Updated: 2026-02-20
In trading, Slippage is the difference between the price you expected to fill at and the price you actually received, measured in basis points — caused by order book depth, market volatility, and position size relative to available liquidity. This glossary entry explains why slippage matters, how traders use it, and how to track it with evidence instead of vibes.
Quick definition
Slippage: the difference between the price you expected to fill at and the price you actually received, measured in basis points — caused by order book depth, market volatility, and position size relative to available liquidity.
Slippage is the difference between the price you expected to fill at and the price you actually received, measured in basis points — caused by order book depth, market volatility, and position size relative to available liquidity. The practical version is: can you define it as a field you can log and audit later?
Most trading terms become confusing when they are used as vibes instead of variables. Your goal is a definition that helps you decide size, stop, entry timing, or whether to skip the trade.
Traders sometimes confuse Slippage with spread. Treat them as separate variables in your journal so your reviews stay honest.
Slippage is the gap between your backtest and your live results. On BTC-USDT perp on Binance, a $100,000 market sell into a thin book during off-hours can slip 5-15 bps ($50-$150) beyond mid-price. On lower-liquidity pairs like DOGE-USDT perp, the same notional can slip 20-40 bps. Slippage compounds: a scalper taking 15 round-trips per day at 8 bps average slippage per side loses 2.4% of notional daily to execution drag alone — before fees. If your strategy edge is under 10 bps per trade, slippage can flip the entire system negative.
If Slippage never changes your decision, it is just jargon. The term earns its place when it improves your process consistency under real market pressure.
A useful mental model: plan first (risk and invalidation), execute second (order type and fills), review last (tags and metrics).
Use it to make one decision pre-trade. Example decisions: where the stop goes, whether to take partials, how to scale size, or whether conditions are too thin to trade.
Write the rule in one sentence, then run it consistently for a week. Consistency matters because it creates comparable data for review.
If the rule fails, adjust slowly. Do not rewrite the whole system after one bad session.
Log expected fill price and actual fill price on every entry and exit. In Tiltless, compute slippage in bps = abs(actual − expected) / expected × 10,000. Aggregate by symbol, time-of-day, and volatility regime (use ATR as proxy). Review weekly: if median slippage on a pair exceeds 5 bps during your usual session, either switch to limit orders, trade during higher-liquidity windows, or reduce size on that pair until slippage drops below your threshold.
Use tags so you can slice results by regime and behavior state. The same term behaves differently when volatility changes or when you are fatigued.
Your review question should be binary: did this variable improve outcomes or reduce rule breaks? If not, simplify.
You market-sell $80,000 notional of ETH-USDT perp on Bybit at an expected price of $3,420. The order fills across multiple levels: $3,419.80 (40%), $3,419.50 (35%), $3,419.00 (25%). Your volume-weighted average fill is $3,419.48 — slippage of $0.52 per ETH, or 1.5 bps. On $80,000 notional, that is $12.14. Not devastating, but during a liquidation cascade the same order might slip to $3,416.00 — 11.7 bps, costing $93.57. Your backtest assumed mid-price fills and showed +0.08% edge per trade. After real slippage, that edge is +0.02% or worse.
The point of an example is not to predict price. It is to show what you would log before the trade and what you would audit after the trade.
Backtesting with mid-price or last-price fills and treating the results as realistic. A strategy that shows +12% monthly on historical data with zero slippage assumptions can drop to +3% or even negative when you add 5-8 bps of slippage per side on real order books — especially on altcoin perps where book depth is a fraction of BTC/ETH.
The fastest way to improve slippage is to remove one failure mode at a time. If you try to fix everything, you will fix nothing.
Slippage matters most when volatility is high and the book is thin. That's where small execution errors compound into expectancy drag.
Before you trade, decide what matters more: price control (limits) or fill certainty (markets/stops). Then trade the choice consistently for one week so your data is comparable.
If you change order types every time you feel stressed, your metrics will lie to you.
Slippage is the difference between the price you expected to fill at and the price you actually received, measured in basis points — caused by order book depth, market volatility, and position size relative to available liquidity. In practice, it matters when it changes a concrete decision like size, stop placement, or whether you skip a trade.
They are related but not identical. In your journal, track Slippage as its own variable and treat spread as a separate context factor so you can audit each cleanly.
Log expected fill price and actual fill price on every entry and exit. In Tiltless, compute slippage in bps = abs(actual − expected) / expected × 10,000. Aggregate by symbol, time-of-day, and volatility regime (use ATR as proxy). Review weekly: if median slippage on a pair exceeds 5 bps during your usual session, either switch to limit orders, trade during higher-liquidity windows, or reduce size on that pair until slippage drops below your threshold.
Backtesting with mid-price or last-price fills and treating the results as realistic. A strategy that shows +12% monthly on historical data with zero slippage assumptions can drop to +3% or even negative when you add 5-8 bps of slippage per side on real order books — especially on altcoin perps where book depth is a fraction of BTC/ETH.
See plans and run one weekly review loop with Tiltless: edges, leaks, and enforceable next actions.
