Using Alphalens to Evaluate Predictive Stock Factors
Article Alphalens
Summary
Alphalens is a Python library for evaluating predictive stock factors. It turns a factor signal and pricing data into a structured dataset of forward returns, optionally assigning observations to quantiles and groups such as sectors. The resulting analysis can examine returns, information coefficients, turnover, and group-level behavior.
The document outlines a workflow: prepare factor data, then generate a full tear sheet of statistics and plots. It points readers to example notebooks as guidance for interpreting those outputs. This is an overview of the library and its analysis workflow, not a worked factor study: it presents no specific factor, empirical results, trading rules, or evidence that a signal predicts returns. Findings from any use depend on the input data and research choices; the document does not discuss validation methods or limitations in detail.
Key ideas
- Alphalens evaluates predictive stock factors using factor signals and pricing data.
- Its data preparation step can calculate forward returns and assign observations to quantiles or groups.
- The tear sheet presents returns, information coefficients, turnover, and grouped analyses.
- The document explains a software workflow but provides no factor-specific results or validation evidence.
Tags
Full text
# Ingest and format data
.. image:: https://media.quantopian.com/logos/open_source/alphalens-logo-03.png
:align: center
Alphalens
=========
.. 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:
- 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 (jstauth@quantopian.com)
- `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:Shown in full with attribution under the source's licence. Licence: Apache-2.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.