsolidator(_symbol, Resolution.Second);
    + 
    self._consolidator = self.resolve_consolidator(self._symbol, Resolution.SECOND)
    -

    - If the security subscription in your algorithm provides - - TradeBar - - and - - QuoteBar - - data, - - ResolveConsolidator - - - resolve_consolidator - - returns a - - TradeBarConsolidator - - . -

    If you consolidate on an hourly basis, the consolidator ends at the top of the hour, not every hour after the market open. For US Equities, that's 10 AM Eastern Time (ET), not 10:30 AM ET. To change this behavior, set the @@ -279948,7 +281766,7 @@

    Consolidate Quote Bars

    start_time - parameter of the QuoteBarConsolidator type constructor to + parameter of the TickConsolidator type constructor to TimeSpan(9, 30, 0) @@ -279988,11 +281806,11 @@

    Consolidate Quote Bars

    Define the consolidation handler.
    • - 
    void ConsolidationHandler(object sender, QuoteBar consolidatedBar)
+    
    void ConsolidationHandler(object sender, TradeBar consolidatedBar)
 {
 
 }
    
-    
    def _consolidation_handler(self, sender: object, consolidated_bar: QuoteBar) -> None:
+    
    def _consolidation_handler(self, sender: object, consolidated_bar: TradeBar) -> None:
     pass

    @@ -280000,7 +281818,7 @@

    Consolidate Quote Bars

    exchange time zone - . If you subscribe to minute resolution data for Bitcoin and create an hourly consolidator, you receive consolidated bars at the top of each hour. However, if you subscribe to minute resolution data for the regular trading hours of US Equities and create a daily consolidator, you receive consolidated bars at market close or midnight, depending on your + . If you subscribe to tick resolution data for Bitcoin and create an hourly consolidator, you receive consolidated bars at the top of each hour. However, if you subscribe to tick resolution data for the regular trading hours of US Equities and create a daily consolidator, you receive consolidated bars at market close or midnight, depending on your DailyPreciseEndTime @@ -280053,7 +281871,7 @@

    Consolidate Quote Bars

     method with a - QuoteBar + Tick object. You can update the consolidator with data from the @@ -280075,25 +281893,29 @@

    Consolidate Quote Bars

    # Example 1: Update the consolidator with data from the Slice object
 def on_data(self, slice: Slice) -> None:
-    quote_bar = slice.quote_bars[self._symbol]
-    self._consolidator.update(quote_bar)
+    ticks = slice.ticks[self._symbol]
+    for tick in ticks:
+        self._consolidator.update(tick)
 
 # Example 2: Update the consolidator with data from a history request
-history = self.history[QuoteBar](self._symbol, 30, Resolution.MINUTE)
-for quote_bar in history:
-    self._consolidator.update(quote_bar)
    +ticks = self.history[Tick](self._symbol, timedelta(minutes=3), Resolution.TICK) +for tick in ticks: + self._consolidator.update(tick) 
    // Example 1: Update the consolidator with data from the Slice object
 public override void OnData(Slice slice)
 {
-    var quoteBar = slice.QuoteBars[_symbol];
-    _consolidator.Update(quoteBar);
+    var ticks = slice.Ticks[_symbol];
+    foreach (var tick in ticks)
+    {
+    	_consolidator.Update(tick);
+    }
 }
 
 // Example 2: Update the consolidator with data from a history request
-var history = History<QuoteBar>(_symbol, 30, Resolution.Minute);
-foreach (var quoteBar in history)
+var ticks = History<Tick>(_symbol, TimeSpan.FromMinutes(3), Resolution.Tick);
+foreach (var tick in ticks)
 {
-    _consolidator.Update(quoteBar);
+    _consolidator.Update(tick);
 }
    @@ -280117,9 +281939,12 @@

    Consolidate Quote Bars

    You can also use the - + Consolidate + + consolidate + helper method to create period consolidators and register them for automatic updates. With just one line of code, you can create data in any time period based on a timedelta @@ -280147,8 +281972,8 @@

    Consolidate Quote Bars

    Periods
    - 
    _consolidator = Consolidate<QuoteBar>(_symbol, TimeSpan.FromDays(1), TickType.Quote, ConsolidationHandler);
    - 
    self._consolidator = self.consolidate(self._symbol, timedelta(days=1), TickType.Quote, self._consolidation_handler)
    + 
    _consolidator = Consolidate(_symbol, TimeSpan.FromMilliseconds(100), ConsolidationHandler);
    + 
    self._consolidator = self.consolidate(self._symbol, timedelta(milliseconds=100), self._consolidation_handler)
  • @@ -280157,8 +281982,8 @@

    Consolidate Quote Bars

    Periods
    • - 
    _consolidator = Consolidate<QuoteBar>(_symbol, Resolution.Daily, TickType.Quote, ConsolidationHandler);
    - 
    self._consolidator = self.consolidate(self._symbol, Resolution.DAILY, TickType.Quote, self._consolidation_handler)
    + 
    _consolidator = Consolidate(_symbol, Resolution.Second, ConsolidationHandler);
    + 
    self._consolidator = self.consolidate(self._symbol, Resolution.SECOND, self._consolidation_handler)

    @@ -280176,36 +282001,36 @@

    Consolidate Quote Bars

    argument.

    - 
    void ConsolidationHandler(QuoteBar consolidatedBar)
+   
    void ConsolidationHandler(TradeBar consolidatedBar)
 {
 
 }
    
-   
    def _consolidation_handler(self, consolidated_bar: QuoteBar) -> None:
+   
    def _consolidation_handler(self, consolidated_bar: TradeBar) -> None:
     pass
    -

    Consolidate Trade Ticks

    +

    Consolidate Quote Ticks

    Tick - consolidators aggregate + quote bar consolidators aggregate Tick - objects into + objects that represent quotes into - TradeBar + QuoteBar objects. Follow these steps to create and manage a Tick - consolidator based on a period of time: + quote bar consolidator based on a period of time:

    1. @@ -280239,13 +282064,13 @@

      Consolidate Trade Ticks

      Periods
      2. - 
      _consolidator = new TickConsolidator(TimeSpan.FromMilliseconds(100));
+     
      _consolidator = new TickQuoteBarConsolidator(TimeSpan.FromMilliseconds(100));
 // Aliases:
-// _consolidator = CreateConsolidator(TimeSpan.FromMilliseconds(100), typeof(Tick));
-// _consolidator = ResolveConsolidator(_symbol, TimeSpan.FromMilliseconds(100));
      
-     
      self._consolidator = TickConsolidator(timedelta(milliseconds=100))
+// _consolidator = CreateConsolidator(TimeSpan.FromMilliseconds(100), typeof(Tick), TickType.Quote);
+// _consolidator = ResolveConsolidator(_symbol, TimeSpan.FromMilliseconds(100), typeof(QuoteBar));
      
+     
      self._consolidator = TickQuoteBarConsolidator(timedelta(milliseconds=100))
 # Aliases:
-# self._consolidator = self.create_consolidator(timedelta(milliseconds=100), Tick)
+# self._consolidator = self.create_consolidator(timedelta(milliseconds=100), Tick, TickType.QUOTE)
 # self._consolidator = self.resolve_consolidator(self._symbol, timedelta(milliseconds=100))

      @@ -280267,12 +282092,12 @@

      Consolidate Trade Ticks

      start_time - parameter of the TickConsolidator type constructor to define a custom start time. + parameter of the TickQuoteBarConsolidator type constructor to define a custom start time.

      - 
      _consolidator = new TickConsolidator(TimeSpan.FromMilliseconds(100),
+     
      _consolidator = new TickQuoteBarConsolidator(TimeSpan.FromMilliseconds(100),
     startTime: new TimeSpan(9, 30, 0));
      
-     
      self._consolidator = TickConsolidator(timedelta(milliseconds=100),
+     
      self._consolidator = TickQuoteBarConsolidator(timedelta(milliseconds=100),
     start_time=timedelta(hours=9, minutes=30))
    2. @@ -280303,7 +282128,7 @@

      Consolidate Trade Ticks

      start_time - parameter of the TickConsolidator type constructor to + parameter of the TickQuoteBarConsolidator type constructor to TimeSpan(9, 30, 0) @@ -280343,11 +282168,11 @@

      Consolidate Trade Ticks

      Define the consolidation handler.
      3. - 
      void ConsolidationHandler(object sender, TradeBar consolidatedBar)
+    
      void ConsolidationHandler(object sender, QuoteBar consolidatedBar)
 {
 
 }
      
-    
      def _consolidation_handler(self, sender: object, consolidated_bar: TradeBar) -> None:
+    
      def _consolidation_handler(self, sender: object, consolidated_bar: QuoteBar) -> None:
     pass

      @@ -280476,9 +282301,12 @@

      Consolidate Trade Ticks

    You can also use the - + Consolidate + + consolidate + helper method to create period consolidators and register them for automatic updates. With just one line of code, you can create data in any time period based on a timedelta @@ -280506,8 +282334,8 @@

    Consolidate Trade Ticks

    Periods
    - 
    _consolidator = Consolidate(_symbol, TimeSpan.FromMilliseconds(100), ConsolidationHandler);
    - 
    self._consolidator = self.consolidate(self._symbol, timedelta(milliseconds=100), self._consolidation_handler)
    + 
    _consolidator = Consolidate<QuoteBar>(_symbol, TimeSpan.FromMilliseconds(100), TickType.Quote, ConsolidationHandler);
    + 
    self._consolidator = self.consolidate(self._symbol, timedelta(milliseconds=100), TickType.Quote, self._consolidation_handler)
  • @@ -280516,8 +282344,8 @@

    Consolidate Trade Ticks

    Periods
    • - 
    _consolidator = Consolidate(_symbol, Resolution.Second, ConsolidationHandler);
    - 
    self._consolidator = self.consolidate(self._symbol, Resolution.SECOND, self._consolidation_handler)
    + 
    _consolidator = Consolidate<QuoteBar>(_symbol, Resolution.Second, TickType.Quote, ConsolidationHandler);
    + 
    self._consolidator = self.consolidate(self._symbol, Resolution.SECOND, TickType.Quote, self._consolidation_handler)

    @@ -280535,36 +282363,36 @@

    Consolidate Trade Ticks

    argument.

    - 
    void ConsolidationHandler(TradeBar consolidatedBar)
+   
    void ConsolidationHandler(QuoteBar consolidatedBar)
 {
 
 }
    
-   
    def _consolidation_handler(self, consolidated_bar: TradeBar) -> None:
+   
    def _consolidation_handler(self, consolidated_bar: QuoteBar) -> None:
     pass
    -

    Consolidate Quote Ticks

    +

    Consolidate Other Data

    - Tick - - quote bar consolidators aggregate - - Tick + <dataType> - objects that represent quotes into + consolidators aggregate various types of data objects into - QuoteBar + <dataType> - objects. Follow these steps to create and manage a + objects of the same size or larger. If you consolidate + + custom data + + or alternative datasets, check the definition of the data class to see its data type. Follow these steps to create and manage a - Tick + <dataType> - quote bar consolidator based on a period of time: + consolidator based on a period of time:

    1. @@ -280598,14 +282426,14 @@

      Consolidate Quote Ticks

      Periods
      2. - 
      _consolidator = new TickQuoteBarConsolidator(TimeSpan.FromMilliseconds(100));
+     
      _consolidator = new DynamicDataConsolidator(TimeSpan.FromDays(1));
 // Aliases:
-// _consolidator = CreateConsolidator(TimeSpan.FromMilliseconds(100), typeof(Tick), TickType.Quote);
-// _consolidator = ResolveConsolidator(_symbol, TimeSpan.FromMilliseconds(100), typeof(QuoteBar));
      
-     
      self._consolidator = TickQuoteBarConsolidator(timedelta(milliseconds=100))
+// _consolidator = CreateConsolidator(TimeSpan.FromDays(1), typeof(<dataType>));
+// _consolidator = ResolveConsolidator(_symbol, TimeSpan.FromDays(1));
      
+     
      self._consolidator = DynamicDataConsolidator(timedelta(days=1))
 # Aliases:
-# self._consolidator = self.create_consolidator(timedelta(milliseconds=100), Tick, TickType.QUOTE)
-# self._consolidator = self.resolve_consolidator(self._symbol, timedelta(milliseconds=100))
      
+# self._consolidator = self.create_consolidator(timedelta(days=1), <dataType>)
+# self._consolidator = self.resolve_consolidator(self._symbol, timedelta(days=1))

      The time period is relative to the @@ -280626,12 +282454,12 @@

      Consolidate Quote Ticks

      start_time - parameter of the TickQuoteBarConsolidator type constructor to define a custom start time. + parameter of the DynamicDataConsolidator type constructor to define a custom start time.

      - 
      _consolidator = new TickQuoteBarConsolidator(TimeSpan.FromMilliseconds(100),
+     
      _consolidator = new DynamicDataConsolidator(TimeSpan.FromDays(1),
     startTime: new TimeSpan(9, 30, 0));
      
-     
      self._consolidator = TickQuoteBarConsolidator(timedelta(milliseconds=100),
+     
      self._consolidator = DynamicDataConsolidator(timedelta(days=1),
     start_time=timedelta(hours=9, minutes=30))
    2. @@ -280650,8 +282478,8 @@

      Consolidate Quote Ticks

      - 
      _consolidator = ResolveConsolidator(_symbol, Resolution.Second);
      - 
      self._consolidator = self.resolve_consolidator(self._symbol, Resolution.SECOND)
      + 
      _consolidator = ResolveConsolidator(_symbol, Resolution.Daily);
      + 
      self._consolidator = self.resolve_consolidator(self._symbol, Resolution.DAILY)

      @@ -280662,7 +282490,7 @@

      Consolidate Quote Ticks

      start_time - parameter of the TickQuoteBarConsolidator type constructor to + parameter of the DynamicDataConsolidator type constructor to TimeSpan(9, 30, 0) @@ -280702,11 +282530,11 @@

      Consolidate Quote Ticks

      Define the consolidation handler.
      3. - 
      void ConsolidationHandler(object sender, QuoteBar consolidatedBar)
+    
      void ConsolidationHandler(object sender, <dataType> consolidatedBar)
 {
 
 }
      
-    
      def _consolidation_handler(self, sender: object, consolidated_bar: QuoteBar) -> None:
+    
      def _consolidation_handler(self, sender: object, consolidated_bar: <dataType>) -> None:
     pass

      @@ -280714,16 +282542,6 @@

      Consolidate Quote Ticks

      exchange time zone - . If you subscribe to tick resolution data for Bitcoin and create an hourly consolidator, you receive consolidated bars at the top of each hour. However, if you subscribe to tick resolution data for the regular trading hours of US Equities and create a daily consolidator, you receive consolidated bars at market close or midnight, depending on your - - DailyPreciseEndTime - - - daily_precise_end_time - - - setting - .

    3. @@ -280737,7 +282555,7 @@

      Consolidate Quote Ticks

      Automatic Updates

      4. - To automatically update a consolidator with data from the security subscription, call the + To automatically update a consolidator with data from the data subscription, call the AddConsolidator @@ -280767,7 +282585,7 @@

      Consolidate Quote Ticks

       method with a - Tick + <dataType> object. You can update the consolidator with data from the @@ -280789,29 +282607,27 @@

      Consolidate Quote Ticks

      # Example 1: Update the consolidator with data from the Slice object
 def on_data(self, slice: Slice) -> None:
-    ticks = slice.ticks[self._symbol]
-    for tick in ticks:
-        self._consolidator.update(tick)
+    if self._symbol in slice:
+        self._consolidator.update(slice[self._symbol])
 
 # Example 2: Update the consolidator with data from a history request
-ticks = self.history[Tick](self._symbol, timedelta(minutes=3), Resolution.TICK)
-for tick in ticks:
-    self._consolidator.update(tick)
      +history = self.history[<dataType>](self._symbol, 30, Resolution.DAILY) +for data in history: + self._consolidator.update(data) 
      // Example 1: Update the consolidator with data from the Slice object
 public override void OnData(Slice slice)
 {
-    var ticks = slice.Ticks[_symbol];
-    foreach (var tick in ticks)
+    if slice.ContainsKey(_symbol)
     {
-    	_consolidator.Update(tick);
+        _consolidator.Update(slice[_symbol]);
     }
 }
 
 // Example 2: Update the consolidator with data from a history request
-var ticks = History<Tick>(_symbol, TimeSpan.FromMinutes(3), Resolution.Tick);
-foreach (var tick in ticks)
+var history = History<<dataType>>(_symbol, 30, Resolution.Daily);
+foreach (var data in history)
 {
-    _consolidator.Update(tick);
+    _consolidator.Update(data);
 }
      @@ -280835,356 +282651,12 @@

      Consolidate Quote Ticks

    You can also use the - - Consolidate - - helper method to create period consolidators and register them for automatic updates. With just one line of code, you can create data in any time period based on a - - timedelta - - - TimeSpan - - or - - Resolution - - object: -

    -
      -
    • - - TimeSpan - - Periods -
      • -
    • - - timedelta - - Periods -
      • -
      - 
      _consolidator = Consolidate<QuoteBar>(_symbol, TimeSpan.FromMilliseconds(100), TickType.Quote, ConsolidationHandler);
      - 
      self._consolidator = self.consolidate(self._symbol, timedelta(milliseconds=100), TickType.Quote, self._consolidation_handler)
      -
      -
    • - - Resolution - - Periods -
      • -
      - 
      _consolidator = Consolidate<QuoteBar>(_symbol, Resolution.Second, TickType.Quote, ConsolidationHandler);
      - 
      self._consolidator = self.consolidate(self._symbol, Resolution.SECOND, TickType.Quote, self._consolidation_handler)
      -
      -
    -

    - If you use the Consolidate consolidate - helper method, the consolidation handler doesn't receive an - - object - - argument. -

    -
    - 
    void ConsolidationHandler(QuoteBar consolidatedBar)
-{
-
-}
    - 
    def _consolidation_handler(self, consolidated_bar: QuoteBar) -> None:
-    pass
    -
    - - - -

    Consolidate Other Data

    - - -

    - - <dataType> - - consolidators aggregate various types of data objects into - - <dataType> - - objects of the same size or larger. If you consolidate - - custom data - - or alternative datasets, check the definition of the data class to see its data type. Follow these steps to create and manage a - - <dataType> - - consolidator based on a period of time: -

    -
      -
    1. - Create the consolidator. -
      2. -

      - To set the time period for the consolidator, you can use either a - - timedelta - - - TimeSpan - - or - - Resolution - - object. The consolidator time period must be greater than or equal to the resolution of the security subscription. For instance, you can aggregate minute bars into 10-minute bars, but you can't aggregate hour bars into 10-minute bars. -

      -
        -
      • - - timedelta - - Periods -
        • -
      • - - TimeSpan - - Periods -
        • -
        - 
        _consolidator = new DynamicDataConsolidator(TimeSpan.FromDays(1));
-// Aliases:
-// _consolidator = CreateConsolidator(TimeSpan.FromDays(1), typeof(<dataType>));
-// _consolidator = ResolveConsolidator(_symbol, TimeSpan.FromDays(1));
        - 
        self._consolidator = DynamicDataConsolidator(timedelta(days=1))
-# Aliases:
-# self._consolidator = self.create_consolidator(timedelta(days=1), <dataType>)
-# self._consolidator = self.resolve_consolidator(self._symbol, timedelta(days=1))
        -
        -

        - The time period is relative to the - - exchange time zone - - , not the - - algorithm time zone - - . If you consolidate Crypto data into daily bars, the event handler receives the consolidated bars at midnight 12:00 AM Coordinated Universal Time (UTC), regardless of the algorithm time zone. -

        -

        - Use the - - startTime - - - start_time - - parameter of the DynamicDataConsolidator type constructor to define a custom start time. -

        -
        - 
        _consolidator = new DynamicDataConsolidator(TimeSpan.FromDays(1),
-    startTime: new TimeSpan(9, 30, 0));
        - 
        self._consolidator = DynamicDataConsolidator(timedelta(days=1),
-    start_time=timedelta(hours=9, minutes=30))
        -
        -
      • - - Resolution - - Periods -
        • -

        - The - - Resolution - - enumeration has the following members: -

        -
        -
        -
        - 
        _consolidator = ResolveConsolidator(_symbol, Resolution.Daily);
        - 
        self._consolidator = self.resolve_consolidator(self._symbol, Resolution.DAILY)
        -
        -
      -

      - If you consolidate on an hourly basis, the consolidator ends at the top of the hour, not every hour after the market open. For US Equities, that's 10 AM Eastern Time (ET), not 10:30 AM ET. To change this behavior, set the - - startTime - - - start_time - - parameter of the DynamicDataConsolidator type constructor to - - TimeSpan(9, 30, 0) - - - timedelta(hours=9, minutes=30) - -

      -
    2. - Add an event handler to the consolidator. -
      3. -
      - 
      _consolidator.DataConsolidated += ConsolidationHandler;
      - 
      self._consolidator.data_consolidated += self._consolidation_handler
      -
      -

      - LEAN passes consolidated bars to the consolidator event handler in your algorithm. The most common error when creating consolidators is to put parenthesis - - () - - at the end of your method name when setting the event handler of the consolidator. If you use parenthesis, the method executes and the result is passed as the event handler instead of the method itself. Remember to pass the name of your method to the event system. Specifically, it should be - - ConsolidationHandler - - - self._consolidation_handler - - , not - - ConsolidationHandler() - - - self._consolidation_handler() - - . -

      -
    3. - Define the consolidation handler. -
      4. -
      - 
      void ConsolidationHandler(object sender, <dataType> consolidatedBar)
-{
-
-}
      - 
      def _consolidation_handler(self, sender: object, consolidated_bar: <dataType>) -> None:
-    pass
      -
      -

      - The consolidation event handler receives bars when the consolidated bar closes based on the - - exchange time zone - - . -

      -
    4. - Update the consolidator. -
      5. -

      - You can automatically or manually update the consolidator. -

      -
        -
      • - Automatic Updates -
        • -

        - To automatically update a consolidator with data from the data subscription, call the - - AddConsolidator - - - add_consolidator - - method of the Subscription Manager. -

        -
        - 
        self.subscription_manager.add_consolidator(self._symbol, self._consolidator)
        - 
        SubscriptionManager.AddConsolidator(_symbol, _consolidator);
        -
        -
      • - Manual Updates -
        • -

        - Manual updates let you control when the consolidator updates and what data you use to update it. If you need to warm up a consolidator with data outside of the - - warm-up period - - , you can manually update the consolidator. To manually update a consolidator, call its - - Update - - - update - - method with a - - <dataType> - - object. You can update the consolidator with data from the - - Slice - - object in the - - OnData - - - on_data - - method or with data from a - - history request - - . -

        -
        - 
        # Example 1: Update the consolidator with data from the Slice object
-def on_data(self, slice: Slice) -> None:
-    if self._symbol in slice:
-        self._consolidator.update(slice[self._symbol])
-
-# Example 2: Update the consolidator with data from a history request
-history = self.history[<dataType>](self._symbol, 30, Resolution.DAILY)
-for data in history:
-    self._consolidator.update(data)
        - 
        // Example 1: Update the consolidator with data from the Slice object
-public override void OnData(Slice slice)
-{
-    if slice.ContainsKey(_symbol)
-    {
-        _consolidator.Update(slice[_symbol]);
-    }
-}
-
-// Example 2: Update the consolidator with data from a history request
-var history = History<<dataType>>(_symbol, 30, Resolution.Daily);
-foreach (var data in history)
-{
-    _consolidator.Update(data);
-}
        -
        -
      -
    5. - If you create consolidators for securities in a dynamic universe and register them for automatic updates, remove the consolidator when the security leaves the universe. -
      6. -
      - 
      SubscriptionManager.RemoveConsolidator(_symbol, _consolidator);
      - 
      self.subscription_manager.remove_consolidator(self._symbol, self._consolidator)
      -
      -

      - If you have a dynamic universe and don't remove consolidators, they compound internally, causing your algorithm to slow down and eventually die once it runs out of RAM. For an example of removing consolidators from universe subscriptions, see the - - GasAndCrudeOilEnergyCorrelationAlpha - - - GasAndCrudeOilEnergyCorrelationAlpha - - in the LEAN GitHub repository. -

      -
    -

    - You can also use the - - Consolidate - helper method to create period consolidators and register them for automatic updates. With just one line of code, you can create data in any time period based on a timedelta @@ -301359,7 +302831,7 @@

    if option_chain: for option in option_chain: # Request historical quote data for signal generation. - history = self.history(QuoteBar, option.symbol, 15, Resolution.Minute) + history = self.history(QuoteBar, option.symbol, 15, Resolution.MINUTE) if not history.empty: # Calculate total bid and ask dollar volume to determine the capital directional force. total_bid_volume = (history['bidclose'] * history['bidsize']).sum() @@ -311031,7 +312503,7 @@

    for option_chain in self.current_slice.option_chains.values(): for option in option_chain: # Request historical quote data for signal generation. - history = self.history(QuoteBar, option.symbol, 15, Resolution.Minute) + history = self.history(QuoteBar, option.symbol, 15, Resolution.MINUTE) if not history.empty: # Calculate the total bid and ask for dollar volume to determine the capital directional force. total_bid_volume = 0 @@ -324948,7 +326420,7 @@

    def on_data(self, slice: Slice) -> None: bar = slice.bars.get(self._symbol) # Place order if not invested. Make sure only trade once a day to avoid chasing the loss. - if not self.portfolio.Invested and self._day != slice.time.day and bar: + if not self.portfolio.invested and self._day != slice.time.day and bar: self.market_order(self._symbol, 10) # Update the day variable to avoid repeat ordering. self._day = slice.time.day @@ -329912,20 +331384,29 @@

    Requirements

    MOO orders don't support the - + GoodTilDate + + good_til_date + time in force . If you submit a MOO order with the - + GoodTilDate + + good_til_date + time in force, LEAN automatically adjusts the time in force to be - + GoodTilCanceled + + GOOD_TIL_CANCELED + .

    @@ -330473,20 +331954,29 @@

    Requirements

    MOC orders don't support the - + GoodTilDate + + good_til_date + time in force . If you submit a MOC order with the - + GoodTilDate + + good_til_date + time in force, LEAN automatically adjusts the time in force to be - + GoodTilCanceled + + GOOD_TIL_CANCELED + .

    @@ -331168,11 +332658,11 @@

    Introduction

    limit orders - for muliple securities. Combo limit orders are different from + for multiple securities. Combo limit orders are different from combo leg limit orders - because you must set the limit price of all the leg orders to be the same with combo limit orders. With combo leg limit orders, you can create the order without forcing each leg to have the same limit price. Combo limit orders currently only work for trading Option contracts and their underlying Equities. + because you set a single limit price for the combined (net) price of all the legs, rather than a separate limit price per leg. Combo limit orders currently only work for trading Option contracts and their underlying Equities.

    @@ -331859,11 +333349,11 @@

    Introduction

    limit orders - for muliple securities. Combo leg limit orders are different from + for multiple securities. Combo leg limit orders are different from combo limit orders - because you can create combo leg limit orders without forcing each leg to have the same limit price. Combo leg limit orders currently only work for trading Option contracts. + because you set a separate limit price for each leg, rather than a single limit price for the combined (net) price of all the legs. Combo leg limit orders currently only work for trading Option contracts.

    @@ -343961,7 +345451,7 @@

    Implementation

    near_call = call_contracts[0] far_call = call_contracts[1] near_put = put_contracts[1] - far_put = [x for x in put_contracts if x.Strike == near_put.strike - far_call.strike + near_call.strike][0] + far_put = [x for x in put_contracts if x.strike == near_put.strike - far_call.strike + near_call.strike][0]

  • In the @@ -344380,7 +345870,7 @@

    Example

    near_call = call_contracts[0] far_call = call_contracts[1] near_put = put_contracts[1] - far_put = [x for x in put_contracts if x.Strike == near_put.strike - far_call.strike + near_call.strike][0] + far_put = [x for x in put_contracts if x.strike == near_put.strike - far_call.strike + near_call.strike][0] iron_condor = OptionStrategies.iron_condor( self._symbol, @@ -345033,7 +346523,7 @@

    Example

    near_call = call_contracts[0] far_call = call_contracts[1] near_put = put_contracts[1] - far_put = [x for x in put_contracts if x.Strike == near_put.strike - far_call.strike + near_call.strike][0] + far_put = [x for x in put_contracts if x.strike == near_put.strike - far_call.strike + near_call.strike][0] short_iron_condor = OptionStrategies.short_iron_condor( self._symbol, @@ -346761,7 +348251,7 @@

    Example

    # Find ATM put with the farthest expiry expiry = max([x.expiry for x in chain]) put_contracts = sorted([x for x in chain - if x.right == OptionRight.Put and x.expiry == expiry], + if x.right == OptionRight.PUT and x.expiry == expiry], key=lambda x: abs(chain.underlying.price - x.strike)) if not put_contracts: @@ -348319,7 +349809,7 @@

    Implementation

    call_contracts = sorted([contract for contract in contracts if contract.right == OptionRight.CALL and contract.strike > chain.underlying.price], - key=lambda x: x.Strike) + key=lambda x: x.strike) if not call_contracts: return @@ -348327,7 +349817,7 @@

    Implementation

    put_contracts = sorted([contract for contract in contracts if contract.right == OptionRight.PUT and contract.strike < chain.underlying.price], - key=lambda x: x.Strike, reverse=True) + key=lambda x: x.strike, reverse=True) if not put_contracts: return @@ -349642,7 +351132,7 @@

    Example

    expiry = sorted(chain, key = lambda x: x.expiry)[-1].expiry # select ATM strike price - strike = sorted(chain, key = lambda x: abs(x.Strike - chain.underlying.price))[0].strike + strike = sorted(chain, key = lambda x: abs(x.strike - chain.underlying.price))[0].strike # Order Strategy conversion = OptionStrategies.conversion(self.symbol, strike, expiry) @@ -350117,7 +351607,7 @@

    Example

    expiry = sorted(chain, key = lambda x: x.expiry)[-1].expiry # select ATM strike price - strike = sorted(chain, key = lambda x: abs(x.Strike - chain.underlying.price))[0].strike + strike = sorted(chain, key = lambda x: abs(x.strike - chain.underlying.price))[0].strike # Order Strategy reverse_conversion = OptionStrategies.reverse_conversion(self.symbol, strike, expiry) @@ -350632,7 +352122,7 @@

    Example

    self.universe_settings.asynchronous = True option = self.add_option("GOOG", Resolution.MINUTE) - self._symbol = option.Symbol + self._symbol = option.symbol option.set_filter(lambda universe: universe.box_spread(30, 5)) def on_data(self, slice: Slice) -> None: @@ -350657,7 +352147,7 @@

    Example

    def on_end_of_day(self, symbol: Symbol) -> None: if symbol == self._symbol.underlying: - self.Log(f"{self.time}::{symbol}::{self.securities[symbol].price}") + self.log(f"{self.time}::{symbol}::{self.securities[symbol].price}")
    • @@ -351168,7 +352658,7 @@

    Example

    self.universe_settings.asynchronous = True self.equity_symbol = self.add_equity("GOOG").symbol - option = self.add_option("GOOG", Resolution.Minute) + option = self.add_option("GOOG", Resolution.MINUTE) self.option_symbol = option.symbol option.set_filter(lambda universe: universe.box_spread(30, 5)) @@ -356494,9 +357984,12 @@

    Time In Force

    good_til_date     time in force, LEAN automatically adjusts the time in force to be - + GoodTilCanceled + + GOOD_TIL_CANCELED + .

    @@ -356613,7 +358106,11 @@

    Tags

    Order - class you can set. To set an order tag, pass it as an argument when you create the order or use the order update methods. + class you can set. To set an order tag, pass it as an argument when you create the order or use the order update methods. This is useful for + + debugging + + your algorithm's trading logic because you can include the indicator values or conditions that triggered the order. 

