Readme update

README.md

@@ -11,9 +11,290 @@ pinned: false
 short_description: h2h assignment
 ---
 
-# Welcome to Streamlit!
+# 📈 Complete Stock Trading & Prediction Platform
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+A sophisticated web-based stock trading platform that combines machine learning price predictions with rule-based trading strategies. Built with Streamlit, this platform offers comprehensive technical analysis, backtesting capabilities, and interactive visualizations for Indian stock markets.
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+![Platform Demo](https://img.shields.io/badge/Platform-Streamlit-red)
+![Python](https://img.shields.io/badge/Python-3.8+-blue)
+![License](https://img.shields.io/badge/License-MIT-green)
+![Status](https://img.shields.io/badge/Status-Active-brightgreen)
+
+## 🌟 Features
+
+### 🔮 Price Prediction Engine
+- **Machine Learning Model**: Logistic regression with 59+ technical features
+- **Advanced Features**: Volatility ratios, momentum indicators, lag features
+- **Confidence Analysis**: Prediction probability with visual confidence gauge
+- **Technical Indicators**: RSI, MACD, SMA, EMA, Bollinger Bands
+
+### 📊 Trading Dashboard
+- **Multiple Strategies**: SMA-based and EMA-based trading strategies
+- **Comprehensive Backtesting**: Historical performance analysis with risk metrics
+- **Risk Management**: Configurable stop-loss, take-profit, and position sizing
+- **Interactive Visualizations**: Plotly-powered charts and analysis tools
+
+### 📈 Technical Analysis
+- **50+ Indian Stocks**: Pre-configured NSE stock symbols
+- **Real-time Data**: Yahoo Finance integration with reliable data fetching
+- **Multiple Timeframes**: Customizable periods for technical indicators
+- **Advanced Charts**: Candlestick patterns, volume analysis, drawdown charts
+
+## 🚀 Quick Start
+
+### Prerequisites
+```bash
+Python 3.8+
+pip package manager
+```
+
+### Installation
+
+1. **Clone the repository**
+```bash
+git clone https://github.com/yourusername/stock-trading-platform.git
+cd stock-trading-platform
+```
+
+2. **Install dependencies**
+```bash
+pip install -r requirements.txt
+```
+
+3. **Set up the project structure**
+```bash
+mkdir -p src/logs src/models
+```
+
+4. **Download or train the ML models**
+```bash
+# Place your trained models in src/models/
+# - logistic_regression_model.pkl
+# - scaler.pkl
+```
+
+5. **Run the application**
+```bash
+streamlit run app.py
+```
+
+6. **Access the platform**
+```
+Open your browser and navigate to http://localhost:8501
+```
+
+## 📁 Project Structure
+
+```
+stock-trading-platform/
+│
+├── app.py                          # Main Streamlit application
+├── requirements.txt                # Python dependencies
+├── README.md                      # Project documentation
+│
+├── src/
+│   ├── indicators/                # Technical indicator modules
+│   │   ├── __init__.py
+│   │   ├── rsi.py                 # RSI calculation
+│   │   ├── sma.py                 # Simple Moving Average
+│   │   ├── ema.py                 # Exponential Moving Average
+│   │   ├── macd.py                # MACD indicator
+│   │   └── enhanced_features.py   # Advanced feature engineering
+│   │
+│   ├── strategy/                  # Trading strategy modules
+│   │   ├── __init__.py
+│   │   └── rule_based_strategy.py # SMA/EMA trading strategies
+│   │
+│   ├── utils/                     # Utility modules
+│   │   ├── __init__.py
+│   │   ├── backtester.py          # Backtesting engine
+│   │   └── logger.py              # Logging configuration
+│   │
+│   ├── models/                    # ML models and scalers
+│   │   ├── logistic_regression_model.pkl
+│   │   └── scaler.pkl
+│   │
+│   └── logs/                      # Application logs
+│       └── trading_app.log
+│
+└── data/                          # Data storage (optional)
+    └── processed/
+```
+
+## 🛠️ Dependencies
+
+### Core Libraries
+```python
+streamlit>=1.28.0              # Web application framework
+pandas>=1.5.0                  # Data manipulation
