R-Multiple

Dollar profits look different depending on account size and position size. A $500 gain means very different things to a $10,000 account trader and a $100,000 account trader. R-multiples normalize all outcomes to a single, comparable unit — the amount risked. A +2R trade always means "I made twice what I risked," regardless of whether R was $50 or $5,000. This normalization enables meaningful aggregation across an entire trading system: you can compare strategies, time periods, and market regimes on equal footing. Two systems with very different average trade sizes become directly comparable when expressed in R-multiples, making it the standard unit of measurement in professional systematic trading.

A trading system's edge is fully captured by its R-multiple distribution: the histogram of all trade outcomes expressed in R. Expected value (expectancy) equals the average R-multiple across all trades — positive expectancy means the system is profitable over time. For example, a system with 40% win rate, average winner of +2.5R, and average loser of -1R has expectancy of (0.40 × 2.5) + (0.60 × -1) = +0.40R per trade. Every $1,000 risked per trade generates an expected $400 profit over a large enough sample. R-multiple distributions also reveal skew: a system with frequent small wins and rare large losses (negative skew) feels comfortable but can blow up; a system with frequent small losses and rare large wins (positive skew) feels uncomfortable but has durable edge.

R-multiples are particularly powerful for exit strategy analysis. If your planned risk/reward was 1:3 (potential +3R) but your actual average exit was at +1.2R, you are systematically leaving 1.8R on the table per winner. This gap between planned and realized R-multiples is a direct diagnostic of exit quality and is often fixable through better trailing stop calibration or target placement. Conversely, if your losers average -1.4R instead of the planned -1R, your stops are either too loose or you are adding to losing positions. Reviewing the R-multiple distribution by exit reason — target hit, trailing stop, time exit, early manual exit — reveals exactly where the system is leaking expected value.

Every trade in Tradewink's system is logged with its realized R-multiple alongside dollar P&L. The LearningEngine analyzes R-multiple distributions segmented by strategy type, market regime, time of day, and sector. Setups that consistently deliver 2R+ outcomes are weighted higher in the composite scoring model for future trade selection. Setups whose R-multiple distribution has deteriorated — perhaps averaging only 0.8R recently versus 1.8R historically — trigger an automatic conviction score downgrade and are flagged for strategy health review. This feedback loop continuously refines which setups receive capital allocation based on demonstrated R-multiple performance rather than theoretical edge.