How to read on-chain data: what blockchain metrics really show
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What the guide covers
CryptoSlate has published an intermediate-level guide on how to read on-chain data, written by Andrej Gjorgievski and updated on September 16, 2026. On-chain data is the information recorded in a blockchain's ledger, such as transactions, balances, fees and block activity.
The guide explains how those raw records are turned into metrics through formulas, time windows and address labels, and why the results still need chain-specific context before they can be trusted.
Key takeaways
- On-chain data comes from transactions, balances, blocks, fees and state changes recorded by a blockchain.
- It can reveal activity and supply movement that price charts alone do not show.
- One address is not one person. Labels can be incomplete, and metric methods can differ across chains and providers.
- Metrics such as active addresses, exchange flows and realized value depend on definitions, address clustering and price sources, not just raw facts.
From raw ledger records to interpretation
The guide describes three data layers, followed by a fourth interpretation step. Raw ledger observations include transaction identifiers, block numbers, timestamps, sending and receiving addresses, transferred values, fees and contract events. Bitcoin's developer documentation describes its blockchain as an ordered, timestamped public transaction ledger, while Ethereum's block documentation explains how ordered transactions update global state.
Labeled data attaches an identity or category to an address, such as exchange, bridge, fund, miner or protocol treasury. The address is public, but the label may come from public disclosures, transaction patterns or provider research.
Derived metrics combine observations through a formula. Daily active addresses, realized value, supply in profit (units whose last movement was at a price below the current one) and exchange net flow are examples. The guide notes that a formula can be transparent while assumptions about labels or chain behavior remain uncertain. A final interpretation step asks what changed, why it may matter and what else could explain it.
Why Bitcoin and Ethereum need different methods
Bitcoin uses unspent transaction outputs, or UTXOs. A transaction consumes earlier outputs and creates new ones, and a wallet may control many addresses and outputs. Because coins moved can include change returned to the sender, the guide says the visible transferred value may overstate the actual economic payment.
Ethereum uses an account-based system. Accounts carry balances and a nonce that counts transactions from the account, while smart contracts store code and state. One transaction can trigger many token transfers and internal calls, so a metric that counts only top-level transactions can miss contract activity, while a metric that counts every event can count one user action many times.
This difference affects cross-chain comparisons. Address activity, transfer count, fees and realized-value calculations each need a method suited to the chain. Coin Metrics' realized capitalization methodology is cited as an example that explicitly uses different last-activity logic for UTXO and account-based assets.
What activity and supply metrics actually show
Activity metrics try to summarize how much a network is being used. Common fields include transaction count, active sending or receiving addresses, transfer value, block space used, fees paid, smart-contract calls and new addresses.
Each field needs a definition. An exchange can move funds among its own wallets and create activity without a new user. One person can use many addresses, and one contract address can serve many people. Fees can indicate competition for block space, but the guide says higher fees are not unambiguously positive. They may reflect genuine demand, congestion, a short-lived token launch, spam or an unusual event, so analysts should compare several periods and check which applications generated the activity.
Supply measures include balances held by long-inactive addresses, balances by cohort, concentration among top addresses, staked supply and newly issued units. A top-address table can be misleading, because one exchange address may custody funds for many customers, a bridge contract may lock assets that correspond to representations on another chain, and a burn address may hold units no one can spend. Entity-adjusted analysis tries to cluster related addresses, but clustering methods can create both missed links and false links.
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Exchange flows and realized value
Exchange inflow usually estimates how much crypto moved into addresses labeled as centralized exchanges, while outflow estimates movement away from those addresses. Net flow subtracts outflow from inflow for a period.
The common interpretation is that inflows may increase supply available for sale and outflows may reflect custody or longer-term holding. The guide calls this only a hypothesis. An inflow can support collateral, settlement, market making or an internal exchange move, and an outflow can go to another exchange, a lending platform or a custodian rather than long-term storage. Label coverage also changes over time: a new deposit address may be missed until identified, exchanges can reorganize wallets, and transfers between exchanges can count as one outflow and another inflow without changing aggregate investor intent.
For realized value, the guide contrasts traditional market cap, which applies the current price to circulating supply, with realized capitalization, which assigns different units a value based on the market price when they last moved. Analysts use it to estimate an aggregate on-chain cost basis. The metric is useful because old units are not valued at today's price.
What is still unclear
The supplied text cuts off mid-sentence while explaining the limits of realized capitalization. The guide's table of contents lists further sections on stablecoin and bridge data, the limits of on-chain analysis, an on-chain analysis workflow and frequently asked questions, but the visible source material does not include the text of those sections.
Why this matters
On-chain data can show network activity and supply movement that price charts alone do not show. But the guide stresses that metrics depend on definitions, address labels and chain-specific calculations, so a number is only as reliable as the method behind it. Readers should check what a metric actually measures before treating it as evidence.
Sources
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