Risk of Ruin Calculator
Free Risk ruin Calculator for risk management. Enter your numbers to see returns, costs, and optimized scenarios instantly.
Risk of Ruin Calculator
Calculator
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Formula: RoR = ((1 - edge) / (1 + edge))^N where edge = WR x (1 + W/L ratio) - 1
Worked example — Risk of ruin: < 0.01%. Kelly fraction: 25%. Very safe position sizing at 1/25th Kelly.
Formula
RoR = ((1 - edge) / (1 + edge))^N where edge = WR x (1 + W/L ratio) - 1
Risk of ruin is calculated by raising the ratio (1-edge)/(1+edge) to the power of N, where N represents the number of risk units needed to reach the ruin threshold. The edge is determined by multiplying win rate by (1 + win/loss ratio) and subtracting 1. Kelly Criterion = WR - LR / (W/L ratio).
Worked Examples
Example 1: Conservative Day Trader
Problem:A day trader has 55% win rate, 1.5:1 reward-to-risk, risks 1% per trade on a $25,000 account. Ruin threshold is 30%.
Solution:Edge = 0.55 x (1 + 1.5) - 1 = 0.55 x 2.5 - 1 = 0.375 Kelly = 0.55 - 0.45/1.5 = 0.55 - 0.30 = 0.25 (25%) Using 1% risk = 1/25th of Kelly (very conservative) Consecutive losses to ruin: log(0.70)/log(0.99) = ~35 trades Risk of ruin at 1% risk is extremely low.
Result:Risk of ruin: < 0.01%. Kelly fraction: 25%. Very safe position sizing at 1/25th Kelly.
Example 2: Aggressive Swing Trader
Problem:A swing trader has 45% win rate, 2:1 reward-to-risk, risks 5% per trade on $10,000. Ruin threshold is 40%.
Solution:Edge = 0.45 x (1 + 2) - 1 = 0.45 x 3 - 1 = 0.35 Kelly = 0.45 - 0.55/2 = 0.45 - 0.275 = 0.175 (17.5%) Using 5% risk = ~28% of Kelly Consecutive losses to ruin: log(0.60)/log(0.95) = ~10 trades RoR = ((1 - 0.35)/(1 + 0.35))^9.96 = 0.4815^9.96
Result:Risk of ruin: ~0.07%. Kelly fraction: 17.5%. Low against a wide 40% ruin threshold, but the figure rises to roughly 4% if ruin is redefined as a 20% loss.
Frequently Asked Questions
What is the risk of ruin in trading and why does it matter?
Risk of ruin is the probability that a trader will lose enough capital to reach a point where they can no longer trade effectively, typically defined as losing a specified percentage of their account balance. This concept is fundamental to trading survival because even profitable strategies can lead to account blowup if position sizing is too aggressive. A trader with a genuine 55 percent win rate and positive expected value can still face ruin if they risk too much per trade, as a string of losses can deplete the account before the statistical edge manifests. The mathematics of risk of ruin demonstrates that capital preservation is not merely conservative advice but a mathematical necessity. Professional traders and fund managers calculate their risk of ruin to set a maximum position size, choosing a probability low enough that it stays negligible across the number of trades they expect to place over a career rather than over a single month.
How does position sizing affect the probability of ruin?
Position sizing has an exponential impact on risk of ruin, making it arguably the most important variable in a trading system. Doubling the risk per trade does not merely double the risk of ruin — it can increase it by an order of magnitude or more. Run this page's own model on a 55 percent win rate with a 1.5 reward-to-risk ratio and a 20 percent ruin threshold: risking 1 percent per trade puts the probability of ruin far below one in a million, while risking 5 percent per trade lifts it to roughly 3 percent. A five-fold change in position size moves the ruin probability by several orders of magnitude. This dramatic escalation occurs because larger position sizes require fewer consecutive losses to reach the ruin threshold, and the probability of experiencing a specific losing streak increases substantially over hundreds or thousands of trades. Professional risk managers typically recommend never risking more than 1 to 2 percent of account equity on any single trade to maintain an acceptably low risk of ruin over a trading career.
What is the Kelly Criterion and how does it relate to risk of ruin?
