ML Prediction of new ERC20 Marketcap

https://github.com/ennriqe/ETHDam-Slither-Token-Contract-to-Predict-Success/blob/master/README.md

Project Overview

In this project completed at ETHDam 2024, I predict the success of newly minted ERC20 tokens on Ethereum using only features extracted from their smart contracts via Slither. Success is defined as reaching a 100k market cap at any point within the same month they were created. I employ a LightGBM model to predict this binary success variable, achieving a model significantly better than random chance.

Performance Metrics

• Accuracy: 63%
• Precision: 69%
• Recall: 80%
• F1 Score: 74%

These metrics are averaged over training using cross-validation across 10 folds to avoid overfitting to the training set.

Data Collection

I collect data by querying the Ethereum blockchain using Web3.py and an RPC. I specifically look for newly minted ERC20 tokens in the first few days of March 2024. I record the highest market capitalization these tokens achieve within the month. Prices are derived from BitQuery for the pool against WETH, and Web3.py is used to determine the supply.

Feature Extraction

I analyze the smart contracts of these tokens using Slither to extract the following features:

• Number of state variables used across all contracts of each token
• Number of contracts each token includes
• Uniqueness of the names of these state variables and contracts, using TF-IDF to compare the state names of each token against a corpus of state variables of all other tokens considered.