Recently, a friend asked me how to start doing on-chain query and analysis, and I think this is a topic worth discussing in depth. Honestly, people who can understand on-chain data really do have an advantage in the market, because this data is completely transparent and tamper-proof—more real than any press release.

Let’s first talk about what on-chain data actually is. Simply put, it’s all transaction and block information stored on the blockchain, including token transfers between wallets, holdings, gas fees, miner rewards, and more. Ever since smart contracts came along, we can also see interactions between wallets and contracts—such as trading on DEXs, lending, minting NFTs, etc. The magic of this data is that you can track the movements of whales and see what “smart money” is doing, which is extremely helpful for investment decisions.

But what I want to say is that on-chain querying may look easy, but in reality it requires some knowledge accumulation. You can’t just look at data from a single tool—you need to compare multiple sources and cross-verify to reach reliable conclusions. Especially when analyzing new-chain projects, the data sources are limited, and sometimes the numbers the project itself provides may not be accurate enough due to marketing reasons—so you should verify them again on blockchain explorers. Also, market behavior is constantly changing, so the data needs to be updated regularly—you can’t treat data from three months ago as if it’s precious.

So which on-chain indicators are worth paying attention to? TVL is a very important metric; it reflects how popular a DeFi protocol is. Wallet information for development teams, miners, and investors is also public—you can see whether they’re accumulating or selling off. Exchange deposit and withdrawal data is also crucial. Large deposits into CEXs may imply an incoming sell-off; conversely, large withdrawals can be a positive signal. The movement of stablecoins is even more interesting: being minted and deposited into exchanges suggests optimistic market sentiment, while the opposite indicates pessimism. Metrics like the number of active wallet addresses, the amount of tokens held by holders, and token distribution can all help you judge a project’s health.

When it comes to specific tools, I recommend that beginners start with these. Defillama is my top recommended entry tool: it focuses on TVL data and also provides CEX deposit/withdrawal statistics. Token Terminal focuses on a project’s financial metrics, especially revenue data—applying a stock-analysis way of thinking to projects. Lunarcrush can analyze a token’s social buzz, including Twitter mention counts and what KOLs are discussing. CryptoSlam specializes in NFT data, letting you see trading volume and floor price trend.

On-chain query also requires blockchain explorers—such as Ethscan, Bscan, and Solscan—because each chain has its own corresponding explorer tool. When you look at these tools, pay attention to parameters like transaction count and daily transaction volume. Generally speaking, the more users and transactions a chain has, the more promising it tends to be.

If you want to go deeper into on-chain query and analysis, you can check out Santiment. The data on this platform has been cleaned and processed, so it’s relatively reliable, and it also offers social tools to analyze public sentiment. Watchers has also been getting quite popular recently—I even wrote an introductory article about it. Arkham claims it can surpass Nansen. It’s powerful and free, and you can customize buy/sell amounts and search within a specific time range.

One level up, Nansen is a tool I often use to track smart money, but it requires more experience to use effectively—otherwise it’s easy to get overwhelmed by data. Dune Analytics uses SQL to query on-chain data and requires some programming foundation. CryptoQuant and Glassnode are both expert tools dedicated to analyzing BTC. CryptoQuant provides detailed data such as miners and holders, while Glassnode introduced innovative indicators like SOPR and HODL Waves.

Finally, my advice is: don’t be intimidated by tools. The core of on-chain data analysis is understanding the behavior logic of whales and smart money—tools are only supplementary. And since on-chain data is transparent to everyone, you also need to be careful not to get fooled by sharks. It’s best to follow some professional on-chain analysis teams, such as The Datafi and The DataNerd—learn from their perspectives so you can pick it up faster.