Monte Carlo Simulation

Monte Carlo simulation is a computational method that models the range of possible outcomes of a process by running it many times with randomised inputs, then summarising the distribution of results. In a trading-journal context it is most often applied by resampling or reordering a trader's historical trade results (the per-trade returns or win/loss sequence) across a large number of iterations, and recording the resulting equity curve, final balance, and maximum drawdown for each iteration. Two common variants are: reshuffling the existing trades into new random orders (which isolates the effect of sequence on drawdown), and bootstrap resampling — drawing trades at random with replacement — which also reflects sampling variability. The aggregated output is a distribution (for example, percentiles of final equity and of worst drawdown) rather than a point estimate. Because the method only rearranges or resamples results that already occurred, its validity depends entirely on the quality and representativeness of the input data; it assumes, often imperfectly, that the statistical character of past trades is informative about the range of future variation. It is a descriptive risk-and-robustness tool, not a forecasting model, and it produces no trade instructions.

In plain English — A Monte Carlo simulation takes a set of results you already have — for example, the list of wins and losses from your trading journal — and shuffles or resamples them over and over to build hundreds or thousands of alternative "what if the same trades had happened in a different order?" histories. Real trading never repeats in the exact sequence you experienced; you might have hit your three biggest losses back-to-back, or spread them out. By replaying the same outcomes in many random orders, the simulation shows how differently the equity curve could have unfolded purely due to the luck of sequencing. The output is not a single forecast — it is a spread of possibilities, usually shown as a fan of equity curves or a distribution of final balances and worst drawdowns. You read it as ranges and probabilities ("in most runs the deepest drawdown was between X and Y"), never as a promise of what will happen next. It is a way to stress-test how fragile or robust a track record is, and to get a feel for outcomes that didn't happen but easily could have. It says nothing about which asset to trade or when — it only re-arranges results you already produced.

Suppose a trader's journal contains 100 closed trades with a slight positive edge. In their actual history, the account peaked, then suffered a run of losses for an 18% drawdown before recovering. A Monte Carlo simulation takes those same 100 trade results and replays them in, say, 5,000 different random orders. Because the trades are the same but the sequence differs each time, each run produces a different equity path and a different worst drawdown. The tool then summarises all 5,000 runs: perhaps the median worst drawdown was around 15%, but the worst 5% of runs reached 30% or deeper, and a small fraction of runs never recovered to a new high within the sample. The takeaway is not "my account will fall 30%" — it is "with these results, a drawdown noticeably worse than the 18% I actually lived through was well within the realm of normal luck, so my risk settings should be able to survive that." These numbers are illustrative only. The simulation does not predict the trader's next 100 trades and offers no buy, sell, or sizing recommendation.