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        "description": ".. image:: http://pmorissette.github.io/ffn/_static/logo.png\n\n.. image:: https://travis-ci.org/pmorissette/ffn.svg?branch=master\n    :target: https://travis-ci.org/pmorissette/ffn\n\nffn - Financial Functions for Python\n====================================\n\nAlpha release - please let me know if you find any bugs!\n\nIf you are looking for a full backtesting framework, please check out `bt\n<https://github.com/pmorissette/bt>`_. bt is built atop ffn and makes it easy\nand fast to backtest quantitative strategies.\n\nOverview\n--------\n\nffn is a library that contains many useful functions for those who work in **quantitative\nfinance**. It stands on the shoulders of giants (Pandas, Numpy, Scipy, etc.) and provides\na vast array of utilities, from performance measurement and evaluation to\ngraphing and common data transformations.\n\n.. code:: python\n\n    >> import ffn\n    >> returns = ffn.get('aapl,msft,c,gs,ge', start='2010-01-01').to_returns().dropna()\n    >> returns.calc_mean_var_weights().as_format('.2%')\n    aapl    62.54%\n    c       -0.00%\n    ge      36.19%\n    gs      -0.00%\n    msft     1.26%\n    dtype: object\n\n\nInstallation\n------------\n\nThe easiest way to install ``ffn`` is from the `Python Package Index <https://pypi.python.org/pypi/ffn/>`_\nusing ``pip`` or ``easy_insatll``:\n\n.. code-block:: bash\n\n    $ pip install ffn\n\nSince ffn has many dependencies, we strongly recommend installing the `Anaconda Scientific Python Distribution <https://store.continuum.io/cshop/anaconda/>`_. This distribution comes with many of the required packages pre-installed, including pip. Once Anaconda is installed, the above command should complete the installation. \n\nffn should be compatible with Python 2.7 and Python 3.\n\nDocumentation\n-------------\n\nRead the docs at http://pmorissette.github.io/ffn\n\n- `Quickstart <http://pmorissette.github.io/ffn/quick.html>`__\n- `Full API <http://pmorissette.github.io/ffn/ffn.html>`__\n\nSpecial Thanks\n--------------\n\nA special thanks to the following contributors for their involvement with the project:\n\n* Jordan Platts `@JordanPlatts <https://github.com/JordanPlatts>`_ \n\nLicense\n-------\n\nMIT",
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