Monte Carlo simulation is a statistical technique that uses random sampling to model the probability of different outcomes in a trading strategy. It takes a set of historical trade results and randomly reorders them thousands of times to generate a distribution of possible equity curves, revealing the range of potential outcomes including worst-case drawdowns and best-case returns.
A Monte Carlo simulation takes your actual trade results (or hypothetical ones from a backtest) and randomly shuffles the order of those trades many times (typically 1,000 to 10,000 iterations). Each shuffle produces a different equity curve because the sequence of wins and losses changes. By analyzing all these equity curves, you can see: the probability of reaching a specific profit target, the probability of hitting a specific drawdown level, the expected range of returns at various confidence levels (e.g., 95th percentile worst-case), and whether your strategy is robust or dependent on a specific sequence of trades.
A trader has 200 historical trades with a 52% win rate and 1:2 R:R. They run a Monte Carlo simulation with 5,000 iterations. Results show: at the 95% confidence level, the maximum drawdown could reach 28% (compared to 15% in the original backtest). The median final equity is $18,500 on a $10,000 account, but the 5th percentile outcome is only $12,200. This tells the trader that while the strategy is profitable on average, they need to be prepared for drawdowns nearly twice as large as the backtest suggested.
Backtesting shows what happened with one specific sequence of trades, but Monte Carlo simulation shows what could happen across thousands of possible sequences. This is critical because the future will not repeat the past in the same order. Monte Carlo analysis helps traders set realistic expectations, size positions conservatively, and determine if a strategy can survive worst-case scenarios. It is one of the most powerful tools for evaluating strategy robustness.