Traders often ask two questions: "What is a good win rate?" and "What risk-reward should I aim for?" Asked separately, neither has an answer. Together they determine expectancy, the average result per trade, and expectancy is what decides whether a strategy makes or loses money over many trades. This article walks through the arithmetic with simple, illustrative numbers.
Defining the terms
- Win rate: the fraction of trades that close in profit.
- Risk (R): the amount you lose if the stop is hit, taken as one unit. See position sizing and risk per trade.
- Reward-to-risk: average winner divided by average loser. If you risk 1R and make 2R on average on winners, the ratio is 2.
- Expectancy: win rate x average win, minus loss rate x average loss, per trade.
The formula in R terms
Expectancy (in R) = (W x B) - (1 - W), where W is the win rate and B is average winner measured in R, and average loser is exactly 1R.
Example one: win rate 40%, average winner 2R. Expectancy = 0.40 x 2 - 0.60 x 1 = 0.80 - 0.60 = +0.20R per trade.
Example two: win rate 70%, average winner 0.4R. Expectancy = 0.70 x 0.4 - 0.30 x 1 = 0.28 - 0.30 = -0.02R per trade. A 70% win rate loses money.
Both cases are hypothetical, but they demonstrate why win rate alone says nothing.
The break-even table
The break-even win rate for a given reward-to-risk ratio B is 1 / (1 + B). Before costs:
| Reward-to-risk | Break-even win rate |
|---|---|
| 0.5 | 66.7% |
| 1.0 | 50.0% |
| 1.5 | 40.0% |
| 2.0 | 33.3% |
| 3.0 | 25.0% |
To earn something, you need a win rate above those figures, and a margin to cover costs.
Costs shift the table
Spread, commission and slippage reduce each winner and enlarge each loser. If a trade risks 10 pips and costs 1.5 pips round trip, the true reward on a 2R target is 20 - 1.5 = 18.5 pips while the true loss is 10 + 1.5 = 11.5 pips, so the effective ratio is about 1.6, not 2. Tight stops make costs proportionally larger. This is one reason very short-term strategies are difficult. See spreads, slippage and commissions.
Why a bigger target is not automatically better
It is tempting to set a 5R target and call the strategy great. But a more distant target is hit less often. The relationship between target distance and win rate is empirical: you need data on your own method. Moving a target further out can raise the ratio while lowering the win rate by more than the table permits. This is why honest backtesting of the full exit rule matters.
Sample size and uncertainty
Estimates of win rate from small samples are noisy. If you have taken 30 trades and won 15, the observed 50% is compatible with a true rate anywhere from about 33% to 67%. Even a real positive expectancy will produce losing streaks, as shown in losing streaks and revenge trading and drawdown math. A profitable strategy at 40% win rate will lose six or seven in a row from time to time; plan the size so that this is survivable.
Using the numbers in practice
- Record every trade in R, not only in currency. A journal with an R column makes comparisons across position sizes possible. See how to keep a trading journal.
- Compute expectancy per setup after at least a few dozen trades each. Setups with negative or flat expectancy are candidates to stop trading.
- Include costs by recording actual fills rather than planned prices.
- Check the distribution: is the profit from many small gains, or from one outlier? A single huge trade can hide a weak strategy.
- Compare expectancy to variance: a small positive edge needs many trades and small size to be realised safely.
Do not confuse ratio with certainty
A planned 3R target is only a plan. Trades that reach 2.5R and reverse to a stop are the real cost of that plan. Some traders take partial profits or move stops to break-even; these choices change the shape of outcomes, and their effect on expectancy should be measured, not assumed.
A worked example with a journal
Imagine a trader logs 40 trades from one setup, in R: 16 winners averaging +1.9R and 24 losers averaging -1.0R (stops were honoured). Total = 16 x 1.9 - 24 = 30.4 - 24 = +6.4R, or +0.16R per trade. After costs of about 0.1R per trade the figure is +0.06R. The setup is positive but thin: a small rise in spread or a slightly lower win rate erases it. Seeing this in numbers is more useful than a vague feeling of "it works". All figures are made up for illustration.
Summary
Win rate and reward-to-risk are two halves of one equation. Expectancy tells you whether the combination makes money after costs, and the break-even table gives a quick sanity check. Measure in R, use large enough samples, and size positions so that normal losing streaks do not end your account.
Educational content only, not investment advice. All numbers are hypothetical illustrations; past performance does not indicate future results.