Skip to content


Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?


Failed to load latest commit information.
Latest commit message
Commit time


GitHub Actions status

Alphalens is a Python Library for performance analysis of predictive (alpha) stock factors. Alphalens works great with the Zipline open source backtesting library, and Pyfolio which provides performance and risk analysis of financial portfolios. You can try Alphalens at Quantopian -- a free, community-centered, hosted platform for researching and testing alpha ideas. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more.

The main function of Alphalens is to surface the most relevant statistics and plots about an alpha factor, including:

  • Returns Analysis
  • Information Coefficient Analysis
  • Turnover Analysis
  • Grouped Analysis

Getting started

With a signal and pricing data creating a factor "tear sheet" is a two step process:

import alphalens

# Ingest and format data
factor_data = alphalens.utils.get_clean_factor_and_forward_returns(my_factor,

# Run analysis

Learn more

Check out the example notebooks for more on how to read and use the factor tear sheet. A good starting point could be this


Install with pip:

pip install alphalens

Install with conda:

conda install -c conda-forge alphalens

Install from the master branch of Alphalens repository (development code):

pip install git+

Alphalens depends on:


A good way to get started is to run the examples in a Jupyter notebook.

To get set up with an example, you can:

Run a Jupyter notebook server via:

jupyter notebook

From the notebook list page(usually found at http://localhost:8888/), navigate over to the examples directory, and open any file with a .ipynb extension.

Execute the code in a notebook cell by clicking on it and hitting Shift+Enter.


If you find a bug, feel free to open an issue on our github tracker.


If you want to contribute, a great place to start would be the help-wanted issues.


For a full list of contributors see the contributors page.

Example Tear Sheet

Example factor courtesy of ExtractAlpha