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, Inflatio

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: This fetches 600 days of data for 9 ETFs. With an FMP key, the client tries FMP first and fetches Treasury rates (~10 API calls total), then falls back to yfinance for unavailable ETF history. Without an FMP key, it runs in yfinance only mode and uses SHY/TLT as the yield curve fallback. The detector fails closed and writes no report when none of its six components has usable data. Do not treat a missing report or non zero exit as a valid low transition regime. 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 Python dependencies (required): install requirements.txt , including yfinance and requests FMP API Key (optional): set FMP API KEY or pass api key to use FMP and Treasury data before the yfinance/SHY TLT fallbacks The FMP free tier may not serve every ETF; unavailable symbols automatically use yfinance 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 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

macro-regime-detector