Which data sources are used for analysis?

The skill scrapes official ETF websites using Selenium for inventory data and utilizes Yahoo Finance via the yfinance library for market pricing.

How is the Stress Score calculated?

The score is a weighted average of divergence severity (60%), decade-low inventory status (20%), and extreme ratio bonuses (20%).

What specifically does this skill detect?

It detects scenarios where the price of a commodity increases by a set percentage while the corresponding ETF's physical inventory decreases, signaling a potential supply-side squeeze.

Can I use this for non-precious metal ETFs?

Yes, as long as the ETF tracks physical holdings and has a corresponding price ticker, the scripts can be configured to monitor various commodities.

ETF Physical Holdings & Drawdown Monitor

Name: ETF Physical Holdings & Drawdown Monitor
Author: fatfingererr

byfatfingererr

Analytics & Monitoring

Monitors and detects bearish divergence between rising commodity prices and declining physical ETF inventories to assess market supply risk.

About

This skill provides advanced macro-analysis by detecting the 'divergence phenomenon' where commodity prices trend upward while physical ETF holdings (like SLV or GLD) decrease. It employs a multi-metric Stress Score that evaluates inventory drawdown severity, 10-year lows, and price-to-inventory ratios. By cross-referencing COMEX/LBMA data and futures market structures, the skill offers a dual-hypothesis framework to help analysts distinguish between simple fund outflows and genuine physical supply tightness or delivery pressure.

Key Features

Automated detection of price-inventory divergence signals
Selenium-based web scraping for direct ETF provider data
Visual report generation in JSON, PNG, and PDF formats
Stress Score calculation (0-100) based on historical extremes
0 GitHub stars
Cross-validation with futures structures and exchange inventories

Use Cases

Validating the sustainability of commodity price rallies
Identifying physical supply shortages in precious metals markets
Monitoring institutional silver and gold inventory trends

About

Key Features

Automated detection of price-inventory divergence signals
Selenium-based web scraping for direct ETF provider data
Visual report generation in JSON, PNG, and PDF formats
Stress Score calculation (0-100) based on historical extremes
0 GitHub stars
Cross-validation with futures structures and exchange inventories

Use Cases

Validating the sustainability of commodity price rallies
Identifying physical supply shortages in precious metals markets
Monitoring institutional silver and gold inventory trends