Planning Reward Targets Honestly: Probability Before Ambition
Posted: Wed Oct 07, 2026 7:59 pm
Planning Reward Targets Honestly: Probability Before Ambition
A target of three times the risk looks attractive. Whether it is realistic depends on how often price actually gets there. Estimate the probability. If your setup reaches 1R about 60 percent of the time, 2R about 35 percent, and 3R about 20 percent, the expectancy of fixed targets differs. With a 1R target and a 1R stop, a 60 percent hit rate gives an expectancy of 0.2R. With a 2R target and a 35 percent rate, the expectancy is 0.35 times 2 minus 0.65 times 1, or 0.05R. With a 3R target and a 20 percent rate, it is 0.2 times 3 minus 0.8, or negative 0.2R.
So the most ambitious target is not the best. The target that maximizes expectancy depends on the distribution of how far trades go in your favor, which is why MFE data is useful.
Structure also matters. Targets placed just before significant levels have higher hit rates than those beyond them.
Combine with the costs: after spread, each of these expectancies is slightly lower.
Use your own data. After thirty or forty trades per setup, compute the share reaching 1R, 1.5R, 2R, and 3R, and pick the target with the best expectancy, avoiding overfitting by choosing a round number.
Revisit periodically, since conditions change.
Practical step: calculate the share of your last forty trades that reached 1R, 2R, and 3R at any point.
A target of three times the risk looks attractive. Whether it is realistic depends on how often price actually gets there. Estimate the probability. If your setup reaches 1R about 60 percent of the time, 2R about 35 percent, and 3R about 20 percent, the expectancy of fixed targets differs. With a 1R target and a 1R stop, a 60 percent hit rate gives an expectancy of 0.2R. With a 2R target and a 35 percent rate, the expectancy is 0.35 times 2 minus 0.65 times 1, or 0.05R. With a 3R target and a 20 percent rate, it is 0.2 times 3 minus 0.8, or negative 0.2R.
So the most ambitious target is not the best. The target that maximizes expectancy depends on the distribution of how far trades go in your favor, which is why MFE data is useful.
Structure also matters. Targets placed just before significant levels have higher hit rates than those beyond them.
Combine with the costs: after spread, each of these expectancies is slightly lower.
Use your own data. After thirty or forty trades per setup, compute the share reaching 1R, 1.5R, 2R, and 3R, and pick the target with the best expectancy, avoiding overfitting by choosing a round number.
Revisit periodically, since conditions change.
Practical step: calculate the share of your last forty trades that reached 1R, 2R, and 3R at any point.