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Einstein Research — Macro Regime Detector

Name: Einstein Research — Macro Regime Detector
Author: clawdiri-ai

作者 RunByDaVinci · GitHub ↗ · v0.1.0 · MIT-0

cross-platform ⚠ suspicious

108

总下载

当前安装

版本数

在 OpenClaw 中安装

/install einstein-research-regime-dv

功能描述

Detect structural macro regime transitions (1-2 year horizon) using cross-asset ratio analysis. Analyze RSP/SPY concentration, yield curve, credit conditions...

使用说明 (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

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)

安全使用建议

This skill appears to implement the advertised macro-regime detector, but there are a few inconsistencies you should address before running it: (1) SKILL.md and README require an FMP API key (FMP_API_KEY) but the registry metadata does not declare any required env vars — treat any API key as sensitive and only provide one with least privilege; (2) there is no install spec or dependency manifest even though the code needs Python packages (pandas, yfinance) — run inside an isolated environment (venv/container) and install deps explicitly; (3) SKILL.md references a different script path than the repository contains — confirm the correct run command; (4) because the skill includes executable Python files, review scripts/fmp_client.py and the main macro_regime_detector.py to confirm network calls are only to expected data providers (FMP/Yahoo) and that no unexpected endpoints or credential reads exist; (5) run the code in a sandboxed environment with a throwaway API key or the minimal-permission key first. If you are not comfortable doing these checks, ask the publisher for clarification or request a version with a clear dependency manifest and explicit, matching metadata.

功能分析

Type: OpenClaw Skill Name: einstein-research-regime-dv Version: 0.1.0 The 'einstein-research-regime-dv' skill bundle is a legitimate financial analysis tool designed to detect macro-economic regime shifts using cross-asset ratio analysis. The code, primarily located in 'scripts/macro_regime_detector.py' and its associated calculator modules, fetches historical ETF and Treasury data from the Financial Modeling Prep (FMP) API and performs quantitative analysis (moving averages, crossovers, and correlations). The behavior is entirely consistent with the documentation in 'SKILL.md' and 'README.md', and the bundle includes a comprehensive test suite. No indicators of data exfiltration, malicious execution, or prompt injection were found.

能力评估

ℹ Purpose & Capability

The code and documentation implement a cross-asset macro-regime detector that matches the name/description: calculators for RSP/SPY, IWM/SPY, HYG/LQD, SPY/TLT, yield curve and sector rotation are present and weighted as described. This capability legitimately needs market data API access (FMP or Yahoo) and Python data libraries.

⚠ Instruction Scope

SKILL.md instructs the agent to load local reference docs and to execute a Python script that will fetch ~600 days of market data (network calls). The run command in SKILL.md references 'skills/macro-regime-detector/scripts/macro_regime_detector.py' but the repository shows 'scripts/macro_regime_detector.py' (path mismatch). The instructions require an API key (or optional Yahoo fallback) and to read local reference files; they do not ask to read unrelated system files or credentials, but the agent will execute third-party Python code from the skill bundle which can perform arbitrary actions unless sandboxed.

ℹ Install Mechanism

There is no install spec (instruction-only from a platform perspective) but the bundle includes ~23 Python files. README mentions installing packages (yfinance, pandas) but these dependencies are not declared in registry metadata or an install step. Running the scripts will require Python packages and will write output files (JSON/MD) to disk; absence of an explicit install step or dependency manifest increases operational risk (agent may fail or execute with missing/older libs).

⚠ Credentials

SKILL.md and README state an FMP API key is required (FMP_API_KEY env var or --api-key) though the registry metadata lists no required env vars / primary credential — a clear mismatch. Requesting an API key for a market-data service is proportionate to the stated purpose, but the missing declaration in metadata is an integrity issue. No other secrets are requested in code/README, and the code appears focused on fetching market data rather than exfiltrating arbitrary secrets.

✓ Persistence & Privilege

The skill does not request always:true and does not declare system-wide config path changes. It is user-invocable and allows autonomous invocation (platform default) — nothing in the files shows it modifies other skills or requests elevated agent privileges.

如何使用

确保已安装 OpenClaw（本地或 Docker 部署）
在对话框中输入安装命令：/install einstein-research-regime-dv
安装完成后，直接呼叫该 Skill 的名称或使用 /einstein-research-regime-dv 触发
根据 Skill 的参数说明提供必要输入，即可获得结构化输出

版本历史

v0.1.0

Initial release

元数据

Slug einstein-research-regime-dv

版本 0.1.0

许可证 MIT-0

累计安装 0

当前安装数 0

历史版本数 1

常见问题