Slippage
Category
Order-Mechanik
Sub-category
Order-Ausführung
Curated by
Last reviewed
Slippage is the difference between the expected and the actual execution price of an order. It occurs when price moves between order placement and execution or when liquidity at the desired price is insufficient. In calm, liquid markets it is minimal – during news, gaps and thin order books it can fill stops well beyond the planned level and noticeably distort trade statistics.
Context & Mechanics
How slippage arises
Between clicking “buy” and the actual execution lie milliseconds to seconds – enough for price to move. Additionally every market order consumes the order book: if liquidity at the best level is insufficient, the rest is filled at worse levels (walking the book). Slippage is thus not a broker error but market mechanics – it can also turn out positive when price moves in the trader's favour.
When it becomes dangerous
Three situations create the big outliers: news events (prices jump, spreads tear open), gaps (the market opens beyond the stop level – the stop is filled at the next tradable price) and illiquid instruments or off-hours (thin order books even at normal order size). For stop-losses this means: the planned 1R risk is a lower bound, not a guarantee – in extreme cases execution is considerably worse than planned.
Managing slippage
Slippage can be reduced via limit orders (price control instead of execution guarantee), liquid trading hours and instruments, appropriate order sizes relative to the book, and avoiding news events. It cannot be eliminated entirely – which is why it belongs in backtesting and trade review: anyone who knows their real average slippage prices it into expectancy and position size instead of dreaming of ideal fills.
Why it matters for traders
Alongside spread and commission, slippage is the third real cost block – and the most underestimated one because it strikes irregularly but at the worst moment. In the GlanWick simulator, order scenarios can be played through by way of example without real capital – GlanWick is a training and simulation tool and not a prop firm itself.
Execution Example
A trader holds 500 shares long, stop-loss at $40.00 (planned risk: $1.00 per share from a $41.00 entry = $500 = 1R). After weak quarterly figures the stock opens the next morning with a gap down at $38.20.
- Stop trigger: the stop at $40.00 is activated by the gap open – but filled at the next tradable price: $38.20.
- Slippage: 40.00 − 38.20 = $1.80 per share → $900 additional loss beyond plan.
- Real loss: instead of −$500 (1R) now −$1,400 (2.8R) – the planning figure 1R was a lower bound.
- Consequence: the trader henceforth avoids earnings dates with open positions and reduces size on overnight trades – slippage risk is treated like a second, invisible stop distance.
Execution Risk & Errors
Ignoring slippage in backtesting and calculating with ideal fills
Placing market orders in illiquid instruments or off-hours
Holding through news events and earnings without pricing in gap risk
Choosing order sizes that overwhelm the visible order book
Blaming the broker after negative slippage instead of adjusting execution times and instruments
Frequently Asked
Is slippage always negative?
No – if price moves in the trader's favour between order placement and execution, positive slippage occurs. Statistically, however, the negative side dominates for market orders in fast markets because orders are triggered procyclically.
How do I avoid slippage on stop-losses?
It cannot be avoided entirely: the classic stop becomes a market order. Liquid markets, avoiding news events and moderate sizes help reduce it. Stop-limit orders control the price but risk non-execution – usually the worse choice for loss limitation.
How much slippage is normal?
In liquid markets during main trading hours often just one tick or less. What matters are the outliers on gaps and news – hence you measure your own average and maximum slippage across your trade history instead of using blanket values.
Does backtesting account for slippage?
Only if you model it explicitly – e.g. as a fixed deduction per trade or via bid/ask data. Backtests without slippage and spread assumptions systematically overestimate returns, especially for high-frequency strategies.