Monte Carlo Simulator (Risk of Ruin)
A single backtest is one roll of the dice – it tells you almost nothing about how often a strategy blows up. This simulator runs your edge thousands of times across random trade sequences and reports the numbers that actually matter: your risk of ruin, the median ending equity, how often you finish profitable and how deep the drawdowns get. Enter your win rate, reward-to-risk and risk per trade to see the full distribution of outcomes, not just the lucky one.
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Each path starts at 100% equity. A winning trade multiplies equity by (1 + risk% × R), a losing trade by (1 − risk%) – fixed-fractional sizing on a constant edge. Risk of ruin is the share of runs that ever fell to the ruin threshold. Outcomes assume a constant edge and fixed-fractional sizing; real markets vary – spreads, slippage, changing volatility and a drifting win rate all push results worse. See Risk of ruin for the theory. Results are an aid, not financial advice.
How the Monte Carlo simulator works
A Monte Carlo simulation replaces a single backtested equity curve with thousands of independently sampled paths from a two-outcome model. Your strategy has the same average edge in every run, but the order of wins and losses is shuffled – so you see the full range of luck, including the streak of losses that bankrupts an otherwise profitable system. The realistic question is not “does my edge make money on average?” but “in what fraction of plausible futures does it survive?”
For each simulated trade the model draws a random number: with probability equal to your win rate the trade wins and equity grows by risk% × reward (R); otherwise it loses risk% of current equity. Because sizing is a fixed fraction of equity, losses shrink in a drawdown and wins compound in a recovery – the same mechanic as real fixed-fractional position sizing.
Reading the outputs
Risk of ruin is the percentage of runs that ever breached your drawdown threshold – the headline survival number. Median final equity is the middle outcome (more honest than the average, which a few lucky runs can inflate). Ended profitable is how often you finished above your starting balance. Average and worst max drawdown show the typical and the nightmare equity dips you should be psychologically and financially prepared for.
Why this beats a single backtest
One backtest is a single sample from a noisy distribution – it can flatter a fragile strategy or bury a robust one under an unlucky sequence. Running the edge many times exposes the variance you will actually live through. If a 55% / 2R system still shows a meaningful risk of ruin at 2% risk per trade, the fix is smaller size, not more hope. Learn the framework in Risk of ruin and Position sizing models.
Frequently asked questions
What is risk of ruin?
What reward per win (R) should I enter?
How many simulations should I run?
Why can real-world results differ?
Reproducibility and two loss measures
This independently samples a two-outcome trade model; it is not a bootstrap of your actual journal. Seed 42 and identical inputs always reproduce the same sample; another seed generates another sample. The loss threshold is measured from starting capital, whereas maximum drawdown is measured from a running peak: these are different quantities. The worst sampled path is not the maximum possible loss. The interval for threshold-hit frequency measures sampling error at fixed parameters, not uncertainty in the strategy itself. More paths cannot repair a wrong win rate or omitted trade dependence. Positive arithmetic expectancy does not guarantee median equity growth at high risk.
