AI Game-Theoretic Oracle & Trading Intelligence
AI-Powered Trading Behavior Analysis
Utilizes LSTM (Long Short-Term Memory) and Transformer models to detect abnormal trading patterns.
Arbitrage behaviors are computed and used to adjust the collaboration coefficient
Integrates Reinforcement Learning to reshape the reward curve, preventing short-term arbitrage from disrupting market stability.
AI-Driven Interest Rate Optimization
Applies Q-learning to calculate optimal liquidity allocation strategies, dynamically adjusting on an hourly basis.
Leverages Multi-Agent Reinforcement Learning to enhance the predictive capability of the AI oracle, enabling it to adapt to complex and evolving market conditions.
Employs zk-SNARKs to protect the AI computation process, ensuring data privacy and computational integrity.
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