Using Monte Carlo Simulation to Estimate Prop Firm Pass Odds
A Monte Carlo simulation estimates your odds of passing a prop firm evaluation by reshuffling your strategy's trades thousands of times to see the full range of ways things could play out. A single backtest gives you one path, one order of wins and losses that happened to hit the profit target. But the order of trades is partly luck, and a different order could bust the drawdown before you ever reach the target. Monte Carlo takes your trade results, randomizes their sequence again and again, and counts how often you'd hit the target before hitting the loss limit. That share is your estimated pass rate. It's an estimate built on assumptions, not a promise, but it's far more honest than trusting one lucky-looking run.
A single backtest tells you one path. Monte Carlo reshuffles the trades thousands of times to estimate the range of outcomes, including how often a strategy would have passed an evaluation.
Key points
- Monte Carlo turns one backtest into thousands of simulated runs by reshuffling the order of your trades, so you see a range of outcomes instead of a single path.
- The order of wins and losses is partly luck, and a bad sequence can hit the drawdown before the profit target even when the strategy is profitable overall.
- The pass-odds estimate is simply the share of simulated runs that reach the target before breaching the loss limit or trailing drawdown.
- A strategy that passes one backtest can still have low pass odds, which is exactly the blind spot Monte Carlo exposes.
- The result depends on assumptions, like your past trades representing the future and each trade being independent, so it's an estimate rather than a guarantee.
- It's most useful for comparing strategies and sizing risk, not for promising a pass, because real markets can behave differently than the sample.
Frequently asked questions
What is a Monte Carlo simulation for trading?
It's a way to test a strategy by running its trades in thousands of different random orders. Instead of one backtest result, you get a spread of possible outcomes, which shows how much luck was in the original run and how rough things could get.
How does it estimate my odds of passing an evaluation?
It counts how many of the simulated runs reach the profit target before hitting the loss limit or trailing drawdown. If 700 out of 1,000 runs pass, that's a rough 70 percent estimate. It's the share of paths that make it, not a certainty.
Why isn't a good backtest enough?
Because one backtest is one order of trades, and that order is partly chance. A different sequence of the same wins and losses might blow the drawdown early. Monte Carlo checks many sequences so a lucky arrangement doesn't fool you.
Can Monte Carlo guarantee I'll pass?
No. It's an estimate built on assumptions, mainly that your past trades resemble future ones. Markets change, and the number is a probability, not a promise. Treat it as a way to judge risk and compare strategies, not as a green light.
How do I run pass-odds on my own strategy?
You can ask AlgoAgent to run a full backtest and then a Monte Carlo-style analysis on the results, applying your evaluation's target and drawdown. It does the reshuffling and shows the range of outcomes, so you get an honest read on the odds before you buy an evaluation.
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This content is for educational purposes only and does not constitute financial advice. Trading involves risk, including possible loss of capital.