What is on-chain analysis? Reading the public ledger in plain English
Every Bitcoin transaction is recorded in public. On-chain analysis is the practice of turning that record into numbers people can study — with clear rules about what the numbers can and cannot prove.
Photo: “British Money and magnify glass” by Images_of_Money, CC BY 2.0, via Flickr (edited: cropped and resized).
On-chain analysis means studying the data a blockchain records publicly — transactions, amounts, timestamps and addresses — and turning it into metrics about how coins move and are held. Bitcoin announces every transaction openly while keeping owners’ identities off the ledger [1], so analysts see activity, not people.
Key points
- 1A public blockchain records every transaction, so anyone can download and study the full history of coin movements.
- 2The ledger shows addresses and amounts, not names. Linking addresses to people or companies is always an estimate.
- 3Analysts turn raw transactions into metrics such as active addresses, exchange flows and realized price.
- 4On-chain metrics describe what happened on the ledger. They do not reveal intentions and they are not trading signals.
On this page
- What does “on-chain” actually mean?
- What data does a blockchain record?
- If the ledger is public, can analysts see who owns what?
- How does raw data become a metric?
- Which metrics do beginners meet first?
- What can on-chain analysis tell you — and what can it not?
- How do you start reading on-chain charts?
- What mistakes do beginners make here?
- Frequently asked questions
- The bottom line
- Sources
What does “on-chain” actually mean?#
“On-chain” describes anything written into the blockchain itself: a transaction that has been included in a block and is now part of the shared record every full node stores. Bitcoin’s design requires that transactions be announced publicly so that everyone can agree on a single history [1]. Because that history is public and permanent, it can be read by anyone — not only by the people who sent the coins.
On-chain analysis is the practice of reading that record systematically. Instead of looking at one transaction, an analyst looks at millions of them and asks aggregate questions: How many addresses were active today? How long have coins been sitting still? How much moved into wallets that belong to exchanges? The answers become metrics — numbers calculated the same way every day so they can be compared over time.
What data does a blockchain record?#
A Bitcoin transaction has at least one input and at least one output. Each input spends coins that an earlier output received, and each output then waits as an unspent transaction output (a UTXO) until a later transaction spends it [3]. When your wallet shows a balance, it is adding up the UTXOs your keys can spend. Every block also carries a timestamp, so each output has a known creation time.
That gives analysts four raw ingredients: amounts, addresses (the destinations written into outputs), timestamps and the links between outputs and the inputs that later spend them. Almost every on-chain metric is a clever combination of those four. Read how Bitcoin transactions work for the mechanics.
| Data point | Recorded on-chain? | Notes |
|---|---|---|
| Amount in each output | Yes, exactly | Down to one satoshi |
| Time a block was mined | Yes | Block timestamps are approximate |
| Which output an input spends | Yes, exactly | This is how coin age is measured |
| Owner’s name | No | Only revealed by off-chain information |
| Which addresses share an owner | No — estimated | Based on heuristics and labels |
| Why the coins moved | No | Any motive is an interpretation |
If the ledger is public, can analysts see who owns what?#
Not directly. The whitepaper compares Bitcoin’s privacy to a stock exchange tape: the public can see that someone sent an amount to someone else, but not who those parties are [1]. Bitcoin.org puts it the other way round: the system is not anonymous, because anyone can see the balance and history of any address, yet the person behind an address stays unknown until information is revealed elsewhere, for example during a purchase [4].
Analysts close part of that gap with heuristics — rules of thumb. The best known comes from the whitepaper itself: a transaction that spends several inputs usually reveals that those inputs had the same owner [1]. Data providers also tag addresses using information exchanges publish and their own clustering algorithms [5]. The result is useful, but it is an estimate, and it changes as new links are discovered.
How does raw data become a metric?#
From blocks to a chart
The cleaning step matters more than beginners expect. Because a UTXO must be spent in full, most payments create a change output that sends the leftover back to the payer [3]. Glassnode notes that change adds a lot of noise to metrics built on the number and size of outputs, so providers try to identify and adjust for it [6].
| Step | Value |
|---|---|
| UTXO spent (input) | 1.0000 BTC |
| Paid to Bob (output 1) | 0.3000 BTC |
| Fee to the miner | 0.0001 BTC |
| Change back to Alice = 1 − 0.3 − 0.0001 (output 2) | 0.6999 BTC |
| Raw output total = 0.3 + 0.6999 | 0.9999 BTC |
| Raw total ÷ real payment = 0.9999 ÷ 0.3 | ≈ 3.33× |
A naive count would report almost 1 BTC of “transfer volume” for a 0.3 BTC payment. Adjusted metrics try to strip the change out, but the adjustment is itself a guess about which output was change.
