Agent-Skills.md

Agent Skills: Macro Regime Detector

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.

UncategorizedID: tradermonty/claude-trading-skills/macro-regime-detector
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pnpm dlx add-skill https://github.com/tradermonty/claude-trading-skills/tree/HEAD/skills/macro-regime-detector

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skills/macro-regime-detector/SKILL.md

Skill Metadata

Name
macro-regime-detector
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.

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)

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