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 Quantopian.com -- 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.
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