# quantopian/alphalens

Performance analysis of predictive (alpha) stock factors

Repository: https://github.com/quantopian/alphalens
Canonical: https://ross.abutalabs.com/products/alphalens
Homepage: http://quantopian.github.io/alphalens
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: finance, python, pandas, numpy, algorithmic-trading, jupyter
Last push: 2024-02-12T06:44:22+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3743, "days_push": 933, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4434, forks 1350 (observed 2026-08-28T04:08:49.228173+00:00)

## What it is
Alphalens is a Python library for performance analysis of predictive (alpha) stock factors. It generates 'tear sheets' with statistics and plots covering returns, information coefficient, turnover, and grouped analysis, integrating well with Zipline and Pyfolio.

## Use cases
- evaluate the predictive power of a stock alpha factor
- generate a factor tear sheet with returns and IC analysis
- analyze factor turnover and quantile performance
- compare alpha factor performance across sectors
- research quantitative trading signals in a Jupyter notebook

## When to choose
- you are researching or validating alpha factors for equity trading strategies
- you want standardized statistics and visualizations for factor performance
- you already work in Python with pandas and pricing data

## When to avoid
- you need full portfolio backtesting rather than factor analysis (use Zipline or a backtesting framework)
- you need active maintenance and support for the latest pandas versions
- you trade asset classes or workflows outside its equity factor assumptions

## Facets
- artifact type: library
- maturity: maintenance
- function: data-science, data-visualization, analytics, trading
- domain: fintech, data-science, data-visualization, analytics
- platform: python
- tags: quantitative-finance, alpha-factor-analysis, tear-sheet, algorithmic-trading, pandas, jupyter

## Member repositories
- quantopian/alphalens (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:49.228173+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 2026-08-29T18:20:53.227590+00:00, confidence not recorded.
  - readme: https://github.com/quantopian/alphalens (fetched 2026-08-28T04:08:49.228173+00:00, sha 7994d77fdf45)
  - homepage: http://quantopian.github.io/alphalens (fetched 2026-08-29T09:08:12.736634+00:00, sha 4fa54b402c47)
  - registry_pypi: https://pypi.org/pypi/alphalens/json (fetched 2026-08-29T09:08:12.745649+00:00, sha 52e5073ca0cf)
- Data as of 2026-08-30T08:39:29.467469+00:00.