Monte Carlo Simulation

The profound shift Monte CarloReshuffling trades thousands of times to see the range of outcomes. forces: *your backtestTesting a trading strategy on historical data.’s equity curveA graph of a strategy’s account value over time. is just one sample from a distribution of possible outcomes — and the order of your trades was largely luck. Your real trades arrived in a particular sequence, but they easily could have come in a different order — and a different order can mean a much* deeper drawdownThe worst peak-to-trough fall in a portfolio. or a much worse return, even with the identical set of trades. By randomly reshuffling the trade sequence (or resampling with replacement) thousands of times, Monte CarloReshuffling trades thousands of times to see the range of outcomes. builds a distribution: not “my max drawdownThe worst peak-to-trough fall in a portfolio. was 18%” but “across 10,000 plausible orderings, my drawdown was usually 15–30% and occasionally hit 40%.” This is transformative for risk understanding: it reveals the bad luck you haven’t experienced yet — the worse-but-entirely-possible paths your single backtestTesting a trading strategy on historical data. didn’t happen to show. It stops you trusting one lucky sequence and prepares you for the drawdowns the *futureA binding agreement to buy or sell at a set price on a future date.* may actually deal. You stopA pre-set exit that caps your loss if a trade goes wrong. asking “what happened?” and start asking “what could happen?”