Slippage in trading is the gap between the price you expected when you placed an order and the price at which that order actually filled. It is one of the quiet costs that separates a backtest from live results: a strategy can look pristine on historical data and then underperform simply because real fills arrive at slightly worse prices than the model assumed. Understanding where slippage comes from, and how automated systems account for it, is essential to trading rules that survive contact with the market.
Why prices move between click and fill
Slippage happens because markets are not static. Between the instant your strategy decides to trade and the instant the order reaches the book, the price can move — sometimes by a little, sometimes by a lot. In fast conditions or around news, the level you were aiming for may simply no longer be available, and your order fills at the next best price instead. This is the ordinary friction of participating in a live market rather than a snapshot of one.
The size of your order matters too. A small order can often be filled at or near the price you see, because there is enough resting liquidity to absorb it. A larger order may need to consume several levels of the order book to complete, with each successive level a little further from where you started. This is why the same strategy can experience very different slippage depending on how much capital it is moving and how deep the market is at that moment.
How automated strategies manage slippage
Automated systems cannot eliminate slippage in trading, but they can be designed to respect it. The most direct tool is the choice between order types. A market order prioritizes certainty of execution and accepts whatever price is available, which can mean more slippage in thin or fast markets. A limit order sets a boundary — it will not fill worse than the price you specify — trading the guarantee of a fill for control over the price.
Beyond order type, well-built strategies account for slippage in how they measure themselves. A backtest that assumes every order fills at the exact target price will overstate real performance, sometimes dramatically for high-frequency rules. Building a realistic allowance for slippage into your testing produces a more honest picture and helps you avoid deploying a strategy whose apparent edge would be eaten entirely by execution costs. Some approaches also throttle order size or avoid trading during the most volatile windows specifically to keep slippage manageable.
When slippage matters most
Slippage is not equally important to every strategy. A system that holds positions for days and trades infrequently may find that a few ticks of slippage per trade barely register against its overall results. The same few ticks can be decisive for a strategy that trades often and aims to capture small moves, because the cost is paid again and again and compounds against a thin margin.
