Zero slippage: the other assumption baked into almost every test
Posted: Sun Nov 01, 2026 12:14 am
Standard backtests fill orders at the requested price. Live trading doesn't. The gap between the two is slippage, and ignoring it turns small edges into mirages.
A scalper taking a thousand trades a month with half a pip of average slippage each way loses a full pip per trade, or a thousand pips a month. If the average gross profit per trade is two pips, that's half the edge gone.
Slippage isn't constant. It increases with volatility, with order size, and at certain hours. It differs for market and limit orders, for entries and exits, for winners and losers. A stop-loss typically suffers more than a take-profit, since stops trigger in fast, adverse moves.
Adding a fixed slippage in the tester is a start. Better is a model calibrated to real fills: collect live data from a month of small trades, compute average slippage by order type and time of day, and use those numbers.
Strategies that survive a pessimistic slippage assumption are much more likely to hold up live.
A calibrated slippage model can be built from the trade log. For each order, compute the difference between the requested price and the fill price, in the direction that hurts. Group by order type, session, and spread level. The averages become inputs for the tester.
Another point is that slippage isn't independent of strategy. A breakout strategy, which enters when price is moving, suffers more than a mean-reversion strategy that enters against the move. A strategy that trades news suffers most.
Adding slippage to a backtest often changes the verdict. Systems that looked marginal turn clearly negative, while systems with a reasonable edge survive with a lower number.
Sorting strategies by how much slippage they can tolerate before turning unprofitable is a useful measure. A system that breaks at 0.2 pips of slippage is a different animal from one that survives 1 pip.
What slippage figure was used in the last backtest, and where did that number come from?
A scalper taking a thousand trades a month with half a pip of average slippage each way loses a full pip per trade, or a thousand pips a month. If the average gross profit per trade is two pips, that's half the edge gone.
Slippage isn't constant. It increases with volatility, with order size, and at certain hours. It differs for market and limit orders, for entries and exits, for winners and losers. A stop-loss typically suffers more than a take-profit, since stops trigger in fast, adverse moves.
Adding a fixed slippage in the tester is a start. Better is a model calibrated to real fills: collect live data from a month of small trades, compute average slippage by order type and time of day, and use those numbers.
Strategies that survive a pessimistic slippage assumption are much more likely to hold up live.
A calibrated slippage model can be built from the trade log. For each order, compute the difference between the requested price and the fill price, in the direction that hurts. Group by order type, session, and spread level. The averages become inputs for the tester.
Another point is that slippage isn't independent of strategy. A breakout strategy, which enters when price is moving, suffers more than a mean-reversion strategy that enters against the move. A strategy that trades news suffers most.
Adding slippage to a backtest often changes the verdict. Systems that looked marginal turn clearly negative, while systems with a reasonable edge survive with a lower number.
Sorting strategies by how much slippage they can tolerate before turning unprofitable is a useful measure. A system that breaks at 0.2 pips of slippage is a different animal from one that survives 1 pip.
What slippage figure was used in the last backtest, and where did that number come from?