# cuemacro/finmarketpy

Python library for backtesting trading strategies & analyzing financial markets (formerly pythalesians)

Repository: https://github.com/cuemacro/finmarketpy
Canonical: https://ross.abutalabs.com/products/finmarketpy
Homepage: http://www.cuemacro.com
Language: Python
License: Apache-2.0
License Family: permissive
Topics: python, trading-strategies, backtesting-trading-strategies
Last push: 2026-04-16T09:29:35+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 77, release rhythm 40, longevity 100
- inputs: {"age_days": 4213, "days_push": 139, "days_rel": 541, "gap_med": 0.5, "n_releases_24m": 3}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3805, forks 523 (observed 2026-08-28T04:08:20.278637+00:00)

## What it is
finmarketpy is a Python library for analyzing financial market data and backtesting trading strategies through a simple, object-oriented API with prebuilt backtest templates. It supports seasonality analysis, event studies, volatility-targeted risk weighting, and integrates with findatapy for market data downloads and chartpy for visualization.

## Use cases
- backtest trading strategies in python
- analyze historical returns of a trading strategy
- study seasonality of assets like gold or fx volatility
- run event studies around economic data releases
- download and analyze market data from Bloomberg or Yahoo
- apply volatility targeting to strategy risk weights

## When to choose
- you need a Python framework for backtesting trading strategies with prebuilt templates
- you want seasonality and market event study analysis out of the box
- you want pluggable charting backends (matplotlib, plotly, bokeh) and market data sources

## When to avoid
- you need production-grade, fully documented trading infrastructure - the API is still under continual development
- you want a standalone library - it requires the author's findatapy and chartpy dependencies
- you need live trading execution rather than research and backtesting

## Facets
- artifact type: library
- maturity: active
- function: data-science, data-visualization, analytics
- domain: fintech, data-science, analytics
- platform: python, cross-platform
- tags: backtesting, trading-strategies, financial-markets, seasonality, event-studies, market-data, quantitative-finance

## Member repositories
- cuemacro/finmarketpy (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:20.278637+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:26:47.139228+00:00, confidence not recorded.
  - readme: https://github.com/cuemacro/finmarketpy (fetched 2026-08-28T04:08:20.278637+00:00, sha ca8c1cab8301)
  - registry_pypi: https://pypi.org/pypi/finmarketpy/json (fetched 2026-08-29T09:21:41.782418+00:00, sha 763422ffce66)
- Data as of 2026-08-30T08:39:29.467469+00:00.