Reviewing Your Spread and Slippage Costs From Your Own Fills

Most cost reviews start from a quoted spread on the order book and stop there. That number tells you what the market looked like at one instant, not what you actually paid. Your real cost lives in the difference between the price you expected when you sent the order and the price printed on your fill, plus the gap between the best bid and best ask at the moment of execution. This guide shows you how to pull that evidence from your own trade history on Binance, OKX, Bybit and Bitget, build a small worksheet, and read the pattern instead of a single number. You will not find fee tables here, because fee tiers change and depend on your account; check the official fee page of the exchange you use and record the tier that applied on the day of each fill. The goal is a repeatable habit: measure, log, compare across sessions, and know when the sample is too thin to conclude anything.

Why the quoted spread is not your cost

A quoted spread is the distance between the best bid and the best ask at the moment you look. It is a snapshot of resting orders, and it changes the instant someone cancels or adds liquidity. When you send a market order, you consume whatever is resting, which may be several price levels deep. The average price you receive can sit well away from the best quote you saw a second earlier.

Your realized cost has two parts. The first is the spread you crossed: the distance between the mid price and your average fill price. The second is slippage: the difference between the price you saw when you decided and the price your order actually got. On a thin book these two blur together, but you can still separate them if you record the mid price at decision time and the average fill price from the confirmation screen.

The reason to measure from your own fills rather than a public spread feed is that your size, your timing and your order type are what determine your number. Two traders on the same pair at the same minute can pay very different amounts. Only your execution history tells you which one you are.

Collecting the raw evidence from your fills

Open your order history or trade history in the exchange interface and filter to the pair and date range you want to study. For each fill, note the timestamp, the side, the order type, the quantity, the average fill price, and the fee charged. If the interface shows a single aggregated price for a partially filled order, expand the detail view to see the individual executions, because the ladder of prices is where slippage hides.

Next to each fill, record the reference price you had in mind when you sent the order: the mid price, the last traded price, or the best quote on the side you were taking. Write down where you read it, since a mid price from the chart and a mid price from the order book widget may differ slightly. This reference column is what turns a list of fills into a cost measurement.

Finally, note the conditions: time of day, whether a major data release or listing event was in progress, and whether the book looked thin. You are building context so that later you can tell whether a large gap was a one-off or a recurring pattern at certain hours. Keep the fee column separate from the spread column so you do not double count. For the fee side of the arithmetic, confirm the schedule that applied to your account on that date on the official fee page rather than relying on memory.

Turning fills into spread and slippage numbers

For each fill, compute the signed difference between your average fill price and your reference price. On a buy, a fill above the reference is a cost; on a sell, a fill below the reference is a cost. Express the result in basis points of the reference price so that a large order and a small order on different pairs can sit in the same column. Write the formula down once and reuse it so your rows stay comparable.

Then split the result into the two components. The spread component is the half-spread you crossed, measured from the mid to the best quote on your side at decision time. The slippage component is what remains after you subtract that half-spread from the total difference. If your order was small and the book was deep, slippage should be close to zero and most of your cost is the spread. If slippage dominates, your problem is size relative to depth, not the headline spread.

Aggregate by session rather than by single trade. A median and a count of how many fills exceeded your own threshold will tell you more than an average dragged around by one bad print. Record the number of fills behind each figure, and mark any session with fewer than a handful of fills as indicative only. When a number looks surprising, go back to the individual executions and check whether a partial fill at an extreme price is skewing it.

Keep the fee line separate throughout. Fee tiers depend on your account and change over time, so do not bake a rate into your spreadsheet as a constant. Check the current schedule on the official fee page and note the tier that applied on the date of each fill, then treat fees as a third column alongside spread and slippage.

Reading the pattern and deciding what to change

Once you have a few weeks of rows, sort them by hour, by pair, and by order size. Look for clusters: the same hour producing wide slippage repeatedly, or one pair where your fills consistently land away from the reference. A cluster is actionable; a single outlier is not. Compare your worst sessions against the market conditions you noted at the time.

Use the pattern to test one change at a time. If slippage dominates on large orders, splitting the order or using a resting limit order changes the arithmetic, and you can measure the result with the same worksheet. If the spread dominates and slippage is near zero, your cost is mostly the price of immediacy, and the question becomes whether you need that immediacy at all.

Set stop conditions in advance so the review does not drift. Stop measuring a pair when you have enough fills to see a stable pattern, and stop trusting a figure when the sample is too small or when market conditions were clearly abnormal. If your recorded numbers disagree with what the interface shows, pause and recheck the export before drawing conclusions, and use the help centre to clarify how a field is defined.

Finally, keep a short log of what you changed and what happened next. The value of this exercise is not one clean number but a habit of checking your own execution against a reference you wrote down before the fact. That habit survives fee changes, interface redesigns and new pairs, because it depends only on your fills and your records.

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Scenario checkpoint

• For each fill, record timestamp, side, order type, quantity, average fill price and fee, and expand partial fills to see individual executions.
• Write down the reference price you used at decision time and where you read it, so the comparison is not made after the fact.
• Compute the signed difference between fill price and reference in basis points, then separate the half-spread you crossed from the remaining slippage.
• Confirm the fee tier that applied on each fill date on the official fee page instead of hard-coding a rate in your sheet.
• Aggregate by session with a median and a fill count, and flag any session with too few fills as indicative only.
• Note market conditions per session so you can tell a recurring cluster from a one-off outlier before changing anything.