☠️ RISK CALCULATOR

Risk of Ruin Calculator

Monte Carlo simulation over 1,000 equity paths. Find out the probability your trading account hits your ruin threshold given your current settings.

Your Trading Parameters
The % drawdown that constitutes "ruin" — e.g. 10% for prop firm max DD, or 20% for personal account.
Risk of Ruin
Run simulation to calculate
0% Safe25%50%100% Likely
Statistics
Expected Value / Trade
Kelly Criterion
Half Kelly (recommended)
Avg Equity at End
Median Equity at End
% Paths Profitable
Monte Carlo Equity Paths (sample of 100 paths shown)

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Frequently Asked Questions

What is risk of ruin in trading?
Risk of ruin is the probability that your account will drop to a defined threshold (e.g., 20% drawdown) before you achieve your target. It is influenced by three factors: your win rate, your average R:R (risk:reward), and the percentage you risk per trade. Even a positive expected value system can have a significant risk of ruin if you risk too much per trade.
What is the Kelly Criterion and should I trade at full Kelly?
The Kelly Criterion is an algorithm for sizing bets to maximise long-term account growth: Kelly % = WinRate - (1-WinRate)/R. At full Kelly, account drawdowns can be extreme — a run of losses can temporarily halve your account even with a positive expectancy system. Most professional traders use half-Kelly (half the Kelly percentage) as their maximum risk, which substantially reduces volatility while still growing the account.
What risk of ruin is acceptable for a prop firm account?
For a prop firm funded account with a 10% max drawdown limit, you want a risk of ruin below 5% — ideally below 2%. At 1% risk per trade with a 55% win rate and 1:2 R:R, the risk of ruin against a 10% drawdown over 100 trades is typically under 1%. Higher risk per trade (2%+) can push this above 10%, making funded account breaches statistically likely over time.
Why does this calculator use Monte Carlo simulation instead of a formula?
Closed-form risk of ruin formulas exist but assume a fixed win rate and R:R with no variance. Monte Carlo simulation accounts for the random sequence of wins and losses — since the same win rate can produce very different equity paths depending on the order of outcomes. Running 1,000+ simulated paths gives a realistic distribution of outcomes, including worst-case and best-case scenarios.

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