📈 Stock Analysis Dashboard

A professional-grade stock analysis web application built with Streamlit, featuring AI-powered recommendations, technical indicators, backtesting, and portfolio tracking.

🌟 Features

• 📊 Stock Overview - Real-time price charts, candlestick patterns, volume analysis
• 💡 Investment Advice - AI-powered BUY/SELL/HOLD recommendations with risk assessment
• 📉 Technical Analysis - 8+ indicators (RSI, MACD, Bollinger Bands, SMA, etc.)
• 🤖 Price Prediction - Machine learning price forecasting using Random Forest
• ⚡ Backtesting - Test trading strategies on historical data
• 💼 Portfolio Analysis - Track multiple stocks, correlation analysis
• 📋 Watchlist Manager - Create and manage custom stock watchlists
• 🔄 Stock Comparison - Compare multiple stocks side-by-side
• 🔍 Stock Screener - Find stocks matching technical criteria
• 🔔 Price Alerts - Set alerts for price targets and indicators
• 💹 Performance Tracker - Track paper trading performance with P&L analysis
• ₿ Crypto Analysis - Dedicated cryptocurrency analysis with 24/7 market data and volatility metrics
• 📰 AI News Sentiment - AI-powered analysis of news headlines with sentiment scoring and trend detection
• 💼 Insider Trading Tracker - Monitor executive and insider transactions with buy/sell signals and multi-timeframe analysis
• 📊 Earnings Calendar - Track upcoming earnings dates, analyze historical earnings performance, and monitor beat/miss rates
• 🔍 Chart Pattern Scanner - AI-powered detection of technical patterns (head & shoulders, double tops/bottoms, triangles) with trading signals
• 🎯 Advanced Market Screener - 7 preset screeners to find gap-ups/downs, unusual volume, momentum stocks, value plays, high beta, and 52-week high breakouts
• 🌐 Global Markets Dashboard - Track 20 international indices (Asia, Europe, Americas including Malaysia KLCI), currencies (including MYR), commodities, and correlations with US markets in real-time
• 📦 ETF Holdings Explorer - Discover what stocks are inside any ETF, view top holdings with weights, analyze sector allocation, and understand fund concentration
• 🌙 Dark Mode - Toggle between light and dark themes

🚀 Quick Start

Installation

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

Run the Application

streamlit run app.py

The dashboard will open in your browser at http://localhost:8501

📁 Project Structure (Refactored!)

The codebase has been refactored from a single 2,428-line file into a clean, modular structure:

stock/
├── app.py                          # Main entry point (152 lines - 94% reduction!)
├── requirements.txt                # Python dependencies
├── README.md                       # This file
├── DEVELOPER_GUIDE.md              # Developer documentation
│
├── data/
│   ├── cache/                      # Cached stock data
│   └── user_data/                  # User preferences & data
│       ├── watchlists.json
│       ├── alerts.json
│       ├── performance_tracker.json
│       └── preferences.json
│
└── src/
    ├── data/
    │   ├── fetcher.py              # Stock data fetching (yfinance)
    │   ├── persistence.py          # User data persistence
    │   ├── loader.py               # Standardized data loading utilities
    │   ├── insider_data.py         # Insider trading data
    │   ├── earnings_data.py        # Earnings calendar & analysis
    │   ├── global_markets.py       # Global markets data
    │   └── etf_data.py             # ETF holdings & info ⭐ NEW
    │
    ├── indicators/
    │   ├── trend.py                # Trend indicators (SMA, EMA, MACD)
    │   ├── momentum.py             # Momentum indicators (RSI, Stochastic)
    │   ├── volatility.py           # Volatility indicators (Bollinger Bands, ATR)
    │   └── volume.py               # Volume indicators (OBV, VWAP)
    │
    ├── models/
    │   ├── random_forest.py        # ML price prediction
    │   └── feature_engineering.py  # Feature creation for ML
    │
    ├── backtesting/
    │   └── strategies.py           # Trading strategies (SMA, RSI)
    │
    ├── analysis/
    │   ├── investment_recommendation.py  # AI recommendation engine
    │   ├── sentiment_analyzer.py   # News sentiment analysis
    │   ├── pattern_detector.py     # Chart pattern detection
    │   └── market_screener.py      # Advanced market screener ⭐ NEW
    │
    ├── fundamental/
    │   └── ratios.py               # Financial ratios
    │
    ├── ui/
    │   ├── themes.py               # Apple-inspired CSS themes ⭐ UPDATED
    │   ├── charts.py               # Reusable chart functions
    │   └── components.py           # UI component library ⭐ NEW
    │
    ├── alerts/
    │   └── checker.py              # Price alert monitoring
    │
    └── pages/                      # 20 page modules
        ├── __init__.py
        ├── home.py
        ├── stock_overview.py
        ├── investment_advice.py
        ├── technical_analysis.py
        ├── price_prediction.py
        ├── backtesting.py
        ├── portfolio.py
        ├── alerts.py
        ├── performance_tracker.py
        ├── stock_screener.py
        ├── stock_comparison.py
        ├── watchlist_manager.py
        ├── crypto_analysis.py
        ├── news_sentiment.py          # AI news sentiment
        ├── insider_trading.py         # Insider trading tracker
        ├── earnings_calendar.py       # Earnings calendar & analysis
        ├── pattern_scanner.py         # Chart pattern detection
        ├── market_screener.py         # Advanced market screener
        ├── global_markets.py          # Global markets dashboard
        └── etf_explorer.py            # ETF holdings explorer ⭐ NEW