+numpy>=1.24.0                  # Numerical computing
+plotly>=5.15.0                 # Interactive visualizations
+yfinance>=0.2.18               # Stock data fetching
+scikit-learn>=1.3.0            # Machine learning
+```
+
+### Additional Dependencies
+```python
+curl-cffi>=0.5.10              # HTTP requests with browser impersonation  
+pickle>=4.0                    # Model serialization
+datetime                       # Date/time handling
+warnings                       # Warning management
+logging                        # Application logging
+```
+
+## 📊 Supported Stocks
+
+The platform supports 50+ major Indian stocks including:
+
+**Banking & Finance**
+- HDFCBANK.NS, ICICIBANK.NS, AXISBANK.NS, KOTAKBANK.NS, SBIN.NS
+
+**Technology**
+- TCS.NS, INFY.NS, HCLTECH.NS, TECHM.NS, WIPRO.NS
+
+**Energy & Materials**
+- RELIANCE.NS, ONGC.NS, COALINDIA.NS, NTPC.NS, POWERGRID.NS
+
+**Consumer Goods**
+- HINDUNILVR.NS, ITC.NS, NESTLEIND.NS, TATACONSUM.NS, BRITANNIA.NS
+
+**And many more...**
+
+## 🔧 Configuration
+
+### Technical Indicators
+- **RSI Period**: 5-30 days (default: 14)
+- **Short-term SMA/EMA**: 5-50 days (default: 20)
+- **Long-term SMA/EMA**: 50-200 days (default: 50)
+
+### Trading Parameters
+- **Initial Capital**: ₹10,000 - ₹10,00,000 (default: ₹1,00,000)
+- **Transaction Cost**: 0.0% - 1.0% (default: 0.1%)
+- **Stop Loss**: 0% - 20% (default: 5%)
+- **Take Profit**: 0% - 50% (default: 15%)
+
+### Risk Management
+- Position sizing based on available capital
+- Automatic stop-loss and take-profit execution
+- Transaction cost simulation
+- Drawdown analysis and monitoring
+
+## 🎯 Usage Guide
+
+### 1. Price Prediction
+1. Select a stock from the dropdown menu
+2. Choose your date range and technical indicator periods
+3. View the ML model's next-day price prediction
+4. Analyze confidence levels and technical charts
+5. Export prediction data for further analysis
+
+### 2. Trading Dashboard
+1. Configure your trading strategy (SMA or EMA based)
+2. Set backtesting parameters and risk management rules
+3. Run the backtest and analyze performance metrics
+4. Compare strategy performance vs buy-and-hold
+5. Review detailed trade analysis and export results
+
+### 3. Technical Analysis
+1. Examine multiple technical indicators simultaneously
+2. Identify trading signals and market trends
+3. Analyze volume patterns and momentum indicators
+4. Use Bollinger Bands for volatility analysis
+5. Monitor drawdown and risk metrics
+
+## 📈 Model Performance
+
+### Machine Learning Metrics
+- **Accuracy**: 55.0%
+- **F1 Score**: 0.4839
+- **AUC Score**: 0.5370
+- **Average Precision**: 0.5300
+
+### Feature Engineering
+- **Total Features**: 59 technical indicators
+- **Feature Categories**: Price, Volume, Volatility, Momentum, Position
+- **Data Processing**: StandardScaler normalization
+- **Model Type**: Logistic Regression with regularization
+
+## 🔍 Advanced Features
+
+### Enhanced Technical Analysis
+- **Volatility Features**: Multi-timeframe volatility ratios and rankings
+- **Lag Features**: Historical price and indicator dependencies
+- **Volume Analysis**: Volume-price relationships and momentum
+- **Position Features**: Price position relative to historical ranges
+
+### Backtesting Engine
+- **Performance Metrics**: Sharpe ratio, maximum drawdown, win rate
+- **Trade Analysis**: Individual trade performance and statistics
+- **Risk Assessment**: Drawdown analysis and volatility measures
+- **Comparison Tools**: Strategy vs buy-and-hold performance
+
+### Interactive Visualizations
+- **Real-time Charts**: Dynamic price action with technical overlays
+- **Signal Visualization**: Buy/sell signals on price charts
+- **Performance Tracking**: Portfolio value progression over time
+- **Distribution Analysis**: Trade returns and risk metrics
+
+## 🚨 Disclaimer
+
+**Important**: This platform is designed for educational and research purposes only. Stock market investments carry inherent risks, and past performance does not guarantee future results. 
