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Performance analysis of predictive (alpha) stock factors

Project description

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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.

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.


Install with pip:

pip install alphalens-reloaded

Install with conda:

conda install -c ml4t alphalens-reloaded

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 Sheets

Example factor courtesy of ExtractAlpha

Peformance Metrics Tables


Returns Tear Sheet


Information Coefficient Tear Sheet


Sector Tear Sheet


Project details

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