Slippage in FX and Crypto: Why Your Fill Price Differs

The price you saw, and the price you got

You watch bitcoin sitting at $85,000. You click buy. The confirmation comes back at $85,200. That $200 gap has a name: slipage — the difference between the price you expected when you decided to trade and the price you actually got when the order filled. It's one of the most quietly expensive things in trading, and it's completely normal.

It isn't a platform malfunction. Slippage is what happens when the market moves between the moment you click and the moment your order reaches a matching engine. The quote you saw was never a contract to trade at that level. It was a snapshot, and by the time your order arrived, the book had moved on.

Four things that open the gap

Most slippage comes from a handful of forces acting at once. Knowing which one is biting tells you where your execution is weak.

The bid-ask spread. Every order that crosses the spread pays the difference between the buying price and the selling price. In a strategy holding for months, this barely registers. In one holding for minutes, the spread can be the entire result.

Thin liquidity. If only a small amount of volume sits at your target price, the rest of your order fills at progressively worse levels — "walking the book." Buy 10 BTC against an order book showing 2 BTC at $85,000, 2 at $85,050, 3 at $85,100 and 3 at $85,200, and your average fill lands around $85,100 even though the screen said $85,000.

Market movement. Prices drift while your order is in flight. Even a 50-millisecond delay lets price discovery run ahead of you in fast markets.

Your own footprint. A large order consumes available liquidity and pushes the price against you. This is market impact — one cause of slippage, not the same thing as it. Slippage is the total measured gap; impact is the slice you caused yourself.

Why crypto and FX behave differently from shares

Asset class shapes how execution costs show up. In forex, trading happens across an over-the-counter network of banks, electronic communication networks and non-bank market makers rather than one central exchange. Major pairs like EUR/USD or USD/JPY usually carry deep liquidity in active sessions. Minor and exotic pairs behave very differently: lower participation and wider gaps between quotes mean even modest orders can slip, especially around session transitions or the Sunday open.

Crypto adds its own wrinkles. Digital assets trade continuously and fragmentation is the norm — the same token can have very different depth on different venues. A large sell order on a smaller exchange can move the local price sharply without touching the global average. Because there are no standard market hours to concentrate volume, thin patches appear unpredictably, including weekend hours when institutional market makers step back.

Decentralised exchanges work differently again. Instead of an order book, automated market makers use a formula — the constant product model, x × y = k — to price trades. In a pool holding 1,000 ETH and 400,000 USDC, buying 1 ETH costs a shade over the $400 spot rate. Buying 10 ETH costs materially more per coin, because you've moved a larger share of the pool. On an AMM, size relative to pool liquidity is everything.

One more note: slippage isn't always against you. Positive slippage happens when price moves in your favour before the fill. Traders remember the bad fills far more vividly than the good ones, so the phenomenon feels more one-sided than it is. Persistent, consistently one-sided slippage, though, is worth a hard look at who is filling your orders.

The habit that costs retail traders the most

Market orders buy certainty of execution with an unknown price. Limit orders fix the price and accept that some orders will go unfilled. Neither is free — you're trading price certainty for execution certainty, not making the cost disappear.

Research has measured this. One study placed 85,000 matched market orders through five US brokers in 2022 and found the full round-trip cost ranged from 0.07% at one broker to 0.46% at another, before commission. Same orders, more than six times the cost. Another dataset, covering $1.7 trillion of live equity orders across 21 developed markets between 1998 and 2016, found market impact averaging 9.97 basis points per order — 18.95 in small caps against 8.90 in large caps. Those were one-way costs, achieved by a well-resourced institution that could choose when to trade.

If you run a backtest in QuantConnect's LEAN, Backtrader or many other engines, the default slippage assumption is often exactly zero until you tell it otherwise. Zipline charges equities a flat five basis points, capped at 10% of the bar's volume — better than nothing, and still a fiction, because a small order and a very large one pay the same rate. Engines that don't model market impact at all will show fills on large orders that look better than they could possibly be live.

Measuring what you actually paid

One trade's slippage is easy arithmetic: for a buy, (actual fill price − expected price) × position size. A 3-pip deviation on a 1-lot EUR/USD trade at 1.0850 is about $30. Individually trivial. Across a hundred trades a month at an average 0.3% on $20,000 positions, that's $6,000 — against a $20,000 expected profit, roughly 30% of the edge, gone before the strategy earned anything.

The richer framework is implementation shortfall, a concept Andre Perold set out in 1988. It measures the whole distance between the portfolio you intended to hold when you made the decision and the portfolio you actually hold once execution is done. It breaks into four parts:

• Explicit costs — commissions, exchange fees and taxes. The only piece that appears on a trade confirmation, and often the smallest.
• Realised execution cost — the slippage between fill price and the benchmark at the time of each fill, driven mainly by market impact.
• Delay cost — the drift in the benchmark while your order waits. Historically the largest single component.
• Missed trade opportunity cost — the alpha forfeited on shares you never managed to buy or sell.

The choice of benchmark matters enormously. Using the arrival price — the midquote when the order reached the market — isolates the execution desk's job. Using the decision price — when the manager decided to trade — stretches the lens to include delays before the order was even released. Two reports showing different shortfall numbers aren't necessarily disagreeing; they may simply be measuring different windows.

It's worth being precise about uncertainty here. You'll never know what the market would have done if you hadn't traded. Market impact and market drift are hard to separate cleanly, and shortfall figures resist direct comparison across asset classes, venues and studies because the imputation rules for unfilled orders differ. Implementation shortfall is a framework for consistent comparison, not a physical constant.

When the gap widens

Slippage isn't steady. It rises fastest precisely when a strategy most needs to trade, and it rises in deep markets as well as thin ones. In April 2025, spreads in US Treasuries — the deepest government bond market in the world — widened after the tariff announcement of 2 April, order book depth fell to its lowest since March 2023, and price impact per $100 million traded spiked. All three measures recovered after the postponement.

Two earlier episodes make the same point harder. In March 2020, selling pressure ran across every advanced sovereign bond market and triggered central bank intervention, concentrated disproportionately in Treasuries. On 5 August 2024, a crowded yen-funded carry trade reversed; the VIX briefly passed 60 in pre-market trading before closing under 40, and Japan's TOPIX banks index took the worst single-day loss in its forty-year history. The BIS noted liquidity deteriorated while trading continued — costs rose sharply and the market kept working. That ordinary case is exactly what a cost model should be built around.

What to actually do with this

Spread large orders into smaller slices so the book can replenish between fills. Trade during the hours when your instrument is genuinely liquid, and treat the closing auction as a deliberate cost if you must use it. Use limit orders where you can tolerate an unfilled outcome, and set a slippage tolerance on AMM swaps so a bad fill fails rather than executes. Check pool or book depth before sizing up, not after.

Then — the part almost everyone skips — log every order. Expected price, fill price, difference in basis points, order type, instrument, time of day. Two hundred rows is enough to show whether your backtest assumption was optimistic. Feed the measured numbers back into the engine, split by instrument, and run the strategy again. If you can't show your own fills from a single stressed week, the honest position is that you don't yet know what your strategy costs to run.

Regulators have been nudging disclosure along. The SEC adopted amendments to Rule 605 in March 2024 that widen execution-quality reporting to far more brokers and add statistics for fractional shares, odd lots and larger orders, with time-to-execution measured in milliseconds or finer. The compliance date was extended to 1 August 2026. Rules and timelines differ by jurisdiction and change — check what applies where you trade.

None of this eliminates slippage. It just changes it from a mystery into a number you can see, argue with, and try to shrink.