Monte Carlo Simulation for Trading Strategy Validation
By Daniel Chau
Founder, NeuroBacktest
Use Monte Carlo simulation to estimate the probability of drawdowns, ruin, and profitable outcomes from your backtest.
A single backtest shows what happened in one historical path. Monte Carlo simulation shows what could have happened by reshuffling trades thousands of times, giving you a distribution of possible outcomes.
What Monte Carlo Reveals
Monte Carlo helps answer questions like: What is the probability of a 20% drawdown? What is the chance of losing money over the next year? How lucky was my original backtest result?
Common Methods
- Trade reshuffling: Randomize the order of historical trades.
- Bootstrapping: Sample trades with replacement to create new equity curves.
- Return resampling: Resample daily returns to simulate alternative paths.
Interpreting Results
Look at the median outcome, the 5th percentile (worst realistic case), and the 95th percentile. If the worst realistic case is still acceptable, your strategy is robust.
Run It in NeuroBacktest
Type: "Run Monte Carlo simulation on my Bollinger Bands strategy for MSFT with 5,000 iterations."
Frequently Asked Questions
What is Monte Carlo simulation in trading?▼
Monte Carlo simulation randomly reshuffles historical trades or price paths thousands of times to estimate the distribution of possible outcomes, including drawdowns and returns.
What can Monte Carlo simulation measure?▼
It can estimate the probability of maximum drawdown, consecutive losses, risk of ruin, and whether a strategy is likely to remain profitable under different scenarios.
Do I need to code Monte Carlo simulations myself?▼
No. Platforms like NeuroBacktest can run Monte Carlo simulations automatically from your backtest results and report the statistical distribution.