Slippage

Quick Answer

Slippage is what execution actually costs beyond the quoted price: the gap between the price your strategy expected and the price it got. It grows with order size, speed and thin liquidity, and it quietly erodes backtested edges. Budget for it explicitly, because a scalping system that ignores a few ticks per trade can look profitable yet lose live.

Definition: Slippage

Slippage is the difference between the price a trade was expected to fill at and the price it actually filled at, measured per order in points or rupees.

Key takeaways: Slippage

• Slippage is the gap between expected and filled price, made of spread, market impact and delay.
• Express it in basis points to compare across instruments and add it to other costs.
• It scales with size and thins with liquidity, so it sets a strategy's capacity limit.
• Model it conservatively and stress it — a strategy that only works with optimistic slippage is not deployable.

Slippage at a glance

Slippage in simple words

Slippage is the money that quietly leaks out between the price you saw and the price you got. Part of it is the bid-ask spread you must cross, part is your own order pushing the price as it eats the book, and part is the market moving in the moments between your decision and your fill. It is usually small per trade but, multiplied across many trades, it is one of the biggest reasons a good-looking backtest disappoints live.

What Slippage is for

Understanding slippage lets a quant separate a strategy's theoretical edge from its net-of-costs edge, budget execution cost realistically, and reject strategies whose paper profit is smaller than the slippage they would incur.

Slippage — professional explanation

Defining the benchmark price

Slippage is only meaningful relative to a reference price, and the choice of reference matters. Common benchmarks are the mid-price at decision time, the last traded price when the signal fired, or the arrival price when the order reached the market. A strategy that decides on a bar close but cannot trade until the next tick will show slippage relative to that close even before any spread. Being explicit about the benchmark is the first discipline; comparing fills against an unrealistic benchmark (like the low of the day) produces the flattering fantasy that ruins live results.

The three sources

Slippage decomposes into three additive parts. Spread cost is half the bid-ask spread you cross to trade immediately. Market impact is the extra adverse move your own order causes by consuming depth — larger and more temporary for aggressive orders, and roughly increasing with the square root of order size relative to typical volume. Delay (or timing) cost is the market's own drift between decision and fill, which can be positive or negative but is adverse on average for signal-following strategies chasing a move. Total slippage is spread plus impact plus delay.

Expressing slippage in basis points

To compare across instruments and price levels, slippage is normalised to basis points of the traded price: one basis point is 0.01 percent. A fill 2 points worse than a 25,000 benchmark is 2 / 25,000 = 0.00008 = 0.8 bps. Working in bps lets you add slippage to other costs (brokerage, STT, exchange fees) on a common scale and set a per-trade cost budget that a strategy's edge must clear. High-turnover strategies are judged on net bps per trade after all frictions.

Impact scales with size and thins with liquidity

For a given instrument, impact grows as your order becomes large relative to available depth and daily volume. A widely used rule of thumb models temporary impact as proportional to the square root of participation rate (your size divided by market volume) scaled by volatility. The practical consequence is a capacity limit: a strategy that works at one lot may be uneconomic at fifty lots in the same illiquid strike, because impact rises faster than the edge. Liquidity and slippage are two views of the same thing.

Modelling slippage in a backtest

A credible backtest injects slippage explicitly rather than assuming perfect fills. The simplest defensible model is a fixed per-trade cost (say a spread-based number of ticks); better models scale slippage with the spread and volatility of each bar, and best models add a size-dependent impact term. Whatever the model, it should be conservative: it is far safer to overestimate slippage and reject a marginal strategy than to underestimate it and deploy one whose edge evaporates on contact with the market. Slippage assumptions should be stress-tested by doubling them and checking the strategy still survives.

Slippage versus other frictions

Slippage is an implicit cost — it never appears on a contract note, unlike brokerage, STT, GST, exchange transaction charges and stamp duty, which are explicit. Total transaction cost is the sum of both. A dangerous error is to model only the explicit charges (which are easy to look up) and ignore slippage (which requires judgement), producing a backtest that undercounts the real cost of trading. For frequent intraday systems, slippage frequently dominates the explicit charges.

Formula

How Slippage looks visually

Worked example: Slippage

Limitations of Slippage

• Slippage is an estimate, not an observed constant — real fills vary trade to trade
• It is invisible on the contract note, so it is easy to omit and hard to attribute
• Impact models are approximations; true impact depends on unseen order-book dynamics
• Assumptions that are too optimistic silently convert losing strategies into apparent winners
• Slippage is regime-dependent — it widens sharply in volatile or thin conditions your backtest may under-sample

Why Slippage matters in practice

• For high-turnover strategies, slippage is often the single largest cost and the main gap between backtest and live
• Slippage sets a strategy's capacity — the size beyond which impact overwhelms the edge

How professionals treat Slippage

Execution desks treat slippage as a measured, attributed cost, not an afterthought. They benchmark every fill against a defined arrival or VWAP price, decompose the shortfall into spread, impact and timing, and feed that back to size orders and choose algorithms. Strategy research includes a transaction-cost model calibrated to the desk's own historical fills, and a candidate strategy must beat that cost model by a comfortable margin — not by a hair — before it is funded. The governing principle is that an edge you cannot capture after realistic slippage is not an edge.

Common misconceptions about Slippage

Common mistakes with Slippage

• Backtesting with zero slippage or perfect close-price fills, overstating every result
• Benchmarking fills against the day's high or low instead of the decision or arrival price
• Modelling only explicit charges (brokerage, STT) and ignoring the larger implicit slippage
• Assuming per-trade slippage stays constant when size grows, ignoring rising market impact
• Using a single average slippage figure that hides the fat tail of bad fills in volatile spells
• Ignoring that slippage compounds with turnover — a tiny per-trade cost becomes huge across thousands of trades

Slippage: frequently asked questions

Voice search: how people ask about Slippage

Natural-language questions people ask about Slippage.

Sources & references

• Robert Kissell, “The Science of Algorithmic Trading and Portfolio Management”, Academic Press (Elsevier), 2013
• Irene Aldridge, “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems”, 2nd ed., Wiley, 2013