Volume tells you what traded. Depth tells you what you can trade. A slippage model that takes the first number and assumes the second will underprice every large order, and it will underprice it worst on the venues that look most liquid on a ranking page. The gap between the two numbers is where execution costs hide.
Two numbers, two questions
24h volume answers a historical question: how much changed hands in the last day. Depth answers a present-tense one: how much can change hands in the next minute without moving the mid by a given amount.
Both are legitimate. They are not interchangeable. Volume is an aggregate over time and over both sides of the book. Depth is a snapshot of resting orders, and only orders that are actually resting count.
A venue can score high on the first and low on the second for ordinary reasons. Flow may be concentrated in one pair with a small average ticket, makers may be obliged to quote only near the touch, and volume may come from participants who hedge elsewhere.
Why a volume ranking can hide a thin book
Every ranking has to sort on something, and volume is the easiest column to compute. Our board sorts the same way: it ranks 24 venues on sampled 24h volume as of October 2026.
The sort key is a choice, and it shows in the shape of the table. The top row carries roughly a third of the sampled volume. Twenty-three venues share what remains, and the drop from the first row to the second is steeper than the drop from the second to the tenth.
That shape is a warning rather than a measurement. It says the sample is dominated by one venue, so the median row describes a book much thinner than the table’s total implies. A trader who sizes to the total is sizing to a number that no single book ever held.
We do not publish per-venue depth, and the board does not claim to. What it can do is stop the volume column from being read as a liquidity column.
Reading depth inside one percent
The ratio between two bands
Depth inside 1% is the notional you can execute before the mid moves one percent. Depth inside 2% is the same test at a wider band. The ratio between them says how fast the book thins.
A book with a large figure inside 1% and almost the same figure inside 2% is a wall with a cliff behind it. A book with a small figure inside 1% and a far larger one inside 2% is thin at the touch and deep behind it. The second shape needs a different execution style from the first.
Where estimates go wrong
Most retail slippage models assume depth is proportional to volume and constant through the day. Neither holds. Depth clusters around the moments when makers refresh, and it thins in the minutes around a large liquidation or a funding timestamp.
A second error is averaging. Slippage is convex in size, so doubling an order more than doubles the cost, because the second half eats worse prices than the first. A model built on one average figure is optimistic exactly where it matters.
What the board can and cannot tell you
The board can tell you which venues are in the sample, how the sample was sorted, and when it was taken. It cannot tell you the depth of a book it did not snapshot.
That limit is worth stating plainly, because a ranking invites the opposite reading. A row is a position in a sort, not a certification of liquidity. The methodology page sets out what the sample covers and what it leaves out.
| Measure | What it answers | Failure mode |
|---|---|---|
| 24h volume | How much traded inside the window | Read as resting size |
| Depth inside 1% | Size tradeable before a one percent move | Snapshot goes stale within minutes |
| Ratio of 1% to 2% depth | How fast the book thins | Hides a cliff behind a wall |
| Maker count at the touch | Whether depth will be replaced | One desk can run several quotes |
| Published withdrawal fee | Cost of the exit | Changes without notice |
What to do next
Pull the book at the size you intend to trade, minutes before the order rather than the day before. Compare depth inside 1% with depth inside 2%, and count makers at the touch to judge whether that depth survives your order.
Then price the exit. A position is only as liquid as the way out, and the exit cost is published. FeeEdge’s survey of 17 August 2026 put exchange withdrawal fees at roughly 0.5 to 1 USDT for Solana SPL, about 0.3 USDT for BEP-20, and highest for ERC-20, with L1 ERC-20 transfers at $2 to $10 before congestion.
Use the venue directory to see how many books an order can be split across. Treat the volume ranking as the start of that comparison, not the end of it. The number a slippage model is missing is the one that describes the book at the moment the order crosses it.
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