correlation-regime

Detect market correlation regimes and attribute crisis first-movers using edge-density analysis.

Updated Jul 29, 2026

Search Tags:#market analysis #risk management #finance #correlation #regime detection #crisis attribution

Ready to run

npx skills add https://github.com/santoosaraujo/vibe-trading-claude --skill correlation-regime

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: correlation-regime
Source: https://github.com/santoosaraujo/vibe-trading-claude/tree/main/.claude/skills/correlation-regime
Command: npx skills add https://github.com/santoosaraujo/vibe-trading-claude --skill correlation-regime

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pandas, numpy.

What problem does it solve?

This skill solves the problem of blind portfolio management during market stress by identifying when assets fuse into a single correlated bloc and pinpointing the specific asset that triggered a crisis.

Core Features & Use Cases

Regime Detection: Uses edge-density and hysteresis to identify when diversification has collapsed, providing a clear state machine for risk monitoring.
Crisis Attribution: Employs a robust honesty protocol to identify the first-mover in a market collapse, distinguishing between macro shocks and specific asset triggers.
Rewiring Analysis: Ranks assets by how much their correlation profile has changed, allowing for the detection of slow, grinding bleed-outs that don't trigger violent alarms.

Quick Start

Use the correlation-regime skill to analyze the current market state and identify potential crisis triggers from the provided return data.

Frequently Asked Questions about correlation-regime

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

FAQPage Schema

What is crisis attribution and how does it identify market stress triggers?▼

Crisis attribution pinpoints the first-mover in a market collapse using robust z-score intensity scoring. It applies a robust honesty protocol to distinguish specific asset triggers from broader macro shocks during post-hoc market event analysis.

Can I use pandas and numpy for post-hoc market event analysis?▼

You analyze correlation rewiring by ranking assets based on how much their correlation profile has changed over time. This detects slow, grinding bleed-outs that erode portfolios without triggering violent market alarms.

How do I analyze correlation rewiring to detect slow asset bleed-outs?▼

What is the best way to identify when diversification collapses across multiple assets?▼

No, the regime detection uses causal, non-look-ahead processing to ensure accurate crisis attribution. It provides clear, non-predictive risk context by relying strictly on historical return data without future information leakage.

Does this regime detection approach use look-ahead bias for crisis attribution?▼