+
+**Risk Warning**: 
+- Always conduct your own research before making investment decisions
+- Consider consulting with a qualified financial advisor
+- Never invest more than you can afford to lose
+- Markets can be volatile and unpredictable
+
+## 🤝 Contributing
+
+We welcome contributions to improve the platform! Here's how you can help:
+
+### Areas for Contribution
+- **New Trading Strategies**: Implement additional algorithmic strategies
+- **Enhanced ML Models**: Add new prediction models and features
+- **UI/UX Improvements**: Enhance the user interface and experience
+- **Performance Optimization**: Improve code efficiency and speed
+- **Bug Fixes**: Report and fix issues in the codebase
+
+### Contribution Process
+1. Fork the repository
+2. Create a feature branch (`git checkout -b feature/amazing-feature`)
+3. Commit your changes (`git commit -m 'Add amazing feature'`)
+4. Push to the branch (`git push origin feature/amazing-feature`)
+5. Open a Pull Request
+
+## 📝 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 👨‍💻 Author
+
+**Zane Vijay Falcao**
+- Email: [email protected]
+- LinkedIn: [Your LinkedIn Profile]
+- GitHub: [Your GitHub Profile]
+
+## 🙏 Acknowledgments
+
+- **Yahoo Finance** for providing reliable stock market data
+- **Streamlit** for the excellent web application framework
+- **Plotly** for interactive visualization capabilities
+- **Scikit-learn** for machine learning tools and utilities
+
+## 📞 Support
+
+If you encounter any issues or have questions:
+
+1. **Check the Documentation**: Review this README and code comments
+2. **Search Issues**: Look through existing GitHub issues
+3. **Create New Issue**: Submit a detailed bug report or feature request
+4. **Contact Developer**: Reach out via email for urgent matters
+
+---
+
+⭐ **Star this repository if you find it helpful!** ⭐
+
+---
+
+*Last Updated: August 2025*

src/README.md

@@ -1 +1,287 @@
-# Placeholder for README.md
+# 📈 Complete Stock Trading & Prediction Platform
+
+A sophisticated web-based stock trading platform that combines machine learning price predictions with rule-based trading strategies. Built with Streamlit, this platform offers comprehensive technical analysis, backtesting capabilities, and interactive visualizations for Indian stock markets.
+
+![Platform Demo](https://img.shields.io/badge/Platform-Streamlit-red)
+![Python](https://img.shields.io/badge/Python-3.8+-blue)
+![License](https://img.shields.io/badge/License-MIT-green)
+![Status](https://img.shields.io/badge/Status-Active-brightgreen)
+
+## 🌟 Features
+
+### 🔮 Price Prediction Engine
+- **Machine Learning Model**: Logistic regression with 59+ technical features
+- **Advanced Features**: Volatility ratios, momentum indicators, lag features
+- **Confidence Analysis**: Prediction probability with visual confidence gauge
+- **Technical Indicators**: RSI, MACD, SMA, EMA, Bollinger Bands
+
+### 📊 Trading Dashboard
+- **Multiple Strategies**: SMA-based and EMA-based trading strategies
+- **Comprehensive Backtesting**: Historical performance analysis with risk metrics
+- **Risk Management**: Configurable stop-loss, take-profit, and position sizing
+- **Interactive Visualizations**: Plotly-powered charts and analysis tools
+
+### 📈 Technical Analysis
+- **50+ Indian Stocks**: Pre-configured NSE stock symbols
+- **Real-time Data**: Yahoo Finance integration with reliable data fetching
+- **Multiple Timeframes**: Customizable periods for technical indicators
+- **Advanced Charts**: Candlestick patterns, volume analysis, drawdown charts
+
+## 🚀 Quick Start
+
+### Prerequisites
+```bash
+Python 3.8+
+pip package manager
+```
+
+### Installation
+
+1. **Clone the repository**
+```bash
+git clone https://github.com/yourusername/stock-trading-platform.git
+cd stock-trading-platform
+```
+
+2. **Install dependencies**
+```bash
+pip install -r requirements.txt
+```
+
