Every trader eventually confronts a result that does not make sense on the surface. A strategy with a 65% win rate that keeps losing money. A strategy that wins less than a third of the time but steadily grows the account. The explanation is always the same: win rate without the payoff attached to it tells you almost nothing. Expectancy is what you actually need.
Expectancy is the average outcome per trade, expressed in dollars or in R-multiples, over a sample large enough to smooth out random variation. It is the single number that predicts whether a trading approach makes money in the long run. Everything else — win rate, profit factor, Sharpe ratio — is either a component of expectancy or a description of how the account behaves while expectancy does its work over time. You can compute it on the trade expectancy calculator.
The formula
Expectancy = (win rate × average win) − (loss rate × average loss)
Loss rate is simply one minus the win rate. Average loss is entered as a positive number representing the magnitude of the typical losing trade. The result is the expected value of a single trade, averaged across a large sample. A positive result means each trade adds value on average. A negative result means each trade subtracts value, regardless of how often you win.
Three strategies make this concrete:
| Strategy A | Strategy B | Strategy C | |
|---|---|---|---|
| Win rate | 30% | 60% | 50% |
| Average win | $450 | $100 | $200 |
| Average loss | $100 | $200 | $200 |
| Expectancy per trade | +$65 | −$20 | $0 |
| Edge | Positive | Negative | Break-even |
Strategy A wins only 30% of the time but has strong positive expectancy because its wins are 4.5 times its losses. Strategy B wins 60% of the time but has negative expectancy because losses are twice the size of wins. Strategy C is exactly break-even regardless of how many trades it takes. Win rate ordered these strategies as B, C, A. Expectancy ordered them as A, C, B. The two rankings are nearly opposite.
Expectancy in R: making it portable
Dollar expectancy depends on position size, which makes it hard to compare across strategies traded at different scales. R-multiples solve this. R is the initial risk on a trade — the distance from entry to stop loss. Expressing wins and losses as multiples of R makes expectancy a dimensionless ratio that works across any instrument, account size, or timeframe.
Strategy A above, recast in R: average win is 4.5R, average loss is 1R, expectancy is (0.30 × 4.5R) − (0.70 × 1R) = 1.35R − 0.70R = +0.65R per trade. That means for every dollar risked, the strategy expects to return 65 cents in profit on average. A system with +0.3R expectancy and 200 trades a year expects to grow the account by 60R annually. Use the R-multiple calculator to express individual trades in these terms, and the win rate calculator to verify you are reading the right base number.
The breakeven relationship between win rate and payoff
Expectancy of zero means the strategy breaks even. Setting the formula to zero and solving for win rate gives the breakeven win rate for any payoff ratio: breakeven win rate = 1 ÷ (1 + payoff ratio). At a 1:1 payoff, you need 50% wins. At 2:1, you need 34%. At 3:1, you need 25%. At 0.5:1 — where the average loss is twice the average win — you need 67% wins just to break even.
This relationship is why certain strategy styles are inherently harder to trade psychologically. A trend-following system with 30% win rate and 4R average wins has strong positive expectancy, but it requires sitting through seven consecutive losses before a big winner arrives. A mean-reversion system with 70% win rate and 0.8R average wins is also positive expectancy, but the occasional large loss is what most traders struggle to accept. Neither is better. Expectancy does not care about the path, only the destination.
Sample size: when expectancy becomes trustworthy
A strategy with +0.6R expectancy on 15 trades could easily be noise. Expectancy stabilises over larger samples. The standard floor most systematic traders use is 50 trades before any metric is taken seriously, and 200 or more before drawing strong conclusions about edge. This is why the number of trades a strategy generates per year matters as much as the per-trade expectancy. A system with +0.5R expectancy that takes 10 trades a year will take many years to confirm its edge. One that takes 200 trades a year confirms it in months.
Sample size is also why backtesting across multiple instruments and time periods matters. A strong expectancy on one instrument over three years might be a regime artifact rather than a genuine edge. The guide on why backtest results differ from live trading covers the other gaps between a backtest number and real-world performance.
Measuring expectancy in your Pine Script strategy
Once your trading rules are coded as a strategy in TradingView, the Strategy Tester computes the components of expectancy automatically. Average trade is visible directly. Win rate and average win and loss are in the performance summary. The Strategy Tester metrics guide covers what each number means and when each misleads.
You can also compute and plot a rolling expectancy directly on the chart using Pine Script's strategy.closedtrades.* built-ins, which give you the gross profit, gross loss, win count, and loss count for all closed trades. If you describe what you want to measure in plain English, PineScripter generates the strategy code that surfaces these numbers, so you spend time reading results rather than debugging syntax. The guide on why win rate alone is misleading shows the same concept from a different angle and is worth reading alongside this one.
Disclaimer: PineScripter is a coding tool for Pine Script development. It does not provide financial advice and does not guarantee trading profits. Always backtest strategies thoroughly and understand the risks before live trading.