Monte Carlo simulator FREE

How Monte Carlo helps

The same strategy can produce wildly different equity curves depending on the order of wins and losses. A sequence of 5 losses upfront feels very different from 5 losses spread across 100 trades — even though the totals are identical. Monte Carlo runs your strategy thousands of times to surface the distribution of outcomes, not just the average.

What the percentile bands mean

• Median (P50) — what a typical run looks like.
• P25–P75 (interquartile range) — half of all runs land here. A reasonable expectation band.
• P5 / P95 — pessimistic / optimistic. 1 in 20 runs is worse than P5; 1 in 20 is better than P95.
• P1 — bad-luck scenario. Pay attention to this when sizing — it's where retail accounts blow up.

Risk of ruin — the number that matters

If your strategy has a positive edge but a 30% chance of dropping below 50% of your starting capital at some point, most traders won't survive psychologically — they'll abandon the strategy at the worst time. Reduce risk per trade until ruin probability is <5% even for an aggressive ruin threshold (e.g. drawdown to 70% of start). The math says you can handle the variance; only with sub-5% ruin will you actually behave that way.

Limitations

• Independent draws — real markets cluster losses (regime changes, correlated drawdowns). Monte Carlo with independent Bernoulli draws is optimistic on max drawdown.
• Static parameters — assumes win rate and R:R stay constant. Real strategies degrade. Use the simulator with the worst parameters you've seen, not the best.
• No commissions / slippage — bake these into your effective R:R before simulating.
• Fixed sizing — fixed-fraction (compounding) sizing differs from fixed-amount sizing in long runs. Both are available above.

Reading the chart

The chart shows 30 random simulations as light grey lines plus the percentile band (P5–P95 shaded, P50 highlighted). The aggregate is more informative than any individual line — focus on the band's width and slope.