forex-scalping.com

Backtests need a slippage assumption. The choice affects results, especially for scalping.

Options:

• Fixed: e.g. 0.2 pips on every fill. Simple, but unrealistic.
• Random: drawn from a distribution, e.g. normal with a mean and standard deviation.
• Empirical: sampled from your own live fills, by time and order type.

Empirical is most realistic if you have enough live data. Stops usually slip more than market entries, and news periods much more.

A practical middle ground for traders without much live data is to use a random distribution with a negative mean for stops and a mean close to zero for limit orders, then tighten the assumptions as live fills accumulate.

Whatever method is used, running the backtest twice — once with optimistic and once with pessimistic slippage — shows how sensitive the strategy is. If it only works in the optimistic case, the edge is probably too thin.

How do you model slippage in backtests?

I'd push back slightly on the normal distribution for stops. In my live fills the slip on stops isn't bell shaped at all. Most stops fill within 0.1 pips, and then a small number slip 1 to 3 pips, nearly all around data releases or the rollover.

A normal distribution with the same mean spreads that cost evenly across every trade, which hides the real risk. Your average looks fine while the tail is where accounts get hurt.

What I do with limited data is a mixture: 95% of stops get a small slip of 0.0 to 0.2 pips, and 5% get a slip drawn from 1 to 3 pips. Not elegant, but it lands much closer to my real record than any single distribution did.

I also tag each backtest trade with whether it falls within 15 minutes of a high impact release, and apply the bad slip there more often. That alone moved one strategy from marginal to clearly not worth trading.

How much live data would you want before switching to empirical? I've been using roughly 300 stop fills as the threshold, but I've never seen anyone justify a number.