Risk of Ruin Calculator: Trading Survival Simulator
Risk of Ruin Calculator: Trading Survival Simulator
What is the probability that my trading account blows up?
Risk of ruin is the estimated probability that a trading strategy reaches a user-defined loss or drawdown threshold within a specified number of trades.
It is a model-based estimate, not a prediction of future trading performance.
Your Trading Assumptions
Trade Statistics
Reward-to-risk ratio (auto)
2.00R
R:R = Average Win ÷ Average Loss — calculated automatically, no separate calculator needed.
Risk per trade in currency
$200
Compounding: as equity falls, the risked amount shrinks with the account.
Risk Settings
Advanced Settings (simulation paths, sizing model, costs, seed, uncertainty)
Calculating…
Estimated Risk of Ruin
—
Estimated Survival Probability
—
—
Strategy Context (calculated automatically)
Drawdown Statistics
| Percentile | P10 | P25 | Median (P50) | P75 | P90 |
|---|
Losing Streaks & When Ruin Happens
| Percentile | P25 | Median | P75 |
|---|
“What if I change my risk?” — Risk Sensitivity
Same strategy, different risk per trade. Notice how ruin probability grows far faster than the risk itself — doubling risk does not double the danger.
| Risk / trade | Risk of ruin | Verdict |
|---|
Scenario Stress Test — “What if my numbers are wrong?”
This does not tell you what risk to take. It shows how sensitive the result is to your assumptions.
| Scenario | Assumptions | Risk of ruin |
|---|
Win-rate uncertainty (sample-size check)
Simulated Equity Curves
Run the simulation first — this chart draws a fan of possible account paths with the median, the P10–P90 band and your ruin threshold.
individual paths median path ruined paths P10–P90 band ruin threshold
The full simulation powers the statistics; a subset of paths is drawn for clarity. Toggle “Ruined paths only” to see exactly how accounts fail.
Formulas Used in the Calculations
All formulas are rendered with MathJax and use your exact inputs. This page shows the math so you can verify every number.
1. Reward-to-risk ratio
2. Expectancy per trade (R and currency)
Example: 55% win rate with a 2R average winner and 1R average loser gives \( E_R = 0.55 \times 2 - 0.45 \times 1 = +0.65R \) per trade. With 2% risk on $10,000 that is +$65/trade. Costs per trade are subtracted for net expectancy.
3. Break-even win rate
For a 2:1 reward-to-risk ratio: \( W_{BE} = 100 \div (200 + 100) = 33.33\% \).
4. Monte Carlo engine (default method)
Each path draws a win with probability \(W\), applies the position-sizing rule (% of current equity or fixed currency), subtracts per-trade costs \(c\), then checks the ruin threshold. \( \text{Risk of Ruin} = \dfrac{\text{ruined paths}}{\text{total paths}} \). This approach naturally handles asymmetric payoffs, compounding, finite horizons and path dependency — which is why it is the default over the closed form.
5. Closed-form approximation (sanity check)
\(U\) = number of risk units between the starting balance and the ruin level. The closed form is exact only under restrictive assumptions (even payoffs, infinite horizon) — treat it as a rough cross-check of the simulation, not a replacement. Non-1:1 payoff cases are approximations.
6. Kelly criterion (context only — not advice)
Shown for context. Full Kelly is usually too aggressive in practice; the simulation — not Kelly — is what this tool is about.
7. Wilson score interval (win-rate uncertainty)
Used to build the plausible range around your observed win rate based on your historical sample size \(n\). A 55% win rate from 30 trades is not the same evidence as 55% from 3,000 trades.
Frequently Asked Questions
What is risk of ruin in trading?
Risk of ruin is the estimated probability that your account reaches a loss or drawdown level you define as “ruin” — 20%, 30%, 50% down, a prop-firm maximum drawdown, or total loss — within a specified number of trades. It is a model-based estimate, not a prediction.
How is risk of ruin calculated?
Two ways. The classic closed-form gambler’s-ruin formula gives a quick approximation. The default here is a Monte Carlo simulation: thousands of randomized trading paths are generated from your win rate, average win/loss, risk per trade and horizon, and the share of paths that ever touch your threshold is reported.