    // Tag an order on creation
@@ -359717,7 +361214,7 @@ 
    Get Trading Days
    
   
    
   
    
    
    var tradingDays = TradingCalendar.GetDaysByType(TradingDayType.OptionExpiration, startDate, endDate);
    
-   
    trading_days = self.TradingCalendar.get_days_by_type(TradingDayType.OPTION_EXPIRATION, start_date, end_date)
    
+   
    trading_days = self.trading_calendar.get_days_by_type(TradingDayType.OPTION_EXPIRATION, start_date, end_date)
    
   
    
   
    
    The
@@ -361052,317 +362549,323 @@ 
    Market Orders
    
      Tick
         
     data and the most recent batch of ticks contains a tick of type
-    
+    
+     TickType.Trade
+    
+    
+     TickType.TRADE
+    
+    , use the last trade price.
+   
+   
  • 
+    If the subscription provides
+    
+     TradeBar
+    
+    data, use the closing bid price of the most recent
+    
+     QuoteBar
+    
+    .
+   
    • 
+  
+  
    
+   The model only fills market orders during regular trading hours.
+  
    
+ 
+
+
+
    Limit Orders
    
+
+ 
+  
    
+   To fill limit orders, the model first gets the best effort
+   
+    TradeBar
+   
+   . To get the best effort
+   
+    TradeBar
+   
+   , the model checks the resolution of your security subscription. If your subscription provies
+   
+    Tick
+   
+   data, it gets the most recent batch of trade ticks and consolidates them to build a
+   
+    TradeBar
+   
+   . If your subscription provies
+   
+    TradeBar
+   
+   data, it gets the most recent
+   
+    TradeBar
+   
+   . If the
+   
+    EndTime
+   
+   
+    end_time
+   
+   of the best effort
+   
+    TradeBar
+   
+   is less than or equal to the time you placed the order, the model waits until the next best effort
+   
+    TradeBar
+   
+   to fill the order.
+  
    
+  
    
+   Once the model has a valid best effort
+   
+    TradeBar
+   
+   , it can fill the order. The following table shows the fill condition and fill price of limit orders. The model only fills the order once the fill condition is met.
+  
    
+  
+   
+    
+     
+     
+     
+    
+   
+   
+    
+     
+     
+     
+    
+    
+     
+     
+     
+    
+   
+  
    
+      Order Direction
+     
+      Fill Condition
+     
+      Fill Price
+     
    
+      Buy
+     
+      low price < limit price
+     
+      min(open price, limit price)
+     
    
+      Sell
+     
+      high price > limit price
+     
+      max(open price, limit price)
+     
    
+  
    
+   The model only fills limit orders when the exchange is open.
+  
    
+  
    
+   The model won't fill limit orders with
+   
+    stale data
+   
+   or data with the order timestamp to avoid look-ahead bias.
+  
    
+ 
+
+
+
    Limit if Touched Orders
    
+
+ 
+  
    
+   To fill limit if touched orders, the model first gets the best effort
+   
+    TradeBar
+   
+   . To get the best effort
+   
+    TradeBar
+   
+   , the model checks the resolution of your security subscription. If your subscription provies
+   
+    Tick
+   
+   data, it gets the most recent batch of trade ticks and consolidates them to build a
+   
+    TradeBar
+   
+   . If your subscription provies
+   
+    TradeBar
+   
+   data, it gets the most recent
+   
+    TradeBar
+   
+   . If the
+   
+    EndTime
+   
+   
+    end_time
+   
+   of the best effort
+   
+    TradeBar
+   
+   is less than or equal to the time you placed the order, the model waits until the next best effort
+   
+    TradeBar
+   
+   to fill the order.
+  
    
+  
    
+   After the model has a valid best effort
+   
+    TradeBar
+   
+   , it can check if the trigger price has been touched. The following table describes the trigger condition of limit if touched orders for each order direction:
+  
    
+  
+   
+    
+     
+     
+    
+   
+   
+    
+     
+     
+    
+    
+     
+     
+    
+   
+  
    
+      Order Direction
+     
+      Trigger Condition
+     
    
+      Buy
+     
+      low price <= trigger price
+     
    
+      Sell
+     
+      high price >= trigger price
+     
    
+  
    
+   Once the limit if touched order triggers, the model starts to check if it should fill the order. The following table shows the fill condition and fill price of limit if touched orders. The model only fills the order once the fill condition is met
+  
    
+  
+   
+    
+     
+     
+     
+    
+   
+   
+    
+     
+     
+     
+    
+    
+     
+     
+     
+    
+   
+  
    
+      Order Direction
+     
+      Fill Condition
+     
+      Fill Price
+     
    
+      Buy
+     
+      Best effort ask price <= limit price
+      
    
+         		
+      min(best effort ask price, limit price)
+     
    
+      Sell
+     
+      Best effort bid price >= limit price
+      
    
+         		
+      max(best effort bid price, limit price)
+     
    
+  
    
+   To get the best effort bid price, the model uses the following procedure:
+  
    
+  
      
+   
    1. 
+    If the subscription provides
+    
+     Tick
+    
+    data and the most recent batch of ticks contains a buy quote, use the bid price of the most recent quote tick.
+   
      2. 
+   
    2. 
+    If the subscription provides
+    
+     QuoteBar
+    
+    data, use the closing bid price of the most recent
+    
+     QuoteBar
+    
+    .
+   
      3. 
+  
    
+  
    
+   To get the best effort ask price, the model uses the following procedure:
+  
    
+  
      
+   
    1. 
+    If the subscription provides
+    
+     Tick
+    
+    data and the most recent batch of ticks contains a sell quote, use the ask price of the most recent quote tick.
+   
      2. 
+   
    2. 
+    If the subscription provides
+    
+     QuoteBar
+    
+    data, use the closing ask price of the most recent
+    
+     QuoteBar
+    
+    .
+   
      3. 
+  
    
+  
    
+   If neither of the preceding procedures yield a result, the model uses the following procedure to get the best effort bid or ask price:
+  
    
+  
      
+   
    1. 
+    If the subscription provides
+    
+     Tick
+    
+    data and the most recent batch of ticks contains a tick of type
+    
      TickType.Trade
     
-    , use the last trade price.
-   
      2. 
-   
    2. 
-    If the subscription provides
-    
-     TradeBar
-    
-    data, use the closing bid price of the most recent
-    
-     QuoteBar
-    
-    .
-   
      3. 
-  
    
-  
    
-   The model only fills market orders during regular trading hours.
-  
    
- 
-
-
-
    Limit Orders
    
-
- 
-  
    
-   To fill limit orders, the model first gets the best effort
-   
-    TradeBar
-   
-   . To get the best effort
-   
-    TradeBar
-   
-   , the model checks the resolution of your security subscription. If your subscription provies
-   
-    Tick
-   
-   data, it gets the most recent batch of trade ticks and consolidates them to build a
-   
-    TradeBar
-   
-   . If your subscription provies
-   
-    TradeBar
-   
-   data, it gets the most recent
-   
-    TradeBar
-   
-   . If the
-   
-    EndTime
-   
-   
-    end_time
-   
-   of the best effort
-   
-    TradeBar
-   
-   is less than or equal to the time you placed the order, the model waits until the next best effort
-   
-    TradeBar
-   
-   to fill the order.
-  
    
-  
    
-   Once the model has a valid best effort
-   
-    TradeBar
-   
-   , it can fill the order. The following table shows the fill condition and fill price of limit orders. The model only fills the order once the fill condition is met.
-  
    
-  
-   
-    
-     
-     
-     
-    
-   
-   
-    
-     
-     
-     
-    
-    
-     
-     
-     
-    
-   
-  
    
-      Order Direction
-     
-      Fill Condition
-     
-      Fill Price
-     
    
-      Buy
-     
-      low price < limit price
-     
-      min(open price, limit price)
-     
    
-      Sell
-     
-      high price > limit price
-     
-      max(open price, limit price)
-     
    
-  
    
-   The model only fills limit orders when the exchange is open.
-  
    
-  
    
-   The model won't fill limit orders with
-   
-    stale data
-   
-   or data with the order timestamp to avoid look-ahead bias.
-  
    
- 
-
-
-
    Limit if Touched Orders
    
-
- 
-  
    
-   To fill limit if touched orders, the model first gets the best effort
-   
-    TradeBar
-   
-   . To get the best effort
-   
-    TradeBar
-   
-   , the model checks the resolution of your security subscription. If your subscription provies
-   
-    Tick
-   
-   data, it gets the most recent batch of trade ticks and consolidates them to build a
-   
-    TradeBar
-   
-   . If your subscription provies
-   
-    TradeBar
-   
-   data, it gets the most recent
-   
-    TradeBar
-   
-   . If the
-   
-    EndTime
-   
-   
-    end_time
-   
-   of the best effort
-   
-    TradeBar
-   
-   is less than or equal to the time you placed the order, the model waits until the next best effort
-   
-    TradeBar
-   
-   to fill the order.
-  
    
-  
    
-   After the model has a valid best effort
-   
-    TradeBar
-   
-   , it can check if the trigger price has been touched. The following table describes the trigger condition of limit if touched orders for each order direction:
-  
    
-  
-   
-    
-     
-     
-    
-   
-   
-    
-     
-     
-    
-    
-     
-     
-    
-   
-  
    
-      Order Direction
-     
-      Trigger Condition
-     
    
-      Buy
-     
-      low price <= trigger price
-     
    
-      Sell
-     
-      high price >= trigger price
-     
    
-  
    
-   Once the limit if touched order triggers, the model starts to check if it should fill the order. The following table shows the fill condition and fill price of limit if touched orders. The model only fills the order once the fill condition is met
-  
    
-  
-   
-    
-     
-     
-     
-    
-   
-   
-    
-     
-     
-     
-    
-    
-     
-     
-     
-    
-   
-  
    
-      Order Direction
-     
-      Fill Condition
-     
-      Fill Price
-     
    
-      Buy
-     
-      Best effort ask price <= limit price
-      
    
-         		
-      min(best effort ask price, limit price)
-     
    
-      Sell
-     
-      Best effort bid price >= limit price
-      
    
-         		
-      max(best effort bid price, limit price)
-     
    
-  
    
-   To get the best effort bid price, the model uses the following procedure:
-  
    
-  
      
-   
    1. 
-    If the subscription provides
-    
-     Tick
-    
-    data and the most recent batch of ticks contains a buy quote, use the bid price of the most recent quote tick.
-   
      2. 
-   
    2. 
-    If the subscription provides
-    
-     QuoteBar
-    
-    data, use the closing bid price of the most recent
-    
-     QuoteBar
-    
-    .
-   
      3. 
-  
    
-  
    
-   To get the best effort ask price, the model uses the following procedure:
-  
    
-  
      
-   
    1. 
-    If the subscription provides
-    
-     Tick
-    
-    data and the most recent batch of ticks contains a sell quote, use the ask price of the most recent quote tick.
-   
      2. 
-   
    2. 
-    If the subscription provides
-    
-     QuoteBar
-    
-    data, use the closing ask price of the most recent
-    
-     QuoteBar
-    
-    .
-   
      3. 
-  
    
-  
    
-   If neither of the preceding procedures yield a result, the model uses the following procedure to get the best effort bid or ask price:
-  
    
-  
      
-   
    1. 
-    If the subscription provides
-    
-     Tick
-    
-    data and the most recent batch of ticks contains a tick of type
-    
-     TickType.Trade
+    
+     TickType.TRADE
     
     , use the last trade price.
    
      2. 
@@ -362126,9 +363629,12 @@ 
      Market on Open Orders
      
      Tick
           
     data and the most recent batch of ticks contains a tick of type
-    
+    
      TickType.Trade
     
+    
+     TickType.TRADE
+    
     , use the last trade price.
    
    
    2. 
@@ -362337,9 +363843,12 @@ 
      Market on Close Orders
      
      Tick
           
     data and the most recent batch of ticks contains a tick of type
-    
+    
      TickType.Trade
     
+    
+     TickType.TRADE
+    
     , use the last trade price.
    
      3. 
    
    3. 
@@ -363898,9 +365407,12 @@ 
      Limit if Touched Orders
      
      Tick
           
     data and the most recent batch of ticks contains a tick of type
-    
+    
      TickType.Trade
     
+    
+     TickType.TRADE
+    
     , use the last trade price.
    
      4. 
    
    4. 
@@ -366805,9 +368317,12 @@ 
      Limit if Touched Orders
      
      Tick
           
     data and the most recent batch of ticks contains a tick of type
-    
+    
      TickType.Trade
     
+    
+     TickType.TRADE
+    
     , use the last trade price.
    
      5. 
    
    5. 
@@ -369712,9 +371227,12 @@ 
      Limit if Touched Orders
      
      Tick
           
     data and the most recent batch of ticks contains a tick of type
-    
+    
      TickType.Trade
     
+    
+     TickType.TRADE
+    
     , use the last trade price.
    
      6. 
    
    6. 
@@ -372639,9 +374157,12 @@ 
      Limit if Touched Orders
      
      Tick
           
     data and the most recent batch of ticks contains a tick of type
-    
+    
      TickType.Trade
     
+    
+     TickType.TRADE
+    
     , use the last trade price.
    
      7. 
    
    7. 
@@ -380107,7 +381628,7 @@ 
      Set Models
      
    
      def initialize(self) -> None:
 	# Set the brokerage model to backtest with the most realistic scenario to handle the order validity, margin rate and transaction fee
 	self.set_brokerage_model(BrokerageName.OANDA_BROKERAGE) # Defaults to margin account
-	self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.MARGIN) # Overrides the default account type, which is AccountType.Cash
+	self.set_brokerage_model(BrokerageName.BITFINEX, AccountType.MARGIN) # Overrides the default account type, which is AccountType.CASH

    @@ -387427,7 +388948,7 @@

    Account Currency

    set the account currency to USDT or another - + Cryptocurrency that Binance supports . @@ -395966,7 +397487,7 @@

    return new Position(symbol, leg.Quantity, 1); }); 
        def get_symbol(leg):
-        return Symbol.CreateOption(
+        return Symbol.create_option(
             self._symbol.underlying, self._symbol.id.market, self._symbol.id.option_style, 
             leg.right, leg.strike, leg.expiration
         )
@@ -396170,7 +397691,7 @@ 
    
     }
 }
    class BuyingPowerModelAlgorithm(QCAlgorithm):
-    def Initialize(self) -> None:
+    def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
         self.settings.seed_initial_prices = True
@@ -397007,8 +398528,8 @@ 
    
         self._traded = False
 
     def _plot_cash(self):
-        self.Plot("Settled Cash", "USD", self.Portfolio.CashBook["USD"].Amount)
-        self.Plot("Unsettled Cash", "USD", self.Portfolio.UnsettledCashBook["USD"].Amount)
+        self.plot("Settled Cash", "USD", self.portfolio.cash_book["USD"].amount)
+        self.plot("Unsettled Cash", "USD", self.portfolio.unsettled_cash_book["USD"].amount)
 
     def on_data(self, data: Slice) -> None:
         if self._traded:
@@ -400604,8 +402125,8 @@ 
    
     def get_assignment(self, parameters: OptionAssignmentParameters) -> OptionAssignmentResult:
         option = parameters.option
         # Check if the contract is ITM.
-        if (option.Right == OptionRight.CALL and option.Underlying.Price > option.StrikePrice or
-            option.Right == OptionRight.PUT and option.Underlying.Price < option.StrikePrice):
+        if (option.right == OptionRight.CALL and option.underlying.price > option.strike_price or
+            option.right == OptionRight.PUT and option.underlying.price < option.strike_price):
             return OptionAssignmentResult(option.holdings.absolute_quantity, "MyTag")
         return OptionAssignmentResult.NULL
    @@ -403535,7 +405056,7 @@ 

    class CustomShortableProviderAlgorithm(QCAlgorithm):
 
-    def Initialize(self):
+    def initialize(self):
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 12, 31)
         self.set_cash(100000)
@@ -403546,7 +405067,7 @@ 
    
     def on_data(self, data: Slice) -> None:
         if self.portfolio.invested:
             return
-        shortable_quantity = self._security.shortable_provider.shortable_quantity(self._security.symbol, self.Time)
+        shortable_quantity = self._security.shortable_provider.shortable_quantity(self._security.symbol, self.time)
         if not shortable_quantity:
             return
         fee_rate = self._security.shortable_provider.fee_rate(self._security.symbol, self.time)
@@ -404459,6 +405980,58 @@ 
    Date Rules
    
       Trigger an event on the last tradable date of each week for a specific symbol minus an offset.
      
