Elliott Wave Trading Bot

An advanced AI-powered trading bot that uses Elliott Wave Theory to perform technical analysis on financial market data. The system detects impulsive and corrective waves, provides future projections, and includes a comprehensive backtesting engine.

Features

• Wave Detection: Identifies Elliott Wave patterns (1-2-3-4-5 impulse and A-B-C corrective waves)
• AI/ML Integration: Uses machine learning models for pattern recognition and wave labeling
• Fibonacci Analysis: Incorporates Fibonacci retracement and extension levels
• Interactive Visualization: Charts with labeled waves using Plotly
• Backtesting Engine: Comprehensive testing framework for strategy validation
• Modular Architecture: Scalable design for easy integration with trading APIs
• Multiple Data Sources: Support for Yahoo Finance, Binance, and other market data providers

Project Structure

elliottBot/
├── src/
│   ├── data/
│   │   ├── data_loader.py          # OHLCV data loading and preprocessing
│   │   ├── indicators.py           # Technical indicators and ZigZag detection
│   │   └── storage.py              # Data storage and retrieval
│   ├── analysis/
│   │   ├── wave_detector.py        # Elliott Wave detection and labeling
│   │   ├── fibonacci.py            # Fibonacci retracement/extension analysis
│   │   └── pattern_recognition.py  # AI/ML pattern recognition models
│   ├── visualization/
│   │   ├── visualizer.py          # Main charting and visualization
│   │   └── dashboard.py           # Optional web dashboard
│   ├── trading/
│   │   ├── strategy.py            # Signal generation logic
│   │   ├── backtester.py          # Backtesting engine
│   │   └── risk_management.py     # Position sizing and risk controls
│   └── utils/
│       ├── config.py              # Configuration management
│       ├── logger.py              # Logging utilities
│       └── helpers.py             # Common utility functions
├── data/                          # Local data storage
├── models/                        # Trained ML models
├── logs/                          # Application logs
├── tests/                         # Unit tests
├── examples/                      # Usage examples
├── config.yaml                    # Configuration file
├── requirements.txt               # Python dependencies
└── main.py                        # Main application entry point

Installation

1. Clone the repository:

git clone <repository-url>
cd elliottBot

2. 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

Quick Start

from src.data.data_loader import DataLoader
from src.analysis.wave_detector import WaveDetector
from src.visualization.visualizer import Visualizer

# Load data
loader = DataLoader()
data = loader.get_yahoo_data("AAPL", period="2y")

# Detect waves
detector = WaveDetector()
waves = detector.detect_waves(data)

# Visualize
viz = Visualizer()
viz.plot_waves(data, waves)

Configuration

Edit config.yaml to customize:

• Data sources and API keys
• Wave detection parameters
• Visualization settings
• Backtesting parameters

Usage Examples

See the examples/ directory for detailed usage examples including:

• Basic wave detection
• Backtesting strategies
• Custom indicator development
• API integration

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Disclaimer

This software is for educational and research purposes only. Trading involves substantial risk of loss and is not suitable for all investors. Past performance does not guarantee future results.