Map stock chart turning points to company events and peer context.
npx skills add https://github.com/Peiwvy/myskills --skill vault-company-analysis
Please help me install this Agent Skill. Skill: vault-company-analysis Source: https://github.com/Peiwvy/myskills/tree/main/vault-company-analysis Command: npx skills add https://github.com/Peiwvy/myskills --skill vault-company-analysis
It helps you understand why a specific company’s stock price changed by mapping chart turning points to real company events and market logic instead of relying on chart interpretation alone.
Tell the skill to analyze a specific company’s recent K-line and generate the node/segment timeline plus 3–5 items of what to watch on its F33-Micro page.
High-intent search queries and answers about installing and using this skill.
Stock chart turning points are mapped by identifying nodes (information shocks) and segments (price fermentation) on daily K-lines, then linking them to verifiable company announcements and peer-driven context to explain price movements.
Alpha and beta driver labeling separates company-specific drivers (alpha) from industry-wide resonance (beta) by comparing a stock's event timeline with at least one peer during identical time windows around chart turning points.
Event-driven timeline writing supports A-shares, Hong Kong stocks, and US stocks by fetching daily OHLC with moving averages, scanning weekly reaction ranges, and mapping announcements to nodes for cross-market coverage.
Generate a what-to-watch section by decomposing recent K-lines into nodes and segments, comparing peers for alpha/beta labeling, and writing 3-5 unique items into the target micro page timeline without duplicating existing entries.
When TradingView OHLC data is missing, event-driven stock analysis falls back to event-based fundamentals by searching announcements and news around identified nodes to reconstruct the timeline without relying solely on chart data.
Stock analysis requires comparing at least one peer during identical time windows around each node to label drivers as company-specific (alpha) or industry-wide resonance (beta) through cross-sectional alignment.
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