Mark contract and user addresses from Etherscan label cloud.
Scraping etherscan is used by Clustering Ethereum Addresses and the jupyter code is on github.
There are about 560 top level labels like 0x protocol and Aave further divided into Accounts and Tokens each having subcategories Main and Others. Leaf label examples: Aave: LEND To AAVE Migrator for an Account and Aave interest bearing USDC (aUSDC) for a Token.
At a minimum the scraper should produce a three column table with
- top label
- leaf label
- contract address
Which will identify a contract like aave.aUSDC and result in an event table like aave.aUSDC_evt_Transfer.
Open questions:
- what to do with Accounts and Tokens designations? Ignore?
- what to do with Main and Others designations? Scrape only Main?
- save all the valuable info like token ticker, issuer data and links to etherscan pages into label description json?
Mark contract and user addresses from Etherscan label cloud.
Scraping etherscan is used by Clustering Ethereum Addresses and the jupyter code is on github.
There are about 560 top level labels like
0x protocolandAavefurther divided into Accounts and Tokens each having subcategories Main and Others. Leaf label examples:Aave: LEND To AAVE Migratorfor an Account andAave interest bearing USDC (aUSDC)for a Token.At a minimum the scraper should produce a three column table with
Which will identify a contract like
aave.aUSDCand result in an event table likeaave.aUSDC_evt_Transfer.Open questions: