alphalens 0.1.0

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,
                                                                   pricing,
                                                                   quantiles=5,
                                                                   groupby=ticker_sector,
                                                                   groupby_labels=sector_names)

# Run analysis
alphalens.tears.create_full_tear_sheet(factor_data)

Learn more

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

Installation

pip install git+https://github.com/quantopian/alphalens

Alphalens depends on:

• matplotlib

• numpy

• pandas

• scipy

• seaborn

• statsmodels

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

• Andrew Campbell

• James Christopher

• Thomas Wiecki

• Jonathan Larkin

• Jessica Stauth (jstauth@quantopian.com)

• Taso Petridis

For a full list of contributors see the contributors page.

Example Tear Sheet

Example factor courtesy of ExtractAlpha