harmonic

Detect XABCD harmonic patterns and generate D-point trading signals from OHLCV data.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/prinzeval/Vibe-Trading --skill harmonic-prinzeval
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: harmonic
Source: https://github.com/prinzeval/Vibe-Trading/tree/main/VALENDATA/agent/src/skills/harmonic
Command: npx skills add https://github.com/prinzeval/Vibe-Trading --skill harmonic-prinzeval

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyharmonics, pandas, numpy, requests, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Harmonic patterns enable traders to anticipate reversals by identifying XABCD five-point structures (Gartley/Bat/Butterfly/Crab) within price data and by signaling entries in the PRZ (Potential Reversal Zone).

Core Features & Use Cases

  • Detect Gartley, Bat, Butterfly, and Crab patterns using Fibonacci geometry to locate high-probability reversal zones and generate D-point signals.
  • Offer both a pyharmonics-based detector (preferred) and a deterministic built-in fallback, producing bullish or bearish signals at the D point.
  • Operates on OHLCV data across assets (e.g., crypto, stocks) and supports configurable sensitivity and direction.

Quick Start

Run the harmonic engine on your price data to detect XABCD patterns and generate trading signals at the D point.

Frequently Asked Questions about harmonic

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect harmonic patterns in OHLCV price data?

To detect harmonic patterns in OHLCV price data, the engine identifies XABCD five-point structures and generates trading signals at the D point. It processes OHLCV DataFrames to locate Gartley, Bat, Butterfly, and Crab reversal zones.

What are harmonic XABCD patterns and how do they generate trading signals?

Harmonic XABCD patterns are five-point geometric price structures using Fibonacci ratios to identify potential reversal zones. Trading signals are generated at the D point, indicating bullish or bearish entry opportunities within the PRZ.

Can I use pyharmonics to find Gartley and Bat patterns in crypto markets?

Yes, you can use the pyharmonics-based detector to find Gartley and Bat patterns across crypto and fiat markets. The engine operates on OHLCV price data across multiple timeframes and includes a deterministic built-in fallback if pyharmonics is unavailable.

Do I need pandas and numpy to scan OHLCV DataFrames for harmonic patterns?

Yes, you need pandas and numpy to process OHLCV DataFrames for harmonic pattern scanning. These dependencies handle price data manipulation and numerical calculations required for Fibonacci geometry detection and signal generation.

What is the best way to configure harmonic pattern sensitivity for different timeframes?

The best way to configure harmonic pattern sensitivity is by adjusting the configurable sensitivity and direction parameters when running the engine. This allows you to optimize XABCD pattern detection accuracy across multiple timeframes for crypto and fiat markets.

Why does my harmonic pattern detector fail without pyharmonics installed?

Your harmonic pattern detector may fail or fall back to a built-in detector without pyharmonics installed. The engine prefers pyharmonics for Fibonacci-based pattern detection but includes a deterministic fallback to ensure continuous signal generation at D points.