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Risk of Ruin Calculator

Estimate how often a trading system could cross a ruin threshold from its win rate, payoff ratio and risk per trade.

Strategy inputs

Closed form is exact for R=1. For R not equal to 1, use Monte Carlo for path-based estimates.
edge = p × R − q · units = ruin threshold ÷ risk per trade

Ruin estimate

Risk of ruin
Capital units
Edge
Method used
Risk of ruin is a model estimate, not a forecast.
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Quick answer

Risk of ruin estimates the chance that a sequence of wins and losses crosses a defined loss threshold. With win rate p, loss rate q, payoff ratio R and capital units U = ruin threshold / risk per trade, the closed-form estimate is ((1 - edge) / (1 + edge))^U, where edge = pR - q.

How it works

What risk of ruin means

Risk of ruin is a probability model for path risk. It asks: if this win rate, payoff ratio and fixed risk per trade repeated many times, how often would equity cross the loss threshold you call ruin? The threshold can be total loss, a 50% drawdown, or any other fractional line you choose.

The formula

q = 1 - p

capital units U = ruin threshold ÷ risk per trade

edge = p × R - q

risk of ruin = ((1 - edge) ÷ (1 + edge)) ^ U, clamped to 100% when needed.

In the symmetric Balsara case where R = 1, this reduces to (q / p)^U when p > q. The clean-room oracle example is p = 0.55, risk = 10%, so U = 10 and (0.45 / 0.55)^10 = 13.4%.

Closed form versus Monte Carlo

The closed form is strict for R = 1. When R is not 1, the edge-normalized closed form is an approximation. Monte Carlo is the better fit for asymmetric payoff paths because it samples each trade directly: wins add risk × R, losses subtract risk, and a path is counted as ruined once it crosses the threshold.

How to use this calculator

  1. Enter the strategy win rate as a percentage.
  2. Enter the payoff ratio, where 1 means average win equals average loss.
  3. Enter the fixed fraction of equity risked per trade.
  4. Set the ruin threshold, such as 100% for total loss or 50% for a half-account drawdown.
  5. Use closed form for the simple R=1 case, or Monte Carlo when the payoff is asymmetric.

Worked example - 55% wins, 1R payoff

A system wins 55% and loses 45%, with average win and average loss both equal to 1R. Risking 10% per trade against a 100% ruin threshold gives U = 1 / 0.10 = 10. The R=1 closed form is (0.45 / 0.55)^10, or about 13.4%.

Common mistakes

  • Using a non-positive edge. If edge <= 0, the closed-form ruin estimate is 100% because the system does not have a positive expectancy under the inputs.
  • Applying the closed form to every payoff shape. The strict closed-form result is for R = 1. For unequal average wins and losses, run the Monte Carlo branch and treat it as a model estimate.
  • Forgetting the path. Two systems can have similar average expectancy but very different losing-streak behavior. Risk of ruin is about the sequence, not just the average trade.

Frequently asked questions

What is risk of ruin in trading?
It is the modeled probability that a sequence of trades crosses a defined loss threshold, based on win rate, payoff ratio, risk per trade and the threshold you set.
Why does the calculator show 100% when edge is not positive?
With edge = pR - q, a non-positive edge means the inputs do not produce positive expectancy. The closed-form ruin estimate is therefore clamped to 100%.
Is the closed-form risk of ruin exact?
It is exact for the symmetric R = 1 case used in the classic Balsara form. When R is not 1, this tool labels the closed form as an approximation and provides Monte Carlo for direct path sampling.
Why use Monte Carlo?
Monte Carlo handles finite sampled paths and unequal average wins and losses by simulating trade outcomes one by one. The result is deterministic for the same seed, but it is still a model estimate.
Does a lower risk of ruin make a strategy good?
No. It only describes one path-risk property under the inputs. Strategy quality still depends on data quality, costs, execution, sample size and whether the assumptions hold out of sample.

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