Visualize how randomness affects your trading results over time.
Monte Carlo simulation is a statistical technique that runs a trading strategy through thousands of randomized trade sequences to see the full range of possible outcomes. Instead of relying on a single backtest — which only shows one specific order of wins and losses — Monte Carlo reshuffles the sequence repeatedly to expose the variance hiding inside your edge.
Two traders using the exact same strategy can end the year with wildly different results simply because wins and losses land in a different order. A cluster of early losses can cripple one account while a lucky early streak carries another. Monte Carlo simulation quantifies this randomness so you can plan for it before it happens.
It helps you understand real risk. A strategy with a positive expectancy still has a range of possible futures. Monte Carlo reveals that range — not just the average result, but the 5th-percentile nightmare scenario and the 95th-percentile dream scenario.
It exposes worst-case scenarios. Your backtest might show a 15% max drawdown. Monte Carlo might show that across 500 random sequences, there is a meaningful chance of a 30%+ drawdown. That's the number you should size your risk to survive, not the single lucky path.
It prevents overconfidence. A winning backtest feels like proof. Monte Carlo reminds you that past performance is one path through a tree of possibilities. Traders who size positions based on a single favorable backtest often blow up when variance bites. Seeing 500 curves at once builds the psychological resilience to keep executing during inevitable drawdowns.
1. Input your real stats. Use your actual win rate, average R, and risk percentage from your trading journal or verified backtest. Made-up numbers produce made-up confidence. Be honest — if your true win rate is 48%, don't input 55%.
2. Run multiple times. Each click of "Run Simulation" generates a fresh set of random curves. Running the simulation several times shows how consistent the distribution is and teaches you to think in probabilities rather than single outcomes.
3. Observe the variability. Pay attention to the spread between best and worst cases, the average max drawdown, and the percentage of simulations that ended in profit. If only 70% of simulations are profitable, your strategy has a real edge — but 30% of the time you'd still finish in the red through pure bad luck.
Monte Carlo simulation runs your trading strategy through thousands of randomly ordered trade sequences using your win rate, reward-to-risk ratio, and position sizing rules. Instead of producing a single equity curve, it produces a full distribution of possible outcomes — so you can see the range of results your edge could realistically deliver.
Each simulation randomizes the order of wins and losses. A 60% win rate system could have its losses clustered early (big drawdown) or late (smooth curve). The order doesn't change your long-term expectancy, but it dramatically changes the psychological and financial experience of trading it.
Yes. Even with positive expectancy, variance means a subset of simulations will finish flat or negative. The smaller your edge and the shorter your trade sample, the more likely a bad run wipes out expected gains. This is why professional traders focus on position sizing and drawdown tolerance, not just expectancy.
Typically 100+ trades per simulation are needed before the law of large numbers starts smoothing out variance. With fewer than 50 trades, noise dominates and results are unreliable. For meaningful Monte Carlo analysis, 200–500 trades per simulation is a good benchmark.
There's no universal "good" win rate — it depends entirely on your reward-to-risk ratio. A 40% win rate is highly profitable at 2.5R, while a 70% win rate can be unprofitable at 0.4R. Focus on expectancy (win rate × avg R minus loss rate) rather than win rate in isolation.
The best-case equity curve is luck you can't rely on. The worst case is the drawdown you must be prepared to survive both financially and psychologically. Sizing your risk against the worst-case scenario — not the average — is what keeps traders in business across long careers.
No. Monte Carlo assumes your win rate and average R stay constant. Real markets shift regimes — a strategy that worked in trending markets may fail in chop. Treat Monte Carlo as a probability-of-variance tool, not a forecast. Always combine it with out-of-sample testing.
Calculate expectancy, projected profit, and max losing streak from your win rate and R.
Open →Estimate the probability of losing your account based on your edge and risk size.
Open →Understand how losses impact your account and what it takes to recover.
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