     
+    
+     
+      
+       self.date_rules.quarter_start(days_offset: int = 0)
+      
+      
+       DateRules.QuarterStart(int daysOffset = 0)
+      
+     
+     
+      Trigger an event on the first day of each quarter plus an offset.
+     
+    
+    
+     
+      
+       self.date_rules.quarter_start(symbol: Symbol, days_offset: int = 0)
+      
+      
+       DateRules.QuarterStart(Symbol symbol, int daysOffset = 0)
+      
+     
+     
+      Trigger an event on the first tradable date of each quarter for a specific symbol plus an offset.
+     
+    
+    
+     
+      
+       self.date_rules.quarter_end(days_offset: int = 0)
+      
+      
+       DateRules.QuarterEnd(int daysOffset = 0)
+      
+     
+     
+      Trigger an event on the last day of each quarter minus an offset.
+     
+    
+    
+     
+      
+       self.date_rules.quarter_end(symbol: Symbol, days_offset: int = 0)
+      
+      
+       DateRules.QuarterEnd(Symbol symbol, int daysOffset = 0)
+      
+     
+     
+      Trigger an event on the last tradable date of each quarter for a specific symbol minus an offset.
+     
+    
     
      
       
@@ -405194,12 +406767,26 @@ 
    Examples
    
                 TimeRules.AfterMarketOpen("SPY", 5),
                 RebalancingCode);
 
-    // Schedule an event to fire at the end of the week, the symbol is optional. 
+    // Schedule an event to fire at the end of the week, the symbol is optional.
     // If specified, it will fire the last trading day for that symbol of the week.
     // Otherwise, it will fire on the first day of the week.
     Schedule.On(DateRules.WeekEnd("SPY"),
                 TimeRules.BeforeMarketClose("SPY", 5),
                 RebalancingCode);
+
+    // Schedule an event to fire at the beginning of the quarter, the symbol is optional.
+    // If specified, it will fire the first trading day for that symbol of the quarter.
+    // Otherwise, it will fire on the first day of the quarter.
+    Schedule.On(DateRules.QuarterStart("SPY"),
+                TimeRules.AfterMarketOpen("SPY"),
+                RebalancingCode);
+
+    // Schedule an event to fire at the end of the quarter, the symbol is optional.
+    // If specified, it will fire the last trading day for that symbol of the quarter.
+    // Otherwise, it will fire on the last day of the quarter.
+    Schedule.On(DateRules.QuarterEnd("SPY"),
+                TimeRules.BeforeMarketClose("SPY"),
+                RebalancingCode);
     }
 
 // The following methods are not defined in Initialize:
@@ -405301,6 +406888,20 @@ 
    Examples
    
                     self.time_rules.before_market_close("SPY", 5),
                     self._rebalancing_code)
 
+    # Schedule an event to fire at the beginning of the quarter, the symbol is optional.
+    # If specified, it will fire the first trading day for that symbol of the quarter.
+    # Otherwise, it will fire on the first day of the quarter.
+    self.schedule.on(self.date_rules.quarter_start("SPY"),
+                    self.time_rules.after_market_open("SPY"),
+                    self._rebalancing_code)
+
+    # Schedule an event to fire at the end of the quarter, the symbol is optional.
+    # If specified, it will fire the last trading day for that symbol of the quarter.
+    # Otherwise, it will fire on the last day of the quarter.
+    self.schedule.on(self.date_rules.quarter_end("SPY"),
+                    self.time_rules.before_market_close("SPY"),
+                    self._rebalancing_code)
+
 # The following methods from examples above should be defined in the algorithm body.
 def _liquidate_unrealized_losses(self) -> None:
     ''' if we have over 1000 dollars in unrealized losses, liquidate'''
@@ -406764,6 +408365,17 @@

    +
    + +
    +
    +

    + Wave Trend Oscillator +

    +
    +
    +     +
    @@ -439375,7 +440987,7 @@

    Using MOSC Indicator

    { _symbol = AddEquity("QQQ", Resolution.Daily).Symbol; _reference = AddEquity("SPY", Resolution.Daily).Symbol; - _mosc = MOSC([_symbol, _reference], 19, 39); + _mosc = MOSC([_symbol, _reference]); } public override void OnData(Slice data) @@ -439397,7 +441009,7 @@

    Using MOSC Indicator

    def initialize(self) -> None: self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol self._reference = self.add_equity("SPY", Resolution.DAILY).symbol - self._mosc = self.mosc([self._symbol, self._reference], 19, 39) + self._mosc = self.mosc([self._symbol, self._reference]) def on_data(self, slice: Slice) -> None: @@ -439658,7 +441270,7 @@

    Indicator History

    { _symbol = AddEquity("QQQ", Resolution.Daily).Symbol; _reference = AddEquity("SPY", Resolution.Daily).Symbol; - _mosc = MOSC([_symbol, _reference], 19, 39); + _mosc = MOSC([_symbol, _reference]); var indicatorHistory = IndicatorHistory(_mosc, new[] { _symbol, _reference }, 100, Resolution.Minute); var timeSpanIndicatorHistory = IndicatorHistory(_mosc, new[] { _symbol, _reference }, TimeSpan.FromDays(10), Resolution.Minute); @@ -439674,7 +441286,7 @@

    Indicator History

    def initialize(self) -> None: self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol self._reference = self.add_equity("SPY", Resolution.DAILY).symbol - self._mosc = self.mosc([self._symbol, self._reference], 19, 39) + self._mosc = self.mosc([self._symbol, self._reference]) indicator_history = self.indicator_history(self._mosc, [ self._symbol, self._reference ], 100, Resolution.MINUTE) timedelta_indicator_history = self.indicator_history(self._mosc, [ self._symbol, self._reference ], timedelta(days=10), Resolution.MINUTE) @@ -443032,7 +444644,7 @@

    Using NHNL Indicator

    { _symbol = AddEquity("QQQ", Resolution.Daily).Symbol; _reference = AddEquity("SPY", Resolution.Daily).Symbol; - _nhnl = NHNL([_symbol, _reference]); + _nhnl = NHNL([_symbol, _reference], 2); } public override void OnData(Slice data) @@ -443053,7 +444665,7 @@

    Using NHNL Indicator

    def initialize(self) -> None: self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol self._reference = self.add_equity("SPY", Resolution.DAILY).symbol - self._nhnl = self.nhnl([self._symbol, self._reference]) + self._nhnl = self.nhnl([self._symbol, self._reference], 2) def on_data(self, slice: Slice) -> None: @@ -443119,7 +444731,7 @@

    Using NHNL Indicator

    public override void OnSecuritiesChanged(SecurityChanges changes) { - _nhnl = NHNL(_universe.Selected); + _nhnl = NHNL(_universe.Selected, 2); } public override void OnData(Slice data) @@ -443145,7 +444757,7 @@

    Using NHNL Indicator

    self._universe = self.add_universe(self.universe.etf('SPY')) def on_securities_changed(self, changes: SecurityChanges) -> None: - self._nhnl = self.nhnl(list(self._universe.selected)) + self._nhnl = self.nhnl(list(self._universe.selected), 2) def on_data(self, slice: Slice) -> None: @@ -443309,7 +444921,7 @@

    Indicator History

    { _symbol = AddEquity("QQQ", Resolution.Daily).Symbol; _reference = AddEquity("SPY", Resolution.Daily).Symbol; - _nhnl = NHNL([_symbol, _reference]); + _nhnl = NHNL([_symbol, _reference], 2); var indicatorHistory = IndicatorHistory(_nhnl, new[] { _symbol, _reference }, 100, Resolution.Minute); var timeSpanIndicatorHistory = IndicatorHistory(_nhnl, new[] { _symbol, _reference }, TimeSpan.FromDays(10), Resolution.Minute); @@ -443324,7 +444936,7 @@

    Indicator History

    def initialize(self) -> None: self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol self._reference = self.add_equity("SPY", Resolution.DAILY).symbol - self._nhnl = self.nhnl([self._symbol, self._reference]) + self._nhnl = self.nhnl([self._symbol, self._reference], 2) indicator_history = self.indicator_history(self._nhnl, [ self._symbol, self._reference ], 100, Resolution.MINUTE) timedelta_indicator_history = self.indicator_history(self._nhnl, [ self._symbol, self._reference ], timedelta(days=10), Resolution.MINUTE) @@ -443414,7 +445026,7 @@

    Using NHNLV Indicator

    { _symbol = AddEquity("QQQ", Resolution.Daily).Symbol; _reference = AddEquity("SPY", Resolution.Daily).Symbol; - _nhnlv = NHNLV([_symbol, _reference]); + _nhnlv = NHNLV([_symbol, _reference], 2); } public override void OnData(Slice data) @@ -443436,7 +445048,7 @@

    Using NHNLV Indicator

    def initialize(self) -> None: self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol self._reference = self.add_equity("SPY", Resolution.DAILY).symbol - self._nhnlv = self.nhnlv([self._symbol, self._reference]) + self._nhnlv = self.nhnlv([self._symbol, self._reference], 2) def on_data(self, slice: Slice) -> None: @@ -443503,7 +445115,7 @@

    Using NHNLV Indicator

    public override void OnSecuritiesChanged(SecurityChanges changes) { - _nhnlv = NHNLV(_universe.Selected); + _nhnlv = NHNLV(_universe.Selected, 2); } public override void OnData(Slice data) @@ -443530,7 +445142,7 @@

    Using NHNLV Indicator

    self._universe = self.add_universe(self.universe.etf('SPY')) def on_securities_changed(self, changes: SecurityChanges) -> None: - self._nhnlv = self.nhnlv(list(self._universe.selected)) + self._nhnlv = self.nhnlv(list(self._universe.selected), 2) def on_data(self, slice: Slice) -> None: @@ -443697,7 +445309,7 @@

    Indicator History

    { _symbol = AddEquity("QQQ", Resolution.Daily).Symbol; _reference = AddEquity("SPY", Resolution.Daily).Symbol; - _nhnlv = NHNLV([_symbol, _reference]); + _nhnlv = NHNLV([_symbol, _reference], 2); var indicatorHistory = IndicatorHistory(_nhnlv, new[] { _symbol, _reference }, 100, Resolution.Minute); var timeSpanIndicatorHistory = IndicatorHistory(_nhnlv, new[] { _symbol, _reference }, TimeSpan.FromDays(10), Resolution.Minute); @@ -443713,7 +445325,7 @@

    Indicator History

    def initialize(self) -> None: self._symbol = self.add_equity("QQQ", Resolution.DAILY).symbol self._reference = self.add_equity("SPY", Resolution.DAILY).symbol - self._nhnlv = self.nhnlv([self._symbol, self._reference]) + self._nhnlv = self.nhnlv([self._symbol, self._reference], 2) indicator_history = self.indicator_history(self._nhnlv, [ self._symbol, self._reference ], 100, Resolution.MINUTE) timedelta_indicator_history = self.indicator_history(self._nhnlv, [ self._symbol, self._reference ], timedelta(days=10), Resolution.MINUTE) @@ -449881,7 +451493,16 @@

    Introduction

    - This indicator computes the n-period population standard deviation. + This indicator computes the n-period population standard deviation of its input. When the input is a price series, the result is the dispersion of price levels, not the asset's volatility. To compute volatility, chain this indicator onto a + + + or + + + indicator using + + + .

    To view the implementation of this indicator, see the @@ -451275,6 +452896,12 @@

    Using SWISS Indicator

    // The current value of _swiss is represented by itself (_swiss) // or _swiss.Current.Value Plot("SwissArmyKnife", "swiss", _swiss); + // Plot all properties of abands + Plot("SwissArmyKnife", "gauss", _swiss.Gauss); + Plot("SwissArmyKnife", "butter", _swiss.Butter); + Plot("SwissArmyKnife", "highpass", _swiss.HighPass); + Plot("SwissArmyKnife", "twopolehighpass", _swiss.TwoPoleHighPass); + Plot("SwissArmyKnife", "bandpass", _swiss.BandPass); } } } @@ -451287,7 +452914,13 @@

    Using SWISS Indicator

    if self._swiss.is_ready: # The current value of self._swiss is represented by self._swiss.current.value - self.plot("SwissArmyKnife", "swiss", self._swiss.current.value) + self.plot("SwissArmyKnife", "swiss", self._swiss.current.value) + # Plot all attributes of self._swiss + self.plot("SwissArmyKnife", "gauss", self._swiss.gauss.current.value) + self.plot("SwissArmyKnife", "butter", self._swiss.butter.current.value) + self.plot("SwissArmyKnife", "high_pass", self._swiss.high_pass.current.value) + self.plot("SwissArmyKnife", "two_pole_high_pass", self._swiss.two_pole_high_pass.current.value) + self.plot("SwissArmyKnife", "band_pass", self._swiss.band_pass.current.value)

    For more information about this method, see the @@ -451338,6 +452971,12 @@

    Using SWISS Indicator

    // The current value of _swissarmyknife is represented by itself (_swissarmyknife) // or _swissarmyknife.Current.Value Plot("SwissArmyKnife", "swissarmyknife", _swissarmyknife); + // Plot all properties of abands + Plot("SwissArmyKnife", "gauss", _swissarmyknife.Gauss); + Plot("SwissArmyKnife", "butter", _swissarmyknife.Butter); + Plot("SwissArmyKnife", "highpass", _swissarmyknife.HighPass); + Plot("SwissArmyKnife", "twopolehighpass", _swissarmyknife.TwoPoleHighPass); + Plot("SwissArmyKnife", "bandpass", _swissarmyknife.BandPass); } } } @@ -451353,7 +452992,13 @@

    Using SWISS Indicator

    if self._swissarmyknife.is_ready: # The current value of self._swissarmyknife is represented by self._swissarmyknife.current.value - self.plot("SwissArmyKnife", "swissarmyknife", self._swissarmyknife.current.value) + self.plot("SwissArmyKnife", "swissarmyknife", self._swissarmyknife.current.value) + # Plot all attributes of self._swissarmyknife + self.plot("SwissArmyKnife", "gauss", self._swissarmyknife.gauss.current.value) + self.plot("SwissArmyKnife", "butter", self._swissarmyknife.butter.current.value) + self.plot("SwissArmyKnife", "high_pass", self._swissarmyknife.high_pass.current.value) + self.plot("SwissArmyKnife", "two_pole_high_pass", self._swissarmyknife.two_pole_high_pass.current.value) + self.plot("SwissArmyKnife", "band_pass", self._swissarmyknife.band_pass.current.value)

    For more information about this indicator, see its @@ -451429,6 +453074,13 @@

    Indicator History

    var indicatorHistory = IndicatorHistory(_swiss, _symbol, 100, Resolution.Minute); var timeSpanIndicatorHistory = IndicatorHistory(_swiss, _symbol, TimeSpan.FromDays(10), Resolution.Minute); var timePeriodIndicatorHistory = IndicatorHistory(_swiss, _symbol, new DateTime(2024, 7, 1), new DateTime(2024, 7, 5), Resolution.Minute); + + // Access all attributes of indicatorHistory + var gauss = indicatorHistory.Select(x => ((dynamic)x).Gauss).ToList(); + var butter = indicatorHistory.Select(x => ((dynamic)x).Butter).ToList(); + var highPass = indicatorHistory.Select(x => ((dynamic)x).HighPass).ToList(); + var twoPoleHighPass = indicatorHistory.Select(x => ((dynamic)x).TwoPoleHighPass).ToList(); + var bandPass = indicatorHistory.Select(x => ((dynamic)x).BandPass).ToList(); } } 
    class SwissArmyKnifeAlgorithm(QCAlgorithm):
@@ -451439,7 +453091,14 @@ 
    Indicator History
    
         indicator_history = self.indicator_history(self._swiss, self._symbol, 100, Resolution.MINUTE)
         timedelta_indicator_history = self.indicator_history(self._swiss, self._symbol, timedelta(days=10), Resolution.MINUTE)
         time_period_indicator_history = self.indicator_history(self._swiss, self._symbol, datetime(2024, 7, 1), datetime(2024, 7, 5), Resolution.MINUTE)
-
    + + # Access all attributes of indicator_history + indicator_history_df = indicator_history.data_frame + gauss = indicator_history_df["gauss"] + butter = indicator_history_df["butter"] + high_pass = indicator_history_df["highpass"] + two_pole_high_pass = indicator_history_df["twopolehighpass"] + band_pass = indicator_history_df["bandpass"]
    @@ -456706,9 +458365,299 @@

