Lightweight footprint optimized for Node.js backends or React frontends.
import yfinance as yf # Fetch historical data for Bitcoin data = yf.download("BTC-USD", start="2025-01-01", end="2026-01-01", interval="1d") Use code with caution. Step 2: Monowave Segmentation (Peak and Trough Detection)
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.
If you want to dive deeper into automating your trading, tell me:
These scripts scan historical candlestick data, identify local highs and lows, and apply Elliott Wave rules (e.g., Wave 3 cannot be the shortest wave; Wave 4 cannot enter the price territory of Wave 1) to validate counts. 2. Auto-Charting and Visualization Toolkits
For a more academic approach, is a mature package that implements labeling based on the paper "Profitability of Elliott Waves and Fibonacci Retracement Levels in the Foreign Exchange Market". It provides specific methods for labeling impulse waves up or down, and crucially, includes helper methods for validating rules (e.g., Fibonacci checks for Waves 2, 3, and 4). This library is ideal for developers who need granular control over the validation process.
Wave 4 must not enter the price territory of Wave 1 (except in highly specific diagonal patterns).
The introduces a novel approach based on "Monowaves"—the smallest impulsive movements in a chart. The algorithm brute-forces combinations of these Monowaves against strict rules (like "Wave 3 cannot be the shortest") to find valid patterns. It requires Python 3.9 and uses Yahoo Finance integration for data fetching. This repository is perfect for educational purposes, as it breaks down the wave construction process into easily digestible components.
Long Short-Term Memory (LSTM) networks are exceptionally good at learning sequences. The project and the Combining-Elliott-Wave-Analysis-with-LSTM-model-for-Stock-Market-Prediction repository both explore this synergy. The latter project specifically develops an "EWP-LSTM" model that reportedly achieves high accuracy in predicting future price points based on detected waves.
Use libraries like Matplotlib or Plotly to overlay the generated 1-2-3-4-5 and A-B-C labels onto your price chart for manual review. Conclusion
(Rust + Python bindings)
: A script specifically for pattern discovery on financial dataframes, featuring visualization via Matplotlib. EW_Dataset
Python is the dominant language for financial data analysis. GitHub repositories in this category typically use libraries like scipy.signal for peak detection and pandas for handling time-series data.
: This tool tests thousands of wave combinations against standard rules (like the 1-2-3-4-5 impulse structure) to find valid counts on OHLC charts. elliottwaves.py
Mastering Elliott Wave Theory with GitHub: Open-Source Tools, Trading Bots, and Libraries
Automatic calculation of Fibonacci retracement and extension levels.