us-market-bubble-detector

Score US and Japanese equity market bubble risk with quantitative indicators and qualitative checks.

Updated Apr 17, 2026
One-click install
npx skills add https://github.com/pasie15/claude-trading-skills-marketplace --skill us-market-bubble-detector-pasie15
Or copy as Structured Prompt for Agent
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Skill: us-market-bubble-detector
Source: https://github.com/pasie15/claude-trading-skills-marketplace/tree/main/plugins/trading-portfolio-risk/skills/us-market-bubble-detector
Command: npx skills add https://github.com/pasie15/claude-trading-skills-marketplace --skill us-market-bubble-detector-pasie15

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill reduces uncertainty about whether equity markets are in a bubble by providing a reproducible, data-driven evaluation and clear risk-budget recommendations that replace subjective impressions and unverified narratives.

Core Features & Use Cases

  • Mechanical Quantitative Scoring: Computes a numeric Phase 2 score from Put/Call, VIX, margin debt YoY, IPO heat, breadth, and price acceleration using defined thresholds.
  • Strict Qualitative Adjustment: Applies a capped Phase 3 adjustment (+0 to +3) with a confirmation-bias prevention checklist requiring measurable evidence such as Google Trends, mainstream coverage, and direct non-investor reports.
  • Actionable Risk Guidance: Produces a final score (0–15), bubble phase (Normal → Critical), recommended risk budget, stair-step profit-taking and ATR stop guidance, and short-selling composite conditions.
  • Reference-backed Implementation: Includes step-by-step guides, historical case studies, quick reference checklists, and data source links for US and Japanese markets.

Quick Start

Use the skill to evaluate current US market bubble risk by collecting Put/Call, VIX, margin debt, breadth, IPO, and price acceleration data and generate the Bubble Evaluation Report.

Frequently Asked Questions about us-market-bubble-detector

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

FAQPage Schema
How do I evaluate US market bubble risk using quantitative indicators?

You evaluate US market bubble risk by collecting Put/Call ratio, VIX, FINRA margin debt, breadth metrics, IPO counts, and price acceleration data to compute a reproducible numeric bubble score. This mechanical scoring applies defined thresholds to generate a final risk rating and actionable guidance.

What data do I need to calculate a market bubble score for equity portfolios?

To calculate a market bubble score, you need current Put/Call ratios, VIX levels, FINRA margin debt year-over-year changes, market breadth metrics, IPO counts, and price acceleration percentiles. These inputs drive the two-phase quantitative scoring system for objective risk assessment.

Does the bubble detection scoring include qualitative market signals?

Yes, the bubble detection scoring includes a strict qualitative adjustment phase capped at +0 to +3 points. This phase requires measurable evidence like Google Trends, mainstream media coverage, and direct non-investor reports, applying a confirmation-bias prevention checklist.

Can I use this bubble risk score for Japanese equity markets and short-selling scenarios?

Yes, you can apply this bubble risk score to both US and Japanese equity markets. It supports portfolio risk decisions, profit-taking guidance, entry timing, and short-selling scenarios by providing stage-based actions and composite short-selling conditions based on the final score.

How do I translate a bubble risk score into a risk budget and profit-taking strategy?

The bubble risk score translates into a risk budget recommendation by mapping the final 0-15 score to a bubble phase ranging from Normal to Critical. It then provides stair-step profit-taking guidance and ATR stop levels to manage equity positions mechanically.

What are the limitations of using margin debt and VIX for bubble detection?

Using margin debt and VIX for bubble detection is limited by the strict two-phase scoring process, which caps qualitative adjustments to prevent subjective bias. The system requires verifiable data sources and documented evidence, ensuring limitations are managed through mechanical thresholds rather than narrative assumptions.