Losing streak probability is the chance of consecutive losing trades under a stated model. If trades are independent and each has loss probability L, the probability of k losses starting at a specified trade is Lk. A 50% loss probability gives a 3.125% chance of five losses from that starting point. The probability of encountering such a run somewhere in a longer sequence is higher.
Part of the trading metrics library, this guide separates a particular run, the longest run across a sample and the money lost during it. Every probability below is conditional on the model, not a forecast for a trading strategy.
Losing streak probability at a glance
Scroll horizontally to read every column.
| Question | Calculation or evidence | Main limit |
|---|---|---|
| Will the next k trades all lose? | Lk | Independent outcomes and a constant loss probability |
| Will a run occur anywhere in N trades? | A run-probability calculation or simulation | Overlapping starting points cannot be treated as independent |
| How long might the longest run be? | Approximation: log(N(1 − L)) ÷ log(1 ÷ L) | A rough scale, not a maximum or an exact mean |
| How much could a run cost? | Loss amounts, costs and open exposure | A count of losses does not specify their size |
How do you calculate consecutive losses probability?
Probability of k losses from a given trade = Lk
L is the probability that one trade loses; k is a positive whole number. The model treats trades as independent, each with the same fixed loss probability. The binomial mass function, which assumes that fixed trial probability, gives Lk when all k of k trials are losses.[1] The next k trades must all lose, but a longer run also qualifies. A run of exactly k losses followed by a non-loss has probability Lk × (1 − L).
With wins and losses only, L = 1 − W, where W is the win rate. With breakevens, calculate L as losing trades divided by all closed trades. In this article, a zero-result trade stays in the denominator and breaks a losing run. Removing it would measure a different sequence.
Decide what counts as one trade before calculating anything. A position closed in three parts need not represent three independent decisions. Also state whether a trade is classified before or after costs: a gross breakeven can be a net loss.
Worked example: five losses now versus somewhere in 100 trades
Illustrative numbers, not a recommendation or strategy results. Assume independent trades, a constant 50% win probability, a 50% loss probability and no breakevens.
- Next five trades: 0.55 = 0.03125, or 3.125%.
- Five-loss windows in 100 trades: there are 100 − 5 + 1 = 96 possible starting points.
- Expected number of all-loss windows: 96 × 0.03125 = 3. A run of six losses contains two overlapping five-loss windows.
- At least one run of five or more: approximately 81.01%, calculated by the recurrence below.
The expected window count is not a probability. Nor is 1 − (1 − 0.55)96 the right answer: neighbouring windows share trades.
An exact calculation that handles overlap
Track five states after each trade: no five-loss run has occurred, and the current trailing run has length 0, 1, 2, 3 or 4. Start with probability 1 in state 0. A non-loss resets the trailing count; a loss advances it.
an+1(0) = (1 − L) × Σ an(j); an+1(j) = L × an(j − 1), for j = 1, …, k − 1
The sum runs from j = 0 to k − 1. Probability leaving state k − 1 on another loss has completed the target run and leaves the tracked states. After N updates, 1 − Σ aN(j) is the probability of at least one qualifying run. With N = 100, k = 5 and L = 0.5, it is 0.8101095992.
What is the expected longest losing streak?
Longest losing streak, approximate scale = log(N(1 − L)) ÷ log(1 ÷ L)
N is the number of trades and 0 < L < 1. Any consistent logarithm base works. The expression is useful when N(1 − L) is comfortably above one; it is unsuitable at the boundaries.
A rough derivation counts N(1 − L) opportunities for a run to start after a non-loss, then asks when N(1 − L)Lk is about one. Solving for k gives the expression above. Boundary effects and the distribution of possible run lengths mean that this is not the exact expected maximum.
The worked simulation below supplies the mean and a tail measure instead of treating the approximation as a ceiling. Each row contains 100,000 synthetic sequences of 100 trades with a fixed win probability and no breakevens.
| Assumed win probability | Approximate scale / simulated mean | Simulated 95th percentile |
|---|---|---|
| 40% | 7.22 / 7.87 losses | 12 losses |
| 50% | 5.64 / 5.99 losses | 9 losses |
| 60% | 4.47 / 4.61 losses | 7 losses |
| 70% | 3.53 / 3.52 losses | 5 losses |
Reproduction method: use Python's random.Random(20260925), resetting the generator for each row. For each trade, draw one uniform value with random(); values below L are losses. Reset the current run on a non-loss and retain the largest run per sequence. Average those maxima; sort them and take observation 95,000 for the nearest-rank 95th percentile.
A mean can be fractional even though each realised streak is a whole number. The 95th percentile is not a largest possible streak, and it is not a confidence interval for the true loss probability. Increasing the number of trades changes the table. See Monte Carlo trading simulation for the wider method.
How does a streak relate to a daily or total loss limit?
