Open Bitcoin Metrics: Verifiable Full-Node-Derived Bitcoin Time Series for Economic Research
2026-07-03 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and Science
AI summaryⓘ
The authors created a new dataset called Open Bitcoin Metrics (OBM) that collects many important Bitcoin-related numbers all in one place. They got these numbers directly from the Bitcoin blockchain using a full node, making the data easy to check and trust. The dataset includes daily details about things like transactions, mining, fees, and supply, and comes with open-source code to help others use it. This work helps researchers and teachers study Bitcoin more accurately and reproducibly.
Bitcoinblockchainon-chain metricsfull nodeBitcoin Coretransactionsminer revenueUTXOBitcoin Days Destroyedhashrate
Authors
Diego R. Llanos
Abstract
Bitcoin research increasingly relies on on-chain indicators to study network activity, monetary issuance, transaction demand, miner incentives, coin-age behavior, and long-run monetary dynamics. However, many commonly used Bitcoin metrics are dispersed across commercial platforms, subject to heterogeneous definitions, or not fully reproducible from primary blockchain data. This manuscript introduces Open Bitcoin Metrics (OBM), a reproducible, full-node-derived dataset and reference guide for Bitcoin on-chain time series designed for economic and econometric research. The dataset provides documented daily series covering block production, block-space usage, transaction counts, supply, issuance, fees, miner revenue, mining difficulty, estimated hashrate, Bitcoin Days Destroyed, dormancy, liveliness, UTXO counts, spent output value, and related UTXO-age indicators. Metrics are reconstructed from a locally maintained Bitcoin Core full node, a persistent spent-output indexer, or deterministic transformations of previously generated OBM series. Each series is accompanied by open-source Python code, stable identifiers, explicit definitions, metadata, validation procedures, interpretive caveats, and comparisons with the closest publicly available metrics. The dataset is intended to support transparent empirical research, replication, teaching, and comparative analysis across monetary economics, financial economics, and blockchain studies.