AI Training Models

How Aster AI Interprets & Predicts Market Movements

To identify high-probability trades and uncover market inefficiencies, Aster AI integrates advanced machine learning models alongside custom-finetuned LLMs. These models are optimized specifically for the BNB Smart Chain (BSC), enabling hyper-relevant insights and predictive power in a fast-moving ecosystem.

Self-Finetuned Large Language Models (LLMs)

Aster AI utilizes its own finetuned LLM stack, supported by OpenAI, Deepseek, and Claude. These models have been adapted to better understand BSC dynamics, token-specific contexts, and real-time DeFi trends—delivering sharper, chain-aware intelligence that goes beyond generic LLM output.

Core AI Models in Aster AI

• AI16Z (Quantitative Market Prediction) – A hedge fund-grade model trained to detect high-value trading patterns, pricing inefficiencies, and cross-DEX arbitrage setups.

• OpenAI GPT, Deepseek & Claude (Social Sentiment Analysis) – Analyzes millions of social data points from X (Twitter), Telegram, and Discord to detect early community traction and token hype cycles.

• Gemini AI (Technical & Volume Analysis) – Uses advanced indicators such as EMA, MACD, and liquidity depth metrics to forecast short-term and long-term market trends with precision.

• Graph Neural Networks (GNNs) (Smart Money & Whale Tracking) – Categorizes wallet behavior based on on-chain activity, distinguishing between accumulation, distribution, and potential wash trading.

Real-Time On-Chain Data Aggregation

• GMGN – Supplies granular blockchain intelligence, such as top token movers, large wallet flows, and liquidity shifts.

• DEXScreener – Monitors real-time trading activity across decentralized exchanges, delivering up-to-date metrics on liquidity, pricing, and token breakouts.

By combining quantitative modeling with sentiment-driven insights, Aster AI anticipates market shifts before they happen, allowing traders to enter early and exit at peak efficiency.