Elliott Wave Github [repack]

GitHub has become a vital hub for traders and developers seeking to automate Elliott Wave Theory, a technical analysis method based on the idea that market prices move in predictable cycles or "waves" driven by investor psychology.

While the theory is famously subjective, open-source projects on GitHub are working to standardize wave counting using algorithms, machine learning, and visualization tools. Core Concepts of Elliott Wave Analysis

Before diving into GitHub repositories, it is essential to understand the basic structure being modeled: Impulse Waves (1, 3, 5): These follow the primary trend.

Corrective Waves (2, 4, A, B, C): These act as counter-trend movements.

The 5-3 Pattern: A complete cycle consists of an 8-wave pattern—five in the direction of the trend and three against it. Top Elliott Wave Projects on GitHub

Developers have created various tools to find, validate, and trade these patterns. 1. Automated Wave Recognition & Scanners

Finding Elliott Wave patterns manually is time-consuming. Several repositories offer automated detection:

ElliottWaveAnalyzer: This Python-based tool uses an iterative scanner to find "monowaves" (the smallest elements of a trend) and validate them against 12345 impulsive movements.

ElliottWaves Python Script: A script specifically designed to find and analyze recurrent price patterns in financial dataframes.

python-taew: A library focused on automated Elliott Wave labeling to fill the gap of missing open-source labeling packages. 2. Machine Learning & Genetic Algorithms

For advanced users, some projects integrate AI to improve forecast accuracy:

EW_Dataset: An open-source dataset designed for training Convolutional Neural Networks (CNNs) to recognize impulse wave structures in financial charts.

PyBacktesting: This project models the theory and uses genetic algorithms to optimize parameters, often using the Sharpe ratio as a fitness function. 3. Strategy Development & Backtesting

These tools help turn Elliott Wave counts into actionable trading systems: Strategy based on the Elliot Wave indicator. - GitHub

Strategy Elliot Waves. Strategy based on the Elliot Waves indicator. Dependencies. Tag. Framework. v1.000. v2.000. v1.001. v2.001.

drstevendev/ElliottWaveAnalyzer: Tools to find Elliot ... - GitHub

Elliott Wave Theory on GitHub encompasses a range of open-source tools designed to automate wave counting, visualize patterns, and backtest trading strategies based on financial market cycles. Core Functionality of GitHub Repositories

Developers and traders utilize these repositories to move beyond manual charting. Common features include: Automated Pattern Detection

: Algorithms that identify the 5-wave impulse and 3-wave corrective structures. Fibonacci Integration : Many tools, such as the elliot-waves-auto

repository, use Fibonacci retracement and extension levels to project future price zones. Machine Learning Optimization : Projects like PyBacktesting

apply genetic algorithms to optimize wave parameters for better forecasting. Validation Rules : Tools like the ElliottWaveAnalyzer

validate identified patterns against strict sets of rules (e.g., ensuring wave 3 is not the shortest). Key Open-Source Projects

The following repositories are notable for their specific contributions to the Elliott Wave ecosystem: ElliottWaveAnalyzer

: A Python-based scanner that finds impulse and corrective movements by trying multiple combinations of price patterns. python-taew elliott wave github

: A package focused on technical analysis that provides wave labeling and backtracking based on established research. ElliottWaves (alessioricco)

: A script specifically for pattern discovery on financial dataframes, featuring visualization via Matplotlib. EW_Dataset

: An open-source dataset of impulse waves designed to train Convolutional Neural Networks (CNNs) for automatic pattern recognition. Strategy-ElliottWave

: An MQL4 strategy implementation for MetaTrader, integrating Elliott Wave indicators for automated trading. Implementation Languages

GitHub hosts these projects in several primary languages, depending on the trader's environment:

drstevendev/ElliottWaveAnalyzer: Tools to find Elliot ... - GitHub

The intersection of financial markets and open-source software has transformed how traders approach technical analysis. For proponents of the Elliott Wave Theory—a complex method of predicting price action through repetitive cycles—GitHub has become the ultimate repository for automation, backtesting, and visualization tools.

