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,
                                                                   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¶

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+https://github.com/stefan-jansen/alphalens-reloaded

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.