Direct answer: can you “make more money” with risk-to-reward?
You can’t ensure more money from risk-to-reward alone, but you can use it to evaluate trade quality in a consistent way. In forex, the basic idea is to compare how much you stand to lose if your plan is wrong (risk) with how much you stand to gain if it is right (reward). If your rule set repeatedly produces positive expected value, then—over many attempts—results can improve. If the probability assumptions don’t hold, higher reward targets may not help.
Mechanics: define risk-to-reward using measurable distances
Risk-to-reward is usually expressed as a ratio:
- Risk (R): the price distance from your entry to your stop, converted into money per trade.
- Reward (W): the price distance from your entry to your target, converted into money per trade.
- Risk-to-reward ratio: W/R (for example, 2:1 means reward distance is twice the risk distance).
To connect this ratio to “making more money,” use expected value (EV). In a simplified two-outcome model:
- If price reaches the target, you receive +W.
- If price hits the stop, you receive -R.
A practical EV expression is:
EV = p·W − (1−p)·R, where p is the probability of hitting the target before the stop.
This shows two key levers:
- Size of reward relative to risk (W/R).
- Probability of success (p), which often changes when you widen targets or move stops.
Example checks: how to verify the idea without guessing
To make this framework useful, focus on independent checks:
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Keep measurements consistent: Use the same method to measure stop distance and target distance (for example, both derived from the same reference points). If you change how you place stops and targets midstream, your risk-to-reward math becomes a different system.
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Separate “ratio” from “probability”: A larger W/R can lower p because targets are harder to reach. To see whether your system truly improves, track outcomes under the same rules and compute an EV-like summary from the observed hit rate.
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Stress test with realistic execution: Risk-to-reward assumes your exits happen near planned levels. In real conditions, spreads and slippage can affect realized risk and reward distances in money terms. If realized losses widen more than realized wins, the effective W/R shrinks.
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Rule quality over one trade: One good or bad outcome doesn’t validate EV. You need repeated application with unchanged criteria.
Relevant limitations and risks
Risk-to-reward is a decision framework, not a guarantee. Key limitations include:
- Probability is not known in advance: EV depends on p, and p varies with market conditions and with how you set targets/stops.
- Execution can alter amounts: Realized risk and reward may differ from your planned distances, especially when trading costs change.
- Two-outcome simplification may fail: Markets can produce partial fills, early exits, or alternative outcomes not captured by a simple stop/target model.
A useful verification approach is to treat your risk-to-reward rules as a testable system: define entries, stop placement, target placement, and exit rules; then evaluate consistency over many occurrences. If the observed outcomes don’t support positive EV under your assumptions, then “more money” is unlikely from the same logic.