MacroTrace

MacroTrace is a Python library for collecting, storing, and analyzing macroeconomic time-series vintages. It is designed for research workflows where the revision history matters just as much as the latest published value.

Documentation: https://john-ramsey.github.io/macrotrace/

Instead of treating a series as a single final dataset, MacroTrace helps you work with the sequence of releases that were available in real time. This makes it easier to study data revisions, reproduce historical analyses, and compare what was known at different publication dates.

Features

• Fetch vintage-aware macroeconomic time series from FRED, ONS, and the Philadelphia Fed's Real-Time Data Set (RTDSM)
• Store releases locally in SQLite for reproducible, offline-friendly workflows
• Retrieve series as they were known on a specific date with as_of(...)
• Filter both vintage windows and data windows when loading a series
• Recover which release an undated block of data came from with identify_vintage(...)
• Export to pandas DataFrames or Series and Darts TimeSeries objects
• Plot vintages and revision comparisons with built-in Plotly tooling

Installation

Install the package from PyPI:

pip install macrotrace

Install the optional ONS Textual interface:

pip install "macrotrace[ons-tui]"

Requirements

• Python 3.11+
• A FRED API key for FRED-backed series

Set your FRED API key before loading FRED series:

export FRED_API_KEY="your_api_key_here"

Quick Start

from macrotrace import MTTimeSeries

payems = MTTimeSeries(
    dataset_id="PAYEMS",
    source="FRED",
)

print(payems)

july_2020 = payems.as_of("2020-07-15")
df = july_2020.to_dataframe()

MacroTrace stores fetched releases in a local SQLite database named MacroTrace.db, making repeated loads faster and keeping vintage histories available for later analysis.

For multi-dimensional datasets such as ONS releases, provide a series_key to select a specific slice of the dataset:

from macrotrace import MTTimeSeries

gdp = MTTimeSeries(
    dataset_id="gdp-to-four-decimal-places",
    source="ONS",
    series_key={
        "geography": "K02000001",
        "unofficialstandardindustrialclassification": "A--T",
    },
)

The Philadelphia Fed's Real-Time Data Set (RTDSM) needs no API key. Use the series mnemonic as the dataset_id and select the vintage frequency with the series_key:

from macrotrace import MTTimeSeries

routput = MTTimeSeries(
    dataset_id="ROUTPUT",
    source="RTDSM",
    series_key={"frequency": "Q"},
)

See the RTDSM source guide for the full list of series and details on vintage frequencies.

Identifying an Unknown Vintage

If you have a block of observations with no release date attached — for example, a series lifted from a replication package — identify_vintage compares it against every stored vintage and reports which release(s) it is consistent with:

from macrotrace import MTTimeSeries

routput = MTTimeSeries(
    dataset_id="ROUTPUT",
    source="RTDSM",
    series_key={"frequency": "Q"},
)

# `unknown` is a date-indexed pandas Series whose vintage you want to recover
match = routput.identify_vintage(unknown)

if match.is_ambiguous:
    print(f"Ambiguous — consistent with {len(match.release_dates)} vintages")
elif match.matched:
    print(f"Matches the {match.release_date.date()} vintage")
else:
    print(f"No matching vintage found (failed on: {match.failure_reason})")

Command-Line Tools

MacroTrace includes command-line tools for exploring ONS datasets:

macrotrace ons explorer

If you installed the optional TUI extra, you can also run:

macrotrace ons tui

Development

For local development, we use uv for dependency management and environment execution.

Install the project with the development, docs, and optional TUI dependencies:

uv sync --extra ons-tui --group dev --group docs

Run tests inside the managed environment with:

uv run pytest

Code formatting is handled with black:

uv run black .

Project Status

MacroTrace is under active development as part of a PhD research project on macroeconomic data revisions.

License

MacroTrace is licensed under the GNU General Public License v3.0 or later (GPL-3.0-or-later).