Thinking in R-Multiples and Expectancy
Measure every trade in units of risk and calculate whether a set of rules has a positive expected result.
What you will learn
- How to express any trade result as an R-multiple.
- How to calculate expectancy from win rate, average win and average loss.
- Why a high win rate alone tells you nothing about profitability.
- How costs reduce expectancy.
Explanation
In the beginner course you learned to risk a fixed share of your account per trade. Now we use that risk as a unit of measurement. R is the amount you planned to lose if the stop-loss is hit. For a long trade, 1R per unit = entry price − stop price.
An R-multiple is the result of a trade divided by its initial risk:
R-multiple = (exit price − entry price) ÷ (entry price − stop price) (for a long trade; reverse the signs for a short trade).
Example: entry €50, stop €48, exit €55. Risk per share = €2, gain per share = €5, so the result is +2.5R. Thinking in R lets you compare trades of different sizes, markets and account balances on one scale.
Expectancy is the average result per trade you would expect if a set of rules behaved in the future as it did in your sample. In R terms:
Expectancy (R) = (win rate × average win in R) − (loss rate × average loss in R)
Here the average loss is written as a positive number. Loss rate = 1 − win rate. A positive expectancy means the rules made money on average in the sample. It does not guarantee anything about the next trade or the next year.
Win rate is the share of trades that ended with a profit. It is only half of the picture. A system that wins 70% of the time can still lose money if its losses are much larger than its wins.
Costs matter too. Spreads (the difference between buy and sell price), commissions and financing fees reduce each result. If costs average 0.1R per trade, subtract 0.1R from expectancy.
Worked example
Hypothetical example: two traders each review 100 past trades. Assumptions: 1R = €100 for both; fees, spreads and taxes are excluded.
| Item | Trader A | Trader B |
|---|---|---|
| Win rate | 40% | 70% |
| Average win | 2.5R | 0.5R |
| Average loss | 1R | 1.5R |
Trader A: (0.40 × 2.5) − (0.60 × 1) = 1.00 − 0.60 = +0.40R per trade. With 1R = €100, that is +€40 per trade on average, or +€4,000 over 100 trades.
Trader B: (0.70 × 0.5) − (0.30 × 1.5) = 0.35 − 0.45 = −0.10R per trade, or −€10 per trade and −€1,000 over 100 trades.
Trader B wins far more often but loses money. Trader B probably moves stops away or takes small profits too early, which makes losses large and wins small.
Common mistake
Practice activity
On paper, calculate the expectancy for this hypothetical record: win rate 55%, average win 1.2R, average loss 1.1R. Then convert it to euros with 1R = €50. Finally, subtract estimated costs of 0.1R per trade.
Show solution
(0.55 × 1.2) − (0.45 × 1.1) = 0.660 − 0.495 = +0.165R per trade.
In euros: 0.165 × €50 = about €8.25 per trade before costs.
After costs: 0.165 − 0.1 = +0.065R, or about €3.25 per trade. Costs removed more than half of the edge.
Limitations: expectancy from a small or old sample may not hold in the future; markets change.
Key takeaways
- 1R is your planned risk; every result can be expressed as a multiple of it.
- Expectancy combines win rate and the size of wins and losses.
- A high win rate with large losses can still mean negative expectancy.
- Costs come out of expectancy and can erase a small edge.
Next step
In the next lesson you will turn 1R into a correct position size for shares, forex, crypto and CFDs.
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