1/4 Let me talk about what I’ve been working on recently. I’ve been experimenting with GMGN’s API, mainly to solve my own trading issues. So I built a system that can query common profitable addresses across multiple CAs and analyze the chip (token holder) distribution of a CA’s main addresses. As shown on the homepage (see image), just enter the CA you want to query and the system will automatically detect it. Currently it only supports BSC and SOL.Official site: https://t.co/HM1T5LEEjB .It might crash if too many people are using it at once because the API has concurrency limits. If your query fails, just wait a bit and try again.
2/4 Main features are as follows:After you enter a CA to query, the system will automatically analyze the health of the addresses holding that CA. This includes: number of main (whale) addresses, their holding percentage, number of profitable addresses, number of low-frequency trading addresses and their holdings, number and holdings of addresses that only buy and never sell, etc. The AI then combines all this data to generate a preliminary analysis of the CA’s main force/smart money situation, which you can use as a reference for trading. In the future, it will also analyze the cost basis of early addresses, etc. (still in development).
3/4: As long as a user has queried a certain CA, the AI system will automatically record all address-related chip structures, data, address behavior patterns, etc. for that CA. It then searches through the big data to find which address groups in this CA have significant overlaps (common addresses) with address clusters from previously queried CAs in the database. By analyzing the approximate profitability of these overlapping address groups, the system can assess and monitor their likely selling (exit) ranges, identify which small clusters of addresses are trading intensively, and then proceed to real-time monitor their average entry cost and the percentage of tokens they have already sold.
4/4: The more everyone uses and queries the system, the more powerful it will become. This will make it much easier for all users to review historical data and pick out strong main addresses.Also welcome friends who are interested in AI development and fun trading-related applications, or those who have relevant products and strategies, to DM me privately. Let’s build a deep discussion group together.
1/4 Let me talk about what I’ve been working on recently. I’ve been experimenting with GMGN’s API, mainly to solve my own trading issues. So I built a system that can query common profitable addresses across multiple CAs and analyze the chip (token holder) distribution of a CA’s main addresses. As shown on the homepage (see image), just enter the CA you want to query and the system will automatically detect it. Currently it only supports BSC and SOL.Official site: https://t.co/HM1T5LEEjB .It might crash if too many people are using it at once because the API has concurrency limits. If your query fails, just wait a bit and try again.
2/4 Main features are as follows:After you enter a CA to query, the system will automatically analyze the health of the addresses holding that CA. This includes: number of main (whale) addresses, their holding percentage, number of profitable addresses, number of low-frequency trading addresses and their holdings, number and holdings of addresses that only buy and never sell, etc.
The AI then combines all this data to generate a preliminary analysis of the CA’s main force/smart money situation, which you can use as a reference for trading. In the future, it will also analyze the cost basis of early addresses, etc. (still in development).
3/4: As long as a user has queried a certain CA, the AI system will automatically record all address-related chip structures, data, address behavior patterns, etc. for that CA. It then searches through the big data to find which address groups in this CA have significant overlaps (common addresses) with address clusters from previously queried CAs in the database. By analyzing the approximate profitability of these overlapping address groups, the system can assess and monitor their likely selling (exit) ranges, identify which small clusters of addresses are trading intensively, and then proceed to real-time monitor their average entry cost and the percentage of tokens they have already sold.
4/4: The more everyone uses and queries the system, the more powerful it will become. This will make it much easier for all users to review historical data and pick out strong main addresses.Also welcome friends who are interested in AI development and fun trading-related applications, or those who have relevant products and strategies, to DM me privately. Let’s build a deep discussion group together.