macro-regime-detector

name	macro-regime-detector
description	Detect structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis. Analyze RSP/SPY concentration, yield curve, credit conditions, size factor, equity-bond relationship, and sector rotation to identify regime shifts between Concentration, Broadening, Contraction, Inflationary, and Transitional states. Run when user asks about macro regime, market regime change, structural rotation, or long-term market positioning.
allowed-tools	["Bash(python3:*)"]

Macro Regime Detector

Detect structural macro regime transitions using monthly-frequency cross-asset ratio analysis. This skill identifies 1-2 year regime shifts that inform strategic portfolio positioning.

When to Use

• User asks about current macro regime or regime transitions
• User wants to understand structural market rotations (concentration vs broadening)
• User asks about long-term positioning based on yield curve, credit, or cross-asset signals
• User references RSP/SPY ratio, IWM/SPY, HYG/LQD, or other cross-asset ratios
• User wants to assess whether a regime change is underway

Workflow

1. Load reference documents for methodology context:

   • references/regime_detection_methodology.md
   • references/indicator_interpretation_guide.md

2. Execute the main analysis script:

   python3 skills/macro-regime-detector/scripts/macro_regime_detector.py
   

   This fetches 600 days of data for 9 ETFs + Treasury rates (10 API calls total).

3. Read the generated Markdown report and present findings to user.

4. Provide additional context using references/historical_regimes.md when user asks about historical parallels.

Prerequisites

• FMP API Key (required): Set FMP_API_KEY environment variable or pass --api-key
• Free tier (250 calls/day) is sufficient (script uses ~10 calls)

6 Components

#	Component	Ratio/Data	Weight	What It Detects
1	Market Concentration	RSP/SPY	25%	Mega-cap concentration vs market broadening
2	Yield Curve	10Y-2Y spread	20%	Interest rate cycle transitions
3	Credit Conditions	HYG/LQD	15%	Credit cycle risk appetite
4	Size Factor	IWM/SPY	15%	Small vs large cap rotation
5	Equity-Bond	SPY/TLT + correlation	15%	Stock-bond relationship regime
6	Sector Rotation	XLY/XLP	10%	Cyclical vs defensive appetite

5 Regime Classifications

• Concentration: Mega-cap leadership, narrow market
• Broadening: Expanding participation, small-cap/value rotation
• Contraction: Credit tightening, defensive rotation, risk-off
• Inflationary: Positive stock-bond correlation, traditional hedging fails
• Transitional: Multiple signals but unclear pattern

Output

• macro_regime_YYYY-MM-DD_HHMMSS.json — Structured data for programmatic use
• macro_regime_YYYY-MM-DD_HHMMSS.md — Human-readable report with:
  1. Current Regime Assessment
  2. Transition Signal Dashboard
  3. Component Details
  4. Regime Classification Evidence
  5. Portfolio Posture Recommendations

Relationship to Other Skills

Aspect	Macro Regime Detector	Market Top Detector	Market Breadth Analyzer
Time Horizon	1-2 years (structural)	2-8 weeks (tactical)	Current snapshot
Data Granularity	Monthly (6M/12M SMA)	Daily (25 business days)	Daily CSV
Detection Target	Regime transitions	10-20% corrections	Breadth health score
API Calls	~10	~33	0 (Free CSV)

Script Arguments

python3 macro_regime_detector.py [options]

Options:
  --api-key KEY       FMP API key (default: $FMP_API_KEY)
  --output-dir DIR    Output directory (default: current directory)
  --days N            Days of history to fetch (default: 600)