StockViz: Market Analysis & Visualization Platform 📈

StockViz is a comprehensive platform designed to fetch, process, visualize, and display stock market and financial news data. It combines a Python backend for data handling with a dynamic web interface for user interaction and visualization.

✨ Features

• Stock Data Fetching: Retrieves historical stock data (Open, High, Low, Close, Volume) using external APIs.
• News Data Aggregation: Fetches relevant financial news articles for specified companies.
• Data Processing: Cleans and transforms raw API data (including non-standard CSV/JSON formats) into structured Pandas DataFrames.
• Data Storage: Saves processed stock and news data into organized CSV files.
• Database Integration: Stores processed data in a structured database for efficient querying and persistence.
• Web Interface: A user-friendly website to:
  • Display stock information and historical charts (using Chart.js).
  • Show latest market news.
  • Compare different stocks.
  • (Potentially) Simulate trading and manage a virtual portfolio.

📸 Screenshots

(Add screenshots of your application here)

Main Dashboard / Stock Chart:

News Section:

Stock Comparison:

Trading Interface:

🛠️ Tech Stack

• Backend:
  • Python 3.x
  • Pandas (Data manipulation and processing)
  • Requests (For interacting with APIs - assumed)
  • SQLAlchemy (Database ORM)
• Database:
  • SQLite (Development)
  • PostgreSQL/MySQL (Production option)
• Frontend:
  • HTML5
  • CSS3
  • JavaScript
  • Chart.js (Interactive charts)
• APIs:
  • Alpha Vantage (For stock data - inferred from data_processor.py)
  • Newsdata.io (For news data - inferred from newsdata_api.py)
• Data Format: JSON, CSV

📁 Project Structure

Sem-2-Project/
├── API/                  # Scripts for fetching data from external APIs
│   ├── stock_data.py
│   ├── news_data.py
│   └── newsdata.py
├── StockProcessing/      # Scripts for data processing and visualization
│   ├── data_processor.py
│   ├── newsdata_processor.py
│   ├── visualizer.py
│   ├── line_graph.py
│   └── pie_chart.py
├── Database/             # Database models and connectivity
│   ├── models.py         # SQLAlchemy models
│   ├── db_connector.py   # Database connection management
│   └── db_operations.py  # CRUD operations
├── website/              # Frontend web application
│   ├── assets/           # CSS, JS, Images
│   ├── *.html            # HTML pages (index, markets, trade, etc.)
├── stock-data-csv-files/ # Output directory for processed stock CSVs
├── news-data-csv-files/  # Output directory for processed news CSVs (if applicable)
├── main.py               # Main script to run the data pipeline
├── companies.json        # Configuration for company symbols/names
├── requirements.txt      # Python dependencies (You should create this)
└── README.md             # This file

⚙️ Setup and Installation

1. Clone the repository:

   git clone <your-repository-url>
   cd Sem-2-Project

2. Set up a Python virtual environment (Recommended):

   python3 -m venv .venv
   source .venv/bin/activate  # On Windows use `.venv\Scripts\activate`

3. Install Python dependencies: (First, create a requirements.txt file if you haven't)

   pip install pandas requests sqlalchemy # Add other libraries if needed
   pip freeze > requirements.txt # Create the file

   Then install:

   pip install -r requirements.txt

4. API Keys:

   • Sign up for API keys from Alpha Vantage and Newsdata.io.
   • Store your API keys securely. You might need to:
     • Modify the API scripts (API/*.py) to read keys from environment variables or a configuration file (recommended).
     • Do not commit your API keys directly into the code.

5. Database Setup:

   • For SQLite (Development):

     python -c "from Database.db_connector import init_db; init_db()"

   • For PostgreSQL/MySQL (Production):
     • Install the required database server
     • Create a database and user with appropriate permissions
     • Update connection string in Database/db_connector.py

6. Run the Data Pipeline:

   • Execute the main script to fetch and process data:

     python main.py

   • This will populate the stock-data-csv-files/ directory and update the database.

7. View the Website:

   • Open the index.html file in your web browser.
   • Alternatively, for features that might require it, run a simple local web server:

     python3 -m http.server 8000 # Or any other available port

     Then navigate to http://localhost:8000 in your browser.

🚀 Usage

1. Data Processing: Run python main.py to update the stock and news data CSV files and database. You might need to configure which companies to process in main.py or companies.json.
2. Web Interface: Open index.html to access the main dashboard. Navigate through the different sections (Markets, Trade, Compare, News) using the navigation bar. The website reads data primarily from the database or generated CSV files, and uses frontend JavaScript for dynamic elements.
3. Database Queries: Use the database models in your Python code to query and analyze stock data programmatically.

🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature-name).
3. Make your changes.
4. Commit your changes (git commit -m 'Add some feature').
5. Push to the branch (git push origin feature/your-feature-name).
6. Open a Pull Request.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details (You should add a LICENSE file).