Self-Learning Capability

The AI agent utilizes reinforcement learning to continually improve over time, analyzing past trades, market outcomes, and external signals—including sentiment data—to refine its strategies and adapt to new patterns without requiring constant retraining.

• Reinforcement Learning (RL): Uses RL algorithms (Q-Learning, PPO) to reward successful trades and penalize losses, iteratively improving strategy parameters, with sentiment data as a key input for reward functions.

• Adaptive Feedback: Post-trade analysis incorporates market feedback, updating models without external supervision. The agent learns from global events, such as economic announcements, crypto-specific news, and evolving sentiment trends.

• Continuous Improvement: Periodic model retraining occurs via decentralized compute, ensuring the agent evolves with market dynamics and incorporates new sentiment patterns.