What Is an R-Multiple in Trading?

What it means

What R-exposure looks like in a log

Once entries carry a stated stop and size, the R view of your trading becomes mechanical:

• Every entry line records planned risk in currency and in % of equity — which makes the R-multiple computable at exit, not estimated.
• Losing trades cluster at -1R (the stop did its job) instead of scattering to -2R, -3R (stops moved or ignored).
• Wins are not all smaller than losses: the R-distribution of winners reaches at least +1.5R somewhere, or the math of costs cannot be covered.
• Rule-break entries are quoted in R too — an oversized unplanned loss shows up as a -4R outlier that no strategy test predicted.
• Weekly review averages R per trade by setup type, so you learn which setup earns its costs, not which setup had a lucky week.

Logging trades in R

The habit is three fields per trade, written at entry, not reconstructed at exit:

1. 1.At entry, write stop price and position size; compute 1R in currency and as % of equity before confirming the order.
2. 2.At exit, compute the R-multiple (net of commissions) and store it with the setup name — this is the number the review sorts by.
3. 3.Cap the outlier risk: if a single trade's planned risk exceeds your normal 1R (for example, doubled size after a loss), log why; those rows are where discipline review lives.
4. 4.Review the R-distribution weekly: average R, worst R, and the count of trades that were never authorized. Expect months of data before per-setup averages mean anything.
5. 5.Practice the computation itself in simulation until placing the stop-first order is automatic — R journaling fails mostly because entries without stops cannot be quoted in R at all.

Frequently asked questions