The Kelly Criterion is a mathematical formula that calculates the optimal fraction of capital to risk on each trade to maximize the long-term geometric growth rate of the portfolio. The formula is Kelly Fraction equals Win Rate minus (Loss Rate divided by Win-to-Loss Ratio). While the full Kelly percentage theoretically maximizes growth, it also produces significant drawdowns and a relatively high risk of ruin in practice. Most professional traders use fractional Kelly, typically one-quarter to one-half of the full Kelly amount, to dramatically reduce volatility and risk of ruin while sacrificing only modest long-term growth. In the standard continuous-time model the trade-off is explicit: betting half of full Kelly keeps three quarters of the maximum growth rate, while the probability of ever drawing the account down to a given fraction of its starting value falls from that fraction to its cube. The chance of ever halving the account therefore drops from about one in two to about one in eight. Surrendering a quarter of the growth rate to cut that probability to a quarter of its former size is the asymmetry that makes fractional Kelly the practical choice.
How do win rate and reward-to-risk ratio interact in risk calculations?
Win rate and reward-to-risk ratio are the two components that define a trading edge, and they interact multiplicatively to determine expected value and risk of ruin. A trader can be profitable with a low win rate if the reward-to-risk ratio compensates sufficiently. For example, a trend-following system with only 35 percent win rate but a 3:1 average winner-to-loser ratio has a positive expected value of 0.35 times 3 minus 0.65 equals 0.40 per unit risked. Conversely, a scalping system with 70 percent win rate but only 0.8:1 reward ratio has an edge of 0.70 times 0.8 minus 0.30 equals 0.26 per unit risked. The first system has a higher mathematical edge despite winning far less frequently. When calculating risk of ruin, both variables feed into the edge calculation, and even small changes in either can dramatically alter the ruin probability, especially at higher risk-per-trade levels.
What is a safe risk of ruin percentage for different types of traders?
There is no industry-standard number to copy, so derive the threshold from consequences rather than from a figure quoted elsewhere. Ask what reaching the modelled loss would actually cost you. If the account is your income and cannot be replaced, the tolerable probability has to stay negligible across the thousands of trades a career contains, because a probability that looks small on one trade still accumulates over a long sequence of them. If the capital is discretionary and replaceable from other earnings, a materially higher probability can be rational. Two adjustments matter more than the exact target. First, run the model with pessimistic inputs: live win rates and reward ratios usually come in below backtested ones once spread, commission and slippage are charged. Second, re-run it whenever your measured statistics change, because ruin probability rises steeply as the edge shrinks. The common mistake is estimating risk of ruin from optimistic performance parameters rather than realistic worst-case figures.
What is the difference between risk of ruin and maximum drawdown?
Maximum drawdown is a backward-looking measurement — the largest peak-to-trough decline an equity curve has already suffered, expressed in percent. Risk of ruin is forward-looking: the probability that future trading will ever push the account down to a chosen threshold, given the win rate, reward-to-risk ratio and fractional risk per trade. Drawdown tells you what happened to one sample path; risk of ruin describes the distribution of all the paths your edge and position size can produce.
How many consecutive losses does it take to hit the ruin threshold?
With fixed-fractional sizing each loss is taken on a smaller balance, so the streak needed is longer than simply dividing the threshold by the risk. The count is log(1 − ruin threshold) / log(1 − risk per trade). Risking 2 percent with a 20 percent ruin threshold gives log(0.80)/log(0.98) ≈ 11.05, so 12 straight losses are required — after 11 the account is down about 19.9 percent, just short of the line.
Does fixed-fractional or fixed-lot sizing carry more ruin risk?
Fixed-fractional sizing risks a constant percentage of current equity, so the dollar risk shrinks automatically as the balance falls and the account approaches the ruin threshold asymptotically rather than crossing zero. Fixed-lot sizing risks the same dollar amount regardless of balance, so each loss becomes a larger share of what remains and the path to ruin is far shorter after a losing run. This is why the ruin threshold in this model is defined as a percentage loss, not a zero balance.
Why is ruin certain when the edge is negative, no matter how small the position size?
Expectancy per trade is win rate × reward-to-risk minus loss rate. When that value is negative the equity curve has a downward drift, and over a large number of trades the law of large numbers makes cumulative loss grow without bound. Reducing risk per trade only slows the decline — it lowers the variance around a falling mean, not the direction of the mean. Only raising the win rate or the average winner relative to the average loser changes the outcome.
How much sample data is needed before the win rate input can be trusted?
A win rate is a proportion, so its standard error is the square root of p(1 − p)/n. At an observed 55 percent over 30 trades that is roughly 9 percentage points at one standard error — wide enough to shift the calculated edge substantially. Because ruin probability rises steeply as the edge shrinks, run the calculation with the pessimistic end of your estimate, and use average loss figures that already include spread, commission and slippage.
References
Reviewed for accuracy by Daniel Agrici, Founder & Lead Developer · Editorial policy
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