Optimizing parameters of a trading strategy via backtesting has one major problem: there are typically not enough historical trades to achieve statistical significance. This talk will discuss a variety of methods of overcoming that, including stochastic control theory and simulations. Simulations may involve either linear or nonlinear time series models such as recurrent neural networks.

About the Speaker:
Dr. Ernest Chan is the Managing Member of QTS Capital Management, LLC., a commodity pool operator and trading advisor. He began his career as a machine learning researcher at IBM’s Human Language Technologies Group, and later joined Morgan Stanley’s Data Mining Group. He was also a quantitative researcher and proprietary trader for Credit Suisse. Ernie is the author of “Machine Trading”, “Algorithmic Trading”, and “Quantitative Trading”, all published by Wiley, and a popular financial blogger at epchan.blogspot.com. He also teaches at the Master of Science in Predictive Analytics program at Northwestern University. He received his Ph.D. in theoretical physics from Cornell University.

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