🤖 AI Summary
This study addresses the lack of transparency and reproducibility in Bitcoin on-chain economic research, which has been hindered by inconsistent definitions and proprietary methodologies used by commercial platforms. To overcome this limitation, the authors construct the first open-source, verifiable framework for native on-chain metrics by leveraging a local Bitcoin Core full node, augmented with a persistent spent output indexer and a deterministic data processing pipeline. The resulting dataset comprises dozens of standardized daily time series across key dimensions—including block production, transaction activity, supply dynamics, miner revenue, and UTXO age—each accompanied by precise definitions, comprehensive metadata, stable identifiers, open-source implementation code, and validation against existing indicators. This resource substantially enhances the reproducibility of empirical analyses and enables meaningful cross-study comparisons in cryptocurrency economics.
📝 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.