Comprehensive Stock Market Analysis & Portfolio Management Platform
StockAnalyzer is a powerful, interactive web application for analyzing stocks, tracking portfolios, comparing equities, and leveraging AI-powered price predictions. Built with Streamlit, Plotly, and yfinance, it empowers investors and enthusiasts with actionable insights and beautiful visualizations.
- Real-Time Stock Data: Access US and NSE (India) stocks with up-to-date prices and company info.
- Technical Indicators: Visualize SMA, EMA, RSI, MACD, Bollinger Bands, and more.
- Portfolio Tracker: Manage your investments, view performance, and analyze diversification.
- Stock Comparison: Compare multiple stocks side-by-side with charts and key metrics.
- AI Price Prediction: Get machine learning-based forecasts with confidence intervals and risk analysis.
- Database Integration: Securely store portfolios, watchlists, and analysis history.
- Customizable Preferences: Set your default market, currency, and chart settings.
- Export/Import: Download your analysis history and manage your data with ease.
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Clone the repository
git clone https://github.com/yourusername/StockAnalyzer.git cd StockAnalyzer -
Install dependencies
pip install -r requirements.txt
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Run the app
streamlit run app.py
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Explore the features
Navigate using the sidebar to access analysis, indicators, portfolio, comparison, predictions, and database management.
.
├── app.py
├── requirements.txt
├── pyproject.toml
├── utils/
│ ├── stock_data.py
│ ├── technical_indicators.py
│ ├── portfolio.py
│ ├── price_prediction.py
│ └── database.py
├── pages/
│ ├── 01_📈_Stock_Analysis.py
│ ├── 02_📊_Technical_Indicators.py
│ ├── 03_💼_Portfolio_Tracker.py
│ ├── 04_🔍_Stock_Comparison.py
│ ├── 05_🔮_Price_Prediction.py
│ ├── 06_🇮🇳_NSE_Stocks.py
│ └── 07_🗄️_Database_Management.py
└── .streamlit/
└── config.toml
- Frontend/UI: Streamlit
- Data Visualization: Plotly
- Data Source: yfinance
- Machine Learning: scikit-learn
- Database: SQLAlchemy, PostgreSQL
- Python Libraries: pandas, numpy, psycopg2-binary
⚠️ Disclaimer:
This application is for educational purposes only. It does not constitute financial advice. Past performance does not guarantee future results.
Contributions are welcome! Please open issues or submit pull requests for improvements and new features.
Happy Analyzing! 📊