🛠️ Technology Stack

• Frontend: Streamlit
• Data: yfinance, pandas, numpy
• Visualization: Plotly, mplfinance
• Machine Learning: scikit-learn, Random Forest
• Technical Indicators: pandas_ta, custom implementations
• Backtesting: backtesting.py library

📊 Usage Examples

Analyze a Stock

1. Navigate to Stock Overview
2. Enter a ticker symbol (e.g., AAPL)
3. Select date range
4. Click "Load Stock Data"

Get Investment Advice

1. Go to Investment Advice
2. Enter ticker symbol
3. Enable "Include AI Prediction"
4. Click "Analyze Stock"
5. Review BUY/SELL/HOLD recommendation with price targets

Create a Watchlist

1. Open Watchlist Manager
2. Create a new watchlist
3. Add stocks to the watchlist
4. View real-time prices

Screen for Opportunities

1. Navigate to Stock Screener
2. Select stock universe (watchlist or popular stocks)
3. Set screening criteria (RSI, MACD, volume, etc.)
4. Run screener to find matches

🎯 Recent Improvements

Code Quality Enhancements (December 2025)

✅ Fixed Deprecation Warnings

• Replaced use_container_width=True with width="stretch" (28 instances)
• Code now compatible with Streamlit 1.40+

✅ Extracted Duplicate Code

• Created src/ui/charts.py with 9 reusable chart functions
• Created src/data/loader.py with 9 standardized data loading utilities
• Reduced code duplication by ~500 lines

✅ Added Type Hints

• Type annotations added to all utility modules
• Improved IDE support and code clarity
• Better error detection

✅ Modular Architecture

• Refactored from 2,428-line monolithic file to 16 organized modules
• app.py reduced to 152 lines (94% smaller!)
• Each page in its own file for easy maintenance

🔧 Configuration

Default Settings

• Default stock list: Tech Giants (AAPL, MSFT, GOOGL, AMZN, META)
• Cache duration: 1 hour
• Dark mode: Off (toggle in sidebar)

Customization

Edit watchlist options in app.py:

if watchlist == "Custom Group":
    default_stocks = ["TICKER1", "TICKER2", "TICKER3"]

📝 Development

Adding a New Page

See DEVELOPER_GUIDE.md for detailed instructions.

Quick steps:

1. Create src/pages/new_page.py with a render() function
2. Add import to src/pages/__init__.py
3. Add page name to navigation list in app.py
4. Add routing entry in page_routes dictionary

Code Quality Tools

The codebase follows these principles:

• Modular structure - Each page is a separate module
• Type hints - Functions have type annotations
• DRY principle - Reusable utilities for common operations
• Consistent styling - Standardized chart creation and data loading

🐛 Troubleshooting

"No data found" errors

• Check internet connection
• Verify ticker symbol is correct
• Try a different date range

Slow performance

• Reduce date range for analysis
• Clear cache: delete data/cache/ directory
• Disable AI prediction for faster results

Import errors

• Ensure all dependencies installed: pip install -r requirements.txt
• Check Python version: 3.8+

⚠️ Disclaimer

This tool is for educational and informational purposes only. It is NOT financial advice. Always:

• Do your own research
• Consult a licensed financial advisor
• Understand the risks of trading/investing
• Never invest more than you can afford to lose

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Made with ❤️ using Streamlit