Size a trade in R, then check whether your win rate and average R actually add up to a process that makes money.
Where you plan to get in.
Where the idea is wrong. The distance from entry to here is 1R.
Where you plan to take profit.
What 1R is worth to your account.
Share of trades that end in profit, taken from your own records rather than memory.
Average size of a winning trade, measured in R.
Average size of a losing trade. Slippage past the stop makes this above 1 in practice.
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R is the distance between entry and stop — what you lose if the trade is wrong. Everything else is measured in units of that risk, so a target three times as far from entry as the stop is a 3R trade whether the instrument is Bitcoin at $90,000 or a token at $0.0004. Thinking in R makes trades comparable: a $400 win tells you nothing on its own, a 3R win means the same thing on every trade you have ever taken.
Win rate on its own is not information either. Winning 80% of trades is a losing process if the losses are five times the wins, and winning 35% is a strong one if the wins are four times the losses. Expectancy combines the two into the only number that decides whether a process makes money: (win rate x average win) minus (loss rate x average loss), in R. The break-even win rate is its mirror — with 3R winners and 1R losers you only need to be right a quarter of the time.
Two inputs make this flattering if you are careless. An average loss of exactly 1R assumes every stop fills at its level, and real stops slip past it during gaps and fast markets, so a measured 1.1R or 1.3R is normal and worth using. And a win rate recalled from memory is almost always too high; take it from a trading journal instead. Costs matter too: the round-trip cost calculator shows how much of a 1R win the fees take.
Expectancy is a long-run average, not a promise about the next ten trades. A process with a real edge still has losing streaks, and position sizing has to survive them. See the position-sizing guide. Illustrative only, and not financial advice.