    Indicator History

     

    -

    Indicators > Supported Indicators > Wilder Accumulative Swing Index

    +

    Indicators > Supported Indicators > Wave Trend Oscillator

    +

    Supported Indicators

    +

    Wave Trend Oscillator

    +
    +
    +

    Introduction

    + + + +

    + The WaveTrend Oscillator (WTO) is a momentum indicator that highlights overbought and oversold conditions by measuring how far the typical price has deviated from a smoothed moving average, normalized by an exponentially smoothed mean absolute deviation. The oscillator's main line (WT1) is an EMA of this normalized channel index, and the signal line (WT2) is an SMA of WT1; crossovers between the two lines are commonly used as entry and exit signals. Formula: HLC3 = (High + Low + Close) / 3 ESA = EMA(HLC3, channelPeriod) D = EMA(|HLC3 - ESA|, channelPeriod) CI = (HLC3 - ESA) / (0.015 * D) WT1 = EMA(CI, averagePeriod) (the indicator's Current.Value) WT2 = SMA(WT1, signalPeriod) (exposed via + + + ) +

    +

    + To view the implementation of this indicator, see the + + LEAN GitHub repository + + . +

    + + + +

    Using WTO Indicator

    + + +

    + To create an automatic indicator for + + WaveTrendOscillator + + , call the + + WTO + + + wto + + helper method from the + + QCAlgorithm + + class. The + + WTO + + + wto + + method creates a + + WaveTrendOscillator + + object, hooks it up for automatic updates, and returns it so you can used it in your algorithm. In most cases, you should call the helper method in the + + Initialize + + + initialize + + method. +

    +

    +

    +
    + 
    public class WaveTrendOscillatorAlgorithm : QCAlgorithm
+{
+    private Symbol _symbol;
+    private WaveTrendOscillator _wto;
+
+    public override void Initialize()
+    {
+        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
+        _wto = WTO(_symbol, 10, 21, 4);
+    }
+
+    public override void OnData(Slice data)
+    {
+
+        if (_wto.IsReady)
+        {
+            // The current value of _wto is represented by itself (_wto)
+            // or _wto.Current.Value
+            Plot("WaveTrendOscillator", "wto", _wto);
+            // Plot all properties of abands
+            Plot("WaveTrendOscillator", "channelaverage", _wto.ChannelAverage);
+            Plot("WaveTrendOscillator", "channeldeviation", _wto.ChannelDeviation);
+            Plot("WaveTrendOscillator", "channelindexaverage", _wto.ChannelIndexAverage);
+            Plot("WaveTrendOscillator", "signal", _wto.Signal);
+        }
+    }
+}
    + 
    class WaveTrendOscillatorAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
+        self._wto = self.wto(self._symbol, 10, 21, 4)
+
+    def on_data(self, slice: Slice) -> None:
+
+        if self._wto.is_ready:
+            # The current value of self._wto is represented by self._wto.current.value
+            self.plot("WaveTrendOscillator", "wto", self._wto.current.value)
+            # Plot all attributes of self._wto
+            self.plot("WaveTrendOscillator", "channel_average", self._wto.channel_average.current.value)
+            self.plot("WaveTrendOscillator", "channel_deviation", self._wto.channel_deviation.current.value)
+            self.plot("WaveTrendOscillator", "channel_index_average", self._wto.channel_index_average.current.value)
+            self.plot("WaveTrendOscillator", "signal", self._wto.signal.current.value)
    +
    +

    + For more information about this method, see the + + QCAlgorithm class + + + QCAlgorithm class + + . +

    +

    + You can manually create a + + WaveTrendOscillator + + indicator, so it doesn't automatically update. Manual indicators let you update their values with any data you choose. +

    +

    + Updating your indicator manually enables you to control when the indicator is updated and what data you use to update it. To manually update the indicator, call the + + Update + + + update + + method. The indicator will only be ready after you prime it with enough data. +

    +
    + 
    public class WaveTrendOscillatorAlgorithm : QCAlgorithm
+{
+    private Symbol _symbol;
+    private WaveTrendOscillator _wavetrendoscillator;
+
+    public override void Initialize()
+    {
+        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
+        _wavetrendoscillator = new WaveTrendOscillator(10, 21, 4);
+    }
+
+    public override void OnData(Slice data)
+    {
+        if (data.Bars.TryGetValue(_symbol, out var bar))
+            _wavetrendoscillator.Update(bar);
+
+        if (_wavetrendoscillator.IsReady)
+        {
+            // The current value of _wavetrendoscillator is represented by itself (_wavetrendoscillator)
+            // or _wavetrendoscillator.Current.Value
+            Plot("WaveTrendOscillator", "wavetrendoscillator", _wavetrendoscillator);
+            // Plot all properties of abands
+            Plot("WaveTrendOscillator", "channelaverage", _wavetrendoscillator.ChannelAverage);
+            Plot("WaveTrendOscillator", "channeldeviation", _wavetrendoscillator.ChannelDeviation);
+            Plot("WaveTrendOscillator", "channelindexaverage", _wavetrendoscillator.ChannelIndexAverage);
+            Plot("WaveTrendOscillator", "signal", _wavetrendoscillator.Signal);
+        }
+    }
+}
    + 
    class WaveTrendOscillatorAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
+        self._wavetrendoscillator = WaveTrendOscillator(10, 21, 4)
+
+    def on_data(self, slice: Slice) -> None:
+        bar = slice.bars.get(self._symbol)
+        if bar:
+            self._wavetrendoscillator.update(bar)
+
+        if self._wavetrendoscillator.is_ready:
+            # The current value of self._wavetrendoscillator is represented by self._wavetrendoscillator.current.value
+            self.plot("WaveTrendOscillator", "wavetrendoscillator", self._wavetrendoscillator.current.value)
+            # Plot all attributes of self._wavetrendoscillator
+            self.plot("WaveTrendOscillator", "channel_average", self._wavetrendoscillator.channel_average.current.value)
+            self.plot("WaveTrendOscillator", "channel_deviation", self._wavetrendoscillator.channel_deviation.current.value)
+            self.plot("WaveTrendOscillator", "channel_index_average", self._wavetrendoscillator.channel_index_average.current.value)
+            self.plot("WaveTrendOscillator", "signal", self._wavetrendoscillator.signal.current.value)
    +
    +

    + For more information about this indicator, see its + + reference + + + reference + + . +

    + + + +

    Visualization

    + + + +

    + The following plot shows values for some of the + + WaveTrendOscillator + + indicator properties: +

    + WaveTrendOscillator line plot. + + + +

    Indicator History

    + + +

    + To get the historical data of the + + WaveTrendOscillator + + indicator, call the + + IndicatorHistory + + + self.indicator_history + + method. This method resets your indicator, makes a + + history request + + , and updates the indicator with the historical data. Just like with regular history requests, the + + IndicatorHistory + + + indicator_history + + method supports time periods based on a trailing number of bars, a trailing period of time, or a defined period of time. If you don't provide a + + resolution + + argument, it defaults to match the resolution of the security subscription. +

    +
    + 
    public class WaveTrendOscillatorAlgorithm : QCAlgorithm
+{
+    private Symbol _symbol;
+    private WaveTrendOscillator _wto;
+
+    public override void Initialize()
+    {
+        _symbol = AddEquity("SPY", Resolution.Daily).Symbol;
+        _wto = WTO(_symbol, 10, 21, 4);
+
+        var indicatorHistory = IndicatorHistory(_wto, _symbol, 100, Resolution.Minute);
+        var timeSpanIndicatorHistory = IndicatorHistory(_wto, _symbol, TimeSpan.FromDays(10), Resolution.Minute);
+        var timePeriodIndicatorHistory = IndicatorHistory(_wto, _symbol, new DateTime(2024, 7, 1), new DateTime(2024, 7, 5), Resolution.Minute);
+
+        // Access all attributes of indicatorHistory
+        var channelAverage = indicatorHistory.Select(x => ((dynamic)x).ChannelAverage).ToList();
+        var channelDeviation = indicatorHistory.Select(x => ((dynamic)x).ChannelDeviation).ToList();
+        var channelIndexAverage = indicatorHistory.Select(x => ((dynamic)x).ChannelIndexAverage).ToList();
+        var signal = indicatorHistory.Select(x => ((dynamic)x).Signal).ToList();
+    }
+}
    + 
    class WaveTrendOscillatorAlgorithm(QCAlgorithm):
+    def initialize(self) -> None:
+        self._symbol = self.add_equity("SPY", Resolution.DAILY).symbol
+        self._wto = self.wto(self._symbol, 10, 21, 4)
+
+        indicator_history = self.indicator_history(self._wto, self._symbol, 100, Resolution.MINUTE)
+        timedelta_indicator_history = self.indicator_history(self._wto, self._symbol, timedelta(days=10), Resolution.MINUTE)
+        time_period_indicator_history = self.indicator_history(self._wto, self._symbol, datetime(2024, 7, 1), datetime(2024, 7, 5), Resolution.MINUTE)
+    
+        # Access all attributes of indicator_history
+        indicator_history_df = indicator_history.data_frame
+        channel_average = indicator_history_df["channelaverage"]
+        channel_deviation = indicator_history_df["channeldeviation"]
+        channel_index_average = indicator_history_df["channelindexaverage"]
+        signal = indicator_history_df["signal"]
    +
    + + + +

     

    +

    Indicators > Supported Indicators > Wilder Accumulative Swing Index

    +
    +

    Supported Indicators

    Wilder Accumulative Swing Index

    @@ -456993,7 +458942,7 @@

    Indicator History

     

    Indicators > Supported Indicators > Wilder Moving Average

    -
    +

    Supported Indicators

    Wilder Moving Average

    @@ -457248,7 +459197,7 @@

    Indicator History

     

    Indicators > Supported Indicators > Wilder Swing Index

    -
    +

    Supported Indicators

    Wilder Swing Index

    @@ -457591,7 +459540,7 @@

    Indicator History

     

    Indicators > Supported Indicators > Williams Percent R

    -
    +

    Supported Indicators

    Williams Percent R

    @@ -457866,7 +459815,7 @@

    Indicator History

     

    Indicators > Supported Indicators > Zero Lag Exponential Moving Average

    -
    +

    Supported Indicators

    Zero Lag Exponential Moving Average

    @@ -458121,7 +460070,7 @@

    Indicator History

     

    Indicators > Supported Indicators > Zig Zag

    -
    +

    Supported Indicators

    Zig Zag

    @@ -458609,7 +460558,7 @@

    Create Indicators

    # Create a manual indicator with the indicator constructor self.manual_bb = BollingerBands(20, 2) # Create an automatic indicator with the helper method - self._symbol = self.add_crypto("BTCUSD").Symbol + self._symbol = self.add_crypto("BTCUSD").symbol self.auto_bb = self.bb(self._symbol, 20, 2, Resolution.DAILY)
    @@ -459249,7 +461198,7 @@ 

    def on_securities_changed(self, changes: SecurityChanges) -> None:
     for security in changes.added_securities:
         # Create an indicator
-        security.indicator = self.sma(security.Symbol, 10)
+        security.indicator = self.sma(security.symbol, 10)
 
         # Warm up the indicator
         self.warm_up_indicator(security.symbol, security.indicator)
@@ -460732,7 +462681,7 @@ 
    
 WarmUpIndicator(new[] {aapl, spy}, beta, Resolution.Daily);
    self._spy = self.add_equity("SPY").symbol
 self._aapl = self.add_equity('AAPL').symbol
-self._beta = self.B(self._aapl, self._spy, 21, Resolution.DAILY)
+self._beta = self.b(self._aapl, self._spy, 21, Resolution.DAILY)
 self.warm_up_indicator([self._aapl, self._spy], self._beta, Resolution.DAILY)

    @@ -463058,7 +465007,7 @@

    # Update the rolling windows for cointegration analysis. window = self._windows.get(input.symbol) if not window: - raise Exception(f"{input.Symbol} is not part of the Cointegration relation.") + raise Exception(f"{input.symbol} is not part of the Cointegration relation.") window.add(IndicatorDataPoint(input.symbol, input.end_time, input.value)) if not all(x == 0 for x in self._coefficients): @@ -463363,6 +465312,276 @@

    def is_ready(self) -> bool: return len(self.positive_money_flow) == self.positive_money_flow.maxlen

    +

    + Example 3: Custom Annualized Volatility Indicator +

    +

    + The following algorithm implements a custom annualized log-return volatility indicator using a rolling window of close prices. + It screens a + + dynamic Equity universe + + for overbought stocks whose annualized volatility exceeds 100% and enters short positions via + + limit orders + + using an equal-weight allocation, exiting when the + + Connors Relative Strength Index + + reverts below the exit threshold. +

    +
    + 
    public class ConnorsCrashAlgorithm : QCAlgorithm
+{
+    private readonly int _rsiPeriod = 3, _streakPeriod = 2, _pctRankPeriod = 100, _volaPeriod = 100, _maxShorts = 40;
+    private readonly decimal _crsiEntry = 90m, _crsiExit = 30m;
+    private Universe _universe;
+    private readonly Dictionary<Symbol, (ConnorsRelativeStrengthIndex Crsi, CustomVolatility Vola)> _indicators = new();
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        SetCash(100_000);
+
+        Settings.AutomaticIndicatorWarmUp = true;
+        Settings.SeedInitialPrices = true;
+        UniverseSettings.Resolution = Resolution.Daily;
+
+        // Select liquid, tradeable-priced equities for the universe.
+        _universe = AddUniverse(fundamentals =>
+            fundamentals.Where(f => f.Price > 5 && f.DollarVolume > 1e6).Select(f => f.Symbol));
+
+        Schedule.On(
+            DateRules.EveryDay("SPY"),
+            TimeRules.At(8, 0),
+            Rebalance
+        );
+    }
+
+    public override void OnSecuritiesChanged(SecurityChanges changes)
+    {
+        foreach (var security in changes.AddedSecurities)
+        {
+            // Attach ConnorsRSI indicator to each security.
+            var crsi = CRSI(security.Symbol, _rsiPeriod, _streakPeriod, _pctRankPeriod);
+            // Initialize and warm up the custom volatility indicator.
+            var vola = new CustomVolatility(_volaPeriod);
+            foreach (var bar in History<TradeBar>(security.Symbol, _volaPeriod + 1))
+            {
+                vola.Update(bar);
+            }
+            RegisterIndicator(security.Symbol, vola, Resolution.Daily);
+            _indicators[security.Symbol] = (crsi, vola);
+        }
+        foreach (var security in changes.RemovedSecurities)
+        {
+            if (_indicators.TryGetValue(security.Symbol, out var pair))
+            {
+                DeregisterIndicator(pair.Crsi);
+                DeregisterIndicator(pair.Vola);
+                _indicators.Remove(security.Symbol);
+            }
+            Liquidate(security.Symbol);
+        }
+    }
+
+    private void Rebalance()
+    {
+        if (!_universe.Selected.Any()) return;
+
+        // Filter to securities with ready indicators.
+        var securities = _universe.Selected
+            .Where(s => Securities.ContainsKey(s) && _indicators.ContainsKey(s)
+                        && _indicators[s].Crsi.IsReady && _indicators[s].Vola.IsReady)
+            .Select(s => Securities[s])
+            .ToList();
+
+        // Filter to securities with above-threshold annualized volatility.
+        var filterVola = securities.Where(s => _indicators[s.Symbol].Vola.Current.Value > 100).ToList();
+
+        // Find currently invested short positions.
+        var shortPositions = securities.Where(s => s.Holdings.IsShort).ToList();
+
+        // Liquidate short positions when ConnorsRSI falls below exit threshold.
+        foreach (var security in shortPositions)
+        {
+            if (_indicators[security.Symbol].Crsi.Current.Value < _crsiExit)
+            {
+                Liquidate(security.Symbol);
+            }
+        }
+
+        // Exclude symbols with pending open orders.
+        var pendingSymbols = Transactions.GetOpenOrderTickets()
+            .Select(t => t.Symbol)
+            .ToHashSet();
+
+        // Find short entry candidates that are not currently invested (high volatility and high CRSI).
+        var shortCandidates = filterVola
+            .Where(s => _indicators[s.Symbol].Crsi.Current.Value > _crsiEntry
+                        && !s.Holdings.Invested
+                        && !pendingSymbols.Contains(s.Symbol))
+            .OrderBy(s => _indicators[s.Symbol].Crsi.Current.Value)
+            .ThenBy(s => _indicators[s.Symbol].Vola.Current.Value)
+            .ToList();
+
+        // Set union: liquidated positions are only counted once.
+        var occupied = shortPositions.Select(s => s.Symbol).ToHashSet();
+        occupied.UnionWith(pendingSymbols);
+        var availableSlots = _maxShorts - occupied.Count;
+
+        if (availableSlots <= 0 || !shortCandidates.Any()) return;
+
+        var nOrders = Math.Min(shortCandidates.Count, availableSlots);
+        var targetWeight = -1m / _maxShorts;
+
+        foreach (var security in shortCandidates.TakeLast(nOrders))
+        {
+            var quantity = (int)CalculateOrderQuantity(security.Symbol, targetWeight);
+            if (quantity != 0)
+            {
+                LimitOrder(security.Symbol, quantity, Math.Round(1.03m * security.Price, 2));
+            }
+        }
+    }
+}
+
+public class CustomVolatility : TradeBarIndicator, IIndicatorWarmUpPeriodProvider
+{
+    private readonly RollingWindow<double> _window;
+
+    public override bool IsReady => _window.IsReady;
+    public int WarmUpPeriod => _window.Size;
+
+    public CustomVolatility(int period) : base("CustomVolatility")
+    {
+        _window = new RollingWindow<double>(period);
+    }
+
+    protected override decimal ComputeNextValue(TradeBar input)
+    {
+        // Annualized log-return volatility.
+        var price = (double)input.Value;
+        if (price <= 0) return Current.Value;
+
+        _window.Add(price);
+        if (!_window.IsReady) return 0m;
+
+        // Collect prices from oldest to newest to compute chronological log returns.
+        var prices = _window.ToArray().Reverse().ToArray();
+        var logDiffs = new double[prices.Length - 1];
+        for (var i = 0; i < logDiffs.Length; i++)
+        {
+            logDiffs[i] = Math.Log(prices[i + 1] / prices[i]);
+        }
+
+        // Annualized standard deviation of log returns.
+        var mean = logDiffs.Average();
+        var variance = logDiffs.Select(d => (d - mean) * (d - mean)).Average();
+        return (decimal)(Math.Sqrt(variance) * Math.Sqrt(252) * 100.0);
+    }
+}
    + 
    class ConnorsCrash(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100_000)
+        self._rsi_period = 3
+        self._streak_period = 2
+        self._pct_rank_period = 100
+        self._vola_period = 100
+        self._crsi_entry = 90
+        self._crsi_exit = 30
+        self._max_shorts = 40
+        self._stop_trading_days_before_end = 1
+        self.settings.automatic_indicator_warm_up = True
+        self.settings.seed_initial_prices = True
+        self.universe_settings.resolution = Resolution.DAILY
+        # Select liquid, tradeable-priced equities for the universe.
+        self._universe = self.add_universe(
+            lambda fundamentals: [f.symbol for f in fundamentals if f.price > 5 and f.dollar_volume > 1e6]
+        )
+        self.schedule.on(
+            self.date_rules.every_day('SPY'),
+            self.time_rules.at(8, 0),
+            self._rebalance
+        )
+
+    def on_securities_changed(self, changes):
+        for security in changes.added_securities:
+            # Attach ConnorsRSI indicator to each security.
+            security.connors = self.crsi(security, self._rsi_period, self._streak_period, self._pct_rank_period)
+            # Initialize and warm up the custom volatility indicator.
+            security.volatility = CustomVolatility(self._vola_period)
+            for bar in self.history[TradeBar](security, self._vola_period + 1):
+                security.volatility.update(bar)
+            self.register_indicator(security, security.volatility)
+        for security in changes.removed_securities:
+            self.deregister_indicator(security.connors)
+            self.deregister_indicator(security.volatility)
+            self.liquidate(security)
+
+    def _rebalance(self):
+        if not self._universe.selected:
+            return
+        securities = [self.securities[symbol] for symbol in self._universe.selected]
+        securities = [s for s in securities if s.connors.is_ready and s.volatility.is_ready]
+        filter_vola = [s for s in securities if s.volatility.value > 100]
+        # Find currently invested short positions.
+        short_positions = [s for s in securities if s.holdings.is_short]
+        # Liquidate short positions when ConnorsRSI falls below exit threshold.
+        for security in short_positions:
+            if security.connors.current.value < self._crsi_exit:
+                self.liquidate(security)
+        # Exclude symbols with pending open orders.
+        pending_symbols = {t.symbol for t in self.transactions.get_open_order_tickets()}
+        # Find short entry candidates that are not currently invested (high volatility and high CRSI).
+        short_candidates = sorted([
+            s for s in filter_vola
+            if (s.connors.current.value > self._crsi_entry and
+            not s.holdings.invested and
+            s.symbol not in pending_symbols)
+        ], key=lambda s: (s.connors.current.value, s.volatility.value))
+        # Set union: liquidated positions are only counted once.
+        short_position_symbols = {s.symbol for s in short_positions}
+        occupied = short_position_symbols | pending_symbols
+        available_slots = self._max_shorts - len(occupied)
+        if available_slots <= 0 or not short_candidates:
+            return
+        n_orders = min(len(short_candidates), available_slots)
+        target_weight = -1 / self._max_shorts
+        for security in short_candidates[-n_orders:]:
+            quantity = int(self.calculate_order_quantity(security, target_weight))
+            if quantity:
+                self.limit_order(security, quantity, round(1.03 * security.price, 2))
+
+
+class CustomVolatility(PythonIndicator):
+
+    def __init__(self, period):
+        super().__init__()
+        self.value = 0
+        self._window = RollingWindow[float](period)
+
+    def update(self, input_: BaseData):
+        # Annualized log-return volatility.
+        price = input_.value
+        if price <= 0:
+            return
+        self._window.add(price)
+        if self._window.is_ready:
+            prices = np.array(list(self._window)[::-1])
+            log_diffs = np.diff(np.log(prices))
+            self.value = np.std(log_diffs) * math.sqrt(252) * 100.0
+        return self.is_ready
+
+    @property
+    def is_ready(self) -> bool:
+        return self._window.is_ready
    +

    Other Examples

    @@ -463828,6 +466047,190 @@

    self.deregister_indicator(removed.ema) self.deregister_indicator(removed.sma)

    +

    + Example 2: Reset Indicators on Corporate Actions +

    +

    + The following example shows how to select a universe of US Equities based on their price and SMA indicator. + The + + SelectionData + + class keeps track of the SMA indicator for each stock in the universe dataset. + When a split or dividend occurs for a stock, the data in its indicator becomes invalid because it doesn't account for the price adjustments that the split or dividend causes. + The + + SelectionData + + class resets and warms up the indicator with the + + ScaledRaw + + + SCALED_RAW + + + data normalization mode + + , which gives you accurate indicator values to use in your universe selection after each corporate action. +

    +
    + 
    class EquityIndicatorUniverseSelectionAlgorithm(QCAlgorithm):
+
+    def initialize(self) -> None:
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.settings.seed_initial_prices = True
+        # Add a universe of US Equities based on an indicator.
+        self._selection_data_by_symbol = {}
+        self._universe = self.add_universe(self._select_assets)
+        # Add a warm-up period to warm up the indicators in the universe selection.
+        self.set_warm_up(timedelta(60))
+
+    def _select_assets(self, fundamentals):
+        # Update the indicator of all stocks in the universe dataset and
+        # get the subset of stocks that have their indicator ready.
+        ready_stocks = [
+            f for f in fundamentals
+            if self._selection_data_by_symbol.setdefault(f.symbol, SelectionData(self, f)).update(f)
+        ]
+        # As assests leave the Fundamental dataset, delete their SelectionData object.
+        for symbol in self._selection_data_by_symbol.keys() - {f.symbol for f in fundamentals}:
+            del self._selection_data_by_symbol[symbol]
+        # During warm-up, keep the universe empty.
+        if self.is_warming_up:
+            return []
+        # Select a subset of the stocks based on the indicator.
+        # Example: 10 stocks furthest above their SMA.
+        factor_by_symbol = {
+            f.symbol: f.price / self._selection_data_by_symbol[f.symbol].indicator.current.value 
+            for f in ready_stocks
+        }
+        return sorted(
+            {k: v for k, v in factor_by_symbol.items() if v > 0}, 
+            key=lambda symbol: factor_by_symbol[symbol]
+        )[-100:]
+
+
+class SelectionData:
+
+    def __init__(self, algorithm, f):
+        self._algorithm = algorithm
+        self._price_scale_factor = f.price_scale_factor
+        self.indicator = SimpleMovingAverage(21)
+
+    def update(self, f):
+        # If there hasn't been a split or dividend since the last trading
+        # day, just update the indicator like normal.
+        if f.price_scale_factor == self._price_scale_factor:
+            return self.indicator.update(f.end_time, f.price)
+        # Otherwise, reset the indicator and warm it up with the new 
+        # adjusted history.
+        self._price_scale_factor = f.price_scale_factor
+        self.indicator.reset()
+        history = self._algorithm.history[TradeBar](
+            f.symbol, 
+            self.indicator.warm_up_period, 
+            Resolution.DAILY, 
+            data_normalization_mode=DataNormalizationMode.SCALED_RAW
+        )
+        for bar in history:
+            self.indicator.update(bar)
+        return self.indicator.is_ready
    + 
    public class EquityIndicatorUniverseSelectionAlgorithm : QCAlgorithm
+{
+    private Dictionary<Symbol, SelectionData> _selectionDataBySymbol = new();
+    private Universe _universe;
+
+    public override void Initialize()
+    {
+        SetStartDate(2024, 9, 1);
+        SetEndDate(2024, 12, 31);
+        Settings.SeedInitialPrices = true;
+        // Add a universe of US Equities based on an indicator.
+        _universe = AddUniverse(SelectAssets);
+        // Add a warm-up period to warm up the indicators in the universe selection.
+        SetWarmUp(TimeSpan.FromDays(60));
+    }
+
+    private IEnumerable<Symbol> SelectAssets(IEnumerable<Fundamental> fundamentals)
+    {
+        // Update the indicator of all stocks in the universe dataset and
+        // get the subset of stocks that have their indicator ready.
+        var readyStocks = new List<Fundamental>();
+        foreach (var f in fundamentals)
+        {
+            if (!_selectionDataBySymbol.TryGetValue(f.Symbol, out var sd))
+            {
+                sd = new SelectionData(this, f);
+                _selectionDataBySymbol[f.Symbol] = sd;
+            }
+            if (sd.Update(f))
+            {
+                readyStocks.Add(f);
+            }
+        }
+        // As assests leave the Fundamental dataset, delete their SelectionData object.
+        var activeStocks = fundamentals.Select(f => f.Symbol).ToHashSet();
+        foreach (var symbol in _selectionDataBySymbol.Keys.Where(s => !activeStocks.Contains(s)).ToList())
+        {
+            _selectionDataBySymbol.Remove(symbol);
+        }
+        // During warm-up, keep the universe empty.
+        if (IsWarmingUp)
+        {
+            return Enumerable.Empty<Symbol>();
+        }
+        // Select a subset of the stocks based on the indicator.
+        // Example: 10 stocks furthest above their SMA.
+        return readyStocks
+            .Select(f => (f.Symbol, Factor: f.Price / _selectionDataBySymbol[f.Symbol].Indicator.Current.Value))
+            .Where(t => t.Factor > 0)
+            .OrderBy(t => t.Factor)
+            .TakeLast(100)
+            .Select(t => t.Symbol);
+    }
+}
+
+public class SelectionData
+{
+    private QCAlgorithm _algorithm;
+    private decimal _priceScaleFactor;
+    public SimpleMovingAverage Indicator { get; }
+
+    public SelectionData(QCAlgorithm algorithm, Fundamental f)
+    {
+        _algorithm = algorithm;
+        _priceScaleFactor = f.PriceScaleFactor;
+        Indicator = new SimpleMovingAverage(21);
+    }
+
+    public bool Update(Fundamental f)
+    {
+        // If there hasn't been a split or dividend since the last trading
+        // day, just update the indicator like normal.
+        if (f.PriceScaleFactor == _priceScaleFactor)
+        {
+            return Indicator.Update(f.EndTime, f.Price);
+        }
+        // Otherwise, reset the indicator and warm it up with the new
+        // adjusted history.
+        _priceScaleFactor = f.PriceScaleFactor;
+        Indicator.Reset();
+        var history = _algorithm.History<TradeBar>(
+            f.Symbol,
+            Indicator.WarmUpPeriod,
+            Resolution.Daily,
+            dataNormalizationMode: DataNormalizationMode.ScaledRaw
+        );
+        foreach (var bar in history)
+        {
+            Indicator.Update(bar);
+        }
+        return Indicator.IsReady;
+    }
+}
    +

    Other Examples

    @@ -465756,11 +468159,14 @@

    Example for Plotting

    // Execute the following command in first
-#load "../Initialize.csx"
-
-// Create a QuantBook object
-#load "../QuantConnect.csx"
-using QuantConnect;
+#load "../Initialize.csx"
    +
    +
    + 
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    +
    +
    + 
    using QuantConnect;
 using QuantConnect.Research;
 
 var qb = new QuantBook();
    @@ -465801,8 +468207,12 @@

    Example for Plotting

    packages.
    - 
    #r "../Plotly.NET.dll"
-using Plotly.NET;
+    
    #r "nuget: Plotly.NET"
+#r "nuget: Plotly.NET.Interactive"
    
+
    +
    + 
    using Plotly.NET;
+using Plotly.NET.Interactive;
 using Plotly.NET.LayoutObjects;
  • @@ -465872,15 +468282,11 @@

    Example for Plotting

    Line - object and create the - - HTML - - object. + object and display the chart. 
    • chart.WithLayout(layout);
-var result = HTML(GenericChart.toChartHTML(chart));
    +display(chart);
    @@ -465997,11 +468403,11 @@

    Example for Logging

    if (_emaShort > _emaLong && !Portfolio[_symbol].IsLong) { - MarketOrder(_symbol, 100, tag: $"BUY: ema-short: {_emaShort:F4} > ema-long: {_emaLong:F4}"); + MarketOrder(_symbol, 100, tag: $"BUY: ema-short: {_emaShort:F4} > ema-long: {_emaLong:F4}"); } else if (_emaShort < _emaLong && !Portfolio[_symbol].IsShort) { - MarketOrder(_symbol, -100, tag: $"SELL: ema-short: {_emaShort:F4} < ema-long: {_emaLong:F4}"); + MarketOrder(_symbol, -100, tag: $"SELL: ema-short: {_emaShort:F4} < ema-long: {_emaLong:F4}"); } } 
    def on_data(self, data: Slice):
@@ -466011,15 +468417,18 @@ 
    Example for Logging
    
     ema_short = self._ema_short.current.value
     ema_long = self._ema_long.current.value
     if ema_short > ema_long and not self.portfolio[self._symbol].is_long:
-        self.market_order(self._symbol, 100, tag=f'BUY: ema-short: {ema_short:.4f}  >  ema-long: {ema_long:.4f}')
+        self.market_order(self._symbol, 100, tag=f'BUY: ema-short: {ema_short:.4f} > ema-long: {ema_long:.4f}')
     elif ema_short < ema_long and not self.portfolio[self._symbol].is_short:
-        self.market_order(self._symbol, -100, tag=f'SELL: ema-short: {ema_short:.4f}  <  ema-long: {ema_long:.4f}')
    + self.market_order(self._symbol, -100, tag=f'SELL: ema-short: {ema_short:.4f} < ema-long: {ema_long:.4f}')
  • In the - + OnOrderEvent + + on_order_event + method, log each fill to the content string.
    • @@ -466071,13 +468480,16 @@

