# pmorissette/ffn

ffn - a financial function library for Python

Repository: https://github.com/pmorissette/ffn
Canonical: https://ross.abutalabs.com/products/ffn
Language: Python
License: MIT
License Family: permissive
Last push: 2026-08-13T14:20:22+00:00

## Health v2 (maintenance only)
Score: 86/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 97, release rhythm 64, longevity 100
- inputs: {"age_days": 4458, "days_push": 20, "days_rel": 162, "gap_med": 60.0, "n_releases_24m": 5}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2636, forks 370 (observed 2026-08-28T04:07:06.251745+00:00)

## What it is
ffn is a Python library of financial functions for quantitative finance, built on Pandas, NumPy, and SciPy. It provides utilities for performance measurement, evaluation, plotting, and common data transformations of financial time series.

## Use cases
- calculate portfolio performance metrics like sharpe ratio and drawdown
- compute mean-variance optimal portfolio weights
- analyze returns of a set of tickers
- transform and resample financial price data
- plot cumulative performance of multiple assets
- evaluate algorithmic trading strategy statistics

## When to choose
- you need quick quantitative finance calculations on pandas data
- you want performance and risk metrics without a full backtesting engine
- you are prototyping portfolio analysis in Python

## When to avoid
- you need a full backtesting framework (use bt instead)
- you need real-time market data feeds
- you need enterprise-grade guaranteed stability (it is an alpha release)

## Facets
- artifact type: library
- maturity: active
- function: data-science, math, data-visualization
- domain: fintech, data-science, analytics
- platform: python, cross-platform
- tags: quantitative-finance, finance, portfolio-analysis, performance-metrics, pandas

## Member repositories
- pmorissette/ffn (main) score 86

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:06.251745+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 2026-08-30T02:19:19.771927+00:00, confidence not recorded.
  - readme: https://github.com/pmorissette/ffn (fetched 2026-08-28T04:07:06.251745+00:00, sha 4f79373870e4)
  - registry_pypi: https://pypi.org/pypi/ffn/json (fetched 2026-08-29T10:02:17.870495+00:00, sha 933656b0aeee)
- Data as of 2026-08-30T08:39:29.467469+00:00.