Manual traders feel slippage as a vague annoyance — the price moved just before you clicked. Algorithmic traders feel it as a systematic tax on every single trade, compounding quietly until a strategy that looked brilliant in backtesting is quietly bleeding out in live markets. Getting this modelling right is not optional; it is the difference between a strategy and an expensive lesson.

The core problem is that a backtest executes at whatever price the historical data shows, with zero friction. Real markets do not work that way. Your order moves the market. Spreads widen at open. Liquidity evaporates exactly when you need it most. A backtest that ignores these realities is essentially a fantasy novel with a P&L attached.

CONCEPTSlippage modelling adds simulated execution friction to backtests — making historical results far more representative of live trading conditions.
WARNINGA strategy showing 20% annual returns in backtest can turn negative once realistic slippage, spread, and market impact costs are applied.
KEY IDEAThe more frequently a strategy trades, the more aggressively slippage compounds — high-frequency logic demands high-precision cost modelling.

Systematic traders approach slippage in layers. The first is the bid-ask spread — the baseline cost of entry and exit on every trade. The second is market impact: larger orders shift price against you before they fill. The third is timing slippage — the gap between signal generation and actual execution. Each layer needs its own estimate, historically grounded and instrument-specific.

Equity Curve: With vs Without SlippageNo slippageWith slippageTrade Count Over TimeReturn

Good slippage models are calibrated against actual fills — not assumed. Traders pull real execution data, compare it to the mid-price at signal time, and build a distribution of observed slippage by instrument, session, and order size. That distribution then gets applied stochastically during backtesting, so results reflect a realistic range of outcomes rather than a single optimistic path. Concepts like slippage on Investopedia, the mechanics of market impact on Wikipedia, and bid-ask spread on Investopedia all provide useful grounding when building those estimates from first principles.

A backtest is not a promise — it is a hypothesis under idealised conditions. Build your slippage model with the same rigour you give your entry signals.

This content is for educational purposes only and does not constitute financial product advice. Past performance is not indicative of future results. Profit Logic Ltd (ACN 688 669 936) accepts no responsibility for errors or omissions in this content or anywhere on this website. Always seek advice from a licensed financial adviser before making investment decisions.