It's tempting to judge a trading strategy by one number: how often it wins. A strategy that wins 70% of the time feels obviously better than one that wins 35% of the time — until you actually run the math and discover the 35%-win-rate strategy is the more profitable one. Win rate on its own says almost nothing about whether a strategy makes money over time. The number that actually answers that question is expectancy, and it takes both how often you win and how much you win or lose each time into account together.
The Trap of Judging a Strategy by Win Rate Alone
Win rate is seductive because it's the easiest statistic to track and the one that feels most emotionally validating trade to trade — nobody enjoys a string of losses, no matter what the math behind it says. But a win rate on its own is an incomplete fraction: it tells you the frequency of wins, but says nothing about their size relative to the losses. A strategy that wins nine times out of ten but risks $100 to make $10 each time is a losing strategy overall the moment that tenth trade hits its Stop Loss, because one $900 loss erases nine $10 wins with room to spare. Meanwhile, a strategy that only wins three times out of ten but risks $100 to make $400 can be comfortably profitable, because the three wins ($1,200) outweigh the seven losses ($700) by a wide margin. Win rate answers "how often was I right?" Expectancy answers the only question that actually matters to an account balance: "was I paid enough when I was right to cover what I lost when I was wrong?"
The Expectancy Formula
Expectancy is calculated per trade, expressed in either currency terms or, more usefully, in R-multiples (multiples of how much was risked on the trade — see Risk Management Basics for how R is defined). The formula is:
Expectancy = (Win% × Average Win) − (Loss% × Average Loss)
A positive result means the strategy is mathematically profitable over a large enough sample of trades, regardless of how it feels on any individual trade. A negative result means the strategy loses money over time even if it happens to string together a run of wins in the short term. Expectancy doesn't predict what the next trade does — it describes what the average trade is worth once win rate and win/loss size are both accounted for.
Worked Example A: High Win Rate, Small Reward
Take a strategy with a 70% win rate, where the average win is 0.5R and the average loss is a full 1R (a fairly common shape for mean-reversion or scalping-style systems that take frequent, small profits and occasionally give one back in full). Plugging into the formula: (0.70 × 0.5) − (0.30 × 1) = 0.35 − 0.30 = +0.05R per trade. That's a real edge, but a thin one — over 100 trades, this strategy is expected to net around 5R, and it's highly sensitive to a small decline in win rate. If the true win rate turns out to be 65% instead of 70%, the same math gives (0.65 × 0.5) − (0.35 × 1) = 0.325 − 0.35 = −0.025R, flipping the strategy negative. High-win-rate, small-reward systems need their win rate estimate to be accurate, because they have very little room for error.
Worked Example B: Low Win Rate, Large Reward
Now take a trend-following-style strategy with a 35% win rate, where the average win is 3R (letting winners run) and the average loss is 1R (a disciplined, consistent Stop Loss). Plugging in: (0.35 × 3) − (0.65 × 1) = 1.05 − 0.65 = +0.40R per trade — eight times the edge of Scenario A, despite winning less than half as often. This is the counterintuitive result experienced traders eventually internalize: a strategy that's "wrong" most of the time can still be the better strategy, because Risk:Reward asymmetry does more work than win rate does. It's also more forgiving of estimation error — even if the true win rate is only 30% instead of 35%, the expectancy is (0.30 × 3) − (0.70 × 1) = 0.9 − 0.7 = +0.20R, still comfortably positive.
How Win Rate and Risk:Reward Trade Off Against Each Other
These two worked examples aren't a coincidence — they illustrate a genuine trade-off that runs through almost every trading approach. Tighter Take Profit targets relative to the Stop Loss (a smaller reward-to-risk ratio) tend to produce higher win rates, because price only has to move a small distance in your favor before the trade closes out profitably. Wider Take Profit targets relative to the Stop Loss (a larger reward-to-risk ratio) tend to produce lower win rates, because price has further to travel before the trade banks a profit, and more setups will reverse and hit the stop before getting there. Neither side of this trade-off is inherently correct — a positive-expectancy strategy can be built from either end, as both worked examples show — but it does mean win rate and Risk:Reward have to be evaluated as a pair, never in isolation. A strategy's Take Profit and Stop Loss settings, of the kind used throughout this site's EA lessons via RiskRewardRatio, directly shape which side of this trade-off a system sits on.
Why Expectancy Needs a Large Enough Sample to Show Up
Expectancy is a statistical average, and averages only become reliable over a meaningful number of trials. A strategy with a genuine +0.20R expectancy can still lose money over its next 10 or even 20 trades purely from variance — a run of bad luck that a positive-expectancy system is not immune to, only less likely to keep suffering indefinitely. This is exactly why Backtesting a Trading Strategy emphasizes running a large enough sample of trades before drawing conclusions, and why Keeping a Trading Journal matters just as much in live trading — a handful of recent trades, good or bad, tells you very little about a strategy's real expectancy, while a journal spanning 50-100+ logged trades starts to reveal the actual win rate and average win/loss size a strategy produces in practice, as opposed to what a trader assumes those numbers are.
From Positive Expectancy to Account Growth
Positive expectancy is necessary but not sufficient for long-term account growth — it has to be paired with consistent, non-erratic position sizing to actually compound. A strategy with solid expectancy can still be ruined by inconsistent lot sizing: risking 0.5% on some trades and 5% on others based on how confident a trade "feels" reintroduces exactly the kind of variance and ruin-risk that expectancy math assumes is controlled for. Compounding and Position Sizing Over Time covers why keeping risk sizing consistent — as a fixed percentage of the current balance, trade after trade — is what allows a small, real per-trade edge to accumulate into meaningful account growth, rather than being undone by a handful of oversized trades that swamp the statistical edge the expectancy calculation depends on.