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Alphalens

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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:

• 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:

.. code:: python

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 <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>__

Installation

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:

• matplotlib <https://github.com/matplotlib/matplotlib>__
• numpy <https://github.com/numpy/numpy>__
• pandas <https://github.com/pandas-dev/pandas>__
• scipy <https://github.com/scipy/scipy>__
• seaborn <https://github.com/mwaskom/seaborn>__
• statsmodels <https://github.com/statsmodels/statsmodels>__

Usage

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 Shift+Enter.

Questions?

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

Contribute

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

Credits

• Andrew Campbell <https://github.com/a-campbell>__
• James Christopher <https://github.com/jameschristopher>__
• Thomas Wiecki <https://github.com/twiecki>__
• Jonathan Larkin <https://github.com/marketneutral>__
• Jessica Stauth ([email protected])
• Taso Petridis <https://github.com/tasopetridis>_

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 :alt: