TRADING • SURVIVAL • MONTE CARLO
Risk of Ruin Calculator
Estimate how often a strategy reaches a defined loss threshold and how drawdown changes as risk per trade increases.
ESTIMATED RISK OF RUIN—
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Sample equity paths
A small sample from the same reproducible simulation set. The dashed line is the configured ruin threshold.
Risk per trade comparison
Same strategy assumptions, four risk levels. This is often more useful than reading one probability in isolation.
Losing streak distribution
Probability that the longest losing streak reaches at least each length, across all simulation paths.
What this result means
Risk of ruin is conditional on the assumptions and the chosen definition of ruin.
MODELCompounding risk model
Each trade risks the selected percentage of current equity. Wins and losses are modeled as average R-multiples, so position size adapts as equity changes.
RUINA configurable capital-loss threshold
Here, “ruin” means equity reaches the chosen percentage loss from starting capital. It is not a universal mathematical definition and it is not bankruptcy probability.
UNCERTAINTYWin rate estimated from a sample
When a sample size is provided, each path draws its own win rate from a Beta(wins+1, losses+1) distribution, so small samples spread wider than large ones.
How the simulation works
Independent trade outcomes with constant win probability and average R outcomes.
Expectancy
E[R] = Win rate × Avg win R − Loss rate × Avg loss RRisk
Risk amount = Current equity × Risk per tradeWin
Equity += Risk amount × Avg win RLoss
Equity −= Risk amount × Avg loss RThe model assumes independent trades and stable win rate / R-multiples. Real strategies may have regime changes, serial correlation, slippage, gaps, variable sizing and execution effects.
RISK WORKFLOW
Move from one trade to strategy survival.
Position Size controls one trade. Risk of Ruin asks what repeated exposure could do across a long sequence.
1
Position SizeDefine risk per trade
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2Liquidation PriceMap the risk boundary
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3Futures PnLEvaluate a trade outcome
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4
Risk of RuinStress-test survival
Method, example and limitations
Model: each path uses a reproducible random sequence of independent wins and losses. Position risk compounds from current equity and the path stops at the first configured ruin-threshold breach.
Win-rate uncertainty: when a sample size is provided, the win rate is no longer a fixed parameter. Each path first draws its own win rate from a Beta(wins+1, losses+1) distribution, where wins ≈ win rate × sample size. A 54% win rate estimated from 40 trades spreads much wider than one estimated from 4,000 trades. The confidence interval uses the normal approximation p ± z·sqrt(p(1−p)/n) with z = 1.96 for 95% and z = 1.64 for 90%.
The 95% interval describes Monte Carlo sampling uncertainty, not uncertainty about the strategy assumptions. Ending-above-start probability, drawdown and losing streaks help prevent a low ruin estimate from being read as low operational risk. With a sample size, the result is a conditional estimate — it conditions on the observed sample of trades, not a forecast of future performance.
Reviewed August 24, 2026. This model does not capture serial correlation, changing regimes or full return distributions.
References: CFA Institute — Backtesting & Simulation · NIST — binomial confidence intervals.