Definition: what “risk reward limitations” means
Risk reward limitations describe the gap between a simplified risk-to-reward calculation (often a ratio) and what can happen in real execution. The limitation is not that the arithmetic is wrong; it is that the inputs and assumptions behind the arithmetic (where you enter, where a stop is filled, what the price path looks like, and what costs apply) can differ from reality.
In this context, “risk” usually means the loss from a defined adverse move between an entry price and a stop level. “Reward” usually means the profit from a defined favorable move between the entry price and a target level. The limitation is that the future market path and fill quality may not match the chosen levels.
Worked example (fully specified) and how the ratio can mislead
Below is one transparent scenario. No live prices are used.
Assumptions (state every input)
- Entry price: 100.00
- Stop-loss level: 98.00 (a 2.00 move down)
- Take-profit/target level: 104.00 (a 4.00 move up)
- Position size is chosen so that a 1.00 price move equals 10 monetary units of profit or loss.
- Costs and execution effects: assumed zero for the first calculation.
- The price either reaches the stop or reaches the target before the other, with no partial fills.
Step-by-step mechanics
- Risk (in price terms) = 100.00 − 98.00 = 2.00
- Reward (in price terms) = 104.00 − 100.00 = 4.00
- Risk-to-reward ratio = Reward / Risk = 4.00 / 2.00 = 2.0
Now convert to monetary units using assumption (4):
- Monetary risk = 2.00 × 10 = 20 loss units
- Monetary reward = 4.00 × 10 = 40 profit units
So, under these assumptions, the setup suggests that if the target is hit and the stop is not hit first, profit is 40 units; if the stop is hit first, the loss is 20 units. The risk reward ratio is 2.0.
Where the limitation appears (same setup, changed assumption)
Now change only one assumption while keeping everything else the same:
- Execution cost assumption: a flat 0.10 “effective” adverse move on entry and exit combined (for example, due to spread and slippage effects). This means your effective stop is 0.10 closer to the entry than planned, and your effective target is also 0.10 less favorable.
Recomputed effective distances:
- Effective risk price move = (2.00 + 0.10) = 2.10
- Effective reward price move = (4.00 − 0.10) = 3.90
- Effective risk-to-reward ratio = 3.90 / 2.10 ≈ 1.86
Monetary outcomes under the same “10 units per 1.00 move” assumption:
- Effective loss = 2.10 × 10 = 21 units
- Effective profit = 3.90 × 10 = 39 units
The worked example limitation is clear: even with an unchanged “planned” stop and target, small execution differences can reduce the effective ratio from 2.0 to about 1.86, and also shift the exact profit/loss amounts.
Material limitations and risks to consider
Here are common failure modes that create risk reward limitations:
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Assumptions about fill quality may be wrong. Stops may not fill at the exact stop level in fast markets, causing the realized loss to exceed the planned risk.
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Costs can change the effective ratio. Spreads, commissions, and slippage act like additional adverse distance, reducing reward and increasing loss compared with a zero-cost model.
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The probability of which level is hit first is not determined by the ratio. A risk-to-reward number does not tell you how often the target or stop is reached; volatility regime changes can alter the path behavior.
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Stable historical relationships may not hold. Even if a simplified risk-to-reward framework seemed to fit past trades, it does not guarantee future outcomes.
How to verify the example independently (and what to ask next)
To verify, recompute everything from the stated assumptions:
- Use your own assumed entry, stop, and target distances.
- Recalculate the ratio as Reward / Risk using those distances.
- If you include costs, express them as an effective change to price distances (as shown with the 0.10 adverse move) and recompute the ratio.