Metadata-Version: 1.1
Name: alphalens
Version: 0.3.4
Summary: Performance analysis of predictive (alpha) stock factors
Home-page: https://github.com/quantopian/alphalens
Author: Quantopian Inc.
Author-email: opensource@quantopian.com
License: UNKNOWN
Description-Content-Type: UNKNOWN
Description: .. image:: https://media.quantopian.com/logos/open_source/alphalens-logo-03.png
            :align: center
        
        Alphalens
        =========
        .. image:: https://travis-ci.org/quantopian/alphalens.svg?branch=master
            :target: https://travis-ci.org/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:
        
        -  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.
        
        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/pydata/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 <http://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 <http://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:
        
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
