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 Load reference documents for methodology context: references/regime_detection_methodology.md references/indicator_interpretation_guide.md 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). Read the generated Markdown report and present findings to user. 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: Current Regime Assessment Transition Signal Dashboard Component Details Regime Classification Evidence 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 ) Resources references/regime_detection_methodology.md — Detection methodology and signal interpretation references/indicator_interpretation_guide.md — Guide for interpreting cross-asset ratios references/historical_regimes.md — Historical regime examples for context