AI-Powered Autonomous Token Analysis and Liquidity Provisioning System

This paper presents an AI-powered autonomous system for token selection and liquidity provisioning within DLMM (Dynamic Liquidity Management Market Maker) pools on the Solana blockchain. The core objective is to optimize real-time liquidity deployment while minimizing risk in volatile DeFi environments. By integrating natural language processing (NLP) for sentiment analysis, time-series forecasting for price prediction, and anomaly detection for abnormal trading patterns, the system offers dynamic, data-driven liquidity decisions. It analyzes social media sentiment (from Twitter, Telegram), on-chain market data (via BirdEye and Meteora APIs), and liquidity trends to identify promising token pairs. The AI agent finds trending tokens and evaluates possible liquidity pools using a dynamic risk-adjusted fee-to-TVL ratio by means of 24-hour volume trends, buy/sell ratios, rug detection metrics, whale/sniper activity, and a computed volume-to-market cap ratio.Once a token is chosen, the agent automatically constructs or verifies a token-SOL pair, applies liquidity with an optimal allocation approach spanning $100 to $1,000, and dynamically rebalances positions anomaly detection. When pre-defined market thresholds are activated, the system actively changes liquidity, claims earned fees, and leaves positions to guarantee adaptive risk management in tumultuous times. Experimental findings show in real-world distributed markets the scalability, durability, and efficiency of our AI-enhanced liquidity provisioning system.