Detect Elliott Wave impulse and corrective patterns in financial time-series data.
npx skills add https://github.com/hxhyyy/Vibe-Trading --skill elliott-wave-hxhyyy
Please help me install this Agent Skill. Skill: elliott-wave Source: https://github.com/hxhyyy/Vibe-Trading/tree/main/agent/src/skills/elliott-wave Command: npx skills add https://github.com/hxhyyy/Vibe-Trading --skill elliott-wave-hxhyyy
This skill addresses the subjectivity and complexity of manual Elliott Wave analysis by providing a deterministic, rule-based engine to identify market wave structures and potential reversal points.
Use the elliott-wave skill to analyze the provided market data and generate buy or sell signals based on current wave structures.
High-intent search queries and answers about installing and using this skill.
Automated Elliott Wave pattern detection identifies swing points in financial time-series data using Zigzag analysis and validates them against Fibonacci ratios. It flags completed 5-wave impulse and 3-wave corrective structures to generate trend reversal signals.
Validating Elliott Wave structures with Fibonacci relationships requires applying strict ratio checks to wave segments. This skill enforces classic technical theory rules to filter market signals and maintain a conservative, low-false-positive approach.
Scanning multiple assets for completed 5-wave impulse structures is supported by processing financial time-series data. The engine evaluates swing points and Fibonacci relationships to identify potential trend tops or ABC correction entry signals.
Automated Elliott Wave analysis works with pandas and numpy to process time-series data. It outputs buy or sell signals based on detected wave structures, supporting automated trading strategy development and trend analysis.
Automated Elliott Wave detection uses a conservative low-false-positive approach to prevent inaccurate trading signals. It implements rigorous technical rules to filter market noise, ensuring only validated impulse and corrective wave completions are flagged.
Identifying ABC correction entry signals involves detecting 3-wave corrective patterns in time-series data. The engine validates these wave structures against Fibonacci ratios to flag potential entry points for trading strategies.
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