Performance analysis of predictive (alpha) stock factors
Under Apache License 2.0
By quantopian

python finance pandas numpy jupyter algorithmic-trading

.. image:: https://media.quantopian.com/logos/open_source/alphalens-logo-03.png
:align: center


.. image:: https://github.com/quantopian/alphalens/workflows/CI/badge.svg
:alt: GitHub Actions status
:target: https://github.com/quantopian/alphalens/actions?query=workflow%3ACI+branch%3Amaster

Alphalens is a Python Library for performance analysis of predictive
(alpha) stock factors. Alphalens works great with the
Zipline <https://www.zipline.io/> open source backtesting library, and
Pyfolio <https://github.com/quantopian/pyfolio>
which provides
performance and risk analysis of financial portfolios. You can try Alphalens
at Quantopian <https://www.quantopian.com> -- a free,
community-centered, hosted platform for researching and testing alpha ideas.
Quantopian also offers a fully managed service for professionals <https://factset.quantopian.com>

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:

Getting started

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

.. code:: python

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 <https://github.com/quantopian/alphalens/tree/master/alphalens/examples> for more on how to read and use
the factor tear sheet. A good starting point could be this <https://github.com/quantopian/alphalens/tree/master/alphalens/examples/alphalens_tutorial_on_quantopian.ipynb>


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+https://github.com/quantopian/alphalens

Alphalens depends on:


A good way to get started is to run the examples in a Jupyter
notebook <https://jupyter.org/>

To get set up with an example, you can:

Run a Jupyter notebook server via:

.. code:: bash

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


If you find a bug, feel free to open an issue on our github
tracker <https://github.com/quantopian/alphalens/issues>


If you want to contribute, a great place to start would be the
issues <https://github.com/quantopian/alphalens/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22>


For a full list of contributors see the contributors page. <https://github.com/quantopian/alphalens/graphs/contributors>_

Example Tear Sheet

Example factor courtesy of ExtractAlpha <https://extractalpha.com/>_

.. image:: https://github.com/quantopian/alphalens/raw/master/alphalens/examples/table_tear.png
.. image:: https://github.com/quantopian/alphalens/raw/master/alphalens/examples/returns_tear.png
.. image:: https://github.com/quantopian/alphalens/raw/master/alphalens/examples/ic_tear.png
.. image:: https://github.com/quantopian/alphalens/raw/master/alphalens/examples/sector_tear.png