Mr MANG

Only after deeply experiencing Chainbase's on-chain analysis tools can one truly understand why it has become the "on-chain radar" for institutional players. Unlike the superficial information piling of ordinary data platforms, Chainbase's core competitiveness lies in the structured reconstruction of on-chain data.

For example, its original "funding map" feature can associate scattered wallet addresses to the same entity through address clustering algorithms. Even funds that have been laundered multiple times through mixers can trace their origins through the interaction traces of smart contracts, making it a powerful tool for identifying the long-term layouts of major investors.

Recently, the upgraded DeFi module has been a focus of testing: the newly added "liquidity health score" system is quite interesting. It not only calculates the absolute value of TVL but also combines 12 dimensions such as the volatility of the staking rate, deviation from liquidation thresholds, and cross-chain asset interconnectivity to generate a dynamic risk coefficient. For instance, a leading lending protocol may have a surface TVL of 500 million dollars, but Chainbase's data shows that the proportion of stablecoin collateral has plummeted by 20%, with alternative tokens mostly being highly volatile altcoins. This underlying liquidity risk is something that ordinary dashboards cannot capture at all.

From a developer's perspective, its API ecosystem deserves high praise: the batch address label query interface supports 100,000 calls per second and comes with an anti-scraping mechanism. Previously, using other tools to scrape 100,000 ETH millionaire list addresses would take 3 hours; now, Chainbase's SDK synchronizes directly and can also include historical interaction labels for each address (such as DEX traders, NFT minters, stablecoin market makers), improving data cleaning efficiency by at least 80%.

It is said that their next step is to launch the "on-chain sentiment index," which will predict market hot spots by analyzing unstructured data such as the frequency of smart contract deployments, abnormal fluctuations in gas fees, and the speed of new address creation. This evolution from "data presentation" to "decision support" may be the key to Chainbase attracting a large number of quantitative teams.

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