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Bitcoin Data GuideBitcoin Reference

On-Chain Analytics

Coin Days Destroyed

Coin days destroyed weights each spent output by how long it sat idle before it moved. The idea is that a coin held for years and then spent carries more information than a coin that moves every day. It is a reasonable idea, and it is still a heuristic.

Last reviewed 2026-09-21Source: Coin Metrics coin days destroyed methodology; Bitcoin Core UTXO setNo figure is quoted here. The measure is a heuristic built on the ledger's output ages, not a direct observation of holder intent.

The definition

For each output that is spent, the measure multiplies the number of coins in the output by the number of days since the output was created. The result is a coin-day figure for that spend, and summing the figures across all spends in a period gives the coin days destroyed for that period. An output of ten coins that sat for a hundred days contributes a thousand coin days when it moves.

The intent is to distinguish between activity that reflects a change in holder behaviour and activity that does not. A coin that moves every day contributes little each time it moves, because the holding period is short. A coin that has not moved for years contributes a large amount when it finally does, which is offered as evidence that a long-term holder has decided to act.

The measure is usually presented as a series, and the interesting events are the spikes. A large spike means a substantial quantity of old coins moved in a short period, which is the kind of event that attracts attention. A quiet series means the old supply is sitting still.

Why it is a heuristic

The measure assumes that the age of an output is a proxy for the conviction of its holder. That assumption holds often enough to be useful and fails often enough to matter. An exchange that consolidates old outputs will produce a large coin-days-destroyed figure without any holder changing their mind, because the coins were never held by the exchange in the sense the metric assumes.

The measure also cannot see intent. A coin that moves after ten years might be sold, might be moved to a new wallet for security, or might be part of a custody migration. The ledger records the movement and not the reason, so the metric captures the former and infers the latter. The inference is the part that requires judgement.

There is a further subtlety in the treatment of change. When a transaction spends an old output and creates a new one back to the sender, the old output's coin days are destroyed and the new output starts its own clock. A wallet that periodically moves its own coins will therefore destroy coin days repeatedly without any economic activity, which is another way the measure can be moved by behaviour that is not what it is trying to capture.

Reading the measure

The measure is most informative when it is read alongside the price and the broader flow data. A spike in coin days destroyed during a price rise is consistent with long-term holders taking profit, and a spike during a decline is consistent with capitulation. Neither reading is established by the metric alone, because the same spike could be produced by an exchange reshuffle.

The measure is least informative when it is treated as a signal. A single spike does not establish that a particular group of holders acted, and it does not predict what happens next. The limits of blockchain analysis page sets out why a metric built on attribution is an inference rather than an observation.

The related measure of realised capitalisation, covered on the realised capitalisation page, shares the same dependence on the ledger's record of when outputs moved. The two are often read together, and they share the same limitation: the ledger records movement, not motive.

Sources and references

The description of the measure and its construction is taken from the published methodology of the providers that compute it, and from the ledger structure it reads.

  • The metric and its methodology. Coin Metrics, Coin Days Destroyed: documents the definition, the holding-period weighting and the treatment of spent outputs.
  • The output model the measure reads. Bitcoin, Bitcoin Developer Guide — Transactions: describes how outputs are created and spent, which is what the measure counts.
  • The attribution assumptions behind ledger metrics. Meiklejohn et al., A Fistful of Bitcoins: characterises the clustering assumptions that on-chain metrics depend on.

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