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.
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.
Read the docs at http://pmorissette.github.io/ffn
FAQs
Financial functions for Python
We found that ffn demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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