moon-dev-trading-agents

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

# 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

# Activate your environment first
python src/agents/[agent_name].py

Each agent is standalone and can run independently.

2. Run Main Orchestrator

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:

EXCHANGE = "hyperliquid"  # or "birdeye", "extended"

Then import corresponding functions:

if EXCHANGE == "hyperliquid":
    from src import nice_funcs_hl as nf
elif EXCHANGE == "extended":
    from src import nice_funcs_extended as nf

4. Switch AI Model

Edit src/config.py:

AI_MODEL = "claude-3-haiku-20240307"  # Fast, cheap
# AI_MODEL = "claude-3-sonnet-20240229"  # Balanced
# AI_MODEL = "claude-3-opus-20240229"  # Most powerful

Or use ModelFactory per-agent:

from src.models.model_factory import ModelFactory
model = ModelFactory.create_model('deepseek')  # or 'openai', 'groq', etc.
response = model.generate_response(system_prompt, user_content, temperature, max_tokens)

5. Backtest Strategy (RBI Agent)

python src/agents/rbi_agent.py

Provide: YouTube URL, PDF, or trading idea text → DeepSeek-R1 extracts strategy logic → Generates backtesting.py compatible code → Executes backtest, returns metrics

See WORKFLOWS.md for more examples.

Development Rules

CRITICAL Rules

1. Keep files under 800 lines - split into new files if longer
2. NEVER move files - can create new, but no moving without asking
3. Use existing environment - don't create new virtual environments, use the one from initial setup
4. Update requirements.txt after any pip install: pip freeze > requirements.txt
5. Use real data only - never synthetic/fake data
6. Minimal error handling - user wants to see errors, not over-engineered try/except
7. Never expose API keys - don't show .env contents

Agent Development Pattern

Creating new agents:

# 1. Use ModelFactory for LLM
from src.models.model_factory import ModelFactory
model = ModelFactory.create_model('anthropic')

# 2. Store outputs in src/data/
output_dir = "src/data/my_agent/"

# 3. Make independently executable
if __name__ == "__main__":
    # Standalone logic here

# 4. Follow naming: [purpose]_agent.py

# 5. Add to config.py if needed

Backtesting

• Use backtesting.py library (NOT built-in indicators)
• Use pandas_ta or talib for indicators
• Sample data: src/data/rbi/BTC-USD-15m.csv

Configuration Files

config.py: Trading settings

• MONITORED_TOKENS, EXCLUDED_TOKENS
• Position sizing: usd_size, max_usd_order_size
• Risk: CASH_PERCENTAGE, MAX_LOSS_USD, MAX_GAIN_USD
• Agent: SLEEP_BETWEEN_RUNS_MINUTES, ACTIVE_AGENTS
• AI: AI_MODEL, AI_MAX_TOKENS, AI_TEMPERATURE

.env: Secrets (NEVER expose)

• Trading APIs: BIRDEYE_API_KEY, MOONDEV_API_KEY, COINGECKO_API_KEY
• AI: ANTHROPIC_KEY, OPENAI_KEY, DEEPSEEK_KEY, GROQ_API_KEY, GEMINI_KEY
• Blockchain: SOLANA_PRIVATE_KEY, HYPER_LIQUID_ETH_PRIVATE_KEY, RPC_ENDPOINT
• Extended: X10_API_KEY, X10_PRIVATE_KEY, X10_PUBLIC_KEY, X10_VAULT_ID

Exchange Support

Hyperliquid (nice_funcs_hl.py)

• EVM-compatible perpetuals DEX
• Functions: market_buy(), market_sell(), get_position(), close_position()
• Leverage up to 50x

BirdEye/Solana (nice_funcs.py)

• Solana spot token data and trading
• Functions: token_overview(), token_price(), get_ohlcv_data()
• Real-time market data for 15,000+ tokens

Extended Exchange (nice_funcs_extended.py)

• StarkNet-based perpetuals (X10)
• Auto symbol conversion (BTC → BTC-USD)
• Leverage up to 20x
• Functions match Hyperliquid API for compatibility

See docs/hyperliquid.md, docs/extended_exchange.md for exchange-specific guides.

Data Flow Pattern

Config/Input → Agent Init → API Data Fetch → Data Parsing →
LLM Analysis (via ModelFactory) → Decision Output →
Result Storage (CSV/JSON in src/data/) → Optional Trade Execution

Common Tasks

Add new package:

# Make sure your environment is activated first
pip install package-name
pip freeze > requirements.txt

Read market data:

from src.nice_funcs import token_overview, get_ohlcv_data, token_price

overview = token_overview(token_address)
ohlcv = get_ohlcv_data(token_address, timeframe='1H', days_back=3)
price = token_price(token_address)

Execute trade (Hyperliquid):

from src import nice_funcs_hl as nf
nf.market_buy("BTC", usd_amount=100, leverage=10)
position = nf.get_position("BTC")
nf.close_position("BTC")

Execute trade (Extended):

from src import nice_funcs_extended as nf
nf.market_buy("BTC", usd_amount=100, leverage=15)
position = nf.get_position("BTC")
nf.close_position("BTC")

Git Operations

Current branch: main Main branch for PRs: main

Recent commits:

• dc55e90: websearch agent
• 921ead6: websearch_agent launched and rbi agent updated
• 6bb55c2: backtest dash

Modified files (current):

• .env_example
• src/agents/swarm_agent.py
• src/agents/trading_agent.py
• src/data/ohlcv_collector.py

Documentation

Main docs (docs/):

• CLAUDE.md: Project overview and development guidelines
• hyperliquid.md, hyperliquid_setup.md: Hyperliquid exchange
• extended_exchange.md: Extended Exchange (X10) setup
• rbi_agent.md: Research-Based Inference agent
• websearch_agent.md: Web search capabilities
• swarm_agent.md: Multi-agent coordination
• [agent_name].md: Individual agent docs

README files:

• Root README.md: Project overview
• src/models/README.md: LLM provider guide

Risk Management

• Risk Agent runs FIRST before any trading decisions
• Circuit breakers: MAX_LOSS_USD, MINIMUM_BALANCE_USD
• AI confirmation for position-closing (configurable)
• Default loop: every 15 minutes (SLEEP_BETWEEN_RUNS_MINUTES)

Philosophy

This is an experimental, educational project:

• No guarantees of profitability
• Open source and free
• YouTube-driven development
• Community-supported via Discord
• No official token (avoid scams)

Goal: Democratize AI agent development through practical trading examples.

Additional Resources

For complete agent list, see AGENTS.md For workflow examples, see WORKFLOWS.md For architecture details, see ARCHITECTURE.md

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Built with 🌙 by Moon Dev

"Never over-engineer, always ship real trading systems."