Python is quickly establishing itself as the lingua franca for quantitative finance. The rich stack of open source tools like Pandas, the Jupyter notebook, and Seaborn, provide quants with a rich and powerful tool belt to analyze financial data. While useful for Quantitative Finance, these general purpose libraries lack support for common financial analyses like the computation of certain risk factors (Sharpe, Fama-French), or plots of portfolio allocations over time.

Pyfolio is a recent open source tool developed by Quantopian to fill this gap. At the core of pyfolio is a so-called tear sheet that consists of various individual plots that provide a comprehensive image of the performance of a trading algorithm/portfolio. In addition, the library features advanced statistical analyses using Bayesian modeling. The software can be used stand-alone, w**ith our open-source backtesting library Zipline and is available on the Quantopian platform.

This talk will be a tutorial of how to get the most out of this library (http://quantopian.github.io/pyfolio/).