.. image:: https://media.quantopian.com/logos/open_source/alphalens-logo-03.png :align: center
.. image:: https://github.com/quantopian/alphalens/workflows/CI/badge.svg :alt: GitHub Actions status :target: https://github.com/quantopian/alphalens/actions?query=workflow%3ACI+branch%3Amaster
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:
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)
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>
__
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>
__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.
If you find a bug, feel free to open an issue on our github tracker <https://github.com/quantopian/alphalens/issues>
__.
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>
__.
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>
__Taso Petridis <https://github.com/tasopetridis>
_For a full list of contributors see the contributors page. <https://github.com/quantopian/alphalens/graphs/contributors>
_
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: