Skip to main content

A Python library for managing and analyzing macroeconomic time series data with vintage awareness.

Project description

MacroTrace

PyPI version Python versions License: GPL-3.0-or-later CI Docs

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).

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

macrotrace-0.2.2.tar.gz (473.3 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

macrotrace-0.2.2-py3-none-any.whl (119.6 kB view details)

Uploaded Python 3

File details

Details for the file macrotrace-0.2.2.tar.gz.

File metadata

  • Download URL: macrotrace-0.2.2.tar.gz
  • Upload date:
  • Size: 473.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for macrotrace-0.2.2.tar.gz
Algorithm Hash digest
SHA256 7fca7e3445a62808060d610d4e9333bf381f5a6f9d785704f79a776db55f63e7
MD5 be5c21a02ff8589c7be9e135464627e4
BLAKE2b-256 84876d2a13b6d03b6d92788acad7b750a885cf1d133ee9a2ce01ce057c13cc58

See more details on using hashes here.

Provenance

The following attestation bundles were made for macrotrace-0.2.2.tar.gz:

Publisher: release.yml on john-ramsey/macrotrace

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file macrotrace-0.2.2-py3-none-any.whl.

File metadata

  • Download URL: macrotrace-0.2.2-py3-none-any.whl
  • Upload date:
  • Size: 119.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.12

File hashes

Hashes for macrotrace-0.2.2-py3-none-any.whl
Algorithm Hash digest
SHA256 bfe54c613ae659d13d01e4af2d7b697d5df5ece0a3537fb76ff63884b12b9a10
MD5 d82a2484c77fb12d278e35c2bc1d1401
BLAKE2b-256 78b8f7cf6905f242a0ca8ae727aca89eb046744e0bf0da83e6ccca9447af3bc6

See more details on using hashes here.

Provenance

The following attestation bundles were made for macrotrace-0.2.2-py3-none-any.whl:

Publisher: release.yml on john-ramsey/macrotrace

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page