dataforge-labs/crypto-execution-costs

Crypto execution costs

What it costs to trade, quoted at the moment someone would have traded.

Executed trades are on chain forever. Quotes for trades nobody placed are computed on demand and kept by nobody, so they only exist if they were recorded when they were served.

Contents

name	one row is
e10_quote_benchmark	one swap quote at a fixed size, with the provider fee separated out
e16_dex_routes	one leg of a route a router chose: venue, pool, amount, and how many venues the trade was split across
e17_perp_depth	one order book snapshot: spread, level counts, resting notional within fixed distances of mid

Reading it

Quotes are what a provider served, which is not what you would necessarily have filled. Provider fees are separated from price so that a fee policy is not mistaken for routing quality.

e16_dex_routes maps where routable liquidity sits rather than where volume went, and it surfaces venues too small to appear in volume rankings. The split widens sharply with size: one observation put the same pair through a single venue at small size and ten venues at large size.

e17_perp_depth stores book shape rather than raw ladders, as cumulative resting notional within a band of mid. Where a spread is wider than a band, that band is correctly zero, which happens often on thinner markets.

Before you build on this

• Not every provider answers every request. Refusals and rate-limit skips are written as explicit error rows rather than omitted, so a provider that was unavailable is distinguishable from one that had nothing to offer. Check the error column before treating absence as meaning.
• One provider is queried on a subset of pairs. Its coverage is deliberately narrower than the others and should not be read as a market-wide view.
• Only some routers expose a leg breakdown. Rows from the others carry the venue name alone, which is why pool and swap_amount_raw are frequently null.
• Depth rows are snapshots at the observation interval, not a tick-level book. A move that reverses between observations is invisible.
• Depth covers a single venue, so it sizes that one book rather than the market. Treat it as one participant's view; a second venue would be a control and there is not one here.

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.

from huggingface_hub import snapshot_download
import pandas as pd, glob

path = snapshot_download("dataforge-labs/crypto-execution-costs", repo_type="dataset",
                         allow_patterns="e10_quote_benchmark/**")
df = pd.concat(map(pd.read_parquet,
                   glob.glob(f"{path}/e10_quote_benchmark/**/*.parquet", recursive=True)))

Coverage

e0_run_manifest lists every collection window with its poll counts and failure counts, and is published in full rather than windowed. Gaps between windows are real, cannot be filled in afterwards, and nothing here is interpolated.

License and contact

ODC-BY: use it freely, credit "DataForge (dataforge-labs)". Questions and requests for the full history via the discussions tab.