Risk of ruin calculator (trader edition)
The probability of blowing up your trading account, given your edge (win rate and R:R) and your risk-per-trade setting. Built on the institutional risk-of-ruin framework derived from gambler's ruin theory. AUD-native. Used by professional desks to size every position; almost never used by retail traders, which is why retail blow-up rates stay high.
Calculator
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What is risk of ruin?
Risk of ruin is the foundational risk metric used by institutional trading desks. It answers a specific question: given my trading edge (win rate and reward-to-risk ratio) and my position sizing (risk per trade as a percentage of account), what is the probability that my account drops below a ruin threshold within a defined number of trades?
The concept comes from gambler's ruin theory, first formalised in the 17th century by Christiaan Huygens. The math was later refined for financial markets by Ralph Vince in Portfolio Management Formulas (1990). Every professional risk function on every prop desk uses some variant of the formula to set position-size limits. Retail traders almost never see this number, which is the practical reason retail blow-up rates remain stubbornly high regardless of broker selection, leverage caps, or platform sophistication.
The framework cuts through three of the most common retail trading mistakes:
- "I'll just trade bigger to recover faster." Risk of ruin shows the math: doubling risk per trade approximately doubles the probability of ruin, not the recovery rate.
- "My win rate is good so I'll be fine." Risk of ruin shows that win rate alone is insufficient. Without reward-to-risk above 1:1, even a 60 percent win rate produces high ruin probability at typical position sizes.
- "Small losses don't matter because I'll come back." Risk of ruin shows the asymmetry: small drawdowns compound non-linearly into large drawdowns when variance is high relative to expectancy.
The formula
Where E is expectancy per trade in R-multiples, U is capital-units to ruin (drawdown threshold ÷ risk per trade), and σ² is the variance of trade outcomes in R-multiples.
The formula is an analytical approximation for fixed-fractional bet sizing under a normally-distributed return assumption. The full derivation requires solving a difference equation on the equity random walk; the exponential form is the closed-form approximation.
Three things drive ruin probability:
- Expectancy per trade (E). The expected gain in R-multiples per trade. For win rate p and reward-to-risk R: E = p × R - (1 - p). Positive expectancy is necessary for finite ruin probability at any horizon.
- Capital units to ruin (U). The number of risk-per-trade losses required to hit the ruin threshold. Equal to drawdown threshold percentage divided by risk per trade percentage. Smaller risk per trade increases U non-linearly, which dramatically reduces ruin probability.
- Variance per trade (σ²). The trade-outcome variance in R-multiples. For win rate p and reward-to-risk R: σ² = p × R² + (1 - p) - E². Higher variance means a higher chance of streaks of bad outcomes; ruin probability rises with variance.
Worked example
An Australian retail trader has the following documented track record over 300 trades:
- Win rate: 52 percent
- Average winning trade: 1.5R
- Average losing trade: 1.0R
- Risk per trade: 2 percent of a AUD 10,000 account (AUD 200 per trade)
- Drawdown threshold: 50 percent (AUD 5,000)
- Trade horizon: 1,000 trades over the next 12 months
Step 1: Compute expectancy per trade.
E = 0.52 × 1.5 - 0.48 × 1.0 = 0.78 - 0.48 = 0.30R per trade.
Step 2: Compute capital units to ruin.
U = drawdown threshold ÷ risk per trade = 50% ÷ 2% = 25 capital units.
Step 3: Compute variance per trade.
σ² = 0.52 × 1.5² + 0.48 × 1² - 0.30² = 1.17 + 0.48 - 0.09 = 1.56.
Step 4: Apply the formula.
P(ruin) = exp(-2 × 0.30 × 25 / 1.56) = exp(-9.62) = 0.0066%.
Ruin probability is well below the institutional 1 percent threshold. This trader's edge, at 2 percent risk per trade, is genuinely sustainable.
Now reduce expectancy to test sensitivity. Same setup but win rate drops to 48 percent (negative expectancy: E = -0.04R). The exponential formula returns P(ruin) above 99 percent over 1,000 trades. Without positive expectancy, no risk-per-trade setting saves the account; the math is simply against the trader.
How institutional desks use it
Institutional risk management uses risk of ruin in three connected ways:
Position sizing. Every position is sized so that the marginal contribution to overall risk of ruin stays below a tight threshold (typically 0.1 percent per position over the firm's trade horizon). Aggregated across positions, total ruin probability stays sub-1 percent.
Strategy allocation. Capital is allocated across strategies to minimise the sum of marginal ruin probabilities. Strategies with similar expected return but lower variance receive higher allocations because their contribution to firm-level ruin is smaller.
Trader evaluation. Prop trading desks evaluate trader edges using a forward risk of ruin estimate based on the trader's track record. New traders typically start at lower risk per trade until enough sample size accumulates to tighten the variance estimate. Track records of fewer than 100 trades are usually insufficient to estimate expectancy with the confidence required for full sizing.
This is the framework retail trading platforms and broker marketing materials almost never expose. ASIC's product intervention order (March 2021) implicitly applied a system-wide risk of ruin cap by limiting leverage to 30:1 on majors. The calculator on this page surfaces the same framework to individual traders.
Why retail blow-up rates stay high
Roughly 76 percent of retail CFD accounts lose money according to the ASIC-mandated disclosure brokers must publish. The risk of ruin framework explains why:
- Edge is rarely positive. Most retail strategies are net-negative expectancy after spreads, commissions, slippage, and swap. Without positive E, any risk per trade produces near-certain ruin.
