Risk of Ruin Calculator
The Risk of Ruin Calculator estimates the probability your trading account hits a chosen loss threshold (default 50%), given win rate, risk:reward, and risk per trade. It reports two figures: a Gambler's Ruin formula, RoR = ((1 − Edge)/(1 + Edge))^(RuinThreshold% ÷ Risk%), and a 10,000-iteration Monte Carlo simulation over your trade count.
- Two methods run together: the mathematical Gambler's Ruin formula (assumes infinite trades) plus a 10,000-iteration Monte Carlo simulation that uses your specific trade count and random sequencing — Monte Carlo usually reads higher over short horizons under 200 trades.
- Edge (expectancy) = (WinRate x AvgWin) − (LossRate x AvgLoss), with AvgLoss set to 1R; capital units = Ruin Threshold% ÷ Risk Per Trade%. A negative or zero edge returns 100% risk of ruin.
- Risk per trade is the dominant lever — its effect is exponential, not linear, so doubling risk can raise ruin probability roughly 10x. Defaults are 50% win rate, 1:2 R:R, 2% risk, 50% ruin threshold, 100 trades.
- Verdict bands (from Monte Carlo RoR): under 1% Very Safe, under 5% Acceptable, under 15% Risky, under 50% Dangerous, and 50% or more Account Death Likely.
- Outputs include per-trade and R-multiple expectancy, expected, median, worst-10% and best-10% account values, 5 sample equity curves, and a sensitivity table testing risk levels of 0.5, 1, 2, 3, 5, 7, and 10%.
Calculate the statistical probability that your trading account will blow up. Uses both mathematical formulas and Monte Carlo simulation (10,000 iterations) for the most accurate result.
How to Use the Risk of Ruin Calculator
Enter Your Win Rate
Use your actual trading history — not what you hope for. If you have 100 trades and 55 were winners, your win rate is 55%. Be honest; the calculator is only as good as the inputs.
Set Your Average Risk:Reward
Calculate your average winning trade divided by your average losing trade. If your average winner is $200 and average loser is $100, your R:R is 1:2. Include all trades, not just your best ones.
Set Your Risk Per Trade
How much of your account you risk on each trade. This is the most important variable — small changes here produce massive changes in risk of ruin. Start with your current risk and see the impact.
Define "Ruin"
The default is 50% — losing half your account. Prop firm traders might set 10% (matching their drawdown limit). Conservative traders might use 25%. Aggressive traders might use 75%.
Click Calculate and Analyze
The Monte Carlo simulation runs 10,000 iterations. Check the sensitivity table to see how adjusting your risk per trade changes the outcome. Find the sweet spot between growth and survival.
What Is Risk of Ruin in Trading?
Risk of ruin is the probability that a trader will lose enough money to reach a point where recovery becomes practically impossible. Unlike maximum drawdown (which measures the worst dip in a single equity path), risk of ruin measures the probability of catastrophic loss across all possible outcomes.
Every trading strategy — no matter how profitable — has some probability of hitting an extended losing streak. The question isn't if you'll have a bad run, but whether your position sizing can survive it when it happens.
The Cold Math of Losing Streaks
With a 50% win rate, there is a 3.1% chance of losing 5 trades in a row, a 0.1% chance of losing 10 in a row, and a non-zero chance of losing 15+ in a row. At 5% risk per trade, a 10-trade losing streak would erase 40% of your account. At 1% risk, the same streak only costs 9.6%.
The Math Behind Risk of Ruin
The mathematical approach uses the edge-based formula derived from the Gambler's Ruin problem:
Edge = (WinRate × AvgWin) - (LossRate × AvgLoss)
// Step 2: Convert ruin threshold to capital units
CapitalUnits = RuinThreshold% / RiskPerTrade%
// Step 3: Calculate risk of ruin
RoR = ((1 - Edge) / (1 + Edge)) ^ CapitalUnits
// Example: 50% win rate, 1:2 R:R, 2% risk, 50% ruin
Edge = (0.50 × 2) - (0.50 × 1) = +0.50
CapitalUnits = 50 / 2 = 25
RoR = (0.50 / 1.50)^25 = (0.333)^25 ≈ 0.0000000001%
This formula assumes infinite trades and constant risk sizing. The Monte Carlo simulation provides a more realistic result by using finite trades and random sequencing.
Why 1-2% Risk Is the Industry Standard
The relationship between risk per trade and risk of ruin is exponential, not linear. Doubling your risk doesn't double your ruin probability — it can increase it by 10x or more.
| Risk/Trade | 50% WR, 1:2 R:R | 55% WR, 1:1.5 R:R | 45% WR, 1:1 R:R |
|---|---|---|---|
| 0.5% | ~0% | ~0% | ~0% |
| 1% | ~0% | ~0% | 3-8% |
| 2% | ~0% | 0.1-1% | 15-25% |
| 5% | 0.5-3% | 5-15% | 50-70% |
| 10% | 10-25% | 30-50% | 80-95% |
The 1% Rule
At 1% risk per trade, even a mediocre strategy with slightly positive expectancy has near-zero risk of ruin over hundreds of trades. At 5% risk, the same strategy has a meaningful chance of account death. The difference between a surviving trader and a blown account is often just position sizing — not strategy quality.
Monte Carlo Simulation Explained
Monte Carlo simulation answers the question: "Given my strategy's parameters, what range of outcomes is possible?" It does this by running thousands of random trading simulations.
- Setup: Define your win rate, R:R ratio, risk per trade, and number of trades.
- Simulate: For each of the 10,000 iterations, randomly determine each trade outcome based on your win rate. Track the equity curve.
- Count: After all simulations, count how many hit your ruin threshold. That percentage is your risk of ruin.
- Analyze: The distribution of final equity values shows your range of possible outcomes — from worst case to best case.
The equity curve chart shows 5 randomly selected simulations to illustrate the range of possible paths your account could take. Some curves soar, some flatline, some crash — all from the exact same strategy parameters. This is the randomness of trading made visible.
Monte Carlo vs. Mathematical Formula
The mathematical formula assumes infinite trades and produces a long-run probability. Monte Carlo uses your specific trade count and captures short-run risk. For 100 trades, Monte Carlo typically shows higher ruin probability because a short losing streak early in a small sample is more devastating than in an infinite sequence.
Risk of Ruin vs. Drawdown — The Difference
| Concept | Risk of Ruin | Maximum Drawdown |
|---|---|---|
| What it measures | Probability of hitting a loss threshold | Largest peak-to-trough decline experienced |
| Forward or backward? | Forward-looking (predictive) | Backward-looking (historical) |
| Single path or many? | Across all possible trade sequences | Single historical equity curve |
| How to use | Determine if your position sizing is survivable | Understand past pain points and worst periods |
| Key limitation | Assumes future matches past parameters | Past drawdown may not be the worst possible |
A backtest showing a 20% maximum drawdown does not mean your risk of ruin is 20%. The actual risk of ruin depends on ongoing trade parameters. The 20% drawdown was one possible path — Monte Carlo reveals all the paths you didn't see.
How to Reduce Your Risk of Ruin
| Strategy | Impact | Difficulty |
|---|---|---|
| Reduce risk per trade | Massive — exponential reduction in RoR | Easy — just trade smaller |
| Improve win rate | Significant but linear | Hard — requires better analysis or entries |
| Improve R:R ratio | Significant but linear | Moderate — wider targets, tighter stops |
| Cut risk during drawdown | High — reduces compounding losses | Moderate — requires discipline |
| Diversify strategies | Moderate — smooths equity curve | Hard — requires multiple uncorrelated edges |
The most effective approach is almost always reducing risk per trade. Going from 5% to 2% can cut risk of ruin from dangerous to near-zero, while only reducing account growth speed by about 60%. Going from 2% to 1% further halves growth speed but makes ruin virtually impossible for strategies with positive expectancy.
Real Examples: Why High Win Rate Doesn't Save You
| Trader | Win Rate | R:R | Risk/Trade | Expectancy | Risk of Ruin |
|---|---|---|---|---|---|
| Trader A | 70% | 1:0.8 | 5% | +0.26R | 15-30% |
| Trader B | 40% | 1:3 | 1% | +0.60R | <0.01% |
| Trader C | 60% | 1:1 | 10% | +0.20R | 40-60% |
Trader A wins 70% of trades but uses 5% risk — a few losing streaks can destroy the account despite the high win rate. Trader B only wins 40% but uses a 1:3 R:R with 1% risk — virtually indestructible. Trader C has a profitable edge but 10% risk makes ruin likely.
The Paradox
Trader B has the worst win rate but the lowest risk of ruin. This is because expectancy × position sizing determines survival, not win rate alone. A low win rate with high R:R and conservative sizing beats a high win rate with aggressive sizing every time over the long run.
