Monte Carlo Simulation — what it is and how to use it in trading
Definition
Monte Carlo simulation in a backtest runs thousands of simulations in which the order of a strategy's historical trades is randomly shuffled, to show the range of possible outcomes rather than the single "lucky" path that actually happened in history.
How it works in the builder
For each of the thousands of simulations the trades are reshuffled and the equity curve is recalculated — the result is a distribution of possible outcomes, from which percentiles are taken: p5 (a bad scenario), p50 (typical, the median), and p95 (a best-case scenario).
Typical use
Helps assess not just "how much the strategy would have made", but how different that result could have turned out with a slightly different trade order — for example, the share of simulations that finished in profit, or the median and 95th-percentile maximum drawdown.
Common pitfalls
Reshuffling changes the order of trades but not the trades themselves — if a strategy systematically lost during some market regime, Monte Carlo won't fix that or surface it separately; the method evaluates resilience to luck in sequencing, not the correctness of the strategy's logic itself.
This block is available in the strategy builder
137 no-code blocks — build an entry condition with this term and test it on history
Historical results do not guarantee future ones. The service is an informational and analytical tool, not an individual investment recommendation; trades are not executed.