Datasets:
crypto-execution-costs: 2026-09-15
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README.md
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license: odc-by
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pretty_name: "Ethereum swap quotes, routing and perpetual order-book depth"
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tags:
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- dex
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- ethereum
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- 10K<n<100K
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# Crypto execution
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## Contents
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| `e10_quote_benchmark` |
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| `e16_dex_routes` |
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| `e17_perp_depth` |
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## Using the data
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Compare quotes for the same pair, direction and size at comparable observation times. Inspect the provider-fee fields before attributing differences in output to routing quality.
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- A price difference between quotes does not establish profitable arbitrage. That requires executable prices for the complete trading cycle and all associated costs.
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##
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```python
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from huggingface_hub import snapshot_download
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## Coverage
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`e0_run_manifest`
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## License and contact
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---
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license: odc-by
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pretty_name: "Ethereum DEX swap quotes, routing, and perpetual order-book depth"
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tags:
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- dex
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- ethereum
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- 10K<n<100K
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# Crypto execution costs
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What it costs to trade, quoted at the moment someone would have traded.
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Executed trades are on chain forever. Quotes for trades nobody placed are computed on demand and
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kept by nobody, so they only exist if they were recorded when they were served.
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## Contents
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| name | one row is |
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| `e10_quote_benchmark` | one swap quote at a fixed size, with the provider fee separated out |
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| `e16_dex_routes` | one leg of a route a router chose: venue, pool, amount, and how many venues the trade was split across |
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| `e17_perp_depth` | one order book snapshot: spread, level counts, resting notional within fixed distances of mid |
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## Reading it
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Quotes are what a provider served, which is not what you would necessarily have filled. Provider
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fees are separated from price so that a fee policy is not mistaken for routing quality.
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`e16_dex_routes` maps where routable liquidity sits rather than where volume went, and it
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surfaces venues too small to appear in volume rankings. The split widens sharply with size: one
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observation put the same pair through a single venue at small size and ten venues at large size.
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`e17_perp_depth` stores book shape rather than raw ladders, as cumulative resting notional within
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a band of mid. Where a spread is wider than a band, that band is correctly zero, which happens
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often on thinner markets.
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## Before you build on this
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- Not every provider answers every request. Refusals and rate-limit skips are written as explicit
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error rows rather than omitted, so a provider that was unavailable is distinguishable from one
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that had nothing to offer. Check the `error` column before treating absence as meaning.
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- One provider is queried on a subset of pairs. Its coverage is deliberately narrower than the
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others and should not be read as a market-wide view.
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- Only some routers expose a leg breakdown. Rows from the others carry the venue name alone,
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which is why `pool` and `swap_amount_raw` are frequently null.
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- Depth rows are snapshots at the observation interval, not a tick-level book. A move that
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reverses between observations is invisible.
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- Depth covers a single venue, so it sizes that one book rather than the market. Treat it as
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one participant's view; a second venue would be a control and there is not one here.
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Partitions are parquet, one file per collection window, under `dataset/YYYY/MM/`. Every dataset here carries a FIXED 7-day sample WINDOW starting at its own first day of collection, together spanning 2026-08-25 to 2026-09-03, so you can check schema, coverage and quality before asking for more. It does not advance, so there is nothing to gain by re-downloading it. The full history is held privately, available on request.
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```python
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from huggingface_hub import snapshot_download
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## Coverage
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`e0_run_manifest` lists every collection window with its poll counts and failure counts, and is
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published in full rather than windowed. Gaps between windows are real, cannot be filled in
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afterwards, and nothing here is interpolated.
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## License and contact
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ODC-BY: use it freely, credit "DataForge (dataforge-labs)". Questions and requests for the full
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history via the discussions tab.
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