AgentX is a hierarchical agent-based system for cryptocurrency trading that functions similarly to a professional hedge fund structure. It leverages multiple AI agents working together to research, analyze, make decisions, and execute trades in the cryptocurrency market.
Live Demo: https://fmqflmhh.manus.space
- Chief Coordinator Agent: Reviews recommendations from lower-level agents and makes final allocation decisions
- Risk Manager Agent: Monitors overall portfolio risk exposure and sets appropriate risk parameters
- Performance Analyst Agent: Tracks system metrics and provides optimization feedback
- Market Research Agent: Monitors news feeds, social media, and market data sources to analyze sentiment and identify trading opportunities
- Technical Analysis Agent: Performs pattern recognition and signal generation using various technical indicators
- Fundamental Analysis Agent: Evaluates crypto projects based on on-chain metrics and development activity
- Macro Analysis Agent: Monitors broader market conditions and economic factors
- Correlation Agent: Tracks relationships between crypto assets and other market variables
- Trade Execution Agent: Executes approved trades with optimal timing using various execution strategies
- Liquidity Monitoring Agent: Tracks exchange conditions across multiple venues
- Order Book Agent: Analyzes market depth and order flow to provide trading insights
- Standardized communication protocols between agents
- Comprehensive logging system
- Interactive dashboard for system monitoring
- Modular and expandable architecture
- Resilient to failures and unexpected market conditions
The AgentX system is organized into three hierarchical layers:
┌─────────────────────────────────────────────────────────────┐
│ EXECUTIVE LAYER │
│ │
│ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐│
│ │ Chief │ │ Risk │ │ Performance ││
│ │ Coordinator │ │ Manager │ │ Analyst ││
│ └───────┬───────┘ └───────┬───────┘ └───────┬───────┘│
└─────────────────────────────────────────────────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────────────────────────────────────────────────┐
│ STRATEGY LAYER │
│ │
│ ┌───────────┐ ┌───────────┐ ┌───────────┐ ┌───────────┐ │
│ │ Market │ │ Technical │ │Fundamental│ │ Macro │ │
│ │ Research │ │ Analysis │ │ Analysis │ │ Analysis │ │
│ └───────────┘ └───────────┘ └───────────┘ └───────────┘ │
│ │
│ ┌───────────┐ │
│ │Correlation│ │
│ │ Agent │ │
│ └───────────┘ │
└─────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────┐
│ EXECUTION LAYER │
│ │
│ ┌───────────────┐ ┌───────────────┐ ┌───────────────┐│
│ │ Trade │ │ Liquidity │ │ Order Book ││
│ │ Execution │ │ Monitoring │ │ Agent ││
│ └───────────────┘ └───────────────┘ └───────────────┘│
└─────────────────────────────────────────────────────────────┘
- Python 3.10+
- Node.js 20.0+
- 8GB RAM minimum (16GB recommended)
- Modern multi-core CPU
- Internet connection for market data access
- Clone the repository
git clone https://github.com/13otKmdr/AgentX.git
cd AgentX- Set up Python environment
python3 -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt- Install Node.js dependencies
cd src/dashboard
npm install
cd ../..- Configure the system
cp config/default.json config/local.json
# Edit config/local.json with your preferred settings- Run the system
python src/main.py- Access the dashboard at
http://localhost:5000
-
Install Docker from docker.com
-
Clone the repository
git clone https://github.com/13otKmdr/AgentX.git
cd AgentX- Configure the system
cp config/default.json config/local.json
# Edit config/local.json with your preferred settings- Build and run with Docker Compose
docker-compose up -d- Access the dashboard at
http://localhost:5000
For detailed installation instructions, see the Installation Guide.
- Configure your system parameters in the configuration file
- Start the system:
python src/main.py - Access the dashboard and click "Start" to begin trading
- Monitor performance, trades, and system status through the dashboard
The main configuration file is located at config/local.json. Key configuration sections include:
- System settings (update interval, log level)
- Agent-specific parameters
- Risk parameters (max drawdown, volatility limits)
- Allocation limits
- Exchange API credentials
- Monitored trading pairs
Example configuration:
{
"system": {
"name": "AgentX Trading System",
"update_interval": 1.0,
"log_level": "INFO"
},
"agents": {
"executive": {
"chief_coordinator": {
"update_frequency": 0.5,
"allocation_limits": {
"max_per_asset": 0.2,
"max_per_strategy": 0.3
}
},
"risk_manager": {
"update_frequency": 0.5,
"risk_limits": {
"max_drawdown": 0.1,
"max_volatility": 0.2,
"max_correlation": 0.7
}
}
}
},
"exchanges": {
"binance": {
"api_key": "YOUR_API_KEY",
"api_secret": "YOUR_API_SECRET"
}
},
"assets": {
"monitored_pairs": [
"BTC/USDT", "ETH/USDT", "BNB/USDT", "SOL/USDT", "ADA/USDT"
]
}
}- User Guide: Comprehensive guide to using the AgentX system
- API Documentation: Details on programmatic access to the system
- Installation Guide: Step-by-step setup instructions
- Create a new agent class that extends the appropriate base class
- Implement required methods (update, handle_message, etc.)
- Register the agent in the system configuration
Example:
from src.strategy.base_strategy import BaseStrategyAgent
from src.protocols import MessageType, AgentType
class MyCustomStrategyAgent(BaseStrategyAgent):
def __init__(self, agent_id, router, config_file=None, logger=None):
super().__init__(
agent_id=agent_id,
agent_type=AgentType.CUSTOM_STRATEGY,
router=router,
config_file=config_file,
logger=logger
)
def update(self):
# Custom strategy logic here
pass
def handle_market_data(self, message):
# Process market data
pass
def generate_signals(self):
# Generate trading signals
recommendation = {
"asset": "BTC/USDT",
"action": "buy",
"confidence": 0.8,
# Additional details...
}
# Send recommendation to chief coordinator
self.broker.send(
to="chief_coordinator",
message_type=MessageType.TRADE_RECOMMENDATION,
content={"recommendation": recommendation}
)Run the test suite:
python -m pytest tests/- Q2 2025: Enhanced sentiment analysis for Market Research Agent
- Q3 2025: Additional technical indicators and pattern recognition
- Q4 2025: Machine learning integration for performance optimization
- Q1 2026: Multi-exchange support expansion
- Q2 2026: Mobile application for monitoring and alerts
Contributions are welcome! Please feel free to submit a Pull Request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/amazing-feature) - Commit your changes (
git commit -m 'Add some amazing feature') - Push to the branch (
git push origin feature/amazing-feature) - Open a Pull Request
This project is licensed under the MIT License - see the LICENSE file for details.
- Built using OpenAI's Agent SDK
- Inspired by professional hedge fund structures
- Special thanks to all contributors and the crypto trading community
© 2025 AgentX. All rights reserved.