Can a profitable trading strategy still go broke?
Yes — this is the core insight of the tool. A strategy with a positive expectancy can still hit a long losing sequence or a deep drawdown if the risk per trade is too large. Variance, not just edge, decides survival.
Does positive expectancy prevent ruin?
No. Expectancy is the average outcome over many trades; ruin is a path event. With 2% risk, even a 55% win rate at 2:1 can carry a meaningful ruin probability over hundreds of trades. Lower the risk per trade and the simulated survival rate rises sharply.
What is a good ruin threshold?
Whatever level ends your ability (or willingness) to keep trading: a personal pain threshold, a prop firm’s maximum drawdown, or a level from which recovery becomes statistically impractical. The calculator lets you set any threshold from 1% to 100% and measure both starting-balance and peak-to-valley versions.
What does 10% risk of ruin mean?
Under the simulated assumptions, roughly 10 out of every 100 accounts trading this strategy would reach your ruin threshold within the chosen number of trades. It is a frequency estimate across hypothetical paths, not a timetable for your specific account.
Is Monte Carlo better than the formula?
For real trading, generally yes. The closed form breaks down with uneven payoffs, finite trade horizons, compounding and path dependency — the simulation handles all four natively. The formula is still shown here as a transparent sanity check.
How does risk per trade affect risk of ruin?
Nonlinearly — this is the most important lesson in the tool. Doubling risk per trade far more than doubles the ruin probability. Use the risk sensitivity table to see simulated ruin rates at 0.5% through 10% with your own statistics.
Why did my result change when I pressed Calculate again?
It will not here. Every simulation uses a fixed seed (the Simulation ID), so identical inputs always reproduce identical results. Press “New Random Scenario” if you want a different random draw.
© AlamToolkit.com — Educational modeling tool only. Nothing here is financial advice or a recommendation of any risk level; it shows how assumptions change simulated outcomes. Trading involves substantial risk of loss.
Risk of Ruin Calculator — User Guide, Formulas & Worked Examples
This guide explains how the Risk of Ruin Calculator works: every input, every formula, every output, and every assumption behind the Monte Carlo simulation. It is written for traders, risk managers, and anyone who wants to understand why a profitable strategy can still blow up — and how to measure that risk in numbers, not feelings.
What the Risk of Ruin Calculation Is For
Risk of ruin is the estimated probability that a trading strategy reaches a loss or drawdown level you define as “ruin” within a specified number of trades. It answers the question every trader faces eventually: “My plan looks profitable on paper — but what is the chance it destroys my account anyway?”
The counter-intuitive insight is that expectancy alone does not prevent ruin. A 55% win rate at a 2:1 reward-to-risk ratio is a genuinely positive edge, yet at 5% risk per trade it can carry a substantial probability of hitting a 30% drawdown within a few hundred trades. Risk of ruin is driven by variance and position size, not by expectancy alone.
Step-by-Step User Guide
The calculator has four tabs. Work through the Simulator tab first, then check the others to validate the result.
Step 1 — Enter your capital and currency
Enter your starting capital and pick the account currency. Currency does not change the percentage result; it only makes the currency-denominated outputs easier to read.
Step 2 — Enter your trade statistics
Choose how you want to enter average win / loss:
- Currency amounts — enter e.g. average win $200 and average loss $100. The calculator computes R:R automatically.
- R-multiples — enter e.g. 2R average winner and 1R average loser. More portable across accounts.
Also enter your win rate (%) and historical sample size (how many trades your win rate is based on). A 55% win rate from 30 trades is not the same evidence as 55% from 3,000 trades — the calculator builds a Wilson confidence interval to show the plausible range.
Step 3 — Set risk and ruin threshold
- Risk per trade (% of equity) — the most important input. Typical range for retail traders is 0.25% to 2%.
- Ruin threshold (% drawdown) — the level you consider a failure. Personal pain threshold, prop-firm maximum, or total loss.
- Ruin measured from — choose between starting balance and equity peak. Both are always reported.
