analyze-retail-inverse-etf-allocation
CommunityQuantify retail leverage via inverse ETFs.
Authorfatfingererr
Version1.0.0
Installs0
System Documentation
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.
Dependency Matrix
Required Modules
numpypandasyfinancematplotlib
Components
scriptsreferences
💻 Claude Code Installation
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Please help me install this Skill: Name: analyze-retail-inverse-etf-allocation Download link: https://github.com/fatfingererr/macro-skills/archive/main.zip#analyze-retail-inverse-etf-allocation Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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