- id
- einstein-research-regime
- name
- einstein-research-regime
- description
- Detect structural macro regime transitions (1-2 year horizon) using cross-asset
- version
- 1.0.0
- author
- DaVinci
- last_amended_at
- null
- trigger_patterns
- []
- pre_conditions
- git_repo_required
- false
- tools_available
- []
- expected_output_format
- natural_language
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.pyThis 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.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:
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)