- Trading horizon (number of trades) — 2% risk over 50 trades is a very different problem from 2% risk over 5,000 trades.
Step 4 — Advanced settings (optional)
Open Advanced Settings to control simulation paths (default 10,000), position-sizing model (fixed-fractional vs fixed currency), trading costs per trade, confidence level for win-rate uncertainty, and the simulation seed. Same seed + same inputs = identical results.
Step 5 — Run the simulation and read the hero
The hero card shows your estimated risk of ruin, the survival probability, and a zone badge (Very Safe < 5%, Acceptable 5–15%, Elevated 15–30%, Danger > 30%). The gauge marker sits at your result on the risk spectrum.
Step 6 — Study context, drawdown, and streaks
Read the strategy context (R:R, expectancy, break-even win rate, Kelly fraction for context only) and the drawdown distribution (median max DD, worst 5%, observed worst). The losing-streak panel tells you what the worst simulated losing streak looked like — useful for mentally preparing for the inevitable bad run.
Step 7 — Sensitivity & stress test
The sensitivity table re-simulates the same strategy at risk levels from 0.5% to 10% so you can see where the danger begins. The stress test runs your strategy under pessimistic scenarios (win rate −5 or −10 points, average win reduced). The win-rate uncertainty block re-simulates at the low and high ends of your confidence interval.
Step 8 — Chart, export, and share
The Equity Simulation tab draws a fan chart of simulated paths, with a median path, P10–P90 band, and ruin threshold. You can filter to ruined paths only, surviving paths, or the worst / best 5%. Copy the result, copy the share URL, download a PNG, or print to PDF.
Formulas Used in Every Result
Every number the calculator produces comes from one of the formulas below. Units are stated explicitly. Percentages are user-facing inputs (e.g. 55 means 55%) and are converted to decimals internally where needed.
1. Reward-to-risk ratio (R:R)
Units: dimensionless ratio. Example: $200 win ÷ $100 loss = 2.00R. In R-multiple mode, this is entered directly.
2. Expectancy per trade (in R and in currency)
E$ = ER × risk$ − cost$
Units: ER in R-multiples, E$ in currency. Example: 55% win, 2R win, 1R loss → ER = 0.55 × 2 − 0.45 × 1 = +0.65R. At 2% risk on $10,000 (=$200) that is +$130 per trade gross, less costs.
3. Break-even win rate
Units: percent. Example: 2:1 R:R → WBE = 1 ÷ (1 + 2) = 33.33%. Any win rate above this is a positive-edge strategy — before costs.
4. Monte Carlo engine (default method)
Loss: Et+1 = Et − riskt × Rloss − cost
Ruin (start): (E0 − Et) ÷ E0 ≥ Druin
Ruin (peak): (peak≤t − Et) ÷ peak≤t ≥ Druin
Risk of Ruin = ruined paths ÷ total paths
Units: E in currency, Druin as decimal, RoR as percent.
Under fixed-fractional sizing, riskt = Et × r (r = risk percent as decimal), so risk shrinks as equity falls.
Under fixed-currency sizing, riskt = fixed amount, independent of equity.
5. Closed-form approximation (sanity check)
edge = W × R:R − (1 − W), and U = number of risk units between start and ruin
Units: RoR as decimal or percent; U dimensionless. The classic gambler’s-ruin formula. Useful as a rough cross-check of the simulation, not a replacement.
6. Kelly criterion (context only, not advice)
Units: fraction of capital (multiply by 100 for %). Full Kelly is usually too aggressive in practice — it maximises log-growth, not survival. The simulation, not Kelly, is what this tool is about.
7. Wilson score interval (win-rate uncertainty)
Units: proportions (0–1). p̂ = observed win rate, n = sample size, z = 1.645 / 1.96 / 2.576 for 90 / 95 / 99% confidence. Used to compute the plausible range around your observed win rate, then re-simulate at both ends.
Worked Example (Calculator Defaults)
The calculator ships with these defaults. Let’s trace every output step by step.
