Skill 详情
market-breadth-analyzer
Assesses market breadth and equity exposure conditions.
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SKILL.md
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--- name: market-breadth-analyzer description: Quantifies market breadth health using TraderMonty's public CSV data. Generates a 0-100 composite score across 6 components (100 = healthy). No API key required. Use when user asks about market breadth, participation rate, advance-decline health, whether the rally is broad-based, or general market health assessment. --- # Market Breadth Analyzer Skill ## Purpose Quantify market breadth health using a data-driven 6-component scoring system (0-100). Uses TraderMonty's publicly available CSV data to measure how broadly the market is participating in a rally or decline. **Score direction:** 100 = Maximum health (broad participation), 0 = Critical weakness. **No API key required** - uses freely available CSV data from GitHub Pages. ## When to Use This Skill **English:** - User asks "Is the market rally broad-based?" or "How healthy is market breadth?" - User wants to assess market participation rate - User asks about advance-decline indicators or breadth thrust - User wants to know if the market is narrowing (fewer stocks participating) - User asks about equity exposure levels based on breadth conditions **Japanese:** - 「マーケットブレッドスはどうですか?」「市場の参加率は?」 - 「上昇は広がっている?」「一部の銘柄だけの上昇?」 - ブレッドス指標に基づくエクスポージャー判断 - 市場の健康度をデータで確認したい ## Prerequisites - **Python 3.9+** with `requests` library (for fetching CSV data) - **Internet access** to reach GitHub Pages URLs - **No API keys required** - uses freely available public CSV data ## Difference from Breadth Chart Analyst | Aspect | Market Breadth Analyzer | Breadth Chart Analyst | |--------|------------------------|----------------------| | Data Source | CSV (automated) | Chart images (manual) | | API Required | None | None | | Output | Quantitative 0-100 score | Qualitative chart analysis | | Components | 6 scored dimensions | Visual pattern recognition | | Repeatability | Fully reproducible | Analyst-dependent | --- ## Execution Workflow ### Phase 1: Execute Python Script Run the analysis script. If using a nested or date-stamped `--output-dir` in cron runs, create it first; the history writer expects the directory to already exist. ```bash mkdir -p reports/<routine-or-date> python3 skills/market-breadth-