Python is quickly becoming the glue language which holds together data science and related fields like quantitative finance. Zipline is a new, 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 -- a free, community-centered platform that allows development and real-time backtesting of trading algorithms in the web browser. Zipline will be released in time for PyData NYC'12.

The talk will be a hands-on IPython-notebook-style tutorial ranging from development of simple algorithms and their analysis to more advanced topics like portfolio and parameter optimization. While geared towards quantitative finance, the talk is a case study of how modern, general-purpose pydata tools support application-specific usage scenarios including statistical simulation, data analysis, optimization and visualization. We believe the talk to be of general interest to the diverse pydata community.

This talk was presented at PyData NYC 2012: If you are interested in this topic, be sure to check out PyData Silicon Valley in March of 2013:

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