To measure the impact of slippage, we'll employ a test that uses a typical price breakout trend-following strategy: A 20-day Donchian channel system. The system enters when price makes a new 20-day high or low and exits when price reverses and registers a 10-day low or high (that is, in the opposite direction). We also will employ an alternative exit of a stop loss of twice the Average True Range (ATR) at entry. Backtesting this system from 1990 over a diversified portfolio of futures gives a compound annual growth rate (CAGR) of 56.3%. This result is obtained without any consideration for slippage, and therefore is not realistic. Some slippage should be worked into all systems, but the key is to limit it as much as possible.
One backtesting school of thought is to try and break the system while testing it. If the system still holds up, it might have value. One method of attempting to "break the system" is to include large slippage assumptions. To measure the gradual effect of slippage, and check if the system described above holds up, a stepped simulation can be run using different slippage assumptions, with values ranging between 0% and 35%. The results are shown in "Slippage simulation" (below).
The impact of slippage is rather dramatic. Even ignoring the extreme cases, consider the difference between a backtest with no slippage and the next one down, with a small 5% number: The performance is cut drastically, to a point where the MAR ratio of the system is more than halved (1.08 vs. 0.51) as both CAGR and drawdown performance deteriorate sharply.
To put things in perspective, imagine setting up a buy order for 100 on tomorrow's open. If the price trades between 99 and 102, your order should be filled. However, with 5% slippage, the fill price would be 100.1 instead of the order price of 100. This is the difference between a good system and a not-so-good one.