Monte Carlo Simulations & Trading Risk Math
A strategy with a 60% win rate can still experience a 10-trade losing streak. Learn how Monte Carlo models randomize sequences.
01. The Monte Carlo Simulation Concept
A typical backtest logs a linear sequence of trades. However, in live trading, the order of wins and losses is completely random. If your backtest has 60 wins and 40 losses, the linear sequence might show steady growth, but a randomized sequence could cluster 10 losses at the very beginning.
Monte Carlo simulations take your historical trade metrics and run thousands of randomized shuffles. This calculates the statistical range of possible equity curves, showing you the realistic volatility of your system.
02. Calculating Maximum Expected Drawdown
Expected drawdown is a crucial metric for account survival. A Monte Carlo generator will calculate the worst-case drawdown across e.g. 5,000 runs. If the simulation states that there is a 95% probability of experiencing a 25% drawdown, you must prepare your psychology and position sizing accordingly.
This protects you from abandoning a strategy during a normal drawdown sequence, as you already know it is within statistical boundaries.
03. Applying Simulation Data to Define Risk
Use Monte Carlo data to adjust your risk per trade. If your goal is to never experience a drawdown exceeding 20%, and the simulation shows a 5% ruin probability at 2% risk, you should reduce your risk per trade to 1% or 0.5%.
This quantitative adjustment aligns your position sizes mathematically with your capital limits, ensuring you stay in business long-term.