R-value

What it is

R-value (sometimes written R-multiple) expresses a trade's outcome as a multiple of its initial risk. If your stop distance represented $200 of risk and the trade gained $400, that's a +2R outcome. If it lost the full $200, that's −1R. If it gained $100, that's +0.5R.

The framework normalises trade results across instruments, position sizes, and account states. A scalp on micro contracts and a longer hold on full-size contracts can both be expressed as a single +1.5R number — directly comparable, directly aggregatable.

R-value exists because raw dollar P&L is contaminated by position sizing. A trader who happens to size larger on winning trades and smaller on losing trades can show good dollar P&L from a strategy that doesn't actually have an edge. R-multiples strip out the sizing variable and measure the quality of the trade idea.

Why it matters

Once a strategy is expressed in R-multiples, you can ask the questions that matter:

• What's the average win in R? The average loss in R? (Expectancy in R-units.)
• What's the win rate at +1R, +2R, +3R cutoffs? (Hit-rate distribution.)
• Where on the R-distribution do most outcomes sit? (Distribution shape — clustered, fat-tailed, skewed.)
• What's the maximum favourable excursion in R that you typically leave on the table?

R-values also make it possible to compare strategies on a like-for-like basis. Strategy A with +0.4R average expectancy on 50 trades per month is directly comparable to Strategy B with +0.8R on 25 trades. Without R-units, that comparison requires re-normalising for position size and instrument.

How traders use it on Sierra Chart

R-value computation is a journaling and analytics layer rather than a charting one. Once a trade is recorded — with entry, stop, contract count, and exit — the R-multiple is a derived value: realised P&L divided by initial planned risk.

The SCS Trading Journal captures the inputs needed to compute R per trade automatically: entry, stop, exit, and contract count are journaled directly from filled orders. The downstream analytics surface R-multiple distributions, average R, and R-anchored win rate over arbitrary time slices.

On the chart itself, R-multiple targets are commonly drawn as horizontal lines at +1R, +2R, +3R from entry, anchored to the stop distance.

Common patterns / pitfalls

• R-values depend on having an honest, pre-committed stop. If you redefine your stop mid-trade, R becomes meaningless.
• A single +5R trade doesn't validate a strategy. The distribution matters more than the tail.
• A high average R with low frequency can lose to a lower average R with high frequency once you factor in capital efficiency and drawdown.
• R-multiples on partial fills and scaled exits get fuzzy — pick a convention (typically: average exit price vs entry, divided by stop distance) and stick to it.
• Don't confuse R-value with reward-to-risk ratio. R-value is realised; reward-to-risk is planned.

Related SCS studies

Trading Journal computes R-multiples per trade from journaled fills and surfaces R-distribution analytics over arbitrary date ranges, making strategy expectancy directly measurable from real execution data rather than backtested approximations.