Trading Tools Dashboard

A comprehensive Streamlit web application for trading analysis and strategy backtesting. This dashboard integrates multiple powerful trading tools into a single, interactive platform.

Live Webapp

https://tradingtools.streamlit.app/

Features

• Stan Weinstein Strategy: Backtest Stan's 30-week moving average strategy
• SMA Backtesting: Test short vs long-term moving average crossover strategies
• Risk vs Reward Analysis: Analyse risk-return profiles of multiple stocks
• Correlation Heatmap: Visualise correlations between different stocks
• Real-time Data: Live stock data from Yahoo Finance

Installation and Setup

1. Clone the repository:

   git clone https://github.com/theredplanetsings/Trading-Tools.git
   cd Trading-Tools

2. Install dependencies:

   pip install -r requirements.txt

3. Run the dashboard:

   streamlit run trading_dashboard.py

4. Open your browser to http://localhost:8501

Project Structure

Trading-Tools/
├── trading_dashboard.py      # Main Streamlit dashboard application
├── StanWeinstein.py         # Stan Weinstein strategy backtesting class
├── mySMAbacktesting.py      # SMA crossover strategy backtesting class
├── riskvsreward.py          # Risk vs reward analysis script
├── correlationHeatMap.py    # Correlation heatmap generation script
├── requirements.txt         # Python dependencies
├── README.md               # this file
└── LICENCE                 # the licence

Dependencies

• streamlit - Web app framework
• pandas - Data manipulation
• numpy - Numerical computing
• yfinance - Stock data
• plotly - Interactive charts
• matplotlib - Additional plotting
• seaborn - Statistical visualisation

Technical Features

• Object-Oriented Design: Modular backtesting classes for easy extension and maintenance
• Interactive Visualisations: Dynamic Plotly charts with zoom, pan, and hover functionality
• Statistical Analysis: Comprehensive performance metrics and correlation matrices
• Responsive Web Interface: Mobile-friendly Streamlit dashboard with intuitive navigation
• Scalable Architecture: Easily extensible codebase for adding new trading strategies

Usage

Stan Weinstein Strategy

1. Enter a stock symbol (e.g., AAPL, TSLA)
2. Select your date range
3. Click "Run Analysis" to see strategy performance vs buy & hold

SMA Backtesting

1. Enter stock symbol and moving average periods
2. Choose date range
3. View performance comparison and trading signals

Risk vs Reward

1. Enter multiple stock symbols (one per line)
2. Set analysis period
3. Explore risk-return scatter plot and statistics

Correlation Analysis

1. Input stock symbols for correlation analysis
2. Adjust correlation threshold
3. View heatmap for portfolio diversification insights

Deployment

Streamlit Cloud (Recommended)

1. Upload files to GitHub
2. Connect repository to Streamlit Cloud
3. Deploy automatically

Required Files for Deployment:

• trading_dashboard.py - Main application entry point
• StanWeinstein.py - Stan Weinstein strategy implementation
• mySMAbacktesting.py - SMA backtesting functionality
• requirements.txt - Python dependencies
• README.md - self-explanatory

Optional Files:

• riskvsreward.py - Standalone risk analysis script
• correlationHeatMap.py - Standalone correlation analysis script

Performance Metrics

The dashboard calculates key financial metrics:

• Total Returns: Strategy vs buy-and-hold comparison
• Volatility Analysis: Risk assessment through standard deviation
• Sharpe Ratios: Risk-adjusted return calculations
• Maximum Drawdown: Worst peak-to-trough decline analysis
• Win Rate: Percentage of profitable trades

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Licence

Creative Commons Zero v1.0 Universal (CC0) - Public domain dedication for maximum freedom

Author

• GitHub: @theredplanetsings