An advanced technique used after initial backtesting to assess a strategy’s robustness. In trading, Monte Carlo simulation involves running the strategy through many randomized scenarios or re-sampled trade sequences to evaluate the range of possible outcomes. For example, trades from the backtest can be shuffled in random order (or with random win/loss flips within the strategy’s statistics) thousands of times. Analyzing the distribution of results (returns, drawdowns, etc.) from these simulations helps estimate the strategy’s variability, probability of extreme losses, and confidence intervals for expected performance. This is especially useful to see if a strategy’s success was due to a lucky sequence of trades or if it remains profitable under varied conditions.

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