| Feature | Must-Have | Nice-to-Have | | :--- | :--- | :--- | | | Readme.md explains the parameters | Jupyter Notebook examples provided | | Testing | Unit tests for basic patterns | Visual chart comparison tools | | Flexibility | Adjustable Zigzag depth | Multi-timeframe (MTF) support | | License | MIT or GPL (Free for trading) | Commercial use allowed |
Avoid repositories that claim "100% Accurate Wave Prediction." Elliott Wave is probabilistic; any code guaranteeing 100% accuracy is either backtested with look-ahead bias or a scam. The Future: Machine Learning Meets Elliott The cutting edge of elliott wave github research lies in Hybrid Models . elliott wave github
pip install numpy pandas scipy Elliott Waves are built on pivots (swing highs/lows). We need to filter out market noise. | Feature | Must-Have | Nice-to-Have | |
However, remember the paradox of Elliott Wave: The market is driven by human emotion, and code struggles to predict emotion perfectly. Use GitHub scripts to alert you to potential patterns, but use your human judgment to filter the signals based on context, volume, and fundamentals. We need to filter out market noise
Elliott waves are self-similar. A "Wave 1" on a daily chart is actually a full 5-wave sequence on an hourly chart. Most GitHub algorithms struggle to differentiate between the "degree" (granularity) of a wave.
Many visual tools on GitHub repaint. Yesterday, the code identified a perfect Wave 4 bottom. Today, price broke lower, so the code deletes that Wave 4 and labels it as part of a larger Wave 3 extension. This makes automated trading dangerous without strict money management. Advanced: Backtesting a Strategy using backtrader and Elliott The most sophisticated GitHub repositories combine Elliott Wave with backtrader or vectorbt .