Worked Example of Slippage Assumptions (Scenario + Assumptions)

Direct answer

A worked example of slippage assumptions is a scenario where you explicitly choose numbers for how much worse (or better) the actual execution price is than the reference price you used in modelling. You then carry those numbers through the calculation so a reader can see exactly how the assumptions affect outcomes.

Because the goal is independent verification, every slippage-related input should be stated separately: the reference price definition, the assumed slippage amount (often in price terms or basis points), whether it applies per order or per fill, and what happens if execution is split across multiple fills.

Mechanism or definition

Slippage is the difference between a reference execution price (for example, the price you used when estimating a trade) and the price at which the market actually fills your order. In modelling, slippage assumptions are the simplified rules you use to convert that difference into numbers.

A practical way to make this transparent is to separate stable mechanics from variable conditions:

• Stable mechanics (what you can control in the model): how you apply slippage to the reference price, how you compute profit/loss from fills, and how you handle quantity.
• Variable market/provider conditions (what you must assume or measure): typical spread, whether orders fill immediately or get delayed, and how likely partial fills are.

Evidence or example (with stated assumptions)

Consider a simple long position model with one entry fill and one exit fill. The worked example below uses invented numbers so you can trace the arithmetic.

Assumptions (state every input)

1. Instrument and price units: Use a single reference price unit where changes are linear (no need for contract specifications in this illustration).
2. Reference price for entry: Entry reference price = 1.2000.
3. Reference price for exit: Exit reference price = 1.2050.
4. Assumed slippage for entry: Order fills worse by 0.0002 (price units). So entry fill price = 1.2000 − 0.0002.
5. Assumed slippage for exit: For a long position, exiting worse means you receive a lower price. Assume exit fills worse by 0.0001. So exit fill price = 1.2050 − 0.0001.
6. Quantity: Quantity is 1 unit. (This keeps the example arithmetic simple.)
7. Costs excluded: This example ignores commissions and other fees; you treat them separately if needed.
8. Single-fill execution: Each side (entry and exit) is executed as one fill at the assumed slippage-adjusted price.

Calculation

• Entry fill price = 1.2000 − 0.0002 = 1.1998
• Exit fill price = 1.2050 − 0.0001 = 1.2049
• Modeled price move (net) = Exit fill price − Entry fill price
• Net modeled move = 1.2049 − 1.1998 = 0.0051

Without slippage assumptions, the reference net move would have been 1.2050 − 1.2000 = 0.0050. In this scenario, slippage assumptions increased the net loss relative to the reference by 0.0001 price units.

Alternate scenario to show assumption sensitivity (partial fills)

Now assume the entry is split into two fills:

• Reference entry price = 1.2000
• First fill: 60% of quantity, slippage worse by 0.0001 → first fill price = 1.1999
• Second fill: 40% of quantity, slippage worse by 0.0003 → second fill price = 1.1997

With quantity = 1, the volume-weighted entry fill price becomes:

• Weighted entry fill = 0.6×1.1999 + 0.4×1.1997 = 1.19982

Even if you used an “average slippage” idea, this split shows a modelling limitation: a single constant slippage per trade may hide timing effects and different execution quality across fills.

Limitations and risks

1. Assumptions can be internally inconsistent: If you assume slippage values that contradict your reference price definition or your fill timing, the model can produce misleading differences.
2. Spread and execution delay are often entangled: Slippage assumptions may unintentionally double-count (or miss) the effect of spread changes, latency, or order book depth.
3. Single-fill simplification may fail: Real execution can produce partial fills, re-quotes, or order cancellations. If you ignore that, your slippage assumptions may understate or overstate the true variability.
4. Historical relationships do not guarantee future results: Even if a similar environment produced certain slippage behaviour in the past, future market conditions and liquidity can differ.