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

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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.

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Read the generated Markdown report and present findings to user.

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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:

• 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 interpretation

• references/indicator_interpretation_guide.md — Guide for interpreting cross-asset ratios

• references/historical_regimes.md — Historical regime examples for context