Monte Carlo Simulator

A 45% win rate with $600 average wins against $400 average losses is a genuinely positive edge: about +$50 of expectancy per trade. But over 100 trades, luck decides whether the winners cluster early or the losers do. One sequencing of those exact stats produces a smooth climb; another produces a 15-trade losing stretch that would convince most traders their strategy is broken. A single backtest — or a single good month — shows you exactly one of those sequences and tempts you to treat it as the truth.

Monte Carlo simulation fixes this by brute force: it replays your stats a thousand times with different random orderings and reports the distribution instead of an anecdote. The question changes from “what happened?” to “what does this edge typically do, and how bad does it get when it’s unlucky?” That second question is the one that determines whether you survive long enough for the edge to pay.

The median final balance tells you what this edge typically delivers. The p95 is the run you’ll remember and screenshot; the p5 is the run you have to be able to survive. If the p5 outcome — the result one unlucky trader in twenty gets from your exact stats — would make you abandon the strategy, cut your size, or blow a risk limit, then your position sizing is wrong for your edge, even though the edge is real.

The drawdown rows matter just as much as the finals. The median max drawdown is what a normal run of this strategy feels like at its worst moment; the worst-path drawdown shows what pure bad luck can do with no change in your edge at all. Traders who haven’t seen those numbers routinely quit profitable systems in the middle of an ordinary drawdown. If you want to test the strategy itself — not just its luck — run it against historical data in the TerraTrade backtester and feed the resulting stats back in here.