http://nbviewer.ipython.org/github/quantopian/zipline/blob/master/docs/tutorial.ipynb PyData Berlin 2014 Python is quickly becoming the glue language which holds together data science and related fields like quantitative finance. Zipline is a BSD-licensed quantitative trading system which allows easy backtesting of investment algorithms on historical data. The system is fundamentally event-driven and a close approximation of how live-trading systems operate. Moreover, Zipline comes "batteries included" as many common statistics like moving average and linear regression can be readily accessed from within a user-written algorithm. Input of historical data and output of performance statistics is based on Pandas DataFrames to integrate nicely into the existing Python eco-system. Furthermore, statistic and machine learning libraries like matplotlib, scipy, statsmodels, and sklearn integrate nicely to support development, analysis and visualization of state-of-the-art trading systems. Zipline is currently used in production as the backtesting engine powering Quantopian.com -- a free, community-centered platform that allows development and real-time backtesting of trading algorithms in the web browser.

1. Algorithmic Trading with Zipline
by
Thomas Wiecki