    Example for Logging

     . -
    + 
    // Execute the following command in first
-#load "../Initialize.csx"
-
-// Create a QuantBook object
-#load "../QuantConnect.csx"
-using QuantConnect;
+#load "../Initialize.csx"
    +
    +
    + 
    // Load the necessary assembly files.
+#load "../QuantConnect.csx"
    +
    +
    + 
    using QuantConnect;
 using QuantConnect.Research;
 
 var qb = new QuantBook();
    @@ -466201,11 +468613,11 @@

    Example for Logging

    // Place a market order when the EMAs cross. if (_emaShort > _emaLong && !Portfolio[_symbol].IsLong) { - MarketOrder(_symbol, 100, tag: $"BUY: ema-short: {_emaShort:F4} > ema-long: {_emaLong:F4}"); + MarketOrder(_symbol, 100, tag: $"BUY: ema-short: {_emaShort:F4} > ema-long: {_emaLong:F4}"); } else if (_emaShort < _emaLong && !Portfolio[_symbol].IsShort) { - MarketOrder(_symbol, -100, tag: $"SELL: ema-short: {_emaShort:F4} < ema-long: {_emaLong:F4}"); + MarketOrder(_symbol, -100, tag: $"SELL: ema-short: {_emaShort:F4} < ema-long: {_emaLong:F4}"); } } @@ -466247,9 +468659,9 @@

    Example for Logging

    ema_short = self._ema_short.current.value ema_long = self._ema_long.current.value if ema_short > ema_long and not self.portfolio[self._symbol].is_long: - self.market_order(self._symbol, 100, tag=f'BUY: ema-short: {ema_short:.4f} > ema-long: {ema_long:.4f}') + self.market_order(self._symbol, 100, tag=f'BUY: ema-short: {ema_short:.4f} > ema-long: {ema_long:.4f}') elif ema_short < ema_long and not self.portfolio[self._symbol].is_short: - self.market_order(self._symbol, -100, tag=f'SELL: ema-short: {ema_short:.4f} < ema-long: {ema_long:.4f}') + self.market_order(self._symbol, -100, tag=f'SELL: ema-short: {ema_short:.4f} < ema-long: {ema_long:.4f}') def on_order_event(self, order_event: OrderEvent) -> None: if order_event.status != OrderStatus.FILLED: @@ -468884,7 +471296,7 @@ 

    # Prepare feature and label data for training.
 def get_features_and_labels(self, n_steps=5):
-    training_df = self.PandasConverter.GetDataFrame[TradeBar](list(self.training_data)[::-1])['close']
+    training_df = self.pandas_converter.get_data_frame[TradeBar](list(self.training_data)[::-1])['close']
 
     features = []
     for i in range(1, n_steps + 1):
@@ -472606,7 +475018,7 @@ 
    
   
    
    
    # Add the latest bar to training data to ensure the model is trained with the most recent market data.
 def on_data(self, slice: Slice) -> None:
-    if self._symbol in slice.Bars:
+    if self._symbol in slice.bars:
         self.training_data.add(slice.bars[self._symbol].close)
    
   
    
  
@@ -473258,9 +475670,9 @@ 
    Predict Labels
    
   
    
    
    # Place orders based on the forecasted market direction.
 if prediction > 0:
-    self.SetHoldings(self._symbol, 1)
+    self.set_holdings(self._symbol, 1)
 elif prediction < 0:            
-    self.SetHoldings(self._symbol, -1)
    
+    self.set_holdings(self._symbol, -1)
    @@ -473845,9 +476257,9 @@

    Predict Labels

    # Place orders based on the forecasted market direction.
 if prediction > 0:
-    self.SetHoldings(self._symbol, 1)
+    self.set_holdings(self._symbol, 1)
 elif prediction < 0:
-    self.SetHoldings(self._symbol, -1)
    + self.set_holdings(self._symbol, -1)
    @@ -476991,7 +479403,7 @@

    Use Cases

    Tiingo and - + Benzinga while reducing noise. @@ -478878,9 +481290,12 @@

    Add Models

    initialize     method, call the - + AddUniverseSelection + + add_universe_selection + method.

    @@ -480094,6 +482509,58 @@

    Schedule

    Trigger an event on the last tradable date of each week for a specific symbol minus an offset. + + + + self.date_rules.quarter_start(days_offset: int = 0) + + + DateRules.QuarterStart(int daysOffset = 0) + + + + Trigger an event on the first day of each quarter plus an offset. + + + + + + self.date_rules.quarter_start(symbol: Symbol, days_offset: int = 0) + + + DateRules.QuarterStart(Symbol symbol, int daysOffset = 0) + + + + Trigger an event on the first tradable date of each quarter for a specific symbol plus an offset. + + + + + + self.date_rules.quarter_end(days_offset: int = 0) + + + DateRules.QuarterEnd(int daysOffset = 0) + + + + Trigger an event on the last day of each quarter minus an offset. + + + + + + self.date_rules.quarter_end(symbol: Symbol, days_offset: int = 0) + + + DateRules.QuarterEnd(Symbol symbol, int daysOffset = 0) + + + + Trigger an event on the last tradable date of each quarter for a specific symbol minus an offset. + + @@ -480594,9 +483061,12 @@

    Add Manual Universe Selection

    initialize     method, call the - + AddUniverseSelection + + add_universe_selection + method. The ManualUniverseSelectionModel @@ -481073,7 +483543,7 @@

    Fundamental Selection

    most_liquid = sorted(filtered, key=lambda x: x.dollar_volume, reverse=True)[:100] # Select the 10 assets with the lowest PE ratio. lowest_pe_ratio = sorted(most_liquid, key=lambda x: x.valuation_ratios.pe_ratio, reverse=True)[:10] - return [x.Symbol for x in lowest_pe_ratio] + return [x.symbol for x in lowest_pe_ratio]

    To return the current universe constituents from the selection function, return @@ -481469,7 +483939,7 @@

    self.volume = 0 # Warm up the EMA indicator. - algorithm.warm_up_indicator(symbol, self._ema, Resolution.Daily); + algorithm.warm_up_indicator(symbol, self._ema, Resolution.DAILY); # Update your variables and indicators with the latest data. # You may also want to use the History API here to warm up the indicator. @@ -482007,7 +484477,7 @@

    Add ETF Constituents Universe Selection

    Ticker of the ETF to get constituents for. To view the available ETFs, see - + Supported ETFs . @@ -482462,7 +484932,7 @@

    symbol = c.symbol if symbol in self.ema_by_symbol and c.weight: # Update EMA with the latest weight. - self.ema_by_symbol[symbol].update(c.EndTime, c.weight) + self.ema_by_symbol[symbol].update(c.end_time, c.weight) else: # Create an EMA to filter by trend. self.ema_by_symbol[symbol] = ExponentialMovingAverage(60) @@ -482986,6 +485456,58 @@

    Date Rules

    Trigger an event on the last tradable date of each week for a specific symbol minus an offset. + + + + self.date_rules.quarter_start(days_offset: int = 0) + + + DateRules.QuarterStart(int daysOffset = 0) + + + + Trigger an event on the first day of each quarter plus an offset. + + + + + + self.date_rules.quarter_start(symbol: Symbol, days_offset: int = 0) + + + DateRules.QuarterStart(Symbol symbol, int daysOffset = 0) + + + + Trigger an event on the first tradable date of each quarter for a specific symbol plus an offset. + + + + + + self.date_rules.quarter_end(days_offset: int = 0) + + + DateRules.QuarterEnd(int daysOffset = 0) + + + + Trigger an event on the last day of each quarter minus an offset. + + + + + + self.date_rules.quarter_end(symbol: Symbol, days_offset: int = 0) + + + DateRules.QuarterEnd(Symbol symbol, int daysOffset = 0) + + + + Trigger an event on the last tradable date of each quarter for a specific symbol minus an offset. + + @@ -486346,9 +488868,12 @@

    Coarse Fundamental Selection

    To return the current universe constituents from the coarse selection function, return - + Universe.Unchanged + + Universe.UNCHANGED + .

    @@ -486639,9 +489164,12 @@

    Add Models

    initialize     method, call the - + AddAlpha + + add_alpha + method.

    @@ -487099,9 +489627,12 @@

    To mark insights as a group, call the - + Insight.Group + + Insight.group + method.

    @@ -487136,9 +489667,12 @@

    cancel / - + Expire + + expire + method with the algorithm's Coordinated Universal Time (UTC).

    @@ -490743,7 +493277,13 @@

    Accumulative Insight Model

     , it decreases the position size by a fixed percent. For each active Insight of direction - InsightDirection.Flat + InsightDirection. + + Flat + + + FLAT + , it moves the position size towards 0 by a fixed percent.

    @@ -492543,9 +495083,12 @@

    Multi-Model Algorithms

    initialize     method, call the - + AddRiskManagement + + add_risk_management + method multiple times.

    @@ -493138,241 +495681,391 @@

    $$

    - 
    using Accord.Statistics;
-using MathNet.Numerics.Distributions;
+   
    using MathNet.Numerics.Distributions;
+
 
 public class FrameworkRiskManagementAlgorithm : QCAlgorithm
 {
+
     public override void Initialize()
     {
         SetStartDate(2024, 9, 1);
         SetEndDate(2024, 9, 15);
         SetCash(1000000);
-        
         // Add a universe of the most liquid stocks since their trend is more capital-supported.
-        AddUniverseSelection(new QC500UniverseSelectionModel());
-        // Emit insights all for selected stocks, rebalancing every 2 weeks.
+        AddUniverseSelection(new ETFConstituentsUniverseSelectionModel("SPY"));
+        // Emit insights for all selected stocks, rebalancing every two weeks.
         AddAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(14)));
         // Equal weighting on each insight is needed to dissipate capital risk evenly.
         SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
-
-        // Liquidate on extreme catastrophic event.
+        // Liquidate on extreme catastrophic events.
         AddRiskManagement(new TailValueAtRiskRiskManagementModel(0.05d, 14d));
     }
+}
 
-    private class TailValueAtRiskRiskManagementModel : RiskManagementModel
-    {
-        // The alpha level on TVaR calculation.
-        private readonly double _alpha;
-        // The number of days that the insight signal lasts for.
-        private readonly double _days;
-        private Dictionary<Symbol, SymbolData> _logRetBySymbol = new();
-
-        public TailValueAtRiskRiskManagementModel(double alpha = 0.05d, double numDays = 14d)
-        {
-            _alpha = alpha;
-            _days = numDays;
-        }
-
-        // Adjust the portfolio targets and return them. If there are no changes, emit nothing.
-        public override IEnumerable<PortfolioTarget> ManageRisk(QCAlgorithm algorithm, IPortfolioTarget[] _)
-        {
-            var targets = new List<PortfolioTarget>();
-
-            foreach (var (symbol, security) in algorithm.Securities)
-            {
-                if (!security.Invested)
-                {
-                    continue;
-                }
-
-                var pnl = security.Holdings.UnrealizedProfitPercent;
-                // If the %PnL is worse than the preset level TVaR, we liquidate it.
-                if (pnl < GetTVaR(symbol))
-                {
-                    // Cancel insights to avoid reordering afterward.
-                    algorithm.Insights.Cancel(new[] { symbol });
-                    // Liquidate.
-                    targets.Add(new PortfolioTarget(symbol, 0, tag: "Liquidate due to TVaR"));
-                }
-            }
 
-            return targets;
-        }
-
-        private decimal GetTVaR(Symbol symbol)
-        {
-            if (!_logRetBySymbol.TryGetValue(symbol, out var symbolData))
-            {
-                return 0m;
-            }
+public class TailValueAtRiskRiskManagementModel : RiskManagementModel
+{
+    private const string LogReturnKey = "LogReturn";
+    private const string LogReturnMeanKey = "LogReturnMean";
+    private const string LogReturnSdKey = "LogReturnSd";
+    private readonly double _alpha;
+    private readonly double _days;
+    private readonly List<Security> _securities = [];
 
-            // TVaR = \mu + \sigma * \phi[\Phi^{-1}(p)] / (1 - p)
-            // Scale up to the days of the signal. By stochastic calculus, we multiply by sqrt(days).
-            var dailyTVaR = symbolData.MeanLogRet + symbolData.SdLogRet * Math.Sqrt(_days) * Normal.PDF(0d, 1d, Normal.InvCDF(0d, 1d, _alpha)) / (1d - _alpha);
-            // We want the left side of the symmetric distribution.
-            return -Convert.ToDecimal(dailyTVaR);
-        }
+    public TailValueAtRiskRiskManagementModel(double alpha = 0.05d, double numDays = 7d)
+    {
+        // Set the alpha level for the TVaR calculation.
+        _alpha = alpha;
+        // Set the number of days the insight signal lasts.
+        _days = numDays;
+    }
 
-        public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
+    public override IEnumerable<PortfolioTarget> ManageRisk(QCAlgorithm algorithm, IPortfolioTarget[] _)
+    {
+        var targets = new List<PortfolioTarget>();
+        foreach (var security in _securities)
         {
-            foreach (var added in changes.AddedSecurities)
+            if (!security.Invested)
             {
-                // Add SymbolData class to handle log returns.
-                _logRetBySymbol[added.Symbol] = new SymbolData(algorithm, added.Symbol);
+                continue;
             }
-
-            foreach (var removed in changes.RemovedSecurities)
+            var pnl = security.Holdings.UnrealizedProfitPercent;
+            // Liquidate when the unrealized loss is worse than the preset TVaR level.
+            if (pnl < GetTVaR(security))
             {
-                // Stop subscription on the data to release computational resources.
-                if (_logRetBySymbol.Remove(removed.Symbol, out var symbolData))
-                {
-                    symbolData.Dispose();
-                }
+                // Cancel insights to avoid reordering afterward.
+                var symbols = new List<Symbol> { security.Symbol };
+                algorithm.Insights.Cancel(symbols);
+                targets.Add(new PortfolioTarget(security.Symbol, 0, tag: "Liquidate due to TVaR"));
             }
         }
+        return targets;
     }
 
