Backtesting is the process of feeding historical data to an automated trading strategy and see how it would have performed. This course 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.

This is a pre-recorded workshop conducted in Adobe Connect by Ernest Chan.

The focus of this course is on discovering and avoiding various pitfalls during the backtesting process that may degrade performance forecasting. Illustrative exercises are drawn from a futures strategy and a stock portfolio trading strategy using MATLAB. Free MATLAB trial licenses will be arranged for extensive in-class exercises.

No prior knowledge of MATLAB is needed, but some experience with programming is necessary. The math requirement is basic college-level statistics.