Inputs
- Starting capital:
$10,000 - Currency:
USD ($) - Win rate:
55% - Average win:
$200| Average loss:$100 - Risk per trade:
2%of equity - Ruin threshold:
30%, measured from starting balance - Horizon:
500trades - Simulations:
10,000 - Sample size:
100trades
Step A — Reward-to-risk ratio
R:R = $200 ÷ $100 = 2.00R.
Step B — Expectancy per trade
ER = 0.55 × 2 − 0.45 × 1 = +0.65R per trade.
Risk in currency: 2% × $10,000 = $200.
E$ = 0.65 × $200 = +$130 per trade gross (before costs).
Step C — Break-even win rate
WBE = 1 ÷ (1 + 2) = 33.33%. At 55% you are comfortably above break-even.
Step D — Kelly fraction (context only)
f* = 0.55 − 0.45 ÷ 2 = 0.55 − 0.225 = 0.325, i.e. 32.5% of capital per trade if you were maximising log-growth. Full Kelly is far too aggressive for real trading. The default 2% is 1/16 of full Kelly.
Step E — Monte Carlo result
Running 10,000 paths over 500 trades with a fixed-fractional 2% risk, the calculator reports roughly risk of ruin ≈ 5–10%, depending on the seed. Survival probability is the complement. Median ending balance is well above start; median max drawdown is usually in the 15–25% range.
Step F — Sensitivity check
At the same strategy, the sensitivity table shows:
| Risk / trade | Approximate risk of ruin | Verdict |
|---|---|---|
| 0.5% | < 1% | Very Safe |
| 1.0% | ≈ 1–2% | Very Safe |
| 1.5% | ≈ 2–4% | Very Safe |
| 2.0% | ≈ 5–10% | Acceptable |
| 3.0% | ≈ 15–20% | Elevated |
| 5.0% | ≈ 35–45% | Danger |
| 10% | > 75% | Danger |
Where Engineers & Risk Managers Apply It
Although the calculator is aimed at trading, the underlying mathematics is used across engineering and quantitative risk fields:
- Reliability & safety engineering — the gambler’s-ruin closed form is the same equation as the classic “probability of reaching an absorbing state” problem, used in failure-mode analysis and Markov chain reliability modelling.
- Structural reliability (AISC / Eurocode / ASCE) — load–resistance factor design uses the same probability-of-exceedance framing, with the resistance factor φ playing the role of risk-per-trade.
- Actuarial science — ruin theory in insurance (Lundberg, Cramér) is the direct ancestor of the trading version: the same adjustment coefficient logic underlies both.
- Basel / Solvency II stress testing — Monte Carlo path simulation is the standard method for computing probability of regulatory capital breach.
- Project finance & capital budgeting — the ratio between reserve buffer and per-period variance determines the same ruin probability.
- Quantitative portfolio management — the Kelly fraction and the risk-of-ruin formula together define the boundary between optimal growth and acceptable survival.
Common Mistakes & Microcopy That Prevents Them
Mistake 1 — Confusing a good strategy with a safe one
A strategy with +0.65R expectancy at 2:1 R:R is genuinely profitable. That does not make it safe at 5% risk. Positive expectancy affects the average outcome; risk of ruin is about the path. A profitable system can still hit a drawdown that ends your career.
Mistake 2 — Using a small sample as proof of edge
A 55% win rate from 30 trades has a 95% confidence interval of roughly 37% to 72% — you cannot conclude much. Enter your real sample size; the calculator will show the plausible range and re-simulate at both ends.
Mistake 3 — Ignoring costs
Commission + spread + slippage typically eat 0.05R to 0.3R per trade. For a 2R average winner, that is a 3–15% hit to expectancy. Enter your per-trade cost in the Advanced panel — the calculator subtracts it from every win and every loss.
Mistake 4 — Choosing the wrong ruin definition
“Ruin from starting balance” and “ruin from equity peak” are not the same. If your account grows to $15,000 then falls to $10,500, that is a 30% peak-to-valley drawdown but a +5% gain versus your $10,000 start. The calculator reports both to avoid exactly this confusion.
Mistake 5 — Treating the output as a forecast
Monte Carlo gives a frequency estimate across hypothetical paths, not a forecast of your next 500 trades. Two traders with identical statistics will experience very different paths. Use the number to compare scenarios, not to predict outcomes.
