Direct answer
“Risk reward limitations” refers to the ways the risk-to-reward concept stops being reliable in practice. Even if a calculation looks consistent, the ratio can fail because its inputs are not fixed: execution quality changes, transaction costs vary, and price movement may not reach stops or targets as modeled. Advanced considerations therefore focus on dependencies, edge cases, and implementation constraints—so you can explain the concept clearly and independently verify what is actually being assumed.
A useful way to think about it: risk reward limitation is not a single rule or indicator. It is a collection of conditions under which a ratio-based expectation becomes misleading.
Mechanism or definition
Risk-to-reward is commonly described as a comparison between the magnitude of a potential loss and the magnitude of a potential gain, based on specific levels and a specific measure of risk.
A simplified form is:
- Risk (R): the distance from an entry price to a defined loss level (often called a stop level), expressed in price terms or converted to account currency.
- Reward (W): the distance from entry to a defined gain level (often called a target level), expressed similarly.
- Risk reward ratio (R:R): W divided by R, or alternatively W − R depending on how the metric is defined.
To discuss implications responsibly, separate stable mechanics from variable conditions:
- Stable mechanics (math and representation)
- The ratio is deterministic given the exact numerical inputs.
- If the inputs truly remain fixed and fills occur as assumed, then the ratio reflects the trade’s modeled payoff relationship.
- Variable conditions (what can change)
- Market movement between order placement and execution.
- Whether orders fill at the intended prices.
- Transaction costs such as spreads and commissions.
- Slippage, partial fills, and delays.
- The behavior of the stop or target mechanism under fast price changes.
Key definition for limitations: a limitation exists when the realized payoff relationship diverges from the modeled relationship because at least one dependency does not hold.
Evidence or example
Consider a scenario where a ratio calculation appears favorable because the modeled reward is larger than the modeled risk. The limitation emerges when the implementation details change the realized distances.
Assumptions for this example (so the logic is checkable):
- No real-time prices are used; the example uses hypothetical numbers.
- You measure risk in the same units as reward (for example, both in price distance, or both converted to account currency using the same contract specifications).
- You assume an entry occurs at the modeled entry price.
Example scenario (hypothetical):
- You model R as the price distance from entry to a stop level.
- You model W as the price distance from entry to a target level.
- Your risk reward ratio is therefore based on R and W.
What can go wrong in a way that creates a risk reward limitation:
- Spread and cost changes
- If the effective entry is higher than expected (for buys) or lower than expected (for sells) due to spread at execution time, then the actual loss relative to the account currency can differ from the modeled R.
- If costs change over time, the “net” reward and net risk can shift even when the raw price distances remain the same.
- Slippage at stop execution
- If the market moves quickly through the stop level, the realized stop fill can occur worse than the modeled stop, increasing the realized loss beyond R.
- That turns an intended ratio into a lower realized ratio, or even an adverse payoff relationship.
- Partial fills and order handling
- If an order is partially filled and the remaining portion fills later at different prices, the realized average entry changes.
- That changes both realized risk and realized reward distances, breaking the assumption that one entry price defines both.
- Target non-fill
- A target may not be reached before exit conditions change (for example, manual exits, time-based exits, or changes in market conditions).
- In that case, the modeled reward W does not occur, and the realized outcome is not represented by the risk reward ratio.
- Statistical mismatch
- Even if the ratio math is correct, a single ratio does not determine outcomes. Real outcomes depend on when stops and targets are reached, and that timing is part of the dependency set.
- Historical behavior of price reaching levels may not remain stable when volatility regimes shift.
These cases show how the limitation arises: the ratio calculation is only as good as the assumptions that map from “intended levels” to “realized fills.”
Limitations and risks
Below are common material limitations and failure modes that readers can independently verify by auditing calculations and execution assumptions.
1) The ratio can be correct while the expectation is misleading
Risk reward ratio is a relationship between modeled loss and modeled gain. It does not guarantee an outcome. The limitation occurs because:
- A favorable ratio can still produce losses if adverse events happen more frequently than assumed.
- A single unfavorable execution event (for example, stop slippage) can swing net results.
2) Net results differ from raw price-distance results
Many ratio descriptions omit transaction costs. If costs are meaningful, then:
- Net risk = modeled risk + costs (or cost impact depending on trade direction and timing).
- Net reward = modeled reward − costs.
Even without using any platform-specific numbers, the conceptual verification is to ask: “Is the ratio based on raw price distance or net account currency after costs?”
3) Hidden assumptions about order execution
Risk reward limitations often come from execution mechanics that are not represented in a simple ratio:
- Whether stop/limit orders act like exact price locks.
- Whether fills can occur at different prices than the displayed levels.
- Whether orders remain active for the assumed duration.
A checkable control point is to list each assumption that connects a level to a realized fill.
4) Edge cases that break “static levels”
Examples of edge cases include:
- Fast market moves through multiple levels.
- Conditions where spreads widen temporarily.
- Gaps or discontinuities that prevent fills at intended prices.
- Partial fills that create a weighted average entry.
Any one of these can make realized risk and reward diverge from modeled R and W.
5) Provider and jurisdiction variability
Execution and cost mechanics can vary by provider and by jurisdiction because market access, order types, and reporting differ.