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detect-us-equity-valuation-percentile-extreme

Convert US equity valuation metrics into historical percentiles and composite risk scores.

3|1|Updated Jan 12, 2026
Search Tags:#valuation#risk#data-sources#US-equities#percentile#CAPE
One-click install
npx skills add https://github.com/fatfingererr/macro-skills --skill detect-us-equity-valuation-percentile-extreme
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: detect-us-equity-valuation-percentile-extreme
Source: https://github.com/fatfingererr/macro-skills/tree/main/skills/detect-us-equity-valuation-percentile-extreme
Command: npx skills add https://github.com/fatfingererr/macro-skills --skill detect-us-equity-valuation-percentile-extreme

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy, requests, yfinance, matplotlib, openpyxl, xlrd, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill assesses whether US equity valuations are historically extreme by turning multiple valuation metrics into historical percentiles and aggregating them into a single risk score. It enables users to understand relative valuation strength, track potential earnings compression, and gauge tail risks.

Core Features & Use Cases

  • Convert indicators like CAPE, trailing P/E, forward P/E, P/B, P/S, and market cap to GDP into 0-100 percentiles based on long-run history.
  • Combine metrics via mean, median, or trimmed_mean to produce a robust composite score and flag extreme periods.
  • Identify historical analogs (e.g., 1929, 1965, 1999, 2021) and output post-event statistics for risk assessment and scenario planning.
  • Provide a risk-interpretation framework that communicates that high valuations do not guarantee a crash but imply upper-tail risk and potential returns compression.

Quick Start

  • cd skills/detect-us-equity-valuation-percentile-extreme
  • pip install pandas numpy yfinance requests matplotlib openpyxl xlrd
  • python scripts/visualize_valuation.py -o output
  • python scripts/valuation_percentile.py --quick
  • python scripts/valuation_percentile.py --as_of_date 2026-01-21 --universe "^GSPC" --metrics "cape,mktcap_to_gdp,trailing_pe,pb" --output result.json

Frequently Asked Questions about detect-us-equity-valuation-percentile-extreme

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

FAQPage Schema
How do I calculate historical percentiles for US equity valuation metrics like CAPE?▼

To calculate historical percentiles for US equity valuation metrics, convert indicators like CAPE, trailing P/E, and P/B into 0-100 percentiles based on long-run history to quantify whether current valuations are historically extreme.

What is the best way to assess if the stock market is historically overvalued?▼

Assessing if the stock market is historically overvalued involves aggregating multiple valuation metrics into a single composite risk score to track relative valuation strength, earnings compression, and tail risks.

Can I use yfinance to detect extreme valuation periods in US equities?▼

Yes, you can use yfinance to detect extreme valuation periods in US equities by running configurable scripts that fetch market data and identify historical analogs like 1929 or 1999 for scenario planning.

How do I combine multiple valuation indicators into a composite risk score?▼

Combine multiple valuation indicators into a composite risk score by aggregating metrics via mean, median, or trimmed_mean calculations to produce a robust score that flags extreme valuation periods.

Does a high historical percentile guarantee an equity market crash?▼

A high historical percentile does not guarantee an equity market crash; the risk-interpretation framework communicates that extreme valuations imply upper-tail risk and potential returns compression rather than immediate crashes.

What Python dependencies do I need for US equity valuation percentile analysis?▼

For US equity valuation percentile analysis, you need Python dependencies including pandas, numpy, requests, yfinance, matplotlib, openpyxl, and xlrd to run the calculation and visualization scripts.

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