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Macro Regime Detector

CommunityPopular
tradermonty
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.

Overview

Publishertradermonty
Repositoryclaude-trading-skills
Skill namemacro-regime-detector
Stars
2.8K
Forks
647
Bundled files
30
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 30 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tradermonty on GitHub. Read the source before you install it.

Installation

Install the Macro Regime Detector AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/tradermonty/claude-trading-skills.git /tmp/claude-trading-skills
mkdir -p .claude/skills
cp -r /tmp/claude-trading-skills/skills/macro-regime-detector .claude/skills/macro-regime-detector
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Macro Regime Detector in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Macro Regime Detector on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Macro Regime Detector is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

    bash
    python3 -m pip install -r skills/macro-regime-detector/requirements.txt
    uv run python3 skills/macro-regime-detector/scripts/macro_regime_detector.py --output-dir reports/

    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

#ComponentRatio/DataWeightWhat It Detects
1Market ConcentrationRSP/SPY25%Mega-cap concentration vs market broadening
2Yield Curve10Y-2Y spread20%Interest rate cycle transitions
3Credit ConditionsHYG/LQD15%Credit cycle risk appetite
4Size FactorIWM/SPY15%Small vs large cap rotation
5Equity-BondSPY/TLT + correlation15%Stock-bond relationship regime
6Sector RotationXLY/XLP10%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

AspectMacro Regime DetectorMarket Top DetectorMarket Breadth Analyzer
Time Horizon1-2 years (structural)2-8 weeks (tactical)Current snapshot
Data GranularityMonthly (6M/12M SMA)Daily (25 business days)Daily CSV
Detection TargetRegime transitions10-20% correctionsBreadth health score
API Calls~10~330 (Free CSV)

Script Arguments

bash
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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Macro Regime Detector AI skill do?

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.

Why use Macro Regime Detector on TypingMind?

Because you install it once and use it with any model. Macro Regime Detector is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Macro Regime Detector in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tradermonty/claude-trading-skills/tree/main/skills/macro-regime-detector. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Macro Regime Detector?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Macro Regime Detector?

As many as you like. As long as a model supports skills, you can use Macro Regime Detector with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Macro Regime Detector AI skill free?

Yes. It is published on GitHub by tradermonty under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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