A professional financial analytics platform for stock market analysis using natural language queries powered by AI.

• Natural Language Queries: Ask questions about stock data in plain English
• Interactive Dashboard: Visualize market trends and sector performance
• Data Explorer: Filter and analyze stock data with an intuitive interface
• AI-Powered Analysis: Get instant insights from Groq LLM
• Modern UI/UX: Clean, responsive interface built with Streamlit

View the StockSense Analyzer Demo (Add your deployed app link here)

1. Clone this repository:

   git clone https://github.com/yourusername/stocksense-analyzer.git
   cd stocksense-analyzer
   

2. Create and activate a virtual environment:

   # On Windows
   python -m venv venv
   venv\Scripts\activate
   
   # On macOS/Linux
   python -m venv venv
   source venv/bin/activate
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Configure API access:

   • Obtain a Groq API key from Groq's platform
   • Create .env file and add your API key:

     GROQ_API_KEY=your_api_key_here
     CSV_FILE_PATH=data/stocks.csv
     RESULTS_FILE_PATH=results/analysis_results.csv
     

streamlit run app.py

This will start the Streamlit server and open the application in your default web browser.

For headless analysis without the web interface:

python main.py --query "What is the average P/E ratio of technology stocks?"

Available options:

• --data PATH: Path to stock data CSV file
• --api-key KEY: Groq API key (if not set in environment)
• --query "QUERY1" "QUERY2": Custom queries to run
• --visualize: Generate data visualizations
• --interactive: Run in interactive mode

1. Push your code to GitHub
2. Go to Streamlit Sharing
3. Connect your GitHub repository
4. Add your Groq API key as a secret
5. Deploy the app

• Heroku: Use the provided Procfile to deploy on Heroku
• AWS/GCP/Azure: Deploy using cloud platform services
• Docker: Use the included Dockerfile for containerized deployment

stocksense-analyzer/
│
├── data/                    # Stock data files
│   └── stocks.csv           # Sample stock dataset
│
├── results/                 # Analysis results
│   ├── analysis_results.csv # Query results
│   └── plots/               # Generated visualizations
│
├── app.py                   # Streamlit web application
├── config.py                # Configuration settings
├── data_processor.py        # Data loading and processing
├── analyzer.py              # LangChain and Groq integration
├── visualizer.py            # Data visualization functions
├── main.py                  # CLI entry point
│
├── requirements.txt         # Project dependencies
└── README.md                # This file

• Python: Core programming language
• Streamlit: Web application framework
• LangChain: Framework for LLM applications
• Groq: Large Language Model provider
• Pandas: Data manipulation and analysis
• Matplotlib/Seaborn: Data visualization
• Scikit-learn: Machine learning utilities (for extensions)

• Portfolio Optimization: Add portfolio construction features
• Predictive Analytics: Implement machine learning for predictions
• Real-time Data: Connect to live market data APIs
• Sentiment Analysis: Analyze news sentiment for stocks
• Advanced Visualization: Add interactive charting with Plotly

This project is licensed under the MIT License - see the LICENSE file for details.

• Stock data curated for educational purposes
• Icons by Icons8