Einstein Research — Macro Regime Detector

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id: 'einstein-research-regime' name: 'einstein-research-regime' 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.' 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'

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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)

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)