A streak count becomes a money calculation only after specifying loss sizes. For k sequential losses, each assumed to lose a fixed cash amount R at its stop, the stop-loss subtotal is k × R. Costs, gaps, slippage and other exposure sit outside that simplified subtotal.
Illustrative budget condition: k × R + C < B, so R < (B − C) ÷ k
B is remaining loss headroom and C is a separately assumed allowance for additional losses and costs. The strict inequality represents a hypothetical rule where touching the limit is a breach. Actual account rules decide the measurement and boundary.
Illustrative, not recommended sizing: with B = 1,000 USD, C = 200 USD and k = 8, the condition requires R below 100 USD. An assumed R of 80 USD gives 8 × 80 + 200 = 840 USD. Neither eight losses nor the 200 USD allowance is a proven worst case.
Daily headroom depends on the rule's reset time and on which losses fall within that day. A total or trailing limit can use a different reference value. Open losses can breach an equity-based limit before any trade closes. A streak table alone cannot establish compliance.
The risk management library connects these definitions to position sizing and daily loss controls. No universal risk percentage follows from a win rate.
Where do TradingView, MT5 and PineConnector fit?
As of 25 September 2026, TradingView's strategy report provides a Trades list and CSV download. Under the default testing range, individual data is retained for only the latest 9000 trades; the Metrics tab is not affected.[2] Count streaks in chronological order and disclose the exported window.
The MT5 Strategy Tester report lists maximal consecutive losses, the money lost in that run, and a separate maximum consecutive monetary loss with its trade count.[3] The longest run and the most expensive run need not be the same. These fields describe the tested history, not the next sequence.
For a TradingView-to-MT5 workflow, the measurable rule, alert condition, alert trigger, webhook delivery, PineConnector processing and EA order request are separate stages. Broker acceptance, a resulting deal and the position need their own evidence. PineConnector's setup test explicitly separates message processing from the broker trade.[4]
PineConnector's loss-based sizing documentation describes a planned loss budget and stop-distance calculation. It also explains that costs, fills, gaps and broker limits can change the actual loss.[5] A strategy-only stop in TradingView is not evidence of a broker-side stop. Reconcile actual closed trades before comparing their streak with the simulated one.
What can make a losing streak calculation misleading?
- Dependence: overlapping positions or shared market conditions can link outcomes. The independent-trade formula then describes the wrong model.
- An estimated loss probability: a historical loss rate is an estimate. More precision in Lk does not remove uncertainty in L. See backtest sample size.
- A changing strategy: combining different rule versions can conceal different loss probabilities.
- The gambler's fallacy: under the independent model, a losing run does not make a win due. The next loss probability remains L.
- Confusing streaks and drawdown: small intervening wins can break a streak without recovering the previous peak. Maximum drawdown measures a different event.
Frequently asked questions
What is the probability of consecutive losses?
For independent trades with constant loss probability L, the probability that the next k trades all lose is L to the power k. At L = 0.5, five consecutive losses have probability 0.5 to the power 5, or 3.125%. The chance of finding a run somewhere in a longer sample requires a separate calculation.
How many losses in a row should I expect?
The answer depends on the loss probability, number of trades and dependence between outcomes. In an illustrative simulation of 100 independent trades with a 50% loss probability, the longest run averaged about six losses across 100,000 sequences. Some sequences had longer runs. An average longest streak is not an upper limit or a sizing recommendation.
What should a losing streak calculator ask for?
A losing streak calculator should identify the loss probability, target run length and total number of trades. It should distinguish losses from one specified starting point from a run anywhere in the sample. The calculator must also state its independence assumption and how it treats breakeven trades. Win rate alone cannot determine the monetary loss.
Does a losing streak mean a strategy has stopped working?
A losing streak alone cannot establish that a strategy has changed. Runs occur even in an independent model with an unchanged loss probability. Compare the observed sequence with the previously specified model, sample window and rule version, then investigate execution differences. A calculation based on historical estimates cannot prove that the underlying probability stayed constant.
Reviewed 25 September 2026. Facts were checked against the linked sources on that date. Nothing in this article was tested on a trading account.
Related reading
- Trading metrics library
- How many trades a backtest needs
- Risk of ruin and its assumptions
- Daily loss limits and circuit breakers
- Monte Carlo trading simulation
Sources
- NIST/SEMATECH – e-Handbook: Binomial Distribution, accessed 25 September 2026.
- TradingView – Pine Script v6 User Manual: Strategies, accessed 25 September 2026.
- MetaQuotes – MetaTrader 5 Help: Testing Report, accessed 25 September 2026.
- PineConnector – Test your setup, accessed 25 September 2026.
- PineConnector – Syntax: loss-based sizing, accessed 25 September 2026.
PineConnector executes the instructions you send it. It does not select trades, manage money, or hold funds. Trading carries risk, and past performance of any strategy does not indicate future results.
