Module 6: On-Chain Analysis
What On-Chain Analysis Is
Chapter 1 of 12 · about 5 minutes
1.1 A market with glass walls
Node had spent years investing in stocks. Company insiders had to report their trades, but with delays. Big funds disclosed holdings every quarter, long after the fact. Most of what happened in the market was invisible.
Then Hash showed Node a dashboard tracking how much bitcoin had moved onto exchanges in the past hour, how long coins had been held before being sold, and the average price at which every bitcoin last moved.
"You can see all of this?" said Node. "Live?"
"Every transaction on a public blockchain is public," said Hash. "Most of the world's financial system is a building with no windows. Crypto is a building with glass walls. You can't always tell who's inside each room, but you can see what's moving."
1.2 What on-chain analysis is
On-chain analysis is the study of blockchain data to understand how participants behave: who holds what, how long they hold it, where funds are moving, and what that might mean for supply, demand, and prices.
It sits alongside the other methods in this series:
| Method | Main data | Main question |
|---|---|---|
| Technical analysis (Module 4) | Price and volume on exchanges | What are traders doing in the market? |
| Fundamental analysis (Module 5) | Tokenomics, revenue, documents | What is this asset worth? |
| On-chain analysis (this module) | Transactions and balances on the blockchain | How are holders and users actually behaving? |
On-chain analysis is especially powerful for assets like Bitcoin, where the full history of every coin is visible, and for DeFi, where protocol activity happens entirely on-chain.
1.3 What you can see
Because blockchains are public ledgers (Module 1, Chapter 3), anyone can see:
| Data | Example |
|---|---|
| Every transaction | Amounts, sender and receiver addresses, time |
| Every address's balance | How much any address holds at any moment |
| Coin history | When each coin last moved (especially on Bitcoin) |
| Smart contract activity | Deposits, borrows, swaps, liquidations, governance votes |
| Token supply | Issuance, burns, and transfers |
| Network activity | Fees, active addresses, transaction counts |
All of this is available in real time and stretches back to each blockchain's beginning.
1.4 What you can't see
On-chain data has important blind spots.
Identities. Addresses are pseudonymous (Module 1, Chapter 15). Analysts infer who owns addresses using patterns and labels, but these inferences can be wrong.
Activity inside exchanges. When users trade on a centralised exchange, those trades happen on the exchange's internal ledger, not on-chain (Module 1, Chapter 7). On-chain data only shows funds moving in and out of the exchange.
Intentions. You can see that someone moved coins to an exchange. You can't see whether they plan to sell, use them as collateral, or simply reorganise wallets.
Derivatives positions. Most futures and perpetual trading happens on centralised exchanges, not on-chain. That data comes from exchanges instead (Module 4, Chapter 15).
Off-chain holdings. ETFs, custodians, and companies hold coins in wallets that may or may not be identified, and their internal activity isn't visible.
"So it's glass walls, but the people inside are wearing masks, and some rooms have curtains," said Mint.
"That's a fair upgrade to the analogy," said Hash.
1.5 Raw data versus metrics
Raw blockchain data is enormous and hard to interpret directly. Most on-chain analysis uses metrics: calculations built on top of raw data that summarise behaviour.
For example, "exchange netflow" adds up all coins entering and leaving addresses identified as belonging to exchanges. "Realized price" estimates the average price at which coins last moved. These metrics are produced by analytics platforms, each with its own methods, labels, and assumptions.
This is important. Two platforms can show different numbers for the same metric, because they've identified different addresses as exchanges, or calculated things slightly differently. Understanding how a metric is built matters as much as the number itself.
1.6 Where the data comes from
| Source | What it offers | Trade-offs |
|---|---|---|
| Block explorers (Module 3, Chapter 6) | Individual transactions and addresses | Hard to see big-picture trends |
| Analytics platforms | Pre-built metrics, charts, and labels | Some data is paid; methods vary |
| SQL-based query platforms | Write your own queries on decoded blockchain data | Requires learning basic SQL |
| Protocol dashboards | Data about a specific protocol | May present the protocol favourably |
| Running your own node | Complete raw data, fully independent | Technical and resource-intensive |
Chapter 11 shows how to use dashboards and write simple queries.
1.7 What on-chain analysis is good for
Understanding supply dynamics. Are long-term holders selling or accumulating? Are coins leaving exchanges?
Gauging market phases. Are most holders in profit or loss? Has the market historically been overheated or deeply undervalued at similar levels?
Tracking capital flows. Is money entering crypto through stablecoins? Which chains and protocols are gaining users?
Due diligence. Are a token's insiders moving tokens to exchanges? Is a protocol's usage real? (Module 5)
Security and investigations. Tracing stolen funds after hacks (Module 2, Chapter 14).
1.8 What it's not
On-chain analysis isn't a crystal ball. Many on-chain signals have worked in past market cycles, but the market keeps changing. More activity now happens through ETFs, custodians, and Layer 2 networks, which can change how on-chain metrics behave. Signals that worked in earlier cycles may weaken as more people watch them.
"So it's another source of evidence, not a source of certainty," said Node.
"Same rule as every module," said Hash. "Evidence, weighed carefully, combined with other evidence."
1.9 A note on privacy and ethics
On-chain analysis can reveal a lot about individuals if their addresses are linked to their identities. This module teaches you to analyse market behaviour, not to track or expose private people. Using on-chain data to harass, threaten, or reveal the identities of individuals is harmful, and in many places illegal. Respect people's privacy, even when the data is public.
1.10 Try it yourself
Open a free analytics platform that shows Bitcoin on-chain metrics. Find three metrics you don't yet understand and write down their names. As you go through this module, come back and see if you can explain each one.
Then find the total amount of bitcoin held by addresses labelled as exchanges, and compare it with the amount from a year ago.
Key takeaways from this chapter
- On-chain analysis studies blockchain data to understand how holders and users behave.
- Public blockchains reveal transactions, balances, coin history, smart contract activity, and supply in real time.
- Identities, exchange-internal trades, intentions, most derivatives, and many off-chain holdings aren't visible.
- Most analysis relies on metrics built by platforms, whose methods and labels can differ.
- On-chain data helps with supply dynamics, market phases, capital flows, due diligence, and investigations.
- Past on-chain signals don't guarantee future results, especially as market structure changes.
- Use on-chain data to analyse markets, not to track or expose individuals.