feat: token risk assessment system for rug pull detection

[manlikeHB]

ML-Powered Token Risk Assessment & Rug Pull Detection System

📋 Overview

This PR implements a comprehensive machine learning-based system for detecting potential cryptocurrency rug pulls and assessing token risks in real-time. The system analyzes multiple risk factors, provides weighted risk scores, and delivers real-time alerts through various channels.

Related Issue

• Closes Rug Pull Risk Detector #41

🎯 Key Features

🧠 Machine Learning Risk Analysis

• Weighted scoring algorithm with 5 key risk factors
• Real-time anomaly detection using statistical analysis
• Confidence scoring based on data quality and historical patterns
• Risk level classification: LOW, MEDIUM, HIGH, CRITICAL

📊 Risk Factors Analyzed (with ML weights)

• Liquidity Withdrawal (35%): Detects sudden liquidity drops indicating potential exit scams
• Contract Changes (25%): Monitors ownership transfers and suspicious contract modifications
• Trading Patterns (20%): Identifies unusual volume spikes and price manipulations
• Holder Distribution (15%): Analyzes concentration risk from large holders
• Price Volatility (5%): Measures price stability and manipulation indicators

⚡ Real-Time Monitoring

• Continuous monitoring every 2 minutes for subscribed tokens
• WebSocket alerts for instant notifications
• Multi-channel alerting: Email, Webhook, Database
• Configurable risk thresholds per token or globally

🔍 Blockchain Analysis

• Smart contract event scanning via Etherscan API
• Suspicious pattern detection: ownership changes, large transfers, excessive approvals
• Historical event analysis for comprehensive risk assessment

Ready for Review ✅

This implementation provides a production-ready foundation for token risk assessment with room for future ML model enhancements and additional blockchain integrations.