Trading Risk of Ruin & Losing Streak Calculator

A risk of ruin calculator estimates the probability that a trading strategy will reach a defined loss threshold over a given number of trades. This calculator uses a finite-horizon Monte Carlo simulation for the threshold-hit estimate and exact dynamic programming for losing-streak probabilities. The results are model estimates, not forecasts.

Risk of ruin depends on position-sizing method, win probability, payoff distribution, trade dependence, ruin definition and time horizon. This calculator uses fixed-fractional position sizing, binary win/loss outcomes based on entered average R values, a finite trade horizon, and a seeded Monte Carlo simulation. There is no single universal risk-of-ruin formula applicable to all trading systems.

No. The threshold-hit probability is a finite-horizon Monte Carlo simulation estimate, not an exact probability. The losing-streak probability is exact under the independent Bernoulli model, but real trades can be correlated. The calculator clearly separates exact deterministic calculations from simulation estimates.

You need your win rate (percentage of winning trades), average win in R multiples, average loss in R multiples, risk per trade as a percentage of equity, starting balance, trade horizon, and a loss threshold. For the simulation, you also choose the number of paths and a seed for reproducibility.

Higher risk per trade increases the equity swing on each trade. This means consecutive losses compound faster, the drawdown from the same losing streak is larger, and the probability of hitting a given threshold within a fixed number of trades generally increases. The Risk Comparison mode demonstrates this relationship across multiple risk levels.

Yes. A strategy with positive mathematical expectancy can still experience significant losing streaks and drawdowns, especially over a large number of trades. Positive expectancy describes the average outcome per trade — it does not guarantee that every sequence of trades will be smooth. This is why risk management matters even for profitable strategies.

It depends on your loss probability and the number of trades. At a 50% win rate, the probability of five specific consecutive trades all being losses is 0.5^5 = 3.125%. But the probability of seeing at least one five-loss streak somewhere across 100 trades is much higher — use the Losing Streak mode for the exact figure under the independent-trades model.

At a 50% win rate, the probability of ten specific consecutive losses is 0.5^10 ≈ 0.098%. But across 200 trades, the probability of at least one ten-loss streak is higher. The Losing Streak mode uses dynamic programming to compute the exact probability under the independent Bernoulli model.

Under fixed-fractional risk, each loss reduces equity by the risk percentage multiplied by the average loss in R. After n consecutive losses, the ending balance is StartingBalance × (1 − r × L)^n, where r is the risk fraction and L is the average loss in R. The Drawdown & Recovery mode shows this step by step.

A 10% drawdown requires an 11.11% gain to recover. If you start with $10,000 and lose 10% ($1,000), you have $9,000. To return to $10,000, you need to gain $1,000 on a $9,000 base — that is 11.11%.

A 20% drawdown requires a 25% gain to recover. From $10,000, a 20% loss leaves $8,000. Returning to $10,000 means gaining $2,000 on an $8,000 base — that is 25%.

Because the gain is calculated on the reduced balance. A 50% loss from $10,000 leaves $5,000. Returning from $5,000 to $10,000 means doubling the remaining balance — a 100% gain. This asymmetry is why drawdown recovery becomes progressively harder as the drawdown deepens.

Drawdown measures how far an account has fallen from a peak (or from the starting balance). Risk of ruin estimates the probability of reaching a specified loss threshold over a number of trades. Drawdown is a measure of what has happened; risk of ruin is a probability estimate of what might happen under the model assumptions.

No. Win rate, average win, average loss, risk per trade, trade horizon and threshold definition all affect the result. A high win rate with a very small average win and large average loss can still have poor risk characteristics. Expectancy (which combines win rate with reward-to-risk) is more informative than win rate alone.

Yes. The average win and average loss in R multiples determine the expectancy per trade and the equity multiplier on each outcome. A higher reward-to-risk ratio means each win contributes more, but if the win rate is low, the losing streaks may be longer. The simulation accounts for both.

Yes. The model works in R multiples and is instrument-agnostic. Enter your strategy statistics (win rate, average win/loss in R, risk per trade) regardless of whether you trade forex pairs or gold. The mathematical model does not depend on the instrument.

The general risk model can illustrate how a strategy behaves around an entered drawdown threshold, but prop firms may use daily loss limits, static drawdown, trailing drawdown, intraday equity, end-of-day balance, and consistency rules. Therefore this tool does not certify prop-firm compliance. For consistency-rule checks, use the Prop Firm Consistency Calculator.

No. The model works in R multiples and does not separately model spread, commission, swap or slippage. Historical win rate and average R values are most useful when calculated from net completed trades — in that case, costs are already reflected in the R values.

The simulation assumes independent trades with constant win rate, average win, average loss and fractional risk. Real trading results can cluster (correlated outcomes), change over time (regime shifts), and include costs or execution differences. The result is a model estimate, not a forecast.