- Risk per trade is too high. Even with positive edge, many retail traders risk 5-10 percent per trade, producing capital-units-to-ruin of 5-10, which combined with retail-typical variance produces ruin probability above 50 percent.
- Variance is under-estimated. Retail traders typically estimate variance from a small recent sample (their last 20-50 trades), which underestimates the true variance and overestimates the expectancy. Forward risk of ruin is therefore higher than the trader's mental model.
- Trade horizon is open-ended. Retail traders don't define a planning horizon; they trade indefinitely. Over an infinite horizon, ruin is certain unless E significantly exceeds variance, which is a much higher bar than most realise.
The fix is not "trade smaller" alone. The fix is build documented edge first, then size against the risk of ruin output rather than against gut-feel risk-per-trade rules.
Related tools
- Kelly Criterion Position Sizer - the upper-bound bet size where long-run growth is maximised. Pairs with risk of ruin to set sustainable position sizing.
- Expectancy + Profit Factor Calculator - inputs win rate and average R-multiples, computes expected return per trade. Pre-step to risk of ruin.
- Drawdown Recovery Calculator - the gain required to recover from a drawdown. Pairs with ruin probability to set realistic loss thresholds.
- Position Size Calculator - translates risk-per-trade percentage into actual broker lot size given account balance and stop loss.
Related guides:
- ASIC-regulated forex brokers - why the regulator caps retail leverage at 30:1 on majors (the systemic risk of ruin framework).
- Best forex brokers Australia 2026 - broker comparison for traders sizing under institutional risk frameworks.
Frequently asked questions
Risk of ruin is the probability that a trader's account drops below a defined drawdown threshold (often 50 percent or full ruin) over a given number of trades, given their win rate, reward-to-risk ratio, and risk per trade. It is derived from gambler's ruin theory and is the foundational risk metric used by institutional trading desks to size every position. The formula combines the trader's expectancy per trade with the variance of trade outcomes to produce a probability between 0 and 1. A risk of ruin above 5 percent over a planned trading horizon is considered unacceptable by most professional risk frameworks. Retail traders almost never compute this number, which is why retail account blow-up rates remain high.
Institutional risk frameworks target a risk of ruin below 1 percent over the trading horizon (typically 1,000 to 10,000 trades). For an aggressive professional trader, risk of ruin up to 5 percent may be acceptable if compensated by very high expectancy. For retail traders, the practical advice is: if the calculator above shows ruin probability above 10 percent at your edge and risk setting, reduce risk per trade until ruin drops below 5 percent. If you cannot get below 5 percent at any risk-per-trade setting, your edge is not strong enough to scale and you should build edge before sizing.
Drawdown is the peak-to-trough decline already experienced. Risk of ruin is the forward-looking probability of experiencing a drawdown larger than a specified threshold over a given trade horizon. Drawdown is backward-looking and observed. Risk of ruin is forward-looking and probabilistic. The two are related: a trader with a high risk of ruin will, in expectation, experience large drawdowns. Institutional desks track realised drawdown to verify the calibration of their forward-looking risk of ruin estimates.
ASIC's retail leverage cap (30:1 on majors, lower elsewhere) and negative balance protection rules exist precisely because risk of ruin is high under uncapped leverage. The product intervention order issued in March 2021 was justified by ASIC's own analysis that retail traders using high leverage experienced ruin probabilities above 70 percent over typical holding periods. The calculator above shows traders why the cap exists: even at the capped 30:1 maximum, risk of ruin can still be high if win rate or R:R is poor. Risk of ruin is the framework that motivates every retail leverage rule from a regulatory standpoint.
Theoretically yes, if the trader has infinite capital, an unbounded edge, and never compounds losses. Practically no for any real-world trader. The closer to zero the better. Halving risk per trade approximately squares the risk of ruin (cuts it dramatically), which is why fractional Kelly position sizing exists as a separate risk-control framework. The calculator above shows the trade-off in real time: as you reduce risk per trade, ruin probability falls non-linearly while expectancy stays flat in R-multiples but falls linearly in absolute dollars.
Related but different. The Kelly criterion answers a different question: given your edge, what bet size maximises long-run log-equity growth? Risk of ruin answers: given your edge and bet size, what is the probability of dropping below a ruin threshold? Kelly identifies the maximum sustainable bet size (above which long-run growth turns negative). Risk of ruin quantifies the survival probability at any bet size. Sophisticated traders use both: Kelly to set the upper bound, then size below Kelly until risk of ruin is acceptably low. The calculator on this page outputs risk of ruin. The Kelly Criterion Position Sizer on SatoshiMacro outputs the Kelly upper bound. Use them together.
The formula used here is the analytical approximation for fixed-fractional bet sizing under a normally-distributed return assumption. It assumes constant edge, constant variance, and independent trade outcomes. Real trading deviates from these assumptions due to edge drift, fat-tailed return distributions, and serial correlation in win/loss streaks. The calculator is therefore an order-of-magnitude tool, not a precise probability. Institutional desks use Monte Carlo simulation to verify the analytical estimate. For retail use the analytical formula is sufficient to surface the rough scale of the risk.
Either your win rate or your reward-to-risk ratio is weak. The calculator shows ruin probability declining as you reduce risk per trade, but only up to a limit. If your expectancy per trade is small (for example, win rate 50 percent at R:R 1:1 has zero expectancy), no risk-per-trade setting produces low ruin probability. The math compounds: zero edge plus any positive risk produces certain ruin given enough trades. The fix is not lower risk per trade; the fix is building a documented edge with positive expectancy. The 1 percent risk-per-trade rule only works for traders with positive expectancy.
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