💹 Multi-Agent Stock Trading System (CrewAI)

A simple multi-agent system built using CrewAI that analyzes stock data and generates trading decisions using real-time market information.

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🚀 Overview

This project simulates a collaborative AI trading workflow using two specialized agents:

• 📊 Analyst Agent → Gathers and interprets live stock data
• 📈 Trader Agent → Makes Buy/Sell/Hold decisions based on analysis

The system uses real-time stock data via Yahoo Finance and processes it through a structured agent pipeline.

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🧠 How It Works

1. User provides a stock name (e.g., TSLA)
2. The Analyst Agent:
   • Fetches live stock data (price, change, volume, etc.)
   • Summarizes key insights
3. The Trader Agent:
   • Uses the analysis
   • Outputs a trading decision:
     • ✅ Buy
     • ❌ Sell
     • 🤝 Hold

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🧩 Tech Stack

• CrewAI → Multi-agent orchestration
• LiteLLM / Groq → LLM backend
• yfinance → Real-time stock data
• Python → Core implementation

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🏗️ Project Structure

Crew_AI_multi_agents/
│
├── agents/
│   ├── analyst_agent.py
│   └── trader_agent.py
│
├── tasks/
│   ├── analyse_task.py
│   └── trader_task.py
│
├── tools/
│   └── stock_research_tool.py
│
├── crew.py
├── main.py
├── requirements.txt
└── .env

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🔧 Features

• Multi-agent collaboration using CrewAI
• Real-time stock data via Yahoo Finance (yfinance)
• Modular architecture (agents, tasks, tools separation)
• Extensible for more financial indicators or strategies

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📦 Installation

1. Clone the repository

git clone <your-repo-url>
cd Crew_AI_multi_agents

2. Create virtual environment (Python 3.12 recommended)

py -3.12 -m venv venv
.\venv\Scripts\activate

3. Install dependencies

pip install -r requirements.txt

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🔑 Environment Variables

Create a .env file:

GROQ_API_KEY=your_api_key_here

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▶️ Usage

Run the system:

python main.py

Default example (to be input inside main.py):

run("TESLA")

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🧪 Example Output

Stock: TSLA
Price: 245.32 USD
Change: +3.45 (1.43%)

Recommendation: BUY
Reason:
- Strong upward momentum
- Positive daily change
- High trading volume

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📌 Future Improvements

• Add technical indicators (RSI, MACD, moving averages)
• Introduce risk management agent
• Backtesting on historical data
• Multi-stock portfolio optimization
• Add memory between agents

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📄 License

This project is for educational and experimental purposes.