Definition and purpose
Slippage assumptions are the stated expectations about how far the executed trade price may differ from the price you expected at the moment you placed an order. In forex execution modeling, the “expected” price is usually the quote or reference price used in your calculation, and the “executed” price is what actually happens once the order fills.
Slippage assumptions matter because real trading rarely matches the reference perfectly. Even if you use the same strategy idea, different execution timing and liquidity conditions can change the fill price, which changes profit and loss (P&L). For backtesting and forward testing, assumptions about slippage are a way to model execution uncertainty instead of assuming ideal fills.
How it works as an input
A simple model treats slippage as an adjustment to the reference price (or to the effective cost). You can express it in different forms, for example:
- Fixed slippage: you assume the executed price is worse by a constant amount.
- Variable slippage: you assume slippage can vary, for example depending on volatility, time of day, or order size.
- Distribution-based slippage: you assume slippage follows a range of outcomes, such as “usually small, occasionally large.”
To use slippage assumptions in a calculation, you must state the rule that converts your reference price into an assumed executed price. This includes clarifying whether you apply slippage to buys, sells, or both in the same direction, and whether it is measured in price units or pips (a pip is a standardized price move in forex).
Material limitation: slippage is not only about “spread.” Spread is the difference between bid and ask quotes at a point in time. Slippage includes additional mismatch caused by how orders fill relative to those quotes, including delays and liquidity changes.
Evidence and example you can check
Consider a backtest that uses a reference price to enter and exit. If your slippage assumption is “execution is worse by 0.5 pips per side,” then each trade costs an additional 1.0 pip for a round trip (0.5 on entry plus 0.5 on exit). If you change the assumption to “0.5 pips on average but sometimes 2.0,” then outcomes become more sensitive to the frequency of the “sometimes” events.
To make this independently verifiable, treat slippage assumptions as a modeling choice and ask three concrete questions:
- What exact rule did you encode (fixed, variable, or distribution)?
- What conditions drive changes in the rule (if any)?
- How do you reflect the uncertainty (for example, by testing multiple plausible slippage scenarios)?
If you only run one narrow assumption set, you cannot tell whether results are robust or simply match a chosen execution outlook.
Limitations and failure modes
Slippage assumptions often fail when market conditions differ from the period or regime you assumed. Common failure modes include:
- Regime shift: liquidity and volatility can change abruptly, increasing execution mismatch.
- Tail events: rare but large slippage can dominate results, especially for strategies with frequent trades.
- Hidden execution effects: real fills depend on order handling, queueing, and whether orders are partially filled.
A further limitation is that historical relationships do not guarantee future results. Even if a slippage pattern looked stable in one dataset, future execution may differ.
Verification and next questions
A practical verification approach is to test multiple slippage assumption sets rather than relying on one. Examples include varying the average slippage, varying the “worst-case” tail, and separating testing by time windows.
Next, you can clarify your own model assumptions by answering: Which reference price do you use, what unit measures slippage, and what directionality do you assume for buys versus sells? These choices define what your slippage assumptions actually mean in your calculations.