Mistake 6 — Ignoring model assumptions
The simulation assumes independent trades with fixed win rate and average win/loss. Real markets exhibit volatility clustering, regime shifts, and correlation. When those are present, simulated ruin probability is usually lower than reality — the strategy fails faster than the model expects.
Mistake 7 — Assuming lower risk is always better
Reducing risk to zero also reduces growth to zero. The point of risk-of-ruin modelling is to find the boundary between “can survive” and “cannot survive” — not to minimise risk itself. Use the sensitivity table to see where the inflection point lies.
Key User Pain Points & How This Calculator Solves Them
| Pain point | How the calculator solves it |
|---|---|
| “I don’t know if my risk per trade is too high.” | Simulates ruin probability at your exact risk level, plus a sensitivity table showing 0.5% through 10%. |
| “My strategy is profitable — surely I’m safe?” | Directly shows that positive expectancy does not prevent ruin; the path matters as much as the average. |
| “My win rate came from a small sample. How sure am I?” | Builds a Wilson score confidence interval and re-simulates at both ends of the range. |
| “What drawdown should I expect?” | Reports median max drawdown, worst 5% max drawdown, and observed worst across all simulated paths. |
| “How bad can my losing streaks get?” | Losing-streak panel reports median, 90th percentile, and worst simulated streak length. |
| “What if I use fixed currency sizing instead of % of equity?” | Sizing toggle switches between fixed-fractional and fixed-currency models without leaving the page. |
| “How do I share this analysis with a client / prop firm / journal?” | Copy result, copy share URL (with encoded inputs), download PNG, print to PDF, or embed the calculator. |
| “I need to reproduce the result exactly.” | Every simulation uses a fixed seed (Simulation ID). Same inputs + same seed = identical results. |
| “Where do the numbers come from?” | Full formulas tab shows every equation with your actual inputs, so nothing is a black box. |
Real-World Usage Scenarios
Retail trader sizing a small account
A trader with a $5,000 account uses a simple breakout system with a 45% win rate and 2.5R average winner. At 2% risk the ruin probability is under 3%; at 5% risk it climbs above 25%. The trader settles on 1.5% risk and saves the share URL for later comparison.
Prop-firm candidate matching a max drawdown rule
A funded trader at a firm with a 10% max drawdown limit needs to know if 1% risk per trade is compatible with a 500-trade evaluation. The calculator’s peak-to-valley ruin mode directly answers the question: at 1% risk the simulated probability of ever touching 10% peak-to-valley is well under 5%.
Hedge fund risk memo
A portfolio manager preparing a monthly risk memo runs the simulation across three scenarios: base case, stressed win rate −5 points, and stressed win rate −10 points with reduced average win. The stress table gives the numbers directly, ready to paste into the memo.
Algorithmic backtest sanity check
A quant has a backtest showing strong returns. Before deploying, she runs the same statistics through the calculator with realistic costs. The result reveals a ruin probability of 12% — higher than she expected — prompting a reduction in position size before going live.
Blogger / educator teaching drawdown math
A finance blogger embeds the calculator alongside an article explaining why position sizing matters more than win rate. Readers can adjust inputs live and immediately see how ruin probability explodes at higher risk levels.
Engineering reliability analogy
A reliability engineer uses the calculator as a teaching analogy for the same equation used in load–resistance factor design: the probability of exceeding a defined failure threshold grows nonlinearly with per-cycle variance, exactly as risk of ruin grows nonlinearly with risk per trade.
FAQ
What exactly is risk of ruin?
It is the estimated probability that your trading account reaches a loss or drawdown threshold you define — commonly 20%, 30%, 50%, or a prop firm’s maximum — within a specified number of trades. It is model-based, not a prediction. Use it to compare risk levels and strategies, not to forecast your specific future.
How is the number calculated?
By default, a Monte Carlo simulation runs thousands of randomized paths from your win rate, average win/loss, risk per trade, and horizon. It counts the fraction of paths that ever touch your ruin threshold. A closed-form approximation is also shown for context, but the simulation is the primary method because it handles compounding, asymmetric payoffs, and finite horizons correctly.
