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macro-regime-detector

Identify macro regime transitions using cross-asset ratio analysis.

2|Updated Apr 6, 2026
Search Tags:#yield curve#macro analysis#regime detection#cross-asset analysis#credit conditions
One-click install
npx skills add https://github.com/k1064190/stock-expectation --skill macro-regime-detector-k1064190
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: macro-regime-detector
Source: https://github.com/k1064190/stock-expectation/tree/main/.claude/skills/macro-regime-detector
Command: npx skills add https://github.com/k1064190/stock-expectation --skill macro-regime-detector-k1064190

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, fmp_client, pandas, numpy, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill identifies macro regime transitions (1-2 year horizon) using cross-asset ratio analysis, helping users understand market structure and make informed investment decisions.

Core Features & Use Cases

  • Cross-Asset Ratio Analysis: Analyze RSP/SPY concentration, yield curve, credit conditions, size factor, equity-bond relationship, and sector rotation.
  • Regime Classification: Identify regime shifts between Concentration, Broadening, Contraction, Inflationary, and Transitional states.
  • Use Case: When a user wants to understand the current macro regime or market regime change, this Skill provides insights based on cross-asset signals.

Quick Start

Use the macro-regime-detector skill to analyze the current macro regime.

Frequently Asked Questions about macro-regime-detector

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect macro regime transitions using cross-asset ratio analysis?▼

Detect macro regime transitions by computing cross-asset ratios like RSP/SPY concentration and yield curve metrics, then applying 6M/12M SMA and ROC to monthly market data to identify regime shifts over 1-2 year horizons for strategic portfolio positioning.

What macro regimes can cross-asset signals identify for portfolio positioning?▼

Cross-asset signals identify five primary macro regimes: Concentration, Broadening, Contraction, Inflationary, and Transitional states, analyzing equity-bond relationships, credit conditions, size factor, and sector rotation to understand market structure changes.

Can I use FMP API market data for macro regime classification with Python?▼

Yes, you can leverage the FMP API to fetch financial market data and use Python libraries like pandas and numpy for statistical computation to classify macro regimes based on cross-asset ratio analysis and trend detection.

What's the best way to analyze yield curve and credit conditions for market regime shifts?▼

Analyze yield curve and credit conditions by processing monthly financial data through simple moving averages and rate of change calculations, detecting trend shifts to classify broad macro regime transitions applicable to long-term strategic investment decisions.

Do I need pandas and numpy to identify market regime changes from cross-asset signals?▼

Yes, pandas and numpy are required dependencies for the statistical analysis and computation needed to process monthly cross-asset ratio data, calculate moving averages, and detect rate of change trends for regime classification.

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