BaggaT236 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.mdreferences/indicator_interpretation_guide.md
Execute the main analysis script:
uv run python3 skills/macro-regime-detector/scripts/macro_regime_detector.py --output-dir reports/This fetches 600 days of data for 9 ETFs + Treasury rates (~10 API calls total). An FMP API key is required to run this skill (the client raises if it is missing). For individual ETFs whose FMP historical-price endpoint returns nothing, the client automatically falls back to yfinance — this fallback needs no additional API key, but it does not remove the FMP key requirement.
Read the generated Markdown report and present findings to user.
Provide additional context using
references/historical_regimes.mdwhen user asks about historical parallels.
Prerequisites
- FMP API Key (required): Set
FMP_API_KEYenvironment 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 usemacro_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 interpretationreferences/indicator_interpretation_guide.md— Guide for interpreting cross-asset ratiosreferences/historical_regimes.md— Historical regime examples for context