Monte Carlo Simulation
A computational method that runs thousands of randomized trade sequences to model the range of possible outcomes for a trading strategy, revealing best-case, worst-case, and median scenarios.
What Monte Carlo Simulation Does
Monte Carlo simulation is a statistical technique that generates thousands of possible outcomes by randomly sampling from a known distribution. Originally developed at Los Alamos National Laboratory in the 1940s by Stanislaw Ulam and John von Neumann for nuclear physics calculations, the method is now the standard for risk analysis in quantitative finance. In trading, it takes your strategy's trade statistics (win rate, average win, average loss) and shuffles them into thousands of different sequences.
Each sequence produces a different equity curve. Together, they show the full range of what your strategy could do.
Why One Backtest Is Not Enough
A traditional backtest produces a single equity curve based on the historical order of trades. But that order was one of millions of possible sequences. As David Aronson demonstrated in *Evidence-Based Technical Analysis* (Wiley, 2006), a single backtest has a confidence interval so wide that it is statistically indistinguishable from random chance in most cases.
Monte Carlo answers: "If I take the same trades in a different order, what happens?"
Key Outputs
- P5 (5th percentile): The outcome in the worst 5% of simulations. If this is survivable, your strategy is robust.
- P50 (median): The most likely outcome. Your realistic expectation.
- P95 (95th percentile): The best 5% of outcomes. Don't plan around this.
- Survival probability: The percentage of simulations where your account stayed above your ruin threshold.
- Maximum drawdown distribution: How deep the worst drawdown goes across all simulations.
How Traders Use It
1. Test a strategy before trading it live
2. Compare different position sizes to find the optimal risk level
3. Set realistic expectations for drawdowns and returns
4. Determine if a strategy's edge is large enough to survive real-world variance
Running Your Own Simulation
The Risk of Ruin Calculator provides a simplified Monte Carlo analysis based on your trading stats. RangeSight runs 10,000 full simulation paths using real ticker data and historical volatility.
Key Takeaway
A computational method that runs thousands of randomized trade sequences to model the range of possible outcomes for a trading strategy, revealing best-case, worst-case, and median scenarios. Use RangeSight Studio's Monte Carlo simulator to model monte carlo simulation with 10,000 simulated paths and real market volatility.
This explains the concept.
But how monte carlo simulation affects your specific strategy depends on your parameters, position sizing, and market conditions.
Test your strategy to see how consistent it actually is → Run survival test
See the full distribution with real market data
10,000 Monte Carlo paths. P5/P50/P95 scenarios. Real ticker volatility.
Related articles
Monte Carlo Backtesting: Why One Backtest Is Never Enough
Your backtest showed a 40% return. But that was one path. Monte Carlo shows the other 9,999.
How Many Trades Until Statistical Failure?
100 trades? 500? The number of trades before statistical failure depends on your edge and position size.
Risk of Ruin Calculator: What Every Trader Should Know
Your win rate means nothing without knowing your risk of ruin. Here's how to calculate the probability of blowing up your account.