Discussing the article: "Stress Testing Trade Sequences with Monte Carlo in MQL5"
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Check out the new article: Stress Testing Trade Sequences with Monte Carlo in MQL5.
A backtest shows only one path among many possible outcomes. This MQL5 script performs 1000 bootstrap Monte Carlo resamples of a trade P&L series, draws a percentile fan chart on the chart via CCanvas, and reports probability of ruin, value at risk, and 95th‑percentile worst drawdown. The result is a practical view of path risk and drawdown exposure beyond a single equity curve.
A backtest ending with 42% net profit and a 1.8 profit factor looks great on paper. But here's the uncomfortable truth: that result is path-dependent. The specific sequence of wins and losses in your historical data produced that particular equity curve—and if the same trades had arrived in a different order, the outcome could've been dramatically different.
We watched this happen firsthand with a trend-following EA on EURUSD M30. The strategy had a positive expectancy, a decent win rate, and a profit factor just above 1.6. Looked fine. But when we ran it through a sequence stress test, we found that roughly 12% of alternative trade orderings would have triggered a margin call before reaching trade 80—out of a 200-trade history. Same trades. Same edge. Just different luck on the order. That's the problem nobody talks about at the backtest stage.
And it's worse than it sounds. A trader looking at a single equity curve has no way of knowing whether the strategy is genuinely robust or just got lucky with the sequence. Honestly, most traders stop there—one backtest, looks good, go live. That's a mistake we've seen end badly more than once.
Quantitative risk managers addressed this decades ago with Monte Carlo simulation. They generate thousands of "what-if" scenarios from the same data to reveal the distribution of outcomes, not just a single historical path. In trading terms, given N trades and their realized P&L, what if the same trades occurred in a different order? Critically, how often would the strategy breach a catastrophic drawdown threshold before the sample ends?
MonteCarlo_RiskAssessor.mq5 reads trade P&L from a CSV file and runs 1000 bootstrap simulations. It renders a multi-percentile fan chart directly in MetaTrader via CCanvas and exports percentile curves to a machine-readable output file. Commission and slippage can be layered in to stress test results under realistic execution conditions. No external libraries needed.
Author: Duy Van Nguy