Risk-Reward Ratio and Trading Expectancy Explained
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Executive Summary
- ✦Use R to compare realised results across different trades.
- ✦A distant target does not create an edge unless price reaches it often enough.
- ✦Expectancy combines win rate, average win and average loss.
- ✦Include spread, fees and slippage before judging the result.
Risk-reward compares the planned loss with the potential gain on one trade. Expectancy combines average wins, average losses and their probabilities across many trades. A large reward target does not create an edge if it is rarely reached.
Learning objective
This lesson shows how to calculate an initial risk-reward ratio, estimate expectancy from a trade sample and avoid choosing targets only because the ratio looks attractive.
Define one unit of risk
Let 1R equal the planned cash loss if the trade reaches its initial invalidation point, including estimated costs. If a trade risks £100, then a £200 gain is +2R and a £75 loss is −0.75R.
Using R makes trades with different prices and position sizes comparable. It does not remove gap or slippage risk; the actual loss can exceed −1R.
Calculate the planned ratio
For a long trade:
Price risk = entry − stop
Potential reward = target − entry
Reward-to-risk ratio = potential reward ÷ price risk
A £50 entry, £48 stop and £56 target gives £2 risk and £6 potential reward, or 3:1 before costs. The target still needs evidence, such as a tested resistance zone or a rule derived from historical data. Moving the target farther away merely to improve the ratio does not improve the setup.
Expectancy connects ratio and win rate
Expectancy = (win rate × average win) − (loss rate × average loss)
Measure average realised wins and losses after costs. Planned targets alone are insufficient because partial exits, early closes, gaps and slippage change the result.
Worked example
Assume a hypothetical strategy completes 100 comparable trades. It wins 40 trades with an average gain of +1.8R and loses 60 with an average loss of −1R.
Expectancy = (0.40 × 1.8R) − (0.60 × 1R) = +0.12R per trade before any unrecorded costs.
Now assume realistic slippage and fees reduce average wins to 1.65R and increase average losses to 1.05R. Expectancy becomes (0.40 × 1.65) − (0.60 × 1.05) = +0.03R. A small cost change has consumed most of the apparent edge.
Break-even win rate
Ignoring costs, a strategy with average wins twice the average loss needs to win more than one third of trades to have positive expectancy. A strategy whose average win equals its average loss needs to win more than half.
Real break-even rates are higher after spread, fees, slippage, financing and mistakes. Estimate them from actual fills rather than a brochure or chart.
Common mistakes
- Treating a distant target as a realistic reward.
- Using planned wins but realised losses in the calculation.
- Ignoring costs and partial exits.
- Comparing unrelated setups in one sample.
- Rejecting a valid method after a short losing sequence.
- Increasing size because a trade appears to offer a high ratio.
- Assuming positive historical expectancy guarantees future profit.
Pre-trade and review checklist
- Is the stop based on a genuine invalidation point?
- Is the target supported by market structure or tested rules?
- What is the ratio after conservative costs?
- How often has the setup reached similar targets?
- Are realised wins and losses recorded in R?
- Is the sample large and comparable enough?
- How sensitive is expectancy to worse fills or a lower win rate?
- Does position size keep one loss within the risk budget?
The Money Man lesson
Use risk-reward to plan one trade and expectancy to judge a repeated process. Both depend on realistic exits and costs. A modest ratio with a repeatable win rate can be stronger than an impressive target that price rarely reaches.
Educational information only. The example is hypothetical. Historical expectancy can change, and losses may exceed the planned risk because of gaps, slippage or leverage.