Explainer, in Market Microstructure
Slippage
Slippage = executed_price − reference_price (sign-adjusted for buy vs sell). The reference is usually the midpoint at the moment the order was sent, or the trigger price for a stop. Slippage decomposes into spread cost (crossing the touch), market impact (your order moves the price), and latency cost (the price moves while your order is in flight).
1 question. Published May 10, 2026. By AI Fin Hub Research.
Definition
Slippage
Slippage = executed_price − reference_price (sign-adjusted for buy vs sell). The reference is usually the midpoint at the moment the order was sent, or the trigger price for a stop. Slippage decomposes into spread cost (crossing the touch), market impact (your order moves the price), and latency cost (the price moves while your order is in flight).
Why it matters
For high-frequency and intraday strategies, slippage often exceeds nominal commission by an order of magnitude. Most retail backtests assume zero slippage and discover the truth in live deployment. Even institutional VWAP execution typically incurs 5-15bps of slippage on liquid names — far from free.
How it works
Model spread cost directly from the order book at decision time. Model market impact with a square-root law: impact (bps) ≈ k × sqrt(order_size / ADV). Model latency cost as a Gaussian shock with σ proportional to volatility-per-second × latency. Backtest with the sum. For real strategies, regress observed slippage against order size, volatility, and time-of-day to get an empirical model.
Example
10,000 shares of a mid-cap stock, ADV 1M, σ_daily 2%
Spread cost (1 cent on $50)
2 bps
Market impact (k=10, sqrt(0.01))
10 bps
Latency cost (50ms, σ_sec)
1 bps
Total slippage
13 bps
13bps round-trip on a 1% target means net edge of 87bps. Halve the order size and impact drops to 7bps — total slippage to 10bps. Order sizing matters more than venue selection at this scale.
Key takeaways
Market impact dominates slippage for institutional sizes; spread cost dominates for retail.
Square-root impact law is the empirical baseline — linear-impact models overpenalize small orders, underpenalize large ones.
Latency cost is small for slow strategies, dominant for HFT.
Related terms
Run the numbers next
- Playgrounds Execution Simulator
Estimate execution cost in closed form — square-root permanent impact, linear temporary impact, half-spread, and a latency-drift band. See the slippage a naive backtest hides.
- Comparators Broker API Comparator
Alpaca vs IBKR vs Tradier vs Schwab vs Robinhood — compare auth, rate limits, order types, market data, MCP, and fees before wiring a line of code.
Questions people ask next
Why a square-root law for market impact?
Empirical regularity. Almenberg, Bouchaud, and others have documented impact scaling as sqrt(volume) across markets and decades. Theoretical justifications involve information arrival and limit-order replenishment, but the empirical evidence is the strongest argument.
Sources
- Optimal Execution of Portfolio Transactions , Almgren, Chriss (2000), Journal of Risk 3(2)
Keep the topic connected
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