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Monte Carlo trading simulator

Your win rate and risk-reward look good on paper — but how bad can the drawdowns get? Simulate thousands of possible futures and see your real risk of ruin.

✓ Free✓ Runs in your browser✓ Risk of ruin · drawdown · percentiles
Your trading stats

R = the amount risked per trade. Spread adds randomness to each win/loss size (0 = fixed).

Money management

Expectancy / trade—
Median ending—
Chance of profit—
Risk of ruin—
Median max DD—
Worst 5% max DD—
Median losing streak—
Worst 5% ending—

Equity paths

40 random paths · bold = median path

Ending capital percentiles

Why traders run Monte Carlo simulations

A backtest gives one sequence of trades. The same statistics in a different order can produce a very different equity curve — including drawdowns far deeper than the one you saw. Monte Carlo analysis shuffles your edge thousands of times to show the full range of outcomes.

How to read the results

  • Expectancy must be positive. With a 45% win rate and 2R winners, expectancy is 0.45 × 2 − 0.55 × 1 = +0.35R per trade.
  • Risk of ruin rises steeply with risk per trade. Compare 1% and 3% risk with the same stats.
  • Losing streaks are longer than most people expect: at a 45% win rate, 8–10 losses in a row happen regularly over a few hundred trades.

Get your real statistics from the trading journal or a strategy backtest, and size positions with the position size calculator.

Simulations assume independent trades with stable statistics. Real markets change. Education only.

FAQ

What is a Monte Carlo simulation in trading?

It shuffles your trading statistics into thousands of possible futures. Each simulated path draws wins and losses at random using your win rate and average win/loss, showing the range of outcomes — not just the average.

What is risk of ruin?

The probability that your account falls by a chosen percentage (for example 50%) at some point. Above a few percent, most professional traders would reduce position size.

What is expectancy?

The average result per trade measured in R (multiples of your risk): win rate × average win − loss rate × average loss. Positive expectancy is required for long-term profit.

How many simulations should I run?

1,000 paths give stable percentiles for most inputs; 5,000 is smoother. Results change slightly on each run because they are random.