Monte Carlo Trading Simulator
One backtest is a single path — one ordering of your wins and losses. Reshuffle that same edge thousands of ways and you see the range of outcomes it could have produced: how often it ends up, the typical and the unlucky results, and how deep the drawdown can dig. A wide spread that straddles zero is a warning the headline result leaned on luck.
- • Each trade is a coin-flip weighted to your win rate: a win adds your average win %, a loss subtracts your average loss %.
- • We compound a sequence of that length, 5,000 times, and read off the distribution of final returns and worst drawdowns.
- • Expectancy = win% × avg win − loss% × avg loss; if it's negative, no bet size saves it.
Worked example: a 55% win rate at +1.5%/−1.0% over 100 trades ends profitable in the large majority of reshuffles — but the 5th-percentile path still shows a real loss. That gap is the risk.
This assumes every trade is independent and your win rate and average win/loss are known exactly and never drift — real edges are estimated from a small, changing sample, so treat a rosy simulation as a best case, not a promise. To measure an edge on real history instead of assumed inputs, run a backtest and its reality-check. Educational tool, not advice.
A calculator shows what a rule should do on paper. Whether a strategy actually beats simply buying and holding — costs on, losses shown, no hindsight — is a different question, and the only one that pays. Most rules don't beat holding; the backtest is how you find the rare few that do — so when you build one that survives, you'll know it isn't a fake edge, then prove it forward before you risk real money. It won't tell you you'll win — nothing honest can.