Frequently Asked Questions
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Risk of ruin is the statistical probability that a trader will lose a specified percentage of their account — typically 50% or more — given their win rate, risk per trade, and risk:reward ratio. It's calculated using mathematical formulas and Monte Carlo simulation. A 5% risk of ruin means there is a 5 in 100 chance your account will be cut in half based on your current trading parameters.
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Professional traders aim for a risk of ruin below 1%. Below 5% is considered acceptable for most serious traders. Above 15% is risky — you have a meaningful chance of catastrophic loss. Above 50% means your account is statistically more likely to fail than succeed. Reducing risk per trade from 5% to 1% can often reduce your risk of ruin from dangerous to near-zero.
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Risk per trade is the single most powerful variable. The relationship is exponential — doubling your risk per trade can increase your risk of ruin by 10x or more. For example, a strategy with 2% risk might have a 0.5% ruin probability, but the same strategy at 5% risk could have a 25% ruin probability. This exponential sensitivity is why professionals universally use 1-2% maximum risk per trade.
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Not if your risk per trade is too high. A 70% win rate with 1:1 R:R and 10% risk per trade still has a significant risk of ruin because losing streaks of 5-7 trades are statistically inevitable. Even the best strategies experience extended losing periods. Position sizing determines whether you survive those periods — not how often you win.
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Monte Carlo simulation runs thousands of random trading scenarios using your strategy's parameters. Each simulation randomly determines trade outcomes based on your win rate, then tracks the equity curve. By running 10,000 simulations, it reveals the full range of possible outcomes — from best case to worst case — and the probability of hitting your ruin threshold. It's more realistic than mathematical formulas because it uses finite trade counts.
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Maximum drawdown is the largest peak-to-trough decline in a single historical equity curve — it tells you what happened. Risk of ruin is the probability of hitting a specific loss threshold across all possible trade sequences — it tells you what could happen. A 30% max drawdown in backtesting does not mean 30% ruin probability. The actual ruin probability depends on your ongoing risk parameters and the randomness of future trades.
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Three primary approaches, ranked by impact: (1) Reduce risk per trade — the most powerful lever, with exponential effect on ruin probability. Going from 5% to 1% can drop RoR from 50% to near 0%. (2) Improve your edge by increasing win rate or R:R ratio — linear effect. (3) Cut position size during drawdown periods — reduces compounding losses. Of these, reducing risk per trade is by far the easiest and most effective.
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Expectancy is the average profit or loss per trade expressed in risk units (R-multiples). Formula: (Win Rate × Average Win in R) - (Loss Rate × 1R). For a 50% win rate with 1:2 R:R: (0.50 × 2R) - (0.50 × 1R) = +0.50R. This means for every $1 risked, you expect to make $0.50 on average. Positive expectancy is required for long-term profitability, but even positive expectancy doesn't prevent ruin if position sizing is too aggressive.
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The mathematical formula assumes infinite trades and simplified conditions. Monte Carlo simulation uses your specific trade count and random sequencing. For short horizons (under 200 trades), Monte Carlo often shows higher ruin probability because a concentrated losing streak early in a small sample is more devastating. As trade count increases, both results converge. Monte Carlo is generally more realistic for practical trading decisions.
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The Kelly Criterion calculates the theoretically optimal bet size to maximize long-term growth. Full Kelly eliminates risk of ruin in theory (with infinite trades) but produces extreme drawdowns in practice — often 50%+ swings. Most traders use half-Kelly or quarter-Kelly for smoother equity curves. Risk of ruin at full Kelly is 0% in theory, but short-term experiences can be psychologically devastating. Half-Kelly roughly halves drawdowns while retaining 75% of growth.
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At least 100 trades for basic insights, 200-500 for statistically reliable results. The simulator runs 10,000 iterations of your specified trade count. If you set 30 trades, it shows what could happen in your next 30 trades — useful but with higher variance. For evaluating a trading system's long-term viability, use 200+ trades. Day traders taking 5 trades per day should simulate at least 200-500 trades (1-2 months of trading).
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Yes — and very solidly so. Expectancy = (0.50 × 2R) - (0.50 × 1R) = +0.50R per trade. You lose half your trades, but winners are twice as large as losers. With 1% risk per trade, this produces approximately 0.5% expected account growth per trade with virtually zero risk of ruin. This parameter set is actually better than many hedge fund strategies. Win rate is not the goal — positive expectancy combined with appropriate sizing is.
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