This guide explores the best Elliott Wave resources on GitHub, how to use them, and why the open-source community is changing the game for "Wave Riders." 🌊 Why Elliott Wave and GitHub are a Perfect Match

Elliott Wave Theory (EWT) is notoriously subjective. What one trader sees as a "Third Wave" impulse, another might label a "C Wave" correction. By using code hosted on GitHub, traders can: Remove Bias: Algorithms apply strict rules to wave counts.

Backtest Strategies: See how specific wave patterns performed historically.

Scale Analysis: Scan hundreds of symbols for "Wave 3" setups simultaneously.

Visualize Complexity: Automatically plot Fibonacci retracements and extensions. 🛠 Top Elliott Wave Projects on GitHub

When searching for "Elliott Wave" on GitHub, the results generally fall into three categories: automated labeling, technical libraries, and trading bots. 1. Automated Labeling Engines

Identifying the 1-2-3-4-5 and A-B-C patterns is the most time-consuming part of EWT.

Key Projects: Look for repositories like elliott-wave-labeller or auto-elliott-wave.

Function: These often use "ZigZag" indicators as a foundation to identify swing highs and lows before applying EWT rules (like Wave 3 never being the shortest). 2. Python Libraries for Quants Python is the language of choice for financial data.

elliottwave (Python Package): Several developers have created lightweight libraries that allow you to pass a Pandas DataFrame and receive a list of potential wave counts.

Integration: These are easily integrated into Jupyter Notebooks for research or Matplotlib for custom charting. 3. Pine Script (TradingView) Repos

Many GitHub users host their TradingView scripts on the platform for version control.

What to find: Custom indicators that draw "Wave Tunnels," "Fibo-Level Clusters," or "Wave Oscillators." 📊 How to Evaluate an Elliott Wave Repository

Not all code is created equal. When browsing GitHub, look for these "Green Flags":

Documentation: Does it explain which EWT rules it follows (Prechter vs. Neely)?

Active Issues/PRs: Is the developer still maintaining the code? GitHub has become a vital hub for traders

Validation: Does the repo include unit tests to ensure the wave logic is sound?

Star Count: A high number of stars usually indicates a reliable and popular tool within the trading community. 🚀 Getting Started with Elliott Wave Code

If you are a trader looking to dive into the technical side, follow these steps: Clone a Library: Start with a Python-based EWT library.

Input Clean Data: Use APIs like Yahoo Finance or Alpaca to feed the algorithm OHLC (Open, High, Low, Close) data.

Define Your Rules: Modify the code to match your specific trading style (e.g., how strictly you enforce the "Wave 4 shouldn't enter Wave 1 territory" rule).

Visualize: Use Plotly or Bokeh to create interactive charts where you can toggle different wave degrees (Grand Supercycle down to Subminuette). ⚠️ The Limitations of Algorithmic EWT

While GitHub offers powerful tools, remember that Elliott Wave is as much an art as it is a science. Most GitHub scripts struggle with: Truncated Waves: When Wave 5 fails to move past Wave 3.

Complex Corrections: Double and triple threes (W-X-Y-X-Z) often confuse basic algorithms.

Fundamental Shocks: Black swan events that break technical structures. 💡 The Verdict

Searching for "Elliott Wave GitHub" is the first step toward professional-grade market analysis. By leveraging the collective intelligence of the open-source community, you can transform a subjective charting method into a rigorous, data-driven trading system. To help you find the best fit, tell me:

I can point you toward a specific repository that matches your skill level!

Elliott Wave Theory predicts financial market trends by identifying recurring 8-wave patterns (5 impulse waves and 3 corrective waves) linked to investor sentiment. Several open-source GitHub projects provide tools for automating this analysis, ranging from pattern recognition to machine learning datasets. Key Open-Source Elliott Wave Projects

alessioricco/ElliottWaves: A Python library used to find and visualize patterns in historical CSV data.

Finds wave patterns using the ElliottWaveFindPattern function.

Integrates with matplotlib for overlaying identified waves on price charts.

ESJavadex/elliot-waves-auto: A web application designed for comprehensive trade planning. Detects impulse and ABC correction structures. Projects future price zones using Fibonacci levels.

Provides actionable trade recommendations including position sizing and stop-loss levels.

A-J-Financial-Solutions/EW_Dataset: An open-source dataset focused on training modern AI models.

Provides impulse wave structures for Convolutional Neural Networks (CNNs).

