AI Training Models
How Aster AI Interprets & Predicts Market Movements
Last updated
How Aster AI Interprets & Predicts Market Movements
Last updated
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.