generate-agent-code

name	generate-agent-code
description	Generates Python modules for the hybrid AI trading agent

When to Use

• When Claude is asked to generate trading logic, risk management, or dashboard code
• When the user requests the full agent codebase or specific modules
• When scaffolding new components that fit into the agent architecture

Instructions

1. Create Python module files under the appropriate directories: modules/, risk/, dashboard/, validation/, simulation/, utils/
2. Include comments and Google-style docstrings for all public functions and classes
3. Implement hybrid logic: combine momentum, mean-reversion, and AI predictor signals
4. Integrate risk_manager checks before any trade execution
5. Ensure proof_logger hashes all trade inputs, outputs, and decisions
6. Create main.py to tie everything together as the entry point
7. Use configuration from config/config.yaml or utils/config.py — never hardcode parameters
8. Include proper logging using Python's logging module
9. Use type hints for all function signatures
10. Ensure each module can be imported and tested independently

Module Checklist

When generating the full agent, ensure these files exist:

• main.py — orchestration entry point
• modules/momentum.py — momentum trading strategy
• modules/mean_reversion.py — mean-reversion trading strategy
• modules/yield_optimizer.py — yield optimization strategy
• modules/ai_predictor.py — AI prediction ensemble
• modules/strategy_manager.py — combines all strategy signals
• risk/risk_manager.py — risk validation and trade gating
• simulation/paper_trader.py — virtual trade execution
• validation/proof_logger.py — SHA256 proof hash logging
• dashboard/dashboard.py — Streamlit/Flask visualization
• utils/config.py — centralized configuration
• utils/data_loader.py — data ingestion
• utils/indicators.py — technical indicator calculations
• utils/logger.py — logging setup

Example

Input: "Generate the full trading agent with all modules"

Output: Complete Python module files for every component listed above, each with:

• Proper imports and type hints
• Docstrings explaining the module's purpose
• Core logic implementation
• Logging integration
• Risk management integration (where applicable)
• Proof logging integration (where applicable)

Input: "Generate the momentum strategy module"

Output: modules/momentum.py with:

• Moving average crossover logic
• Volume confirmation filters
• Signal generation with confidence score
• Configurable parameters from config