Aims to bridge classical technical analysis with machine learning research.

philippe-ostiguy/PyBacktesting: Focuses on optimizing Elliott Wave forecasting using genetic algorithms.

Tests parameters using Walk forward optimization and the Sharpe ratio.

Evaluated on EUR/USD currency pairs to assess model profitability and overfitting. Advanced AI Research Papers Stars : ~120 Author : intuitiv Features :

Recent developments integrate Elliott Wave principles with Large Language Models (LLMs) and specialized AI agents:

ElliottAgents (2024/2025): A multi-agent system described in papers on MDPI and arXiv.

Combines deep reinforcement learning (DRL) with natural language processing (NLP).

Specialized agents collaborate via dialogue to identify patterns and formulate investment strategies.

Enhances interpretability by providing human-comprehensible natural language explanations for market trends.

Several GitHub repositories offer automated Elliott Wave analysis, ranging from pattern recognition scripts machine learning datasets Top Elliott Wave Repositories alessioricco/ElliottWaves : A Python script ( elliottwaves.py

) designed to find and analyze recurrent long-term price patterns using sentiment and psychology-based rules. Core Feature ElliottWaveFindPattern

function subsets financial data and uses an automated discovery process to identify waves. drstevendev/ElliottWaveAnalyzer

: An iterative scanner that breaks market movements into "MonoWaves" and chains them to validate classic patterns like 1-2-3-4-5 impulses or ABC corrections. A-J-Financial-Solutions/EW_Dataset

: A community-driven project focused on creating a labeled image dataset of impulse waves for training Convolutional Neural Networks (CNNs).

: A Java-based library that includes advanced indicators like ElliottSwingIndicator ElliottFibonacciValidator

to provide continuous proximity scoring rather than just boolean pass/fail checks. DrEdwardPCB/python-taew

: Implements an iterative approach to identify valid waves of different sizes without requiring pre-filtering or denoising of price data. Key Technical Approaches Genetic Algorithms : Repositories like philippe-ostiguy/PyBacktesting

use machine learning to optimize wave parameters based on the Sharpe ratio. Rule Validation

: Most tools enforce classic rules (e.g., Wave 3 cannot be the shortest) using lambda functions and inheritance-based classes. Scoring Systems

: Modern implementations often use weighted factors—such as Fibonacci proximity (35%) and time proportions (20%)—to assign a confidence score to potential scenarios. Learning Resources Visual Guide to Elliott Wave Trading (PDF) : A hosted digital version of a popular trading guide. Elliott Wave Course

: A markdown-based educational resource covering market sentiment and turning point prediction. Python-specific implementation to integrate into your own trading bot, or do you need a labeled dataset for a machine learning project?

alessioricco/ElliottWaves: Elliott Wavers pattern ... - GitHub

2. elliott-wave-js (JavaScript/TypeScript)

🤝 How to Contribute

  1. Fork the repo and open an issue describing a wave detection bug or missing pattern.
  2. Improve the zigzag algorithm – less repainting, better micro‑wave filtering.
  3. Add more corrective patterns – e.g., running flats, expanded flats.
  4. Write tests for wave 3 extensions & truncations.

Join the discussion in the GitHub Discussions tab – we already have 45+ algo traders sharing wave counts for SPX, BTC, and EURUSD.

3. Notable Repositories & Libraries

While new repos appear frequently, here are the types of established projects you should look for:

Step‑by‑Step: Running ewave on Bitcoin Data

  1. Clone and install:

    git clone https://github.com/michaelmachlin/ewave.git
cd ewave
pip install -r requirements.txt

  2. Get price data (using yfinance or ccxt):

    import yfinance as yf
import pandas as pd
btc = yf.download("BTC-USD", period="6mo", interval="1d")

  3. Detect waves:

    from ewave import ewave
btc['wave_label'] = ewave.get_ewave(btc['High'], btc['Low'])

  4. Plot results with Matplotlib:

    import matplotlib.pyplot as plt
plt.plot(btc['Close'])
# Overlay detected wave numbers at swing points

📜 License

MIT – Use freely, but trading decisions are your own responsibility. Past waves never guarantee future moves.

5. FractalWave (Rust + Python bindings)

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