Alpha release - please let me know if you find any bugs!

If you are looking for a full backtesting framework, please check out bt. bt is built atop ffn and makes it easy and fast to backtest quantitative strategies.

ffn is a library that contains many useful functions for those who work in quantitative finance. It stands on the shoulders of giants (Pandas, Numpy, Scipy, etc.) and provides a vast array of utilities, from performance measurement and evaluation to graphing and common data transformations.

>> import ffn
>> returns = ffn.get('aapl,msft,c,gs,ge', start='2010-01-01').to_returns().dropna()
>> returns.calc_mean_var_weights().as_format('.2%')
aapl    62.54%
c       -0.00%
ge      36.19%
gs      -0.00%
msft     1.26%
dtype: object

The easiest way to install ffn is from the Python Package Index using pip or easy_insatll:

$ pip install ffn

Since ffn has many dependencies, we strongly recommend installing the Anaconda Scientific Python Distribution. This distribution comes with many of the required packages pre-installed, including pip. Once Anaconda is installed, the above command should complete the installation.

ffn should be compatible with Python 2.7 and Python 3.

Read the docs at http://pmorissette.github.io/ffn

• Quickstart
• Full API

A special thanks to the following contributors for their involvement with the project:

• Jordan Platts @JordanPlatts

MIT