Slippage in Trading Explained

At a glance

Definition: The gap between the expected and realised price
Main causes: Spread, latency, order size, and volatility
Worst case: Stop orders in fast markets and gaps
How to measure: Fill versus the midpoint at decision time

Key takeaways

Slippage has several distinct sources: crossing the spread, delay between decision and execution, consuming depth, and volatility during the wait.
Stop orders slip most, because they trigger precisely when price is moving quickly in the adverse direction.
Backtests routinely assume fills that live trading does not achieve, which is why measured slippage should replace assumed slippage.
Slippage scales with order size relative to displayed depth, which is why capacity is a real constraint even at retail size.
The measurement that matters is fill versus midpoint at decision time, averaged across many trades.

The sources of slippage

Source	Mechanism	Typical size	Controllable?
Spread crossing	Buying at the ask rather than the midpoint	Half the spread	Yes, with limit orders
Latency	Price moves between decision and arrival	Varies with volatility	Partly, with faster execution
Depth consumption	Order larger than the size at the best price	Grows with order size	Yes, by splitting orders
Volatility during execution	Price drifts while the order works	Grows with duration	Trade-off against impact
Gap	Market reopens at a different price	Can be very large	Only through position size
Queue position	Limit order not reached before price moves away	Opportunity cost	Partly, through placement

Measuring your own slippage

For every trade, record:
   - midpoint between bid and ask at decision time
   - your actual average fill price
   - order type and size
   - time of day

Slippage (in your favour is negative):
   buys:  fill - midpoint
   sells: midpoint - fill

Report in basis points of price so trades are comparable:
   slippage_bps = 10000 x slippage / midpoint

After 30+ trades, compute:
   mean slippage by order type
   mean slippage by time of day
   mean slippage for stops specifically
   worst decile

Put the MEAN into your backtest as the assumption, and
size positions so the WORST DECILE is survivable.

The measurement, and what to do with it.

Most traders discover two things from this exercise. Their entry slippage is smaller than expected, particularly with limit orders, and their stop slippage is considerably larger. Both are useful corrections to a backtest that assumed a single number for everything.

When slippage is worst

The first minutes of the session. Spreads are widest, quotes are least reliable, and overnight imbalances are still clearing.
Around scheduled releases. Liquidity providers withdraw ahead of the announcement, so the book is thin at exactly the moment volume spikes.
During fast moves. The liquidity that would have filled you is consumed by the move that triggered your order.
In thin instruments. Small caps, deferred futures months, and illiquid options can slip by percentages rather than ticks.
On stop orders. Triggered by adverse movement, filled into depleted depth.
Overnight and across weekends. A gap is slippage without any execution at all between your stop and the open.
In crypto during cascades. Liquidation-driven moves consume the book rapidly and stops fill far from their trigger.

Reducing slippage

1
Use limit orders where the strategy tolerates missed fills
The single largest reduction available. Track your fill rate so you know what the missed trades cost.
2
Split orders larger than the displayed depth
Three or four slices is a simple and effective manual approach at retail size.
3
Avoid the widest windows
The opening minutes and the period around scheduled releases, unless the strategy specifically targets them.
4
Trade more liquid instruments
Slippage differences between large caps and small caps are far larger than any signal improvement you are likely to find.
5
Reconsider stop placement
Stops clustered at obvious levels are filled into the worst liquidity. Volatility-derived placement away from the crowd fills better.
6
Reduce position size rather than chasing
If price has moved beyond your intended entry, the risk-reward has changed. Skipping the trade is usually better than accepting a worse entry with the same stop.

Frequently asked questions

What causes slippage?

Four main things: crossing the bid-ask spread, the delay between your decision and the order arriving, your order being larger than the size available at the best price, and price moving during execution. Each is a distinct effect and each responds to different remedies.

Why do my stop losses fill so far from my stop price?

Because a stop becomes a market order when triggered, and it is triggered precisely when price is moving rapidly against you. The liquidity at your level has typically just been consumed by the move that triggered it. Stop fills are systematically worse than other fills and should be modelled separately.

How much slippage should I assume in a backtest?

Measure your own rather than assuming. Before you have data, half the spread plus an impact term for order size is a reasonable starting point, with a larger allowance for stops and for fast markets. The measured figure typically differs enough from the assumption to change conclusions.

Can slippage ever be positive?

Yes. Retail orders routed to wholesalers frequently receive prices better than the public quote, and limit orders sometimes fill at better prices than requested. Positive slippage is real but smaller and less frequent than the negative kind, so net expected slippage remains a cost.

Does slippage matter for long-term strategies?

Much less. A position trading strategy making ten round trips a year pays slippage ten times; a day trading strategy pays it hundreds of times. Slippage sensitivity is essentially a function of trading frequency, which is why slow strategies tolerate execution quality that would destroy fast ones.

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Educational use only. This guide explains how a strategy works. It is not investment advice, not a recommendation, and no result described here is a forecast. Test any approach on historical and out-of-sample data, size positions conservatively, and never risk money you cannot afford to lose.