<commit_before>Alphalens
=========

Alphalens is a Python Library for performance analysis of predictive
(alpha) stock factors. Alphalens works great with the
`Zipline <http://zipline.io/>`__ open source backtesting library, and
`Pyfolio <https://github.com/quantopian/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:

-  Information Coefficient Analysis
-  Returns Analysis
-  Turnover Analysis
-  Sector Analysis

Getting started
---------------

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

.. code:: python

    import alphalens

    alphalens.tears.create_factor_tear_sheet(my_factor, pricing)


Learn more
----------

Check out the `example notebooks <https://github.com/quantopian/qfactor/tree/master/examples>`__ for more on how to read and use<commit_msg>MAINT add travis badge<commit_after>Alphalens
=========
.. image:: https://travis-ci.com/quantopian/alphalens.svg?token=tw8BdqzqoxGpJiLUMKdN&branch=master
    :target: https://travis-ci.com/quantopian/alphalens
    
Alphalens is a Python Library for performance analysis of predictive
(alpha) stock factors. Alphalens works great with the
`Zipline <http://zipline.io/>`__ open source backtesting library, and
`Pyfolio <https://github.com/quantopian/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:

-  Information Coefficient Analysis
-  Returns Analysis
-  Turnover Analysis
-  Sector Analysis

Getting started
---------------

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

.. code:: python

    import alphalens

    alphalens.tears.create_factor_tear_sheet(my_factor, pricing)


Learn more
----------

Check out the `example notebooks <https://github.com/quantopian/qfactor/tree/master/examples>`__ for more on how to read and use
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