What Is Monte Carlo Simulation?
A Monte Carlo simulation doesn't give you one expected outcome - it gives you thousands. By randomly generating trade sequences based on your win rate and risk-reward ratio, it shows the full distribution of possible results. You see not just what "should" happen on average, but what could happen in the best and worst cases.
Each trade: Win = +Risk × R:R | Loss = −RiskWith 40% win rate and 1:2 R:R on a $10,000 account risking 2% per trade over 100 trades: the median outcome after 500 simulations is roughly $14,200 - but one unlucky simulation might end at $5,800 while one lucky run hits $41,000+.
Understanding the Output Metrics
| Metric | What It Tells You |
|---|---|
| Best Case | Best outcome across all simulations - unrealistic but aspirational |
| Worst Case | Worst outcome - can be extreme in one bad run |
| Median | 50th percentile - half the runs did better, half worse |
| Average | Mean of all outcomes - often skewed by extreme winners |
| 95% VaR | 5th percentile - 5% chance your result is worse than this |
| Risk of Ruin | % of simulations where the account went to zero |
| Profit Probability | % of simulations ending above starting balance |
Why This Matters for Real Trading
Your expected value might say +$4,000 over 100 trades, but Monte Carlo shows there's a meaningful chance of ending at $8,300 (VaR95) or worse. This gap between expectation and worst-case scenarios is why position sizing and risk management exist - not to maximize returns, but to survive the bad paths that inevitably occur.