Trading Risk of Ruin Calculator
The odds your strategy blows up before it pays off.
How it works
This simulation estimates the probability of losing the specified fraction of starting capital within the chosen number of trades. It samples independent trades with constant win rate and payoff, risking a fixed fraction of current equity. A finite sample gives an estimate, not an exact probability.
Higher risk per trade can make the same losing sequence reach the threshold sooner. Positive expectancy does not guarantee survival. The ruin threshold uses starting capital; maximum drawdown measures declines from a previous peak.
Simulated over 10,000 runs with a fixed seed, so the number is stable while you type.
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How the Trading Risk of Ruin Calculator works
This trading risk of ruin calculator estimates how often an account loses the fraction of its starting balance you specify within a chosen number of trades. It uses 10,000 Monte Carlo runs with your win rate, reward-to-risk ratio and risk per trade.
Five inputs drive the Monte Carlo. Win rate and reward-to-risk describe the edge. Risk per trade is the fraction of current equity a single loss costs — the number that actually decides survival. Trades per run sets how long you are asking the strategy to last, and the ruin threshold defines what counts as ruin: a 50% threshold means half the starting balance gone.
How to calculate risk of ruin with this simulator
Enter the win rate and average winning trade in units of risk, then choose the percentage of current equity lost on each losing trade. Set the number of trades and the loss of starting capital that counts as ruin. With a $1,000 starting balance and a 50% ruin threshold, a run is ruined as soon as equity reaches $500 or less.
Each loss multiplies current equity by (1 − risk); each win multiplies it by (1 + risk × reward-to-risk). The estimated ruin probability is the number of runs that reach the threshold divided by 10,000. This threshold is measured from starting capital; maximum drawdown is measured from each run's highest balance.
Example: the same strategy at 2%, 5% and 10% risk
The table uses a 45% win rate, 2R wins, 200 trades per run and a 50% loss-of-starting-capital threshold. Expected return per trade is 0.45 × 2 − 0.55 = +0.35 R before any costs outside the entered payoff. Only risk per trade changes.
These are the current simulator's fixed-seed results, rounded to two decimals. At 2% risk, no run reaches the threshold in this sample; that does not establish zero probability. Increasing risk raises both the observed frequency of ruin and the typical peak-to-trough drawdown.
| Risk per trade | Ruined runs / 10,000 | Estimated ruin probability | Median max drawdown |
|---|---|---|---|
| 2% | 0 | 0.00% | 17.90% |
| 5% | 156 | 1.56% | 40.13% |
| 10% | 1,581 | 15.81% | 65.13% |
Why this is simulated rather than solved
The textbook risk-of-ruin formula assumes even-money outcomes: every win and loss the same size. Real trading almost never looks like that — a 40% win rate with 2.5R winners is a perfectly good strategy, and the classical formula cannot describe it.
This tool samples ten thousand sequences of independent trades, with position size set as a fixed fraction of current equity. The result is an estimate for those assumptions, with sampling uncertainty. The generator uses a fixed seed, so repeating the same inputs reproduces the same sample.
Position size is the dominant lever
A positive expected payoff does not determine the order of wins and losses. Larger positions can reach the loss threshold during a bad sequence even when the strategy has positive expectancy. Use a payoff measured after trading costs if those costs are to be reflected in the simulation.
The edge never changed. Only the sizing did. This is the single most important idea in trading risk management: your edge determines whether you win, your position size determines whether you survive long enough to collect. Compare what you are risking against the Kelly fraction for the same inputs.
Reading the other outputs
The median outcome is the middle result across all runs — the typical experience, not the average, which a few extreme winners would distort. The worst 5% figure shows the fifth-percentile ending balance: one run in twenty finishes at least this badly. Most traders find that number more informative than the ruin probability itself.
Median maximum drawdown is the deepest peak-to-trough decline in a typical run. If it is larger than what you could tolerate without abandoning the strategy, your real risk of ruin is higher than the model says — because the model assumes you keep trading, and you would not.
FAQ
Does 0% risk of ruin mean the strategy is safe?
No. It means no simulated run reached your threshold among the 10,000 sampled runs. Rare outcomes can be missed. The estimate also assumes independent trades and constant win rate and payoff; it does not model changing market regimes, gaps or liquidation.
Is the ruin threshold the same as maximum drawdown?
No. A 50% ruin threshold means losing half the starting balance. Maximum drawdown measures the largest decline from a previous peak. Starting at $1,000, rising to $2,000 and falling to $1,000 is a 50% drawdown without reaching the $500 ruin threshold.
What counts as an acceptable risk of ruin?
Under 1% over a realistic trade horizon is the usual benchmark, and many professionals target far lower. Anything in double digits means the strategy is one bad stretch from ending, regardless of how good the expectancy looks.
Why does ruin stay possible with a positive expectancy?
Because outcomes are random in sequence. A positive edge tells you where the distribution is centred, not what order results arrive in. A long losing streak is improbable but never impossible, and if your size is large enough, one is sufficient.
What ruin threshold should I use?
50% is the common convention, on the reasoning that few traders continue with the same strategy after halving an account. If you would stop at 30%, use 30 — the honest threshold is the drawdown at which you would actually quit, not the point of literal zero.
Does this account for losing streaks clustering?
No. Every trade is sampled independently at a constant win rate. Correlated losses or changing conditions can produce different results. The estimate is not a proven lower or upper bound on the risk of a live strategy.