Label Malicious tokens on Uniswap V3. Moreover, we provide Machine Learning algorithm to predict potential rug pulls.
Uniswap, like other DEXs, has gained much attention this last year because it is a non-custodial and publicly verifiable exchange that allows users to trade digital assets without trusted third parties. However, its simplicity and lack of regulation also makes it easy to execute initial coin offering scams by listing non-valuable tokens. This method of performing scams is known as rug pull, a phenomenon that already existed in traditional finance but has become more relevant in DeFi. In this repo you will find a follow up of the work done in Do not rug on me. The main purpose of this work in progress is to label and predict rug pulls on Uniswap V3 on any EVM compatible network. Using the tools provided, you can download all the important data related to a token. Moreover, the model trained in this repo will allow users to protect themself from potential malicious and scam tokens!
How it's made
This project use polygonscan/etherscan and infura nodes. With these endpoints, we have been able to download all the necessary data to compute important features and train our Machine Learning model. Afterwards, we have used python and XGBoost libraries to train the model obtaining very decent results.