Risk of Ruin Calculator
Estimate the probability of a fatal drawdown by simulating many sequences of trades at a fixed risk level.
Quick answer: Risk of ruin is the probability that a run of trades drives your capital down to a chosen ruin threshold before it recovers. This tool estimates it by Monte Carlo simulation: it plays out thousands of long sequences of trades using your win rate, per-trade risk and payoff ratio, and reports the fraction of those sequences that ever breached the threshold. A higher win rate, smaller risk or better payoff lowers the figure.
How to use it
Enter your win rate, the percentage of current capital risked on each trade, the payoff ratio (average win divided by average loss), and the drawdown that you define as ruin. The tool simulates thousands of trade sequences and reports the approximate probability that equity ever falls to the ruin threshold. It is an estimate from random simulation, so the figure moves slightly each time inputs change.
Formula
Risk of ruin ≈ ( number of simulated sequences that hit the ruin threshold ) ÷ ( total sequences )
Each trade wins with the given probability and multiplies equity by (1 + risk% × b) on a win or (1 − risk%) on a loss. Ruin is reached when equity falls to (1 − threshold%) of its start.
Limitations of the Risk of Ruin Calculator
The Risk of Ruin Calculator is a teaching aid, not a live risk system. It does not model the following:
- The assumption of a fixed win rate and payoff — real edges vary over time
- Independence between trades; correlated losing streaks raise the true probability of ruin
- The definition of ruin used (a chosen drawdown threshold), which is a modelling choice
- Trading costs and gaps, which worsen the real loss distribution
Frequently asked questions
Why estimate risk of ruin by simulation rather than a formula?
Closed-form risk-of-ruin formulas rely on simplifying assumptions about fixed bet sizes and payoffs. A Monte Carlo estimate handles the compounding fixed-fractional case directly and draws on the shape of the backtest's own results, at the cost of a small random wobble in the answer.
How does this connect to Monte Carlo on a backtest?
Risk of ruin is really a Monte Carlo question: resample or reorder the backtest's trades many times and count the fraction of paths that breach your capital floor. That fraction is the estimated probability of ruin for the given starting capital and sizing.
Why can a backtest with positive expectancy still show a real risk of ruin?
Because an unlucky early ordering of the same trades can breach the threshold before the edge compounds. Since the historical sequence is just one draw, resampling reveals paths the single backtest never showed — which is exactly the danger sizing must survive.
How do I lower the risk of ruin a backtest implies?
Reduce the per-trade risk fraction: because capital in bet-units sits in the exponent of the ruin probability, cutting the fraction slashes ruin far more than proportionally. Improving win rate or payoff helps, but re-running the backtest at a smaller fraction usually moves the number most.
Can this figure be trusted for options-selling backtests?
Only with care. Option-selling risk clusters in rare gap and volatility events, so a ruin estimate built from calm-period trades is dangerously reassuring. Perturbing the stress trades or block-bootstrapping through volatile periods gives a far more honest tail.
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