Overview of the AI Trading Agent Development Pipeline
The AI trading agent pipeline is a multi-stage evolution designed to move from manual blockchain interactions to a fully autonomous, AI-driven trading system. The development path progresses through the following levels:
- Manual Swap: Basic understanding of Uniswap V4 mechanics via MetaMask.
- Bot Script Automation: Programmatic execution of one-off ETH-USDC swaps using predefined rules.
- Stateless AI Agent: Integration of local LLMs (via Ollama) to make trading decisions based on real-time data without memory of previous trades.
- Stateful AI Agent: Addition of persistent memory, trading history, and performance tracking to manage context and strategy effectiveness.
- Data Collection: Extraction of historical swap events from the Base mainnet to prepare for model training.
- Synthetic Data Generation: Using Generative Adversarial Networks (GANs) and WGAN-GP to create realistic training datasets for time-series market data.
- Model Distillation: Using a teacher-student architecture (e.g., QwQ 32B teacher to Qwen 2.5 3B student) with LoRA adaptation and 'Chain of Draft' prompting.
- Reinforcement Learning (RL): Optimizing strategies using a custom Gymnasium environment and Deep Q-Networks (DQN) with experience replay.
- Final Autonomous Agent: A complete system combining fine-tuned models, real-time data, risk management, and performance monitoring.