Skill detail
moon-dev-trading-agents
Guides setup and operation of crypto trading agents.
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SKILL.md
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---
name: moon-dev-trading-agents
description: Master Moon Dev's Ai Agents Github with 48+ specialized agents, multi-exchange support, LLM abstraction, and autonomous trading capabilities across crypto markets
---
# 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
```bash
# 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)
**LLM Integration** (src/models/)
- ModelFactory provides unified interface
- Supports: Claude, GPT-4, DeepSeek, Groq, Gemini, Ollama
- Pattern: `ModelFactory.create_model('anthropic')`
**Trading Utilities**
- `nice_funcs.py`: Core functions (Solana/BirdEye)
- `nice_funcs_hl.py`: Hyperliquid exchange
- `nice_funcs_extended.py`: Extended Exchange (X10)
**Configuration**
- `config.py`: Trading settings, risk limits, agent behavior
- `.env`: API keys and secrets (never expose these)
## Agent Categories
**Trading**: trading_agent, strategy_agent, risk_agent, copybot_agent
**Market Analysis**: sentiment_agent, whale_agent, funding_agent, liquidation_agent, chartanalysis_agent
**Content**: chat_agent, clips_agent, tweet_agent, video_agent, phone_agent
**Research**: rbi_agent (codes backtests from videos/PDFs), research_agent, websearch_agent
**Specialized**: sniper_agent, solana_agent, tx_agent, million_agent, polymarket_agent, compliance_agent, swarm_agent
See AGENTS.md for complete list with descriptions.
## Common Workflows
### 1. Run Single Agent
```bash
# Activate your environment first
python src/agents/[agent_name].py
```
Each agent is standalone and can run independently.
### 2. Run Main Orchestrator
```bash
python src/main.py
```
Runs multiple agents in loop based on `ACTIVE_AGENTS` dict in main.py.
### 3. Change Exchange
Edit agent file or config:
```python
EXCHANGE = "hyperliquid" # or "birdeye", "extended"
```Read the full source on GitHub (opens external page)