---
title: "R expectancy"
description: "What is R-expectancy in trading? R-expectancy is the average profit or loss a trading strategy makes per trade, measured in units of risk (R) , where 1R is the amount you risk from entry to your stop-..."
url: https://quantmonitor.net/r-expectancy/
date: 2023-01-06
modified: 2026-07-21
author: "Kornel"
image: https://quantmonitor.net/wp-content/uploads/2023/01/r_expectancy-jpg.webp
categories: ["Trading encyclopedia"]
type: post
lang: en
---

# R expectancy

## What is R-expectancy in trading?

**R-expectancy is the average profit or loss a trading strategy makes per trade, measured in units of risk (R)**, where 1R is the amount you risk from entry to your stop-loss. An R-expectancy of 0.30R means the strategy makes, on average, 0.30 times the amount it risks on each trade. It is a measure of the expected return of a trading strategy, expressed per unit of risk rather than in currency.

| R-expectancy | Interpretation | Over 100 trades (risking 1R each) |
| --- | --- | --- |
| 0 or below | Negative edge — the strategy loses money | Net loss |
| 0.00 – 0.10 | Marginal — fragile to costs and slippage | +0 to +10R |
| 0.10 – 0.25 | A solid, tradeable edge | +10 to +25R |
| 0.25 – 0.50 | Strong | +25 to +50R |
| Above 0.50 | Exceptional — check for overfitting | +50R and up |

*These bands are practitioner heuristics, not fixed rules. Expectancy × number of trades ≈ total R gained.*

## The R-expectancy formula

R-expectancy = (Win rate × Average winning R-multiple) − (Loss rate × Average losing R-multiple)

An R-multiple is a trade’s result expressed in units of the initial risk: a trade that makes twice what you risked is +2R, and a trade stopped out at your initial stop is −1R. If your losses always close at the initial stop, the average losing R-multiple is simply 1R.

## Worked example

Suppose a strategy produces the following six trades, measured in R: +2R, −1R, +1.5R, −1R, +3R, −1R. The total is +3.5R over 6 trades, so:

R-expectancy = 3.5R / 6 trades ≈ 0.58R per trade.

Over 100 trades risking 1R each, that expectancy would be worth roughly +58R before costs — a strong result you would still confirm out-of-sample and in live trading.

## Frequently asked questions

### What is a good R-expectancy value?

Any R-expectancy above 0 is a positive edge. Around 0.10–0.25R is a solid, tradeable edge, 0.25–0.50R is strong, and above 0.50R is exceptional — and worth double-checking for overfitting.

### How do you calculate R-expectancy?

Express each trade’s result in R-multiples and take the average, or use the formula (win rate × average winning R) − (loss rate × average losing R). Both give the expected result per trade in units of risk.

### What is the difference between R-expectancy and win rate?

Win rate only measures how often you win. R-expectancy also accounts for how big your wins and losses are, so it reflects the strategy’s actual edge — not just its hit rate.

### Can a low-win-rate strategy have positive R-expectancy?

Yes. A trend-following strategy might win only 40% of its trades yet have strong positive R-expectancy if its winners are much larger than its 1R losers. That is the whole point of measuring expectancy in R rather than in win rate.

### What is the difference between R-expectancy and profit factor?

R-expectancy is a per-trade figure in units of risk, while [profit factor](https://quantmonitor.net/profit-factor/) is the ratio of gross profit to gross loss across all trades. They describe the same edge from different angles and are best read together.

Read R-expectancy alongside [the Sharpe ratio](https://quantmonitor.net/sharpe-ratio-understanding-using-investing-decisions/), [profit factor](https://quantmonitor.net/profit-factor/), [Z-score](https://quantmonitor.net/z-score/) and [CAGR](https://quantmonitor.net/understanding-cagr-concept-practical-applications/) and the [System Quality Number (SQN)](https://quantmonitor.net/system-quality-number-sqn/). See how these metrics play out on real money in our [monthly live trading portfolio updates](https://quantmonitor.net/blog/).