analyze-retail-inverse-etf-allocation

Computes retail inverse ETF allocation proxy and evaluates SPX downside risk.

3|1|Updated Jan 12, 2026

Search Tags:#analyze #risk-analysis #ETFs #retail-sentiment #SPX #event-study

Ready to run

npx skills add https://github.com/fatfingererr/macro-skills --skill analyze-retail-inverse-etf-allocation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: analyze-retail-inverse-etf-allocation
Source: https://github.com/fatfingererr/macro-skills/tree/main/skills/analyze-retail-inverse-etf-allocation
Command: npx skills add https://github.com/fatfingererr/macro-skills --skill analyze-retail-inverse-etf-allocation

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a quantitative proxy for retail risk sentiment by analyzing how much inverse leveraged ETFs (做空) participate relative to long ETFs (做多) and how that relates to SPX downside risk.

Core Features & Use Cases

Compute the short allocation proxy by aggregating dollar-volume (Close × Volume) across inverse ETFs against long ETFs.
Apply rolling smoothing and percentile normalization to identify extreme low short allocation events.
Evaluate SPX downside risk with forward-looking measures (returns, drawdowns) and report historical analogs.
Output both human-readable Markdown and machine-consumable JSON reports for dashboards and analyst briefs.

Quick Start

Run the analysis with a date range and default ETF lists.
Inspect the current state, events, and forward risk statistics in the generated report.
Use the provided templates to publish results to teammates or dashboards.

Frequently Asked Questions about analyze-retail-inverse-etf-allocation

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

FAQPage Schema

How do I measure retail leverage sentiment using inverse ETFs?▼

Inverse ETF allocation analysis identifies extreme low short allocation events by applying rolling smoothing and percentile normalization to ETF dollar-volume data. This helps detect unusual retail sentiment shifts that may signal potential SPX downside risk.

Can I use yfinance to download ETF data for calculating short allocation proxies?▼

Yes, yfinance downloads historical ETF price and volume data needed to compute dollar-volume contributions for inverse and long ETFs. The Skill uses pandas and numpy to aggregate Close × Volume and calculate the short allocation proxy for retail sentiment analysis.

What is the relationship between inverse ETF volume and SPX downside risk?▼

Inverse ETF dollar-volume relative to long ETF dollar-volume forms a short allocation proxy used to evaluate SPX downside risk through forward-looking returns and drawdowns. Historical analogs of extreme low short allocation events provide context for assessing future market risk.

How do I identify extreme low short allocation events in ETF data?▼

Rolling smoothing and percentile normalization applied to the inverse-to-long ETF dollar-volume ratio identify extreme low short allocation events. These statistically significant deviations serve as signals for evaluating forward-looking SPX downside risk metrics.

Does this inverse ETF analysis output both JSON and Markdown reports?▼

The Skill outputs both machine-consumable JSON and human-readable Markdown reports containing current state, historical analogs, forward risk statistics, and interpretation caveats. This dual format supports dashboard integration and analyst briefing distribution.

What are the limitations of using inverse ETF allocation as a retail sentiment proxy?▼

Interpretation caveats accompany the analysis because inverse ETF dollar-volume is an indirect retail sentiment proxy subject to distortions from institutional hedging activity and structural ETF mechanics. The report explicitly flags these limitations alongside forward risk statistics.