The R multiple is the amount a trade returns relative to what it risked. A 2R winner returned twice the risk. A 0.5R winner returned half the risk. A 1R loser lost exactly what it risked. This unit is timeframe-independent, symbol-independent, and account-size-independent, which is why professionals think in R.
R and Expectancy
Expectancy in R = (Win% × Avg Winner in R) − (Loss% × Avg Loser in R). If your average winner is 2R, your average loser is 1R, and your win rate is 45%, expectancy is (0.45 × 2) − (0.55 × 1) = 0.35R per trade. Over 200 trades, that is 70R of expected profit — regardless of currency or account size.
Why High Win Rates Are a Trap
A 90% win rate at 0.2R per winner and 3R per loser is a losing system. A 30% win rate at 4R per winner and 1R per loser is a strong system. The market pays for asymmetry, not accuracy.
The Minimum R Filter
A useful rule: reject any setup that does not offer at least 2R of reward relative to your intended stop. This single filter eliminates 60–80% of marginal trades and forces you to hunt only for setups with structural asymmetry.
Think in R multiples. Measure in R multiples. Filter setups by R multiples. The reward-to-risk ratio, not the win rate, is what determines whether your edge compounds.
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