+3. **Set up the project structure**
+```bash
+mkdir -p src/logs src/models
+```
+
+4. **Download or train the ML models**
+```bash
+# Place your trained models in src/models/
+# - logistic_regression_model.pkl
+# - scaler.pkl
+```
+
+5. **Run the application**
+```bash
+streamlit run app.py
+```
+
+6. **Access the platform**
+```
+Open your browser and navigate to http://localhost:8501
+```
+
+## 📁 Project Structure
+
+```
+stock-trading-platform/
+│
+├── app.py                          # Main Streamlit application
+├── requirements.txt                # Python dependencies
+├── README.md                      # Project documentation
+│
+├── src/
+│   ├── indicators/                # Technical indicator modules
+│   │   ├── __init__.py
+│   │   ├── rsi.py                 # RSI calculation
+│   │   ├── sma.py                 # Simple Moving Average
+│   │   ├── ema.py                 # Exponential Moving Average
+│   │   ├── macd.py                # MACD indicator
+│   │   └── enhanced_features.py   # Advanced feature engineering
+│   │
+│   ├── strategy/                  # Trading strategy modules
+│   │   ├── __init__.py
+│   │   └── rule_based_strategy.py # SMA/EMA trading strategies
+│   │
+│   ├── utils/                     # Utility modules
+│   │   ├── __init__.py
+│   │   ├── backtester.py          # Backtesting engine
+│   │   └── logger.py              # Logging configuration
+│   │
+│   ├── models/                    # ML models and scalers
+│   │   ├── logistic_regression_model.pkl
+│   │   └── scaler.pkl
+│   │
+│   └── logs/                      # Application logs
+│       └── trading_app.log
+│
+└── data/                          # Data storage (optional)
+    └── processed/
+```
+
+## 🛠️ Dependencies
+
+### Core Libraries
+```python
+streamlit>=1.28.0              # Web application framework
+pandas>=1.5.0                  # Data manipulation
+numpy>=1.24.0                  # Numerical computing
+plotly>=5.15.0                 # Interactive visualizations
+yfinance>=0.2.18               # Stock data fetching
+scikit-learn>=1.3.0            # Machine learning
+```
+
+### Additional Dependencies
+```python
+curl-cffi>=0.5.10              # HTTP requests with browser impersonation  
+pickle>=4.0                    # Model serialization
+datetime                       # Date/time handling
+warnings                       # Warning management
+logging                        # Application logging
+```
+
+## 📊 Supported Stocks
+
+The platform supports 50+ major Indian stocks including:
+
+**Banking & Finance**
+- HDFCBANK.NS, ICICIBANK.NS, AXISBANK.NS, KOTAKBANK.NS, SBIN.NS
+
+**Technology**
+- TCS.NS, INFY.NS, HCLTECH.NS, TECHM.NS, WIPRO.NS
+
+**Energy & Materials**
+- RELIANCE.NS, ONGC.NS, COALINDIA.NS, NTPC.NS, POWERGRID.NS
+
+**Consumer Goods**
+- HINDUNILVR.NS, ITC.NS, NESTLEIND.NS, TATACONSUM.NS, BRITANNIA.NS
+
+**And many more...**
+
+## 🔧 Configuration
+
+### Technical Indicators
+- **RSI Period**: 5-30 days (default: 14)
+- **Short-term SMA/EMA**: 5-50 days (default: 20)
+- **Long-term SMA/EMA**: 50-200 days (default: 50)
+
+### Trading Parameters
+- **Initial Capital**: ₹10,000 - ₹10,00,000 (default: ₹1,00,000)
+- **Transaction Cost**: 0.0% - 1.0% (default: 0.1%)
+- **Stop Loss**: 0% - 20% (default: 5%)
+- **Take Profit**: 0% - 50% (default: 15%)
+
+### Risk Management
+- Position sizing based on available capital
+- Automatic stop-loss and take-profit execution
+- Transaction cost simulation
+- Drawdown analysis and monitoring
+
+## 🎯 Usage Guide
+
+### 1. Price Prediction
+1. Select a stock from the dropdown menu
+2. Choose your date range and technical indicator periods
+3. View the ML model's next-day price prediction
+4. Analyze confidence levels and technical charts
+5. Export prediction data for further analysis
+
+### 2. Trading Dashboard
+1. Configure your trading strategy (SMA or EMA based)
