/alphalens

Performance analysis of predictive (alpha) stock factors

Primary LanguageJupyter NotebookApache License 2.0Apache-2.0

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

Alphalens

https://travis-ci.org/quantopian/alphalens.svg?branch=master

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
  • Sector Analysis

Getting started

With a signal and pricing data creating a factor "tear sheet" is just:

import alphalens

alphalens.tears.create_factor_tear_sheet(my_factor, pricing)

Learn more

Check out the example notebooks for more on how to read and use the factor tear sheet.

Installation

pip install alphalens

Alphalens depends on:

Usage

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.

Questions?

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

Contribute

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

Credits

For a full list of contributors see the contributors page.

Example Tear Sheet

Example factor courtesy of ExtractAlpha

https://github.com/quantopian/alphalens/raw/master/alphalens/examples/table_tear.png

https://github.com/quantopian/alphalens/raw/master/alphalens/examples/returns_tear.png

https://github.com/quantopian/alphalens/raw/master/alphalens/examples/ic_tear.png