Elliott Wave Github May 2026

Key research, such as "ElliottAgents" and studies on Forex profitability, utilizes computational methods to automate Elliott Wave Theory (EWP) analysis. Notable GitHub repositories for implementing these techniques include python-taew, ElliottWaveAnalyzer, and projects focusing on machine learning, such as EW_Dataset. Explore these resources and more on GitHub. an open source dataset of Elliott Wave Impulses · GitHub

Elliott Wave theory is a method of technical analysis that seeks to identify recurrent price patterns driven by investor psychology. On GitHub, you can find various open-source tools and datasets designed to automate wave detection, backtest strategies, and even train machine learning models to recognize these patterns. Notable Elliott Wave GitHub Repositories

Several developers have shared libraries and applications to help traders move away from subjective, manual wave counting.

alessioricco/ElliottWaves: A Python-based analysis tool that uses a main function, ElliottWaveFindPattern, to discover and filter wave chains from pandas DataFrames. It relies on matplotlib to visualize identified patterns overlaid on price charts.

drstevendev/ElliottWaveAnalyzer: This repository provides a scanner that breaks down charts into "MonoWaves" (the smallest trend elements) to find 12345 impulsive movements. It includes a get_data.py helper to pull financial data directly from Yahoo Finance.

ESJavadex/elliot-waves-auto: A comprehensive web application that detects wave structures and automatically projects future price zones using Fibonacci retracements and extensions. It also offers trade recommendations, including suggested entry and stop-loss levels.

DrEdwardPCB/python-taew: A specialized package for Elliott wave labeling and backtracking based on academic research into the profitability of wave theory in foreign exchange markets.

A-J-Financial-Solutions/EW_Dataset: An open-source dataset designed for training Convolutional Neural Networks (CNNs) to recognize impulse waves. It consists of labeled chart images and historical price data.

Elliott Wave Theory Explained | Patterns, Waves & Trading Strategy

Here’s a social/technical post you can use for LinkedIn, Twitter (X), or a trading community forum like Reddit’s r/algotrading:

📈 Post Title: Finding Elliott Wave Code & Tools on GitHub

If you’re automating Elliott Wave analysis—or just backtesting wave counts—GitHub has some solid open-source resources.

🔍 What to look for:

• elliottwave – Python libraries for zigzag detection & swing identification
• fractal wave + github – Alternative labeling logic
• wave trend indicator – Often combined with EW rules

⭐ Top finds (as of 2026):

• ew-python – Basic wave labeling using price swing points
• TradingView EW scripts (converted to Python) – Manual rule checkers
• LSTM + Elliott Wave experiments – Mostly academic, but useful for pattern recognition

⚠️ Caveats:

• No GitHub repo can perfectly auto-label waves (Elliott is subjective).
• Best used for swing detection and rule validation, not fully automated trading.

💡 Pro tip:
Search GitHub with:
"elliott wave" language:python
or
zigzag indicator waves

Then filter by recent commits (last year) to avoid abandoned code.

Want me to turn this into a short LinkedIn caption or a Reddit-style comment instead?

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

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.

Since "Elliott Wave GitHub" isn't a single official repository, this guide breaks down how to use GitHub to find, evaluate, and utilize Elliott Wave tools for trading and analysis.

GitHub is the best place to find open-source code for Elliott Wave analysis, ranging from simple pattern recognition scripts to full-fledged automated trading bots.

🧭 Final Word

Elliott Wave on GitHub is more than just code – it’s an attempt to bring objectivity to a famously subjective discipline. Whether you’re a skeptic or a practitioner, this toolkit will help you backtest wave counts without emotional bias.

Star this repo, try it on your favorite chart, and let’s collectively improve algorithmic pattern recognition in financial markets.

