moon-dev-trading-agents

Moon Dev's AI Trading Agents System

Expert knowledge for working with Moon Dev's experimental AI trading system that orchestrates 48+ specialized AI agents for cryptocurrency trading across Hyperliquid, Solana (BirdEye), Asterdex, and Extended Exchange.

When to Use This Skill

Use this skill when:

• Working with Moon Dev's trading agents repository
• Need to understand agent architecture and capabilities
• Running, modifying, or creating trading agents
• Configuring trading system, exchanges, or LLM providers
• Debugging trading operations or agent interactions
• Understanding backtesting with RBI agent
• Setting up new exchanges or strategies

Environment Setup Note

For New Users: This repo uses Python 3.10.9. If using conda, the README shows setting up an environment named tflow, but you can name it whatever you want. If you don't use conda, standard pip/venv works fine too.

Quick Start Commands

# Activate your Python environment (conda, venv, or whatever you use)
# Example with conda: conda activate tflow
# Example with venv: source venv/bin/activate
# Use whatever environment manager you prefer

# Run main orchestrator (controls multiple agents)
python src/main.py

# Run individual agent
python src/agents/trading_agent.py
python src/agents/risk_agent.py
python src/agents/rbi_agent.py

# Update requirements after adding packages
pip freeze > requirements.txt

Core Architecture

Directory Structure

src/
├── agents/              # 48+ specialized AI agents (<800 lines each)
├── models/              # LLM provider abstraction (ModelFactory)
├── strategies/          # User-defined trading strategies
├── scripts/             # Standalone utility scripts
├── data/                # Agent outputs, memory, analysis results
├── config.py            # Global configuration
├── main.py              # Main orchestrator loop
├── nice_funcs.py        # Core trading utilities (~1,200 lines)
├── nice_funcs_hl.py     # Hyperliquid-specific functions
├── nice_funcs_extended.py # Extended Exchange functions
└── ezbot.py             # Legacy trading controller

Key Components

Agents (src/agents/)

• Each agent is standalone executable
• Uses ModelFactory for LLM access
• Stores outputs in src/data/[agent_name]/
• Under 800 lines (split if longer)