📈 Real-Time Multi-Agent Trading & Risk Management System

🚀 Overview

This project is an advanced AI-powered trading and risk management system that leverages Phi-Agents, Groq LLM, OpenBB, TextBlob, and YFinance to analyze market data, assess news sentiment, and compute risk metrics. It provides real-time financial insights and trade recommendations based on aggregated intelligence from multiple agents.

🏗️ Project Structure

trading-system/
│── agents/
│   ├── market_data_agent.py
│   ├── news_sentiment_agent.py
│   ├── risk_management_agent.py
│── trading_ai.py
│── requirements.txt
│── .env
│── README.md

🧩 Agent Responsibilities

1️⃣ Market Data Agent (market_data_agent.py)

• Fetches real-time stock market data using YFinanceTools
• Computes technical indicators (e.g., RSI, MACD, moving averages)
• Provides market trend insights

2️⃣ News Sentiment Agent (news_sentiment_agent.py)

• Collects recent news articles from Google News, OpenBB, or other sources
• Analyzes sentiment using TextBlob
• Scores news impact on stock price movement

3️⃣ Risk Management Agent (risk_management_agent.py)

• Computes risk scores based on sentiment analysis and market volatility
• Fetches beta, volatility, and other risk metrics from OpenBB
• Suggests a risk-aware strategy for trading

🤖 Parent Orchestrator Agent (trading_ai.py)

The TradingRiskOrchestrator combines the three agents to generate a consolidated report:

trading_system = Agent(
    name="TradingRiskOrchestrator",
    team=[market_agent, news_agent, risk_agent],
    model=Groq(id="deepseek-r1-distill-llama-70b"),
    instructions=[
        "1. Collect and aggregate real-time stock market data, news sentiment analysis, and risk metrics.",
        "2. Cross-analyze financial indicators, sentiment trends, and risk factors to identify market patterns.",
        "3. Generate a structured report summarizing market conditions, sentiment insights, risk evaluation, and potential trading opportunities.",
        "4. If uncertainty is high, highlight risk factors and suggest a cautious approach instead of trade execution.",
        "5. Ensure transparency in decision-making by showing key data sources and justifications for recommendations.",
    ],
    show_tool_calls=True,
    markdown=True,
    monitoring=True,
)

🛠️ Installation & Setup

1️⃣ Clone the Repository

git clone https://github.com/yourusername/trading-system.git
cd trading-system

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Set Up Environment Variables

Create a .env file and add your API keys (e.g., OpenBB, Phi, YFinance, etc.)

GROQ_API_KEY=your_phi_api_key

or

export GROQ_API_KEY=your_phi_api_key

4️⃣ Run the Trading System

python trading_ai.py

🏆 Features

✅ Real-time stock market data aggregation
✅ Automated news sentiment analysis
✅ Risk assessment using market volatility & sentiment
✅ Multi-agent architecture using Phi-Agents
✅ Orchestrated trade recommendations with Groq LLM

📌 Future Enhancements

• 📊 Web dashboard to visualize insights
• 🤝 Integration with brokerage APIs for live trade execution
• 🔍 AI-powered anomaly detection for market movements

🚀 Built with Phi-Agents, Groq LLM, OpenBB, YFinance, and TextBlob