📈 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.

🌟 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

Python 3.8+
pip package manager

Installation

Clone the repository

git clone https://github.com/yourusername/stock-trading-platform.git
cd stock-trading-platform

Install dependencies

pip install -r requirements.txt

Set up the project structure

mkdir -p src/logs src/models

Download or train the ML models

# Place your trained models in src/models/
# - logistic_regression_model.pkl
# - scaler.pkl

Run the application

streamlit run app.py

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

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

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

Select a stock from the dropdown menu
Choose your date range and technical indicator periods
View the ML model's next-day price prediction
Analyze confidence levels and technical charts
Export prediction data for further analysis

2. Trading Dashboard

Configure your trading strategy (SMA or EMA based)
Set backtesting parameters and risk management rules
Run the backtest and analyze performance metrics
Compare strategy performance vs buy-and-hold
Review detailed trade analysis and export results

3. Technical Analysis

Examine multiple technical indicators simultaneously
Identify trading signals and market trends
Analyze volume patterns and momentum indicators
Use Bollinger Bands for volatility analysis
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

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

📝 License

This project is licensed under the MIT License - see the 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:

Check the Documentation: Review this README and code comments
Search Issues: Look through existing GitHub issues
Create New Issue: Submit a detailed bug report or feature request
Contact Developer: Reach out via email for urgent matters

⭐ Star this repository if you find it helpful! ⭐

Last Updated: August 2025