quantopian/alphalens
Performance analysis of predictive (alpha) stock factors observed · 2026-08-28
Health v2 · maintenance only
23/100
- Activity 0
- Release rhythm 8
- Longevity 100
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 3743
- days_rel: n/a
- days_push: 933
- n_releases_24m: 0
Adoption not part of the score
4434 stars · 1350 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
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
library · maturity maintenance
data-science data-visualization analytics trading fintech data-science data-visualization analytics python quantitative-finance alpha-factor-analysis tear-sheet algorithmic-trading pandas jupyter
3 sources
- readme: https://github.com/quantopian/alphalens · fetched 2026-08-28 · 7994d77fdf45
- homepage: http://quantopian.github.io/alphalens · fetched 2026-08-29 · 4fa54b402c47
- registry_pypi: https://pypi.org/pypi/alphalens/json · fetched 2026-08-29 · 52e5073ca0cf
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| quantopian/alphalens | main | 23 |
For agents
markdown · JSON · MCP: product_card(name="quantopian/alphalens")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem