Your backtest is one lucky ordering of wins and losses. This simulator shuffles the same strategy into 1,000 possible futures — and shows the median outcome, the bad-luck tail, and how often the account blows up. Free, in your browser, no signup.
Be honest here — the simulator is only as truthful as the inputs. Use net figures after costs where you can.
Take your strategy's two numbers — how often it wins, and how much it wins versus loses — and deal them into 1,000 different orderings. Same win rate, same payoff, but the wins and losses land in different sequences each time. Every ordering produces its own equity curve. Stack all 1,000 curves together and you stop seeing a future and start seeing the distribution of futures: the median path, the lucky paths, and the unlucky ones where five losers arrive back-to-back in month two.
A backtest is a single shuffle of history. It tells you what happened in that order of trades — but you will never trade that exact order again. Two traders running the identical strategy with the identical edge can have wildly different years, purely because one's losers clustered early. Monte Carlo makes that ordering risk visible before capital is committed: if 15% of your simulated futures hit a 50% drawdown, that is not a flaw in the simulation — it is a property of your strategy you were about to discover with real money.
P50 is the median: half the futures finished above it, half below. It is the honest centre of your strategy. P10 is the bad-luck boundary — only one future in ten did worse. P90 is the good-luck boundary. The shaded band between P10 and P90 is where eight of your ten realistic years live. Judge every strategy by its P10, not its P50: if the account survives the bad-luck case, the median takes care of itself. A strategy whose P10 is still above starting capital is genuinely robust; one whose P10 is a smoking crater is a coin flip wearing a backtest's clothes.
Run the simulator twice with identical numbers except risk per trade: 1%, then 4%. Watch what happens to the chance of a 50%+ drawdown. The expectancy didn't change — the strategy is the same — but the ruin probability explodes, because larger bets turn normal losing streaks into account-ending ones. This is the entire philosophy of Monks Of Market in one chart: edge is what you earn, position sizing is whether you survive long enough to earn it.
The simulator loads with this scenario: ₹1,00,000 starting capital, 40% win rate, 2R average win against 1R average loss, 1% risked per trade, 500 trades.
Now change only the win rate to 30% and the payoff to 1:1 — a strategy with negative expectancy (−0.4R per trade). The median future ends near ₹13,000, the chance of profit collapses to ~0%, and every single simulated future suffers a 50%+ drawdown. Same simulator, same honesty: it tells you the strategy is broken before the account does.
A Monte Carlo simulation runs your trading strategy thousands of times with randomised trade order, so you see the full range of possible outcomes instead of one lucky or unlucky sequence. Your backtest shows one path through history; Monte Carlo shows 1,000 possible paths with the same win rate and payoff — revealing the median outcome, the bad-luck tail, and how often the strategy blows up.
A single backtest is one specific ordering of wins and losses. Shuffle the same trades into a different order and the equity curve — especially the drawdown — changes completely, even though the win rate and average win/loss are identical. A strategy can be profitable on average yet still hit a 40% drawdown in one unlucky ordering. Monte Carlo exposes that ordering risk before real money does.
P50 is the median outcome: half of the 1,000 simulated futures finished above it, half below. P10 is the bad-luck boundary — only 10% of futures did worse. P90 is the good-luck boundary — only 10% did better. The band between P10 and P90 is where 80% of your realistic futures live. Judge a strategy by its P10, not its P50: if you can survive the bad-luck case, the median takes care of itself.
No — it models pure trade outcomes from your win rate, average win and average loss. Real-world friction (brokerage, STT, slippage) makes every outcome slightly worse, which means the simulator is optimistic by design. The practical fix: reduce your average win slightly before entering it, or run the numbers through the Monks Of Market cost calculators first and use the net figures here.
There is no universal number — it depends on your win rate and payoff — but the simulator makes the trade-off visible: raise risk per trade and watch the ruin probability climb. As a starting point, most robust retail strategies keep risk at 1% or less per trade; the simulation usually shows ruin probability near zero there for any strategy with genuine positive expectancy, while 3–5% risk turns even good strategies fragile.
The simulator shows you what can happen. The articles explain why — expectancy, risk of ruin, drawdown recovery and the streaks every trader underestimates.
Browse the articles →