Identify XABCD harmonic patterns and generate long/short signals at the D point.
npx skills add https://github.com/loanntc/Paave --skill harmonic-patterns
Please help me install this Agent Skill. Skill: harmonic-patterns Source: https://github.com/loanntc/Paave/tree/main/skills/harmonic Command: npx skills add https://github.com/loanntc/Paave --skill harmonic-patterns
It helps analysts automatically find XABCD harmonic chart structures and convert them into actionable long/short signals near the PRZ reversal zone, reducing manual pattern scanning effort.
Run the harmonic-patterns signal engine on your OHLCV DataFrame(s) and return a signal series where 1 indicates long, -1 indicates short, and 0 indicates stand aside.
High-intent search queries and answers about installing and using this skill.
To detect harmonic patterns in OHLCV time series data, the Skill identifies XABCD chart structures using Fibonacci retracement and extension rules. It processes batch OHLCV inputs with high, low, open, and close prices to find Gartley, Bat, Butterfly, and Crab patterns across instruments.
Fibonacci geometry and PRZ logic generate trading signals by identifying the XABCD swing points and emitting reversal signals at the D point. A bullish D bottom outputs a 1 for long, a bearish D top outputs -1 for short, and 0 stands for neutral.
Yes, you can scan crypto OHLCV data for Gartley or Bat patterns for backtesting. The Skill applies batch processing to OHLCV time series for BTC, ETH, or SOL, generating a per-timestamp signal series suitable for trading signal research and strategy prototypes.
You do not need pyharmonics installed to identify XABCD chart patterns. The Skill prefers pyharmonics for richer detection but includes a built-in fallback swing-combination detector that automatically processes Fibonacci geometry when the dependency is unavailable.
Harmonic pattern detection specifically identifies structural XABCD chart formations and PRZ reversal zones using Fibonacci geometry, whereas general time series analysis evaluates broader statistical trends. This targeted approach outputs discrete long, short, and neutral signals for market scanning.
A limitation of using harmonic patterns for reversal signals is that outputs are strictly 1, -1, or 0 at the D point, lacking continuous price targets. It requires structured OHLCV inputs with timestamps and focuses purely on Fibonacci-based PRZ setups without guaranteed outcomes.
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