Direct answer
Slippage assumptions are simplified estimates of how much worse (or sometimes better) an order will be filled compared with an expected reference price (often the last quoted or mid price). The main risk is that these assumptions may not match how orders actually execute. When that mismatch happens, results from backtesting, cost modeling, or performance expectations can be inaccurate.
Mechanism: what slippage assumptions do
A slippage assumption typically turns a “reference price” into an “assumed execution price” by adding or subtracting a fixed amount (for example, a fixed number of pips) or a rule (for example, a percentage of spread or volatility). This assumed execution price then feeds into key calculations such as entry/exit levels, realized profit and loss, risk estimates, and order-level metrics.
For clarity, distinguish stable mechanics from variable conditions:
- Stable mechanic: translating a reference price into an assumed fill using an explicit assumption.
- Variable conditions: market movement during order handling, liquidity depth, spread changes, and differences between quote data and actual execution.
If the assumption is not explicitly stated, different people (or different tools) can interpret the same “slippage” differently, producing inconsistent outcomes.
Evidence via realistic scenarios (example-based)
Consider four realistic ways assumptions can fail:
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Market move during the time gap: Even if you assume a constant slippage, the market can move between the moment a decision is made and the moment the order is filled. The assumed slippage can be too small when volatility spikes.
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Liquidity and spread expansion: Slippage is often linked to how easily orders can be matched. During thinner liquidity or fast spread widening, fills can deviate materially from an assumption based on calmer periods.
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Execution mechanics: Order type, partial fills, requotes, and the way fills are reported can all change realized execution. If your assumption ignores these mechanics, calculated trading outcomes can diverge from what actually happens.
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Data and interpretation mismatch: Backtesters and live environments may use different price feeds, timestamp resolution, or bar-construction logic. If the reference price in your model is not the same as what execution uses, “slippage” can be an artifact of the modeling pipeline rather than a realistic execution cost.
Limitations and risks to watch
Material limitations and failure modes include:
- Understated tail risk: Fixed or “average” slippage can miss rare but costly events when execution becomes unfavorable.
- Overfitting to historical conditions: Slippage patterns may not remain consistent across regimes (quiet vs. volatile markets).
- Hidden assumptions: If you do not state whether slippage is applied at entry only or at both entry and exit, or whether it depends on spread/liquidity, verification becomes difficult.
- Counterparty and provider effects: Execution quality can differ due to routing and matching behavior. Without separating modeling inputs from execution realities, conclusions can be unreliable.
Verification and next question
To independently verify whether your slippage assumptions are plausible, check that the assumption is explicit, consistent with the order process, and stress-tested across multiple market conditions. A useful control question is: What exact reference price does your model use, what exact execution path does it represent, and how does the assumed slippage behave during fast markets? If you cannot answer these precisely, the associated risk of interpretation error increases.