Loss-streak probability
If each trade is an independent Bernoulli trial with loss probability 1 − win rate, the probability of k losses in a row is (1 − win rate)^k. That identity ignores dependence, averaging, and floors. The widget counts streaks that actually reach a stated floor.
Win rate in the widget is the Bernoulli p. The table is (1 − win rate)^k for a stated k, not a count of floor hits.
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At a 50% win rate, P(k losses) = 50%^k. The 6-loss row is 1.56%.
| Consecutive losses k | P(k losses) at 50% win rate |
|---|---|
| 3 | 12.5% |
| 4 | 6.25% |
| 5 | 3.13% |
| 6 | 1.56% |
| 8 | 0.39% |
| 10 | 0.1% |
A 6-loss streak at 1% risk is 6 lives consumed. Whether that reaches the floor depends on the allowance, not on this probability alone.
Independence is an assumption
The table assumes independent trades. Clustered losses (the usual empirical complaint) make long streaks more common than (1 − win rate)^k. The widget does not model clustering either — it draws independent sequences from the entered win rate. The free personal-rule engine currently models a stated Max Drawdown allowance as a fixed floor measured from your starting balance, not a floor that ratchets upward with a new equity peak.
Risk of ruin calculator · methodology.
FAQ
- How is loss-streak probability computed here?
- Under independence, (1 − win rate) raised to the streak length k. That is the probability of k losses in a specific sequence of k trades, not the probability that a k-streak appears somewhere in a longer sample.
- Does the simulator use this formula?
- No. The simulator draws sequences and checks a floor. The table is the closed-form Bernoulli identity, printed so the two can be compared.
- Does this say a streak will happen?
- No. It reports a probability under an independence assumption for numbers you enter. It does not predict a next trade.
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