moon-dev-trading-agents

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
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"
```