Risk-reward ratio: formula, break-even win rate and expectancy

1. Fix the ratio convention before comparing numbers

Two opposite notations coexist. Some platforms display risk:reward, so risking $100 to target $200 appears as 1:2. Others display reward/risk, producing 2.0. Both describe the same trade, but an unlabeled '2:1 ratio' can mean opposite things. TradeSizing reports the relationship as risk : reward and labels the monetary values separately.

2. Calculate price risk and reward for long and short trades

A long entry at 5,200, stop at 5,180 and target at 5,250 has 20 points of risk and 50 points of reward: 2.5R, or 1:2.5 risk-to-reward. Quantity changes the dollar amounts but not this gross price ratio when the same size exits entirely at one stop or target.

3. Include costs on both sides of the trade

Spread, commissions and adverse slippage increase the amount lost and reduce the amount gained. Their impact is largest when the stop and target are close. Using gross chart distances can therefore exaggerate the ratio even if the arithmetic is correct.

Suppose the chart shows $100 risk and $200 reward, but estimated total costs are $6 when stopped and $6 when the target fills. Total risk becomes $106 and net reward $194, so the ratio falls from 2.00R to 1.83R. A gap can make the realized loss worse than either estimate.

4. Derive the break-even win rate

If every loss equals 1R and every win equals the same net R multiple, the break-even win rate is the fraction of wins that makes average expectancy zero. This simplified threshold assumes binary outcomes and stable averages; real trading includes scratches, partial exits and variable losses.

5. Combine average outcome with win probability

Expectancy estimates the average result per trade across a defined sample. Use realized net wins and losses—not only the initial targets—because targets may not fill, stops may slip and discretionary exits change R. A positive historical estimate is not a guarantee that the future distribution will remain the same.

Now reduce the average win to 1.4R while keeping the same 40% win rate and 1R average loss: expectancy becomes −0.04R. The planned chart may still show 2R, yet the realized process loses on average because actual exits and costs differ from the plan.

6. Record realized R-multiples correctly

Define 1R as the full planned monetary risk at entry. A $150 budget makes a +$225 net result +1.5R and a −$90 result −0.6R. This normalizes trades of different sizes, but only if the denominator is frozen at entry and all costs are included.

• Record planned R at entry and realized R after the final exit.
• Weight partial exits by their quantities and net prices.
• Keep scratches, small wins and losses instead of forcing binary labels.
• Separate strategy variants, instruments and market regimes before pooling data.
• Report sample size and dispersion alongside the average expectancy.

7. Avoid ratios that look good only on paper

• Moving the target farther away solely to manufacture a higher ratio without evidence it is reachable.
• Tightening the stop solely to increase R, while placing it inside ordinary market noise.
• Assuming a 3R target needs only a 25% win rate without checking costs and realized average outcomes.
• Ignoring that target probability usually falls as target distance increases.
• Comparing ratios with different conventions or before/after-cost definitions.
• Treating a planned stop as a guaranteed maximum loss.

8. A repeatable pre-trade and review workflow

1. Set direction, entry, invalidation stop and evidence-based target.
2. Validate price increments and convert distances into monetary outcomes.
3. Add spread, slippage and round-turn fees to both scenarios.
4. Calculate net R and the corresponding simplified break-even rate.
5. Size the position from the loss budget, not from the desired gain.
6. After exit, record realized R and update statistics only for comparable trades.
7. Review whether realized win rate and average outcomes remain above the cost-adjusted break-even threshold.