Happy wave hunting – and remember, wave 5 is the place to take profits, not chase highs. 🐋

Links:
📘 Full Documentation
🐞 Issue Tracker
💬 Discord for Real‑Time Waves

# config.yaml example
zigzag:
  depth_pct: 0.03  # 3% reversal to consider a pivot
  extended_depth: 0.05
fibonacci:
wave3_min_ratio: 1.0
wave3_max_ratio: 2.618
wave2_retrace_max: 1.0

“Markets are never wrong, but opinions often are.” – The Wave Principle elliott wave 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?

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!

GitHub hosts several "Elliott Wave" projects that range from automated pattern scanners to machine learning datasets. Because Elliott Wave Theory is subjective, these repositories use different algorithmic approaches to identify impulse and corrective waves.  Top Elliott Wave Repositories 

ElliottWaveAnalyzer: An iterative scanner that finds "monowaves" in financial data. It validates combinations of waves against rules for 12345 impulsive movements and ABC corrections.

python-taew: A specialized package for Elliott Wave labeling. It uses an iterative approach to identify valid sequences (Wave 1 through Wave 5) and can handle different wave sizes without needing to denoise the data first.

PyBacktesting: A project focused on forecasting markets by optimizing Elliott Wave parameters using genetic algorithms. It has been tested on FOREX pairs like EUR/USD.

EW_Dataset: An open-source contribution that provides labeled chart images of impulse wave structures. It is designed for training Convolutional Neural Networks (CNNs) to recognize patterns automatically.

ElliottWaves: A core Python script (elliottwaves.py) used to detect recurrent long-term price patterns based on investor sentiment.

Strategy-ElliottWave: Contains MQL files (like Stg_ElliottWave.mq4) for implementing automated Elliott Wave strategies in MetaTrader.  Key Implementation Types 

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

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 Key research, such as "ElliottAgents" and studies on

: 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

Searching for Elliott Wave implementations on GitHub reveals several high-quality open-source projects ranging from basic pattern recognizers to advanced machine learning models.

Below is a review of the top-performing repositories categorized by their specific utility. Top Elliott Wave Repositories on GitHub 1. Automated Pattern Analysis & Scanning ElliottWaveAnalyzer (drstevendev)

: This tool is designed to find 12345 impulsive movements and ABC corrections in financial data. Highlights

: It uses a concept called "MonoWaves" to identify micro-trends. Customization

: You can create custom validation rules via class inheritance, making it highly flexible for specific trading styles. python-taew (DrEdwardPCB) : A dedicated library for labeling Elliott Waves in Python. Highlights

: Unlike some versions that rely on simple SMA/EMA filters, this uses an iterative approach to identify valid wave structures, though it may take longer to compute. ElliottWaves (alessioricco)

: A script focused on finding patterns in financial data using a function called ElliottWaveFindPattern Highlights

: It allows for granular control over the data start/end and measure parameters, suitable for historical analysis. 2. Machine Learning & Quantitative Research PyBacktesting (philippe-ostiguy)

: Uses genetic algorithms to optimize Elliott Wave parameters. Performance

: In tests on EUR/USD hourly data, it achieved a Sharpe ratio above 3 during training.

: The author notes potential overfitting, as testing results were significantly mixed compared to training performance. EW_Dataset

: An open-source dataset of impulse wave images designed to train Convolutional Neural Networks (CNNs). Highlights

: Perfect for developers looking to build their own AI-based wave recognition tools rather than relying on manual rules. 3. Platform-Specific Implementations tradingview-pine-scripts

: Contains Pine Script code for an "Elliot Wave - Impulse Strategy". : Best for traders who prefer using TradingView directly for automated alerts. Strategy-ElliottWave

: A multi-language implementation (Jinja, MQL4, MQL5, C) for MetaTrader platforms. Expert Summary & Considerations

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

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

While there isn't a single "official" paper titled "Elliott Wave GitHub," there are several high-quality research papers and open-source projects on GitHub that bridge the gap between Elliott Wave Theory and modern computational finance. Featured Research & Projects

ElliottAgents: A Natural Language-Driven Multi-Agent System: This 2025 paper introduces a multi-agent AI system that uses Natural Language Processing (NLP) and Large Language Models (LLMs) to collaboratively interpret Elliott Wave patterns.

