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Can you quantify the likelihood your trading system will continue to perform well in the future? Do you think Monte Carlo simulation, walk-forward testing, or out of sample testing can provide the answer? In this session, Mr. Walton discusses why common system validation methods often provide dangerously misleading results and then introduces an alternative called stochastic modeling, a process commonly used by insurance companies to price insurance policies and other financial products. When properly applied to trading systems, stochastic modeling can overcome common development challenges including data mining bias, stateful luck, buying power limitations, and position sizing / system dependencies.