Ask two traders how they are doing and one will lead with their win rate. "I win seven out of ten trades." It sounds decisive. It is not. A trader can win most of their trades and still lose money over time, and another can lose more often than they win and still be net positive. What separates the two is the size of their wins relative to their losses, and how those two ingredients combine into expectancy. This article explains the three numbers that work together — the risk-reward ratio, the breakeven win rate, and expectancy — and where each one hides a trap.

The risk-reward ratio, and what "1R" means

The risk-reward ratio compares what you stand to lose if a trade hits your stop against what you stand to gain if it reaches your target. A trade with a 20-pip stop and a 60-pip target is a 1:3 risk-reward trade: one unit of risk chasing three units of reward.

Traders often express outcomes in multiples of their risk, called R. A loss is −1R. A 1:3 winner is +3R. A trade closed early at half the stop distance is −0.5R. Using R matters because it strips position size out of the picture and lets you judge the quality of trade decisions separately from the size of the account. A $100 loss and a $10,000 loss can both be exactly 1R if the position sizing did its job.

One practical warning belongs right here: the ratio on the ticket is not the ratio you will realize. A target is not a guarantee. Price may approach +3R, stall, and reverse before the target fills, turning a would-be winner into a scratch or a loss. Published risk-reward ratios describe the plan, not the distribution of closed trades.

The breakeven win rate

Every risk-reward ratio implies a minimum win rate at which the strategy stops losing money. The logic is simple: wins of R units must cover losses of 1 unit.

Breakeven win rate = 1 / (1 + reward-per-risk)

Computed values:

Read the table in both directions. At 1:2, you can be wrong two times out of three and still break even before costs. At 1:1, you need to win exactly half of your trades just to stand still. This is why win rate alone says almost nothing: a 70% win rate on 1:1 trades loses money after costs, while a 40% win rate on 1:2 trades is comfortably profitable before costs. (Worked examples below; all figures are illustrative arithmetic, not performance data.)

Expectancy: the number that actually decides

Expectancy is the average result you expect per trade, in R units, if you repeated the same trade many times under the same conditions. It is the standard concept of expected value — the long-run average of results from independent repetitions of the same random experiment — applied to your trade outcomes. The expected value interpretation comes from the law of large numbers: over many repetitions, the average of results converges toward the expectation. That is exactly the sense in which traders use it, and exactly the sense in which it can disappoint: convergence happens over many trades, not over the next three.

For a simple model with a fixed win rate p and a fixed reward of R units per win:

Expectancy per trade = p × R − (1 − p)

Worked illustrative examples (computed, not observed):

• Win rate 40%, reward 1:2 → 0.40 × 2 − 0.60 = +0.20R per trade

• Win rate 35%, reward 1:2 → 0.35 × 2 − 0.65 = +0.05R per trade

• Win rate 30%, reward 1:3 → 0.30 × 3 − 0.70 = +0.20R per trade

• Win rate 55%, reward 1:1 → 0.55 × 1 − 0.45 = +0.10R per trade

• Win rate 45%, reward 1:2 → 0.45 × 2 − 0.55 = +0.35R per trade

To turn expectancy into something tangible, add position sizing. Suppose a $10,000 account risks 1% per trade — $100, or 1R. At 40% wins with 1:2 rewards, expectancy is +0.20R, so the expected contribution of each trade is $20. Over 100 such trades the expected total is 100 × 0.20 × $100 = $2,000, or 20% of the account, before costs. This is an illustrative calculation with assumed numbers, not a prediction: real trade sequences deviate widely from the expectation, and the expectation itself is only an estimate.

Costs come out of a thin margin

Expectancy calculations usually ignore transaction costs, and the margin is often thinner than people assume. Spread and commission are paid on every trade, win or lose. If a round trip costs 0.10R — for example, a 0.8-pip effective cost on a trade with a 25-pip stop is 0.8/25 ≈ 0.03R, while wider spreads, slippage, or tighter stops can push the cost well above 0.1R — then the 40%-win, 1:2 example drops from +0.20R to +0.10R per trade. The 100-trade expected result falls from 20% of the account to 10%. At the 35%-win example, +0.05R before costs, the same 0.10R cost turns expectancy negative: the trader is structurally profitable before costs and losing after them.

This is one of the quieter connections between trade design and broker choice. A stop distance of 25 pips on EUR/USD makes a 0.8-pip spread a 3% surcharge on risk; the same spread on a 5-pip stop is a 16% surcharge. Costs do not change your win rate, but they change what your win rate needs to be.

Why a bigger ratio is not automatically better

The breakeven table seems to point one way: widen the target, and you can afford to be wrong far more often. The trade-off runs the other way too. As the target moves further from entry, the probability of reaching it before the stop falls — that is precisely why the breakeven win rate is lower. A 1:5 setup with a 20% breakeven win rate does not hand you money; it asks you to find trades that reach five times their stop distance often enough to clear 16.67% — and then some, once costs are paid.

There is also an execution asymmetry worth stating plainly: the loss side is usually reliable — the stop is (except for gaps and slippage) a firm exit — while the profit side is aspirational. Targets can be shortened, trailed, or missed entirely. So the realized reward-per-risk of a completed trade set is often below the planned ratio, and the expectancy you actually experience is built from realized R, not planned R. Measuring your own closed trades in R units is the honest way to estimate it; assuming the plan's ratio is the optimistic way.

Sizing, and a caution about Kelly-style math

Expectancy tells you the average result per unit of risk. How much risk per trade is a separate decision, and it interacts with the first. If expectancy is positive, risking more per trade raises the average growth rate — up to a point. The Kelly criterion, described by John Kelly Jr. at Bell Labs in 1956, is the well-known formula for sizing bets to maximize the long-term expected growth rate of wealth rather than raw expected value.

The caution is that Kelly's formula assumes you know the true probabilities and payoffs — in trading, neither is known; both are estimated from a sample with real uncertainty. A trader who overestimates their win rate even slightly will compute an oversized "optimal" bet, and the penalty for oversizing is asymmetric: drawdowns grow faster than the model predicts. In practice, traders who reference Kelly at all use small fractions of it, and fixed-fractional risk (a fixed percentage of the account per trade, often in the 0.5%–2% range) remains the more common and more forgiving default. None of these numbers is a recommendation of what you should use; they are context for how sizing and expectancy interact.

The limits, stated plainly

• Expectancy is an estimate from past observations. It can shift when market conditions change — a strategy with a positive sample expectancy can turn negative without warning.

• Small samples lie. Twenty or fifty trades tell you very little about the true win rate; the arithmetic above uses round numbers for illustration, not results from any tested strategy.

• Costs, slippage and gaps are part of the math. Any expectancy figure that ignores them is flattered.

• A positive expectancy does not remove risk of ruin. With fixed-fractional sizing the sequence of trades still matters, and losing streaks longer than intuition suggests happen at ordinary win rates.

The takeaway

Win rate and risk-reward are two halves of one number. Neither predicts anything alone. The breakeven table shows why: at 1:2 you need only 33.33% wins; at 1:1 you need 50%; the same 55% win rate is a different business depending on the ratio it comes with. Expectancy — wins times their size, minus losses times theirs, minus costs — is the combined figure worth tracking in R units, estimated honestly from realized trades rather than planned ones. And sizing is the multiplier that decides whether a modest positive expectancy builds an account or a drawdown destroys it. None of it promises profit; it just gives you a way to tell, with clear eyes, whether the next hundred trades are expected to pay for themselves.