Optimizing Elliott Wave Theory via Genetic Algorithms: A project by Philippe Ostiguy that models the theory for forecasting and optimizes parameters using genetic algorithms.

Elliott Wave Impulses Dataset: An open-source contribution focused on recognizing wave patterns using Convolutional Neural Networks (CNNs), providing a labeled dataset of impulse wave structures.

Combining Elliott Wave with LSTM: A technical repository exploring the fusion of traditional Elliott Wave points with Long Short-Term Memory (LSTM) deep learning models for price prediction. 📈 Post Title: Finding Elliott Wave Code &

python-taew: Elliott Wave Labelling: A Python implementation of the methods discussed in the paper Profitability of Elliott Waves and Fibonacci Retracement Levels in the Foreign Exchange Market. Core Implementation Libraries

ElliottWaveAnalyzer: An algorithmic tool that validates possible wave combinations against established rules (e.g., 1-2-3-4-5 impulsive movements).

ElliottWaves Python Script: A script specifically designed for finding and analyzing recurrent long-term price patterns based on investor sentiment.

elliot-waves-auto: A web application that visualizes patterns, validates sequences, and projects Fibonacci-based price zones. Academic Background

For the theoretical foundation these GitHub projects are built upon, you can refer to the following studies: DrEdwardPCB/python-taew: elliott wave labelling - GitHub

Searching for "Elliott Wave" on GitHub provides access to various open-source implementations for automated pattern recognition, backtesting, and quantitative analysis. These repositories generally fall into three categories: automated labeling scripts, machine learning-driven models, and educational datasets. Automated Recognition & Labeling

These projects focus on the algorithmic identification of impulse and corrective waves based on historical price data. python-taew

: A specialized library for labeling Elliott Waves in Python. It returns structured data including price levels and wave indices for easier integration into trading bots. ElliottWaveAnalyzer

: 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

: A script designed for recurring pattern analysis to track investor sentiment and market psychology. Machine Learning & Strategy Testing

Advanced repositories utilize genetic algorithms and neural networks to optimize wave parameters or predict future movements. PyBacktesting : Models Elliott Wave Theory using genetic algorithms

for parameter optimization. A notable experiment on EUR/USD showed excellent training results (Sharpe ratio > 3), though results were mixed in live testing due to overfitting. elliot-waves-auto

: A Python tool that combines wave theory with indicators like

. It provides price projection zones based on Fibonacci levels and automated trade recommendations. Strategy-ElliottWave

: An implementation of automated trading strategies specifically built around Elliott Wave indicators for platforms like MetaTrader. Educational Resources & Datasets

For developers looking to build their own models, GitHub hosts curated data and comprehensive guides. DrEdwardPCB/python-taew: elliott wave labelling - GitHub

Automating Elliott Wave Theory with GitHub Tools Elliott Wave Theory (EWT) is a staple of technical analysis that identifies fractal price patterns based on investor psychology. While powerful, manual wave counting is often criticized for being subjective. Developers on GitHub are bridging this gap by creating open-source libraries to automate wave detection, validation, and backtesting. Top Elliott Wave Repositories on GitHub

For developers and traders looking to implement EWT programmatically, several Python-based projects provide robust frameworks for pattern recognition.

ElliottWaveAnalyzer: This tool scans financial data to find "monowaves" and validates them against rules for 12345 impulse movements and ABC corrections.

Core Feature: Uses a rule-based engine where users can define custom constraints, such as ensuring "wave 3 is not the shortest".

Automation: Includes a scanner that tries millions of wave combinations to find the best fit for a given chart.

elliot-waves-auto: A comprehensive web application designed for both visualization and trade planning.

Analytics: Combines EWT with technical indicators like RSI and ATR to provide entry, stop-loss, and take-profit levels.

Projections: Generates future price zones based on Fibonacci retracement and extension levels.

python-taew: A dedicated package for Elliott Wave labeling and backtracking.

Focus: Specifically built to facilitate private research projects by providing a clean implementation of wave labeling rules.

