SkillsAdd

mphinance/alpha-skills

macro-regime-detector

Detect structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis.

First seen May 9, 2026

Installation

$ npx skills add mphinance/alpha-skills --skill macro-regime-detector
View on skills.sh ↗ GitHub repository ↗

Summary

  • 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.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Also listed on

Alternate registries and mirrors of this skill.

Repository health

Stars 21
License LICENSE
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 4,169 B
  • docs SUMMARY.md 474 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 83 installs

SKILL.md

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/regimedetectionmethodology.md - references/indicatorinterpretationguide.md

  1. Execute the main analysis script:

``bash python3 skills/macro-regime-detector/scripts/macroregimedetector.py `` This fetches 600 days of data for 9 ETFs + Treasury rates (10 API calls total).

  1. Read the generated Markdown report and present findings to user.
  1. Provide additional context using references/historical_regimes.md when user asks about historical parallels.

Prerequisites

  • FMP API Key (required): Set FMPAPIKEY 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

  • macroregimeYYYY-MM-DD_HHMMSS.json — Structured data for programmatic use
  • macroregimeYYYY-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/regimedetectionmethodology.md — Detection methodology and signal interpretation
  • references/indicatorinterpretationguide.md — Guide for interpreting cross-asset ratios
  • references/historical_regimes.md — Historical regime examples for context