Can a profitable strategy really blow up?
Yes — this is the central insight. A positive-expectancy system can still hit a drawdown that ends your career if the risk per trade is too large. Expectancy determines the average; risk of ruin determines the path. The higher the variance and the larger the position size, the faster ruin probability grows.
What is a good risk of ruin number?
Professional traders commonly target risk of ruin below 5%. Institutional funds often target below 1%. Above 15% is generally considered elevated; above 30% is a warning sign that risk per trade should be reduced. There is no universal threshold — it depends on your capital, personal tolerance, and how much drawdown you can functionally survive.
Why does doubling risk more than double the danger?
Because ruin is a path event, and the path depends on the product of consecutive outcomes. Larger positions amplify both the wins and the losses multiplicatively. Doubling risk squares the effect of a losing sequence on your account, so ruin probability grows super-linearly. The sensitivity table in the calculator makes this visible at 0.5% through 10%.
What is the difference between “from starting balance” and “from equity peak”?
They measure different things. Starting-balance ruin = you drop X% below your starting capital. Peak-to-valley ruin = you drop X% below your highest-ever equity, even if that peak was well above your start. Prop firms usually care about the starting-balance version; personal pain thresholds are often best expressed as peak-to-valley. The calculator always reports both.
Is Monte Carlo better than the closed-form formula?
For real trading, generally yes. The closed form is exact only under restrictive assumptions: even payoffs, infinite horizon, no compounding. Real trading has asymmetric payoffs, finite horizons, compounding equity, and trading costs. The Monte Carlo engine handles all of these. The closed form is shown in the Formulas tab as a transparent sanity check.
Why do I get the same number every time?
Because the calculator uses a fixed random seed (the Simulation ID). Same inputs + same seed = identical results. If you want a fresh random draw, click “New Random Scenario”. If you want to share a reproducible scenario with someone else, use the share URL — it encodes the seed along with your inputs.
What are the model assumptions?
Trades are independent (no autocorrelation), statistics are constant (no regime change), win rate and average win/loss do not drift, and trading costs are constant per trade. In reality, markets cluster volatility and change regime. When those effects are present, simulated ruin probability is usually lower than reality — the strategy fails faster than the model predicts. Treat the number as an optimistic reference.
Does this account for trading costs?
Yes, if you enter them. Open Advanced Settings and set “Trading costs per trade”. The engine subtracts this from both winning and losing trades before updating equity. Leave it at 0 for the abstract version; enter a realistic combined commission + spread + slippage estimate for the practical version.
How is this different from the Drawdown Recovery Calculator?
The Drawdown Recovery Calculator answers “I am already down X% — how much do I need to gain to recover?”. This calculator answers the forward-looking question “Given my strategy, what is the probability I will ever hit X%?”. They complement each other: use this one before trading, and the recovery calculator after a drawdown.
Is the calculator safe to embed on Blogger?
Yes. Everything is scoped under #ehub-ror-wrap and #ehub-ror-guide. There are no
external fonts, no images, and no CSS frameworks. The only external resource is MathJax (for formula rendering
in the Formulas tab), which loads asynchronously and does not block page render.
Summary — What to Remember
- Risk of ruin is a path probability, not a forecast. It answers “could this kill my account?”, not “will it?”.
- Positive expectancy does not prevent ruin. Position sizing dominates everything else.
- Doubling risk per trade more than doubles ruin probability — the relationship is super-linear.
- At 10,000 simulation paths, results typically converge within roughly ±0.5 percentage points.
- The model assumes independent, statistically stationary trades. Reality is worse, not better.
- Costs, drift, and regime change make the real number higher than the simulated number.
- Use the sensitivity and stress tables to see how much the result depends on your assumptions.
- Always share the URL if you want someone else to reproduce the exact scenario — it encodes the seed.
Educational reference only. Neither this guide nor the Risk of Ruin Calculator constitute financial advice. Formulas shown use the same conventions as the calculator’s code. Verify all numbers before relying on them for real decisions. Trading involves substantial risk of loss.
