📈 Elliott Wave Analyzer

This is a Python-based web application for Elliott Wave analysis of financial markets. It detects wave structures (impulses and corrections), projects future price zones using Fibonacci retracements/extensions, and provides trade setup recommendations with entry, stop-loss, and take-profit levels.

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🎯 Purpose

This tool is designed to help technical traders, analysts, and developers visualize Elliott Wave patterns, validate potential wave sequences, and simulate or plan trades using:

• Elliott Wave Theory (impulse + ABC correction)
• ATR and RSI indicators
• Price projection zones based on Fibonacci levels
• Trade recommendations with position sizing and risk control
• Historical backtests for validation

It's useful for strategy development, market pattern research, and educational purposes.

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⚙️ Installation

1. Clone the repository:

   git clone https://github.com/ESJavadex/elliot-waves-auto.git
   cd elliott-wave-analyzer

2. (Optional) Create a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

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🚀 How to Use

1. Run the Flask application:

   python app.py

2. Open your browser and go to:
   http://127.0.0.1:5000

3. Enter a stock ticker (e.g. AAPL, ^SPX), date range, and interval (1d, 1wk, etc.).

4. Click "Submit" to:

   • Visualize detected Elliott Waves
   • See projected future price zones
   • Optionally simulate trade setups or run backtests

5. You can also toggle:

   • Trade signal overlays (entry/SL/TP)
   • Backtest mode to compare historical forecasts with real outcomes
   • Multi-stock analysis for batch processing

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🛡️ Yahoo Finance Rate Limit Solution

The application now uses curl_cffi to avoid Yahoo Finance rate limiting issues. This solution:

1. Creates a session that impersonates Chrome browser
2. Bypasses the "Too Many Requests" errors that occur with frequent API calls
3. Is implemented in the get_stock_data function

You can also use the standalone utility to test this approach:

python fix_yfinance_ratelimit.py AAPL --start 2023-01-01 --end 2023-12-31 --interval 1wk

This utility demonstrates how to use the solution with any yfinance application.

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🐳 Docker Deployment

You can also run the application using Docker, which is especially useful for deployment on a Raspberry Pi:

1. Build and start the Docker container:

   docker-compose up -d

2. Access the application in your browser at: http://your-host-ip:5001

3. Stop the Docker container:

   docker-compose down

The Docker configuration:

• Uses Python 3.11 as the base image
• Exposes port 5001 for web access
• Mounts the templates directory as a volume
• Includes resource limits for Raspberry Pi compatibility

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💡 This app is for educational and strategy development only. Do not use it for live trading.