Monte Carlo Simulation for Trading Strategy Validation

Monte Carlo simulation is one of the most powerful tools for understanding the range of possible outcomes from your trading strategy. Here's how to implement it. Why Monte Carlo? Backtesting gives you one path through history. But that path won't repeat. Monte Carlo generates thousands of possible equity curves from your actual trade results, showing you the probability distribution of outcomes. Implementation import numpy as np import matplotlib.pyplot as plt def monte_carlo_trades ( trade_results , num_simulations = 1000 , num_trades = 500 ): """ Simulate equity curves by randomly sampling from actual trade results. """ simulations = np . zeros (( num_simulations , num_trades )) for i in range ( num_simulations ): # Random sampling with replacement sampled = np . random . choice ( trade_results , size = num_trades , replace = True ) simulations [ i ] = np . cumsum ( sampled ) return simulations # Example: your last 200 trades in R-multiples trade_results = np . array ([ 1.5 , - 1 , 2