Crypto On

Daily on-chain fundamentals for Bitcoin AND Ethereum in one clean panel - computed directly from the public-domain blockchains (via Google BigQuery public datasets, not a restrictive wrapper API). BTC: transaction count, block count, block size, fees, on-chain volume, average transaction value, outputs created / inputs spent, and ACTIVE ADDRESSES. ETH: transaction count, gas used, average gas price (gwei), ETH transferred, active addresses, and ETH burned (EIP-1559). Active-address counts and on-chain throughput are the metrics analytics vendors (Glassnode, Coin Metrics, DefiLlama, Blockchain.com) sell behind paywalls and forbid you to resell - but the ledger itself is public-domain data, so these derived aggregates are freely redistributable. That licence gap is the moat: the clean, resellable on-chain-activity layer.

What's included

• Two chains, one panel: BTC + ETH daily on-chain fundamentals (2013/2015→present)
• BTC: tx/block counts, block size, fees, on-chain volume, avg tx value, UTXOs created/spent, active addresses
• ETH: tx count, gas used, avg gas price (gwei), ETH transferred, active addresses, ETH burned (EIP-1559)
• Active-address & throughput metrics are the paywalled layer (Glassnode/Coin Metrics) - here, resellable
• Computed from the public-domain blockchains via Google BigQuery public datasets
• Public domain (the ledger is public-domain data); derived aggregates freely redistributable

Schema

Sample & preview

Every purchase ships as Apache Parquet with a data dictionary and the full QC report. A free sample (first rows + schema) is downloadable here - confirm fit before you buy. Source: On-chain (BigQuery) (redistributable open data; attribution passes through - see our Licence).

import pandas as pd
df = pd.read_parquet("crypto-network-fundamentals_sample.parquet")
df.info()   # typed columns, gap-aware, ready to join