Previous section: AI trading agent: Stack
Project repository: Web3 AI trading agent
This is the pipeline overview with little to no hands-on. If you are looking to get your feet wet, feel free to skip this section.
This section maps out our complete development pipeline, tracing the evolution from manual trading to autonomous AI agents. Our approach mirrors the broader Web3 industry progression while giving you hands-on experience with each technological advancement.

​
The three-stage evolution

Our tutorial follows the natural progression of Web3 trading, letting you experience how the industry evolved from manual interactions to scripted bots to an LLM agent.

​
Stage 1: Manual trading era

Direct MetaMask interactions
This one might be of interest to you likely only if you’ve never done a swap on blockchain before. Otherwise feel free to skip this section.
The foundation of Web3 trading begins with manual transactions. Users connect their wallets directly to decentralized exchanges, manually selecting trading pairs, amounts, and executing transactions. To do a manual ETH-USDC swap on the exact Uniswap V4 pool that we use in a bot script and in the trading agent later in the tutorial, do the following:
  1. Install MetaMask.
  2. Connect to the BASE mainnet. See Chainstack tooling or use Chainlist.
  3. Get some ETH on your BASE account.
  4. Do the ETH-USDC pool swap.

​
Stage 2: Bot automation era

Programmatic ETH-USDC swaps The DeFi summer of 2020 sparked widespread adoption of trading bots. Developers began automating repetitive tasks, leading to the MEV (maximum extractable value) revolution.
Make sure you have the private key and the RPC node endpoints set up in the config.py file. And then run the usdc_to_eth_swap.py and eth_to_usdc_swap.py respectively to get the Uniswap V4 programmatic swap experience.
Bot scripts excel at executing predefined strategies but lack adaptability to changing market conditions.

​
Stage 3: AI agent era

Intelligent decision-making systems The current frontier combines traditional Web3 infrastructure with artificial intelligence. AI agents analyze market data, adapt to changing conditions, and execute complex strategies autonomously.

​
Development pipeline architecture

Our pipeline progresses through increasingly sophisticated implementations, each building upon previous foundations.

​
Pipeline breakdown

Each stage in our pipeline serves a specific learning objective while building toward the final autonomous trading system.

​
Foundation: Manual swap implementation

Learning objective: Understand basic Uniswap V4 mechanics You’ll start by executing ETH-USDC swaps manually through MetaMask, then replicate the same operations programmatically.

​
Level 1: Bot script automation

Learning objective: Script a bot to do one-off ETH-USDC swaps Transform manual operations into automated scripts that execute swaps based on predefined rules.

​
Level 2: Stateless AI agent

Learning objective: Integrate AI decision making Replace static rules with dynamic AI-driven decisions using local language models. The stateless agent:
  • Queries Ollama models for trading decisions
  • Processes real-time market data
  • Executes trades based on AI recommendations
  • Operates without memory between decisions

​
Level 3: Stateful AI agent

Learning objective: Add memory and context management Enhance the agent with persistent memory and strategy tracking. The stateful agent:
  • Maintains trading history and performance metrics
  • Tracks long-term strategy effectiveness
  • Manages context window limitations
  • Summarizes performance when memory fills up

​
Level 4: Data collection and processing

Learning objective: Collect raw Uniswap V4 data and prepare for synthetic data generation Collect real on-chain data from BASE mainnet to fine tune custom models:
  • Historical swap event extraction
  • Data preprocessing for synthetic data generation

​
Level 5: Synthetic data generation

Learning objective: Create enhanced training datasets Use generative adversarial networks (GANs) to create synthetic trading data:

​
Level 6: Model distillation pipeline

Learning objective: Create custom trading models Distill knowledge from large teacher models into efficient student models:
  • Chain of Draft prompting for efficiency
  • QwQ 32B teacher model via OpenRouter
  • Qwen 2.5 3B student model fine-tuning
  • LoRA adaptation for parameter efficiency

​
Level 7: Reinforcement learning enhancement

Learning objective: Optimize strategies through trial and error Stack reinforcement learning on top of supervised fine-tuning:
  • Custom Gymnasium trading environment
  • DQN (Deep Q-Network) strategy optimization
  • Experience replay for stable learning
  • Multi-layer fine-tuning

​
Final system: Autonomous trading agent

Learning objective: Deploy your trading system Integrate all components into an autonomous trading system:
  • Custom fine-tuned models with domain expertise
  • Real-time market data processing
  • Risk management and position sizing
  • Performance monitoring and strategy adaptation