dataforge-labs/fiat-onramp-pricing

Fiat on-ramp pricing

What it costs a retail buyer to turn a card payment into crypto, quoted at the moment they would have bought.

An on-ramp quote is computed per request against a rate, a spread and a fee schedule that all move. Nobody keeps the quotes, so the only record of what a buyer was actually offered is one made as it was offered.

Contents

name	one row is
e12_onramp_quotes	one provider quoting one fiat amount into one asset: rate, fees, and what the buyer receives

Reading it

The amount received is the figure that matters. Providers split their margin differently between the exchange rate and the explicit fee, so comparing on the advertised fee alone will mislead you, and comparing on rate alone will mislead you in the other direction.

Rows carry the quoted rate and the fee separately as well, so the split itself can be studied.

Before you build on this

• A small number of providers, and they are the ones that quote without an API key. That is a selection, not a market: the providers requiring a key are absent and are not a random subset.
• Quotes are indicative. A real purchase adds identity checks, card-issuer behaviour and limits that a quote does not reflect, and any of those can change the outcome or block it entirely.
• Refusals 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 error before reading an absence as a decline.
• Coverage per asset changes as providers list and delist pairs.

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 2026-08-26 to 2026-09-01, 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/fiat-onramp-pricing", repo_type="dataset",
                         allow_patterns="e12_onramp_quotes/**")
df = pd.concat(map(pd.read_parquet,
                   glob.glob(f"{path}/e12_onramp_quotes/**/*.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.