macro-regime-detector

name: macro-regime-detectordescription: 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.

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 -m pip install -r skills/macro-regime-detector/requirements.txt
   uv run python3 skills/macro-regime-detector/scripts/macro_regime_detector.py --output-dir reports/
   

   This fetches 600 days of data for 9 ETFs. With an FMP key, the client tries FMP first and fetches Treasury rates (~10 API calls total), then falls back to yfinance for unavailable ETF history. Without an FMP key, it runs in yfinance-only mode and uses SHY/TLT as the yield-curve fallback.

   The detector fails closed and writes no report when none of its six components has usable data. Do not treat a missing report or non-zero exit as a valid low-transition regime.

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

• Python dependencies (required): install requirements.txt, including yfinance and requests
• FMP API Key (optional): set FMP_API_KEY or pass --api-key to use FMP and Treasury data before the yfinance/SHY-TLT fallbacks
• The FMP free tier may not serve every ETF; unavailable symbols automatically use yfinance

6 Components

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

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