StockAnalyzer 📈

Comprehensive Stock Market Analysis & Portfolio Management Platform

🚀 Overview

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.

✨ Features

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.

🖥️ Screenshots

⚡ Quick Start

Clone the repository

git clone https://github.com/yourusername/StockAnalyzer.git
cd StockAnalyzer

Install dependencies
```
pip install -r requirements.txt
```
Run the app
```
streamlit run app.py
```
Explore the features
Navigate using the sidebar to access analysis, indicators, portfolio, comparison, predictions, and database management.

🧩 Project Structure

.
├── 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

🛠️ Tech Stack

Frontend/UI: Streamlit
Data Visualization: Plotly
Data Source: yfinance
Machine Learning: scikit-learn
Database: SQLAlchemy, PostgreSQL
Python Libraries: pandas, numpy, psycopg2-binary

📚 Educational Purpose

⚠️ Disclaimer:
This application is for educational purposes only. It does not constitute financial advice. Past performance does not guarantee future results.

🤝 Contributing

Contributions are welcome! Please open issues or submit pull requests for improvements and new features.

📄 License

MIT License

🌟 Acknowledgements

Happy Analyzing! 📊

rakeshf/StockAnalyzer