Metadata-Version: 2.1
Name: alphalens-reloaded
Version: 0.4.1
Summary: Performance analysis of predictive (alpha) stock factors'
Home-page: https://alphalens.ml4trading.io
Author: Quantopian Inc
Maintainer: Applied AI, LLC
License: Apache 2.0
Description: <p align="center">
        <a href="https://zipline.ml4trading.io">
        <img src="https://i.imgur.com/uf8PmQO.png" width="35%">
        </a>
        </p>
        
        Alphalens
        =========
        
        [![CI Tests](https://github.com/stefan-jansen/alphalens-reloaded/actions/workflows/unit_tests.yml/badge.svg)](https://github.com/stefan-jansen/alphalens-reloaded/actions/workflows/unit_tests.yml)
        [![PyPI Wheels](https://github.com/stefan-jansen/alphalens-reloaded/actions/workflows/distribution.yml/badge.svg)](https://github.com/stefan-jansen/alphalens-reloaded/actions/workflows/distribution.yml)
        
        Alphalens is a Python library for performance analysis of predictive
        (alpha) stock factors. Alphalens works great with the
        [Zipline](https://www.zipline.ml4trading.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:
        
        -   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:
        
        ```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/stefan-jansen/alphalens-reloaded/tree/master/alphalens/examples)
        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:
        
        -   [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:
        
        ```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/stefan-jansen/alphalens-reloaded/issues).
        
        Contribute
        ----------
        
        If you want to contribute, a great place to start would be the
        [help-wanted
        issues](https://github.com/stefan-jansen/alphalens-reloaded/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/stefan-jansen/alphalens-reloaded/graphs/contributors)
        
        Example Tear Sheet
        ------------------
        
        Example factor courtesy of [ExtractAlpha](https://extractalpha.com/)
        
        ![image](https://github.com/stefan-jansen/alphalens-reloaded/raw/master/alphalens/examples/table_tear.png)
        
        ![image](https://github.com/stefan-jansen/alphalens-reloaded/raw/master/alphalens/examples/returns_tear.png)
        
        ![image](https://github.com/stefan-jansen/alphalens-reloaded/raw/master/alphalens/examples/ic_tear.png)
        
        ![](https://github.com/stefan-jansen/alphalens-reloaded/raw/master/alphalens/examples/sector_tear.png)
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python
Classifier: Topic :: Utilities
Classifier: Topic :: Office/Business :: Financial
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: test
Provides-Extra: dev
Provides-Extra: docs
