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macro-regime-detector

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

读凭据执行命令联网写文件严重 0 · 高危 6tradermonty/claude-trading-skills

它会碰到什么

扫了多少31 个文本文件,215 KB
它会碰到什么读凭据执行命令联网写文件
命中总数14 处
命中统计严重 0 · 高 6 · 中 3 · 低 4
逐条看命中(6 条严重或高危)
  • scripts/fmp_client.py:142cred-envread
    self.api_key = api_key or os.getenv("FMP_API_KEY")
  • scripts/tests/test_packaged_deps.py:49exec-spawn
    completed = subprocess.run(
  • scripts/tests/test_packaged_deps.py:65cred-envread
    env["PYTHONPATH"] = str(stub_dir) + os.pathsep + env.get("PYTHONPATH", "")
  • scripts/tests/test_packaged_deps.py:66exec-spawn
    completed = subprocess.run(
  • scripts/tests/test_packaged_deps.py:107exec-spawn
    completed = subprocess.run(
  • scripts/tests/test_packaged_deps.py:122exec-spawn
    completed_help = subprocess.run(

这一栏是扫描器报的事实,不是结论。命中多不等于有毒(安全工具、规则库、示例脚本本来就会包含危险写法),命中少也不等于干净。它和你手上的凭据、文件、网络有什么关系,需要你自己看。

技能内容

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
  1. Execute the main analysis script:
   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.

  1. Read the generated Markdown report and present findings to user.
  1. 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

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

想直接用这个技能?

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