Which metrics do beginners meet first?#
Most on-chain dashboards group metrics into a few families. You do not need all of them; it helps to know what question each family answers.
| Family | Question it asks | Example on this site |
|---|---|---|
| Network activity | How much is the chain being used? | Active addresses |
| Capital flows | Are coins moving towards or away from exchanges? | Exchange netflow |
| Valuation | How does price compare with what holders paid? | Realized price |
| Holder behaviour | How long are coins held before moving? | HODL waves |
| Profit and loss | Are coins being spent at a gain or a loss? | SOPR |
Coin age deserves a special mention because it is unusual. Since every output is timestamped, analysts can measure how long each coin has gone without moving. Unchained Capital’s 2018 study of Bitcoin’s age distribution pointed out that this kind of chart cannot be built for traditional assets — only public blockchains keep the full record [7].
What can on-chain analysis tell you — and what can it not?#
Strengths and blind spots
Address counts are a good example of the limits. Coin Metrics describes active addresses as a popular proxy for the number of users, but warns that on chains where addresses and transactions are cheap or free, address counts can be trivially forged [8]. Exchange data has its own caveats: Glassnode says its reported exchange balances should largely be treated as lower bounds and can be revised after the fact [5]. Our guide to the limits of on-chain data covers these problems one by one.
How do you start reading on-chain charts?#
- Pick one question
For example: are more coins sitting still than a year ago? Choose the metric that answers that question, not the one with the most dramatic chart.
- Read the definition
Check exactly what is counted, which chain, and whether change outputs and exchange wallets are adjusted.
- Use a long view
Daily values are noisy. Compare a weekly or monthly average with the same metric’s own history.
- Cross-check
Look for a second, independent metric that should move the same way if your reading is right.
- Write down the limits
Note what the metric cannot see — labels, lost coins, off-chain trades — before drawing any conclusion.
What mistakes do beginners make here?#
- Treating addresses as people
One person can control hundreds of addresses, and one exchange address can hold coins for thousands of customers.
- Reading motives into movements
A large transfer might be a sale, a move to cold storage or an exchange reshuffling its own wallets. The ledger does not say which.
- Comparing providers’ numbers directly
Different labelling and cleaning choices produce different values for the “same” metric. Compare a metric with its own history from one provider.
- Using historical thresholds as rules
A level that coincided with a few past turning points is a pattern in a small sample, not a law of the market.
Frequently asked questions#
Is on-chain analysis legal?
Yes. It reads data that public blockchains publish to everyone by design. Bitcoin requires transactions to be announced publicly so the network can agree on one history [1].
Do I need to run a node to do on-chain analysis?
No. A node gives you the raw data, but most beginners start with charts from data providers. Just read each provider’s methodology notes, because their cleaning choices differ.
Does on-chain analysis work for every coin?
Only for chains whose data is public. Coin Metrics, for example, does not publish address metrics for privacy coins such as Monero [8]. Account-based chains like Ethereum also need different methods from UTXO chains like Bitcoin.
Can on-chain data predict the price?
No metric reliably predicts price. On-chain data describes what holders did; markets also respond to off-chain trading, news and leverage that the ledger never records.
What is the difference between on-chain and technical analysis?
Technical analysis studies price and trading volume reported by exchanges. On-chain analysis studies coin movements recorded on the blockchain itself.
The bottom line#
On-chain analysis turns a public ledger into measurements of activity, holding behaviour and flows. The raw data is exact; the labels, cleaning steps and interpretations layered on top are not. Read every metric as an estimate with a methodology behind it.
Next, see how a single ledger rule shapes many metrics in UTXO vs account model, then learn the most-cited valuation idea in realized price and cost basis.
Sources#
Grade A = primary source (regulator, protocol specification, client code, original author). Grade B = expert secondary source used for explanation only.
- ASatoshi Nakamoto. Bitcoin: A Peer-to-Peer Electronic Cash System, 2008.
- AJoseph Poon and Thaddeus Dryja. The Bitcoin Lightning Network: Scalable Off-Chain Instant Payments, 2016.
- Abitcoin.org developer documentation. Developer Guide: Transactions, 2026.
- Abitcoin.org. Some things you need to know, 2026.
- BGlassnode Docs. Exchange Data Transparency Notice, 2026.
- BGlassnode Docs. UTXO vs. Account-Based Chains, 2026.
- AUnchained (Dhruv Bansal). Bitcoin Data Science (Pt. 1): HODL Waves, 2018.
- BCoin Metrics Data Knowledge Base. Active Addresses (network data definitions), 2026.
- AEuropean Supervisory Authorities (EBA, ESMA, EIOPA). EU financial regulators warn consumers on the risks of crypto-assets, 2022.
Keep reading
On-chain data limitations: seven blind spots every beginner should know
Blockchain data is exact about movements and silent about people, motives and anything that happens off the chain. Knowing the blind spots is what separates careful analysis from chart-reading.
UTXO vs account model: two ways a blockchain keeps score
Bitcoin tracks coins like banknotes; Ethereum tracks balances like a bank account. The choice decides what analysts can measure — especially how old coins are.
How Bitcoin transactions work, from your wallet to a block
A Bitcoin payment is not a balance moving between accounts. It spends earlier outputs and creates new ones. Here is the anatomy of a transaction, a worked example with change, and the checks every node runs.