dataforge-labs/fiat-onramp-pricing

Fiat on-ramp pricing

Indicative fiat-to-crypto pricing observations from public provider endpoints. The panel includes Mercuryo buy and sell quotes, plus Ramp reference prices and fee bounds.

Contents

Using the data

Separate rows by provider, kind and side before comparing prices. A Ramp reference price and fee range should not be treated as a complete, amount-specific purchase quote.

effective_rate is expressed as fiat currency per whole coin and is derived from returned amounts. The panel retains the quoted rate and explicit fee separately. Mercuryo's fee inclusion in returned amounts is not fully documented, so inspect effective_rate, rate_net_of_fee and the underlying amount fields rather than assuming one represents the final charge.

Compare like-for-like currencies, assets, amounts and observation times. Account for both sides' fees when evaluating a buy-and-sell calculation.

Limitations

• —Coverage is limited to endpoints accessible without a customer API key. It is not a survey of the full on-ramp market.
• —Quotes are indicative. Identity checks, payment method, issuer charges, limits and price changes can affect an actual purchase or sale.
• —Failed requests are retained in error. They should not be interpreted as zero prices or successful quotes.
• —Provider and asset coverage can change over time. These records alone do not demonstrate executable arbitrage.

Files and access

Data is stored as Parquet files under table_name/YYYY/MM/, with partitions for collection windows. Each measurement table has a fixed 7-day sample beginning at its configured collection start date. The sample windows in this repository span 2026-08-26 to 2026-09-01. Availability within each window depends on successful collection. The public sample dates remain fixed as additional history accumulates privately. Contact DataForge through the discussions tab to enquire about additional history.

Load a table

Install datasets and pandas to run this example. The train split contains all observations in the selected table; it is not a predefined modelling split.

from datasets import load_dataset

data = load_dataset("dataforge-labs/fiat-onramp-pricing",
                    "fiat_onramp_quotes", split="train")
df = data.to_pandas()

Coverage

collection_runs records collection windows, poll counts and failures. It is published in full and may cover dates beyond the fixed data sample. Collection gaps are not interpolated. Use this table together with measurement timestamps and error fields to assess coverage.

License and contact

The public sample is published under ODC-BY. Attribute it to "DataForge (dataforge-labs)". For questions about the data or access to additional history, open a discussion in this repository.

<details> <summary>Earlier file paths</summary>

Each table is stored under a directory with the same descriptive name. The file contents and date partitions are unchanged. Scripts using an earlier directory name should use the corresponding table name below, or pin downloads to revision before-folder-rename-20260915 to access the original layout. Internal collector IDs are retained in the private archive and may appear in raw coverage records.

</details>