Cost drag in algorithmic trading: how fees destroy your edge
Cost drag from fees, spread, and slippage: how small edges vanish after costs, and how to model drag honestly before deployment.
Cost drag trading fees slippage and trading fees impact on returns are foundational SEO topics because they decide whether a "great" backtest is economically real. Cost drag is the performance lost to fees, spread, and slippage versus a frictionless simulation.
The failure mode
You optimize on mid prices, then pay spread and fees in live trading. High-frequency edges die first; slower edges may survive with smaller size.
What to include in a serious model
- Maker vs taker fees and whether your logic can actually rest liquidity
- Funding for perpetuals when relevant
- Slippage that scales with size and volatility, not only a constant
How cost drag interacts with metrics
Backtest transaction costs impact changes:
- Profit factor
- Expectancy per trade
- Sharpe and Sortino (Sharpe limitations)
Freqtrade note
Freqtrade backtest slippage fees settings must match your venue. If drag is large relative to per-trade expectancy, prioritize robustness under costs over raw in-sample return (How to read Freqtrade results).