Interactive toolRuns in your browser

Monte Carlo Simulator

Simulate many random trade sequences to see the range of terminal returns and worst drawdowns a system can produce.

Quick answer: A Monte Carlo simulation replays a system many times with the same statistics but a different random order of wins and losses. This tool applies your average win and loss multiplicatively over a chosen number of trades, across up to two thousand runs, and reports the median and percentile terminal returns plus the worst drawdown seen. It shows that a single backtest equity curve is just one path among many the same edge could have produced.

Win rate (%)Average win (%)Average loss (%)

Trades per runSimulations (max 2000)

How to use it

Enter the win rate, the average percentage gained on a win and lost on a loss, the number of trades in a run, and how many runs to simulate (capped at 2000). Each trade multiplies equity up or down; the tool ranks the ending results and reports the median, 5th, 25th, 75th and 95th percentile terminal returns and the worst intra-run drawdown. The chart draws a sample of the simulated equity paths.

Formula

Each trade: Equity ×= ( 1 + Average win% ) on a win, or ×= ( 1 − Average loss% ) on a loss. Repeated over Trades, across Simulations runs.

Terminal return of a run = final equity ÷ starting equity − 1. Percentiles are read from the sorted terminal returns of all runs.

Frequently asked questions

Why simulate when I already have a backtest?

A backtest is one particular ordering of wins and losses. Monte Carlo reshuffles that ordering thousands of times to reveal the spread of outcomes the same edge could produce, including drawdowns your single historical path happened to avoid.

What do the percentiles mean?

The 5th percentile is a pessimistic outcome (only five percent of runs did worse), the 50th is the median, the 95th is optimistic. The gap between them shows how much luck of ordering affects results even with fixed statistics.

Why is the worst drawdown often larger than in my backtest?

With enough re-orderings, unlucky clusters of losses appear that your single historical sequence did not contain. This is a feature: it stress-tests whether you could psychologically and financially survive a plausible bad run.

Does this model assume trades are independent?

Yes. It draws each trade independently from the same win probability, so it ignores autocorrelation, changing market regimes and clustered volatility. Real returns often cluster, which can make actual drawdowns worse than this simple model suggests.

Why cap simulations at 2000?

The whole simulation runs in your browser on every input change. Two thousand runs is enough for stable percentiles while staying fast; far more would slow the page without changing the conclusions much.

Runs entirely in your browser — no data leaves your device. Illustrative and educational only; real-world charges and market conditions apply in practice.