-    private class SymbolData
+    public override void OnSecuritiesChanged(QCAlgorithm algorithm, SecurityChanges changes)
     {
-        private readonly QCAlgorithm _algorithm;
-        private readonly Symbol _symbol;
-        // Since the return is assumed log-normal, we use the log return indicator to calculate TVaR later.
-        private LogReturn _logRet = new(1);
-        // Set up a rolling window to save the log return for calculating the mean and SD for TVaR calculation.
-        private RollingWindow<double> _window = new(252);
-
-        public bool IsReady => _window.IsReady;
-
-        public double MeanLogRet => _window.Average();
-
-        public double SdLogRet => Measures.StandardDeviation(_window.ToArray());
-
-        public SymbolData(QCAlgorithm algorithm, Symbol symbol)
+        foreach (var security in changes.AddedSecurities)
         {
-            _algorithm = algorithm;
-            // Register the indicator for automatic updating for daily log returns.
-            algorithm.RegisterIndicator(symbol, _logRet, Resolution.Daily);
-            // Add a handler to save the log return to the rolling window.
-            _logRet.Updated += (_algorithm, point) => _window.Add((double)point.Value);
-            // Warm up the rolling window.
-            var history = algorithm.History<TradeBar>(symbol, 253, Resolution.Daily);
+            // Store the log return indicator and rolling window directly on the security.
+            var logReturn = algorithm.LOGR(security.Symbol, 1, Resolution.Daily);
+            var logReturnMean = IndicatorExtensions.SMA(logReturn, 252);
+            var logReturnSd = IndicatorExtensions.Of(new StandardDeviation(252), logReturn);
+            security.Set(LogReturnKey, logReturn);
+            security.Set(LogReturnMeanKey, logReturnMean);
+            security.Set(LogReturnSdKey, logReturnSd);
+            var history = algorithm.History<TradeBar>(security.Symbol, 253, Resolution.Daily);
             foreach (var bar in history)
             {
-                _logRet.Update(bar.EndTime, bar.Close);
+                logReturn.Update(bar.EndTime, bar.Close);
             }
+            _securities.Add(security);
         }
-
-        public void Dispose()
+        foreach (var security in changes.RemovedSecurities)
         {
-            // Stop subscription on the data to release computational resources.
-            _algorithm.DeregisterIndicator(_logRet);
+            if (!_securities.Contains(security))
+            {
+                continue;
+            }
+            // Stop updating the log return indicator to release resources.
+            algorithm.DeregisterIndicator(security.Get<LogReturn>(LogReturnKey));
+            _securities.Remove(security);
         }
     }
+
+    private decimal GetTVaR(Security security)
+    {
+        var logReturnMean = security.Get<SimpleMovingAverage>(LogReturnMeanKey);
+        var logReturnSd = security.Get<StandardDeviation>(LogReturnSdKey);
+        // Calculate TVaR from the log return mean and standard deviation.
+        var dailyTVaR = (double)logReturnMean.Current.Value + (double)logReturnSd.Current.Value * Math.Sqrt(_days) * Normal.PDF(0d, 1d, Normal.InvCDF(0d, 1d, _alpha)) / (1d - _alpha);
+        // Use the left side of the symmetric distribution.
+        return -Convert.ToDecimal(dailyTVaR);
+    }
 }
    
    
    from scipy.stats import norm
 
+
 class FrameworkRiskManagementAlgorithm(QCAlgorithm):
+
     def initialize(self) -> None:
         self.set_start_date(2024, 9, 1)
         self.set_end_date(2024, 9, 15)
         self.set_cash(1000000)
-
         # Add a universe of the most liquid stocks since their trend is more capital-supported.
-        self.add_universe_selection(QC500UniverseSelectionModel())
-        # Emit insights all for selected stocks, rebalancing every 2 weeks.
+        self.add_universe_selection(ETFConstituentsUniverseSelectionModel('SPY'))
+        # Emit insights for all selected stocks, rebalancing every two weeks.
         self.add_alpha(ConstantAlphaModel(InsightType.PRICE, InsightDirection.UP, timedelta(14)))
-        # Equal weighting on each insight to dissipate capital risk evenly.
+        # Equal weighting on each insight is needed to dissipate capital risk evenly.
         self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
-
         # Liquidate on extreme catastrophic events.
         self.add_risk_management(TailValueAtRiskRiskManagementModel(0.05, 14))
 
+
 class TailValueAtRiskRiskManagementModel(RiskManagementModel):
-    _log_ret_by_symbol = {}
 
     def __init__(self, alpha: float = 0.05, num_days: float = 7) -> None:
-        # The alpha level on TVaR calculation.
-        self.alpha = alpha
-        # The number of days that the insight signal lasts for.
-        self.days = num_days
+        # Set the alpha level for the TVaR calculation.
+        self._alpha = alpha
+        # Set the number of days the insight signal lasts.
+        self._days = num_days
+        self._securities = []
 
-    # Adjust the portfolio targets and return them. If there are no changes, emit nothing.
-    def manage_risk(self, algorithm: QCAlgorithm, targets: List[PortfolioTarget]) -> List[PortfolioTarget]:
+    def manage_risk(self, algorithm: QCAlgorithm, targets: List[IPortfolioTarget]) -> List[IPortfolioTarget]:
         targets = []
-        for kvp in algorithm.securities:
-            security = kvp.value
+        for security in self._securities:
             if not security.invested:
                 continue
-
             pnl = security.holdings.unrealized_profit_percent
-            symbol = security.symbol
-            # If the %PnL is worse than the preset level TVaR, we liquidate it.
-            if pnl < self.get_tvar(symbol):
+            # Liquidate when the unrealized loss is worse than the preset TVaR level.
+            if pnl < self._get_tvar(security):
                 # Cancel insights to avoid reordering afterward.
-                algorithm.insights.cancel([symbol])
-                # Liquidate.
-                targets.append(PortfolioTarget(symbol, 0, tag="Liquidate due to TVaR"))
-
+                algorithm.insights.cancel([security])
+                targets.append(PortfolioTarget(security, 0, tag="Liquidate due to TVaR"))
         return targets
 
-    def get_tvar(self, symbol: Symbol) -> float:
-        symbol_data = self._log_ret_by_symbol.get(symbol)
-        if not symbol_data:
-            return 0
-        
-        # TVaR = \mu + \sigma * \phi[\Phi^{-1}(p)] / (1 - p)
-        # Scale up to the days of the signal. By stochastic calculus, we multiply by sqrt(days).
-        daily_tvar = symbol_data.mean_log_ret + symbol_data.sd_log_ret * np.sqrt(self.days) * norm.pdf(norm.ppf(self.alpha)) / (1 - self.alpha)
-        # We want the left side of the symmetric distribution.
-        return -daily_tvar
+    def _get_tvar(self, security: Security) -> float:
+        # Calculate TVaR from the log return mean and standard deviation.
+        return -(
+            security.log_return_mean.current.value 
+            + security.log_return_sd.current.value 
+            * np.sqrt(self._days) 
+            * norm.pdf(norm.ppf(self._alpha)) 
+            / (1 - self._alpha)
+        )
 
     def on_securities_changed(self, algorithm: QCAlgorithm, changes: SecurityChanges) -> None:
-        for added in changes.added_securities:
-            # Add SymbolData class to handle log returns.
-            self._log_ret_by_symbol[added.symbol] = SymbolData(algorithm, added.symbol)
+        for security in changes.added_securities:
+            # Store the log return indicator and rolling window directly on the security.
+            security.log_ret = algorithm.logr(security, 1, Resolution.DAILY)
+            security.log_return_mean = IndicatorExtensions.sma(security.log_ret, 252)
+            security.log_return_sd = IndicatorExtensions.of(StandardDeviation(252), security.log_ret)
+            history = algorithm.history[TradeBar](security, 253, Resolution.DAILY)
+            for bar in history:
+                security.log_ret.update(bar.end_time, bar.close)
+            self._securities.append(security)
+        for security in changes.removed_securities:
+            if security not in self._securities:
+                continue
+            # Stop updating the log return indicator to release resources.
+            algorithm.deregister_indicator(security.log_ret)
+            self._securities.remove(security)
    
+
    +
    +

    + Example 3: Bracket Risk Model +

    +

    + The following algorithm implements a custom bracket risk management model that combines a trailing stop-loss with a take-profit target. + Positions are exited when unrealized profit exceeds a threshold or when the price falls by more than a specified percentage from the trailing high. + It is used alongside an XGBoost Alpha model that trades Equities near their earnings dates. +

    +
    + 
    from sklearn.model_selection import RandomizedSearchCV, train_test_split
+from sklearn.preprocessing import MinMaxScaler
+import xgboost as xgb
 
-        for removed in changes.removed_securities:
-            # Stop subscription on the data to release computational resources.
-            symbol_data = self._log_ret_by_symbol.pop(removed.symbol, None)
-            if symbol_data:
-                symbol_data.dispose()
+class ModulatedNadionReplicator(QCAlgorithm):
 
-class SymbolData:
-    def __init__(self, algorithm: QCAlgorithm, symbol: Symbol) -> None:
-        self.algorithm = algorithm
-        self.symbol = symbol
+    def initialize(self):
+        self.set_start_date(2024, 9, 1)
+        self.set_end_date(2024, 12, 31)
+        self.set_cash(100_000)
+        self.settings.free_portfolio_value_percentage = 0.05
+        self.settings.seed_initial_prices = True
+        self._spy = self.add_equity("SPY", Resolution.HOUR)
 
-        # Since the return is assumed log-normal, we use the log return indicator to calculate TVaR later.
-        self.log_ret = LogReturn(1)
-        # Register the indicator for automatic updating for daily log returns.
-        algorithm.register_indicator(symbol, self.log_ret, Resolution.DAILY)
-        # Set up a rolling window to save the log return for calculating the mean and SD for TVaR calculation.
-        self.window = RollingWindow(252)
-        # Add a handler to save the log return to the rolling window.
-        self.log_ret.updated += lambda _, point: self.window.add(point.value)
-        # Warm up the rolling window.
-        history = algorithm.history[TradeBar](symbol, 253, Resolution.DAILY)
-        for bar in history:
-            self.log_ret.update(bar.end_time, bar.close)
+        # Create universe parameters.
+        self.universe_settings.resolution = Resolution.HOUR
+        self.universe_settings.asynchronous = True
+        date_rule = self.date_rules.month_start(self._spy)
+        self.universe_settings.schedule.on(date_rule)
+        self.add_universe_selection(EarningsVolumeUniverseSelectionModel(10))
 
-    @property
-    def is_ready(self) -> bool:
-        return self.window.is_ready
+        # Add the other framework models.
+        self.add_alpha(XGBoostAlphaModel(self, date_rule, self._spy))
+        self.set_portfolio_construction(InsightWeightingPortfolioConstructionModel())
+        self.set_risk_management(BracketRiskModel(0.05, 0.15))
 
-    @property
-    def mean_log_ret(self) -> float:
-        # Mean log return for TVaR calculation.
-        return np.mean(list(self.window))
+        # Add a warm up so the algorithm has insights on deployment day.
+        self.set_warm_up(timedelta(45))
 
-    @property
-    def sd_log_ret(self) -> float:
-        # SD of log return for TVaR calculation.
-        return np.std(list(self.window), ddof=1)
 
-    def dispose(self) -> None:
-        # Stop subscription on the data to release computational resources.
-        self.algorithm.deregister_indicator(self.log_ret)
    +class EarningsVolumeUniverseSelectionModel(FundamentalUniverseSelectionModel): + # Selects symbols by liquidity and nearest earnings report date. + def __init__(self, universe_size=10): + self._universe_size = universe_size + super().__init__(self._select) + + def _select(self, fundamental): + # Sort the top 30 by price and dollar volume. + liquid = sorted( + [f for f in fundamental if f.has_fundamental_data and 20 <= f.price <= 200 and f.dollar_volume > 5_000_000], + key=lambda f: f.dollar_volume + )[-30:] + + # Select symbols with nearest earnings report dates. + selected = sorted( + [f for f in liquid if f.earning_reports.file_date], + key=lambda f: str(f.earning_reports.file_date) + )[:self._universe_size] + + return [f.symbol for f in selected] + + +class XGBoostAlphaModel(AlphaModel): + + def __init__(self, algorithm, date_rule, spy): + self._algorithm = algorithm + self._universe = [] + self._insights = [] + self._feature_window = 24 + self._history_bars = 48 + self._rsi_period = 12 + self._scaler = MinMaxScaler(feature_range=(-1, 1)) + self._spy = spy + + # Create rate-of-change indicator for SPY with specified window. + self._spy.rocp = algorithm.rocp(spy, self._feature_window) + self._spy.rocp.window.size = self._feature_window + algorithm.indicator_history(self._spy.rocp, spy, self._spy.rocp.period + self._spy.rocp.window.size) + + algorithm.train(date_rule, algorithm.time_rules.at(8, 0), self._train_models) + algorithm.schedule.on(date_rule, algorithm.time_rules.after_market_open(self._spy, 30), self._create_insights) + + def _create_insights(self): + self._insights.clear() + + # Extract predictions from all trained models. + for security in self._universe: + # Skip prediction if model not yet trained. + if not (security.model and security.rocp.window.is_ready and self._spy.rocp.window.is_ready): + continue + features, _ = self._build_features(security) + # Get prediction from the trained model. + magnitude = security.model.predict(features)[-1] + # Determine signal direction based on prediction magnitude. + direction = InsightDirection.FLAT + if magnitude > 0.05: + direction = InsightDirection.UP + elif magnitude < -0.05: + direction = InsightDirection.DOWN + # Generate price insights with computed weights and directions. + self._insights.append(Insight.price(security, timedelta(1), direction, weight=0.3 * abs(magnitude))) + + def update(self, algorithm, data): + if algorithm.is_warming_up: + return [] + + insights = self._insights.copy() + self._insights.clear() + + return insights + + def on_securities_changed(self, algorithm, changes): + for security in changes.added_securities: + if security == self._spy or security in self._universe: + continue + # Add security and register indicators to universe. + self._universe.append(security) + security.model = None + security.rocp = algorithm.rocp(security, self._feature_window) + security.rsi = algorithm.rsi(security, self._rsi_period, MovingAverageType.SIMPLE) + security.atr = algorithm.atr(security, self._feature_window, MovingAverageType.SIMPLE) + # Configure window sizes and populate history for indicator and window. + bars = algorithm.history[TradeBar](security, self._feature_window * 2) + for indicator in [security.rocp, security.rsi, security.atr]: + indicator.window.size = self._feature_window + for bar in bars: + indicator.update(bar) + + for security in changes.removed_securities: + if security not in self._universe: + continue + # Remove security and deregister indicators from universe. + self._universe.remove(security) + for indicator in [security.rocp, security.rsi, security.atr]: + algorithm.deregister_indicator(indicator) + + def _build_features(self, security): + # Compute momentum and volatility indicators and normalize all features to consistent range. + scaled = self._scaler.fit_transform(np.hstack( + [self._get_indicator_history(self._spy.rocp)] + + [self._get_indicator_history(indicator) for indicator in [security.rocp, security.rsi, security.atr]] + )) + + return scaled[:, :3], scaled[:, [3]] + + def _get_indicator_history(self, indicator): + return np.array([x.value for x in indicator.window])[::-1].reshape(-1, 1) + + def _train_models(self): + # Iterate through all security in the universe for model training. + for security in self._universe: + if not (security.rocp.window.is_ready and self._spy.rocp.window.is_ready): + continue + + features, scaled_rocp = self._build_features(security) + target = scaled_rocp.ravel() + + # Prepare training and validation splits from the feature matrix. + x_train, x_valid, y_train, y_valid = train_test_split( + features, target, test_size=0.35, random_state=42, + ) + + # Define hyperparameter grid for randomized search optimization. + parameters = { + 'n_estimators': [100, 200, 300, 400], + 'learning_rate': [0.001, 0.005, 0.01, 0.05], + 'max_depth': [8, 10, 12, 15], + 'gamma': [0.001, 0.005, 0.01, 0.02], + 'random_state': [42] + } + eval_set = [(x_train, y_train), (x_valid, y_valid)] + base_model = xgb.XGBRegressor(objective="reg:squarederror", verbosity=0) + model = RandomizedSearchCV( + estimator=base_model, param_distributions=parameters, n_iter=5, scoring="neg_mean_squared_error", cv=4, verbose=0, + ) + + # Execute randomized search for optimal model hyperparameters. + model.fit(x_train, y_train, eval_set=eval_set, verbose=False) + security.model = model + + +class BracketRiskModel(RiskManagementModel): + + def __init__(self, drawdown_pct, profit_pct): + # Store drawdown and profit thresholds as percentage values. + self._drawdown_pct = -abs(drawdown_pct) + self._profit_pct = abs(profit_pct) + + def manage_risk(self, algorithm, targets): + # Build list of risk-adjusted targets. + adjusted_targets = [] + + # Iterate through all securities to evaluate positions. + for symbol, security in algorithm.securities.items(): + # Reset trailing high for non-invested securities. + if not security.invested: + security.trailing_high = None + # Take profit when unrealized gains exceed the threshold. + elif security.holdings.unrealized_profit_percent > self._profit_pct: + adjusted_targets.append(PortfolioTarget(symbol, 0)) + algorithm.insights.cancel([symbol]) + # Initialize trailing high from the entry price. + elif security.trailing_high is None: + security.trailing_high = security.holdings.average_price + # Update trailing high if a new high is reached. + elif security.trailing_high < security.high: + security.trailing_high = security.high + # Exit position when drawdown from trailing high exceeds limit. + elif (security.low / security.trailing_high) - 1 < self._drawdown_pct: + adjusted_targets.append(PortfolioTarget(symbol, 0)) + algorithm.insights.cancel([symbol]) + + return adjusted_targets + +