ElliottWaves (alessioricco): A script-based tool that uses pandas and matplotlib to discover and plot wave patterns.

Functionality: Offers an ElliottWaveFindPattern function that subsets data and finds the best-fit wave chain set. Integrating Machine Learning and EWT

Recent GitHub trends show a shift toward using Machine Learning to solve the subjectivity of wave counting.

EW_Dataset: An open-source project dedicated to building a large dataset of impulse wave structures to train Convolutional Neural Networks (CNNs).

PyBacktesting: Uses genetic algorithms to optimize EWT parameters for better market forecasting. Key Elliott Wave Patterns to Automate

When building or using these tools, the software typically checks for these primary structures:

2. Pine Script: ElliottWaveDrawer (by @kor3k)

Best for: TradingView users. Pine Script is the native language of TradingView. This script plots automatic wave counts directly on your chart using Zigzag indicators to identify pivot points.

• Key Feature: Visual drawing of channels and fib extensions.
• Limitation: Pine Script has a security limitation (cannot look forward), so it will repaint past waves as new data arrives.

🔧 Key Features of This Repository

✅ Automated Impulse Wave Detection – Identifies 5‑wave structures (with rules: wave 2 cannot retrace >100% of wave 1, wave 3 is never the shortest, wave 4 doesn’t overlap wave 1 in price).

✅ Corrective Pattern Recognition – Zigzags (5‑3‑5), Flats (3‑3‑5), Triangles, and Double Threes.

✅ Fibonacci Ratio Validation – Checks if waves adhere to common retracements (0.382, 0.5, 0.618, 0.786) and extensions (1.272, 1.618).

✅ Multi‑Timeframe Fractal Analysis – From 1‑minute to weekly bars, via configurable zigzag thresholds.

✅ Visual Labeling – Plotly and Matplotlib outputs with wave numbers (1,2,3,4,5) and corrective letters (A,B,C).

✅ Backtesting Engine – Simulate entry/exit at wave 3 or wave C completions.

✅ Live Data Integration – CCXT (crypto), Yahoo Finance (stocks), and OANDA (forex).

5. Step-by-Step: Running a Python Elliott Wave Script

If you find a Python repository you want to use, here is the standard workflow:

Prerequisites: Install Python and Git on your computer.

1. Clone the Repo:

   git clone https://github.com/username/elliott-wave-repo.git
   

2. Install Dependencies: Navigate to the folder and look for a requirements.txt file. Run:

   pip install -r requirements.txt
   

   (This installs pandas, matplotlib, numpy, etc.)
3. Prepare Data: Most scripts require a CSV file with market data (Date, Open, High, Low, Close). You can download this from Yahoo Finance or your broker.
4. Run the Script:

   python main.py
   

5. Analyze Output: The script will likely output an image (chart) or a text file predicting the next wave count.

1. The Challenge of Automation

Before diving into repositories, it is important to understand why "Elliott Wave GitHub" is a complex search term. Unlike Simple Moving Averages or RSI, Elliott Wave theory is highly subjective. Rules such as "Wave 2 cannot retract more than 100% of Wave 1" are strict, but rules regarding wave degree and internal structure often rely on the analyst's discretion.

Consequently, repositories on GitHub generally fall into two categories:

1. Plotting/Drawing Libraries: Tools that make it easier for a human to draw waves on a chart.
2. Detection Algorithms: Scripts that attempt to identify wave counts algorithmically (often with varying degrees of success).

2. Top Python Repositories

Python is the dominant language for quantitative finance on GitHub. Most Elliott Wave libraries here are built on top of pandas for data handling and matplotlib or plotly for visualization.

3. TradingView-Elliott-Wave (Pine Script)

• Stars: ~450
• Author: flyingwst
• Features:
  • Full Elliott Wave oscillator (EWO) and automatic labeling.
  • Detects wave degree (Grand Supercycle to Subminuette).
  • Includes alerts for potential wave completions.
• Installation: Copy script into TradingView Pine Editor → Add to chart.