Slippage: The Hidden Cost That Destroys Backtests

You run your algo trading strategy on historical data. It returns 80% annually with a Sharpe of 2.5. You go live and immediately start underperforming. What happened? Slippage. This is the single biggest gap between paper and live trading in algorithmic trading python or any platform.

What Causes Slippage

Slippage happens when your executed price differs from the price you assumed at signal generation. Causes include: market impact (your order moves the price), latency (price changed while your order was in flight), and partial fills (you only got part of the order at the best price).

Modeling Slippage in Backtests

A simple model: assume you execute at the next bar's open plus a fixed cost. A more sophisticated model uses bid-ask spread and a square-root market impact formula: Impact = s × v(Q/V), where s is daily volatility, Q is order size, and V is daily volume.

Testing Your Model

Compare your backtest assumptions to your live fills over 100+ trades. If live costs are consistently 2x your model, adjust. Accurate slippage modeling is the difference between a crypto algo trading edge and an expensive illusion.