+2. Set backtesting parameters and risk management rules
+3. Run the backtest and analyze performance metrics
+4. Compare strategy performance vs buy-and-hold
+5. Review detailed trade analysis and export results
+
+### 3. Technical Analysis
+1. Examine multiple technical indicators simultaneously
+2. Identify trading signals and market trends
+3. Analyze volume patterns and momentum indicators
+4. Use Bollinger Bands for volatility analysis
+5. Monitor drawdown and risk metrics
+
+## 📈 Model Performance
+
+### Machine Learning Metrics
+- **Accuracy**: 55.0%
+- **F1 Score**: 0.4839
+- **AUC Score**: 0.5370
+- **Average Precision**: 0.5300
+
+### Feature Engineering
+- **Total Features**: 59 technical indicators
+- **Feature Categories**: Price, Volume, Volatility, Momentum, Position
+- **Data Processing**: StandardScaler normalization
+- **Model Type**: Logistic Regression with regularization
+
+## 🔍 Advanced Features
+
+### Enhanced Technical Analysis
+- **Volatility Features**: Multi-timeframe volatility ratios and rankings
+- **Lag Features**: Historical price and indicator dependencies
+- **Volume Analysis**: Volume-price relationships and momentum
+- **Position Features**: Price position relative to historical ranges
+
+### Backtesting Engine
+- **Performance Metrics**: Sharpe ratio, maximum drawdown, win rate
+- **Trade Analysis**: Individual trade performance and statistics
+- **Risk Assessment**: Drawdown analysis and volatility measures
+- **Comparison Tools**: Strategy vs buy-and-hold performance
+
+### Interactive Visualizations
+- **Real-time Charts**: Dynamic price action with technical overlays
+- **Signal Visualization**: Buy/sell signals on price charts
+- **Performance Tracking**: Portfolio value progression over time
+- **Distribution Analysis**: Trade returns and risk metrics
+
+## 🚨 Disclaimer
+
+**Important**: This platform is designed for educational and research purposes only. Stock market investments carry inherent risks, and past performance does not guarantee future results. 
+
+**Risk Warning**: 
+- Always conduct your own research before making investment decisions
+- Consider consulting with a qualified financial advisor
+- Never invest more than you can afford to lose
+- Markets can be volatile and unpredictable
+
+## 🤝 Contributing
+
+We welcome contributions to improve the platform! Here's how you can help:
+
+### Areas for Contribution
+- **New Trading Strategies**: Implement additional algorithmic strategies
+- **Enhanced ML Models**: Add new prediction models and features
+- **UI/UX Improvements**: Enhance the user interface and experience
+- **Performance Optimization**: Improve code efficiency and speed
+- **Bug Fixes**: Report and fix issues in the codebase
+
+### Contribution Process
+1. Fork the repository
+2. Create a feature branch (`git checkout -b feature/amazing-feature`)
+3. Commit your changes (`git commit -m 'Add amazing feature'`)
+4. Push to the branch (`git push origin feature/amazing-feature`)
+5. Open a Pull Request
+
+## 📝 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+## 👨‍💻 Author
+
+**Zane Vijay Falcao**
+- Email: [email protected]
+- LinkedIn: [Your LinkedIn Profile]
+- GitHub: [Your GitHub Profile]
+
+## 🙏 Acknowledgments
+
+- **Yahoo Finance** for providing reliable stock market data
+- **Streamlit** for the excellent web application framework
+- **Plotly** for interactive visualization capabilities
+- **Scikit-learn** for machine learning tools and utilities
+
+## 📞 Support
+
+If you encounter any issues or have questions:
+
+1. **Check the Documentation**: Review this README and code comments
+2. **Search Issues**: Look through existing GitHub issues
+3. **Create New Issue**: Submit a detailed bug report or feature request
+4. **Contact Developer**: Reach out via email for urgent matters
+
+---
+
+⭐ **Star this repository if you find it helpful!** ⭐
+
+---
+
+*Last Updated: August 2025*