#metrics

The long-run on-chain economics of Bitcoin as time series, served from the public blockchain.com charts feed. Where snapshot APIs report the chain state right now, this is the history: how the hash rate, mining difficulty, miners' revenue, daily transaction count, transaction fees, market price, market capitalisation, circulating supply, mempool size, average block size, estimated on-chain transaction volume, daily unique addresses, UTXO count and median confirmation time have moved over months and years. The metric endpoint returns one metric's full daily time series over a chosen window (30 days to all-time) with summary statistics — first, last, change, percent change, minimum, maximum and average. The latest endpoint returns a metric's current value with its change versus the previous reading and versus 30 days ago. The metrics endpoint lists every available metric with its unit and category. This is the historical and charting view of Bitcoin's network economics — distinct from the live mempool-snapshot, the multi-chain network-stats and the price-feed APIs in the catalogue. Live, no key on the upstream, nothing stored.

api.oanor.com/bitcoinmetrics-api

Live Bitcoin on-chain economics and network-activity statistics, built on the open blockchain.com dataset — the macro on-chain layer, not raw address or mempool lookups: a live network snapshot (24h transaction count and USD volume, hash rate, market price and cap, total mined supply, miners' revenue), the historical time series of any curated on-chain metric (active addresses, transaction volume, UTXO-set size, mempool size, miner revenue, fees and more), the catalog of available metrics, and Bitcoin's issuance state (total mined, share of the 21M cap, current block reward and estimated next halving).

api.oanor.com/bitcoinstats-api

Classifier-evaluation maths as an API, computed locally and deterministically. The confusion endpoint turns the four cells of a binary confusion matrix — true and false positives and negatives — into the full metric suite: accuracy, precision, recall (sensitivity), specificity, the F1 score, the Matthews correlation coefficient (robust to class imbalance), balanced accuracy, negative predictive value, the false-positive and false-negative rates and the prevalence. The diagnostic endpoint applies Bayes' theorem to a medical or screening test: from its sensitivity, specificity and the prevalence (pre-test probability) it gives the positive and negative predictive values, the positive and negative likelihood ratios and the diagnostic odds ratio. The fbeta endpoint computes the Fβ score from precision and recall (or from the raw counts) for any β — β = 1 is F1, larger β weights recall, smaller β weights precision. Metrics whose denominator is zero are returned as null rather than erroring. Everything is computed locally and deterministically, so it is instant and private. Ideal for machine-learning, data-science, medical-testing and analytics app developers, model-evaluation and screening tools, and statistics education. Pure local computation — no key, no third-party service, instant. Live, nothing stored. 3 endpoints. This is classifier evaluation; for descriptive statistics and regression use a statistics API and for hypothesis tests an inference API.

api.oanor.com/classifier-api