Backtesting is the process of feeding historical data to an automated trading strategy and see how it would have performed. We will study various common backtest performance metrics. Backtest performance can easily be made unrealistic and un-predictive of future returns due to a long list of pitfalls, which will be examined in this course. The choice of a software platform for backtesting is also important, and criteria for this choice will be discussed. Illustrative examples are drawn from a futures strategy and a stock portfolio trading strategy.

Upon completion, students will be able to backtest their own strategies, and use that to decide whether to trade them live.

This is a pre-recorded workshop conducted by Ernest Chan (www.epchan.com). Free MATLAB trial licenses will be arranged for extensive in-class exercises. No prior knowledge of MATLAB is assumed, but some programming experience is necessary. The math requirement assumed is basic college-level statistics. Q&A via emails is included.

Total hours: 7 hours of recorded session.

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