Einstein Research — Macro Regime Detector

通过 FMP 数据源抓取 9 只 ETF 与国债收益率数据，按月度频率计算市场集中度、收益率曲线、信用条件、规模因子、股债关系与行业轮动共 6 项跨资产比率指标，判定集中、扩散、收缩、通胀与过渡五类宏观制度，输出结构化 JSON 与 Markdown 报告。

clawdiri-ai

热度 37.38

最近更新 2026-05-11 17:35:07

分类：数据依赖：代码执行 / 第三方 API / 读写文件应用场景：市场分析 / 报告撰写 / 财务 / 数据处理

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add einstein-research-regime-dv

Metadata

id: einstein-research-regime
name: einstein-research-regime
description: 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.
version: 1.0.0
author: DaVinci
last_amended_at: null
trigger_patterns: []
pre_conditions: {"git_repo_required":false,"tools_available":[]}
expected_output_format: natural_language

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
Execute the main analysis script:
```
python3 skills/macro-regime-detector/scripts/macro_regime_detector.py
```
This fetches 600 days of data for 9 ETFs + Treasury rates (10 API calls total).
Read the generated Markdown report and present findings to user.
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:
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)