https://github.com/rooneyrulz/agentic-stock-research-system
A sophisticated multi-agent AI system for analyzing Indian NSE-listed stocks using real-time data, technical indicators, news sentiment, and advanced AI reasoning.
https://github.com/rooneyrulz/agentic-stock-research-system
ai-agents groq langchain langgraph llms mcp openai python streamlit
Last synced: 2 months ago
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A sophisticated multi-agent AI system for analyzing Indian NSE-listed stocks using real-time data, technical indicators, news sentiment, and advanced AI reasoning.
- Host: GitHub
- URL: https://github.com/rooneyrulz/agentic-stock-research-system
- Owner: rooneyrulz
- Created: 2025-08-07T14:21:10.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2025-08-07T15:48:13.000Z (11 months ago)
- Last Synced: 2025-08-07T16:24:32.307Z (11 months ago)
- Topics: ai-agents, groq, langchain, langgraph, llms, mcp, openai, python, streamlit
- Language: Python
- Homepage:
- Size: 93.8 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# 📈 NSE Stock Research & Analysis System
A sophisticated multi-agent AI system for analyzing Indian NSE-listed stocks using real-time data, technical indicators, news sentiment, and advanced AI reasoning.
## 🌟 Features
### 🤖 Multi-Agent Architecture
- **Stock Finder Agent**: Identifies promising NSE stocks based on liquidity, market cap, and momentum
- **Market Data Agent**: Gathers real-time pricing, volume, and technical indicators
- **News Analyst Agent**: Analyzes recent news sentiment and market impact
- **Recommendation Agent**: Provides actionable BUY/SELL/HOLD recommendations with target prices
### 📊 Advanced Analytics
- Real-time NSE stock data integration
- Technical indicators (RSI, Moving Averages, MACD)
- Volume and volatility analysis
- News sentiment classification
- Risk-reward assessment
### 🎯 Smart Recommendations
- Specific entry/exit price points
- Stop-loss levels and risk management
- Confidence scoring for each recommendation
- Time horizon-based analysis (short-term to medium-term)
### 🎨 Modern UI
- Clean, responsive Streamlit interface
- Interactive charts and visualizations
- Real-time status updates
- CSV export functionality
- Mobile-friendly design
## 🚀 Quick Start
### Prerequisites
- Python 3.8+
- Bright Data API account ([Sign up here](https://brightdata.com))
- OpenAI API key ([Get one here](https://platform.openai.com))
### Installation
1. **Clone the repository**
```bash
git clone https://github.com/rooneyrulz/agentic-stock-research-system
cd nse-stock-research-system
```
2. **Install dependencies**
```bash
pip install -r requirements.txt
```
3. **Set up environment variables**
```bash
cp .env.example .env
# Edit .env with your API keys
```
4. **Install Bright Data MCP**
```bash
npm install -g @brightdata/mcp
```
### Running the Application
1. **Start the Streamlit app**
```bash
streamlit run streamlit_app.py
```
2. **Access the application**
- Open your browser to `http://localhost:8501`
- Enter your API keys in the sidebar
- Select analysis parameters
- Click "Start Analysis" and wait for results!
## 🔧 Configuration
### API Keys Setup
#### Bright Data API Token
1. Sign up at [Bright Data](https://brightdata.com)
2. Navigate to your dashboard
3. Go to "Zones" → "Web Unlocker"
4. Copy your API token
#### OpenAI API Key
1. Sign up at [OpenAI Platform](https://platform.openai.com)
2. Go to "API Keys" section
3. Create a new API key
4. Copy the key (starts with 'sk-')
### Analysis Types
- **Short-term Trading (1-7 days)**: Focus on momentum, technical breakouts, and news catalysts
- **Medium-term Investment (1-4 weeks)**: Emphasis on earnings, sector trends, and technical setups
- **General Market Analysis**: Broad market overview with top stock picks across sectors
## 📈 Sample Output
```
🎯 TRADING RECOMMENDATIONS
═══════════════════════════════════
RELIANCE - Reliance Industries Limited
─────────────────────────────────
📋 RECOMMENDATION: BUY
🎯 TARGET PRICE: ₹2,650
⏰ TIME HORIZON: 1-3 days
📊 CONFIDENCE: HIGH
📈 ENTRY STRATEGY:
Current Price: ₹2,450
Suggested Entry: ₹2,430 - ₹2,460
Stop Loss: ₹2,380 (3.2% below entry)
Target: ₹2,650 (8.2% upside potential)
💡 RATIONALE:
Technical: Breakout above 50-day MA with strong volume
Fundamental: Positive earnings guidance + new project announcements
Risk-Reward: 1:2.6 ratio
```
## 🏗️ System Architecture
```
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Streamlit UI │────│ Supervisor │────│ Bright Data │
│ │ │ Agent │ │ MCP Server │
└─────────────────┘ └──────────────────┘ └─────────────────┘
│
┌─────────┼─────────┐
│ │ │
┌───────▼───┐ ┌───▼───┐ ┌───▼────┐
│Stock Finder│ │Market │ │News │
│ Agent │ │Data │ │Analyst │
└────────────┘ │Agent │ │Agent │
└───────┘ └────────┘
│
┌───────▼────────┐
│ Recommendation │
│ Agent │
└────────────────┘
```
## 🔍 Agent Details
### Stock Finder Agent
- Scans NSE universe for liquid, high-potential stocks
- Filters by market cap, volume, and momentum criteria
- Avoids penny stocks and illiquid securities
- Focuses on large-cap and mid-cap opportunities
### Market Data Agent
- Real-time price, volume, and market data
- Technical indicators (RSI, MACD, Moving Averages)
- Support/resistance level identification
- Trend analysis and momentum assessment
### News Analyst Agent
- Scrapes recent financial news and announcements
- Sentiment classification (Positive/Negative/Neutral)
- Impact assessment on stock prices
- Catalyst identification for price movements
### Recommendation Agent
- Synthesizes all data into actionable recommendations
- Provides specific entry/exit strategies
- Risk management and position sizing guidance
- Confidence scoring and time horizon analysis
## 🛡️ Risk Management Features
- **Stop-loss recommendations** for every trade suggestion
- **Position sizing guidance** based on volatility
- **Risk-reward ratio analysis** (minimum 1:2 ratio)
- **Confidence scoring** to help with decision making
- **Time horizon specification** for each recommendation
## 📊 Export & Reporting
- **CSV Export**: Download analysis results for further analysis
- **Interactive Charts**: Visualize current vs target prices
- **Performance Tracking**: Monitor recommendation accuracy
- **Historical Analysis**: Compare predictions with actual outcomes
## ⚠️ Important Disclaimers
- This tool is for **educational and research purposes only**
- Always consult with a qualified financial advisor before investing
- Past performance does not guarantee future results
- The Indian stock market involves substantial risk of loss
- Do your own due diligence before making any investment decisions
## 🤝 Contributing
We welcome contributions! Please see our contributing guidelines:
1. Fork the repository
2. Create a feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🆘 Support
For support and questions:
- Open an issue on GitHub
- Check the documentation
- Review the troubleshooting guide below
### Troubleshooting
**Common Issues:**
1. **API Key Errors**
- Ensure your Bright Data token is valid and has sufficient credits
- Verify OpenAI API key starts with 'sk-' and has available quota
2. **MCP Installation Issues**
```bash
# Reinstall MCP globally
npm uninstall -g @brightdata/mcp
npm install -g @brightdata/mcp
```
3. **Streamlit Issues**
```bash
# Clear Streamlit cache
streamlit cache clear
```
4. **Import Errors**
```bash
# Reinstall dependencies
pip install -r requirements.txt --force-reinstall
```
## 🔄 Version History
- **v1.0.0** - Initial release with multi-agent architecture
- **v1.1.0** - Added Streamlit UI and export functionality
- **v1.2.0** - Enhanced recommendation parsing and visualization
---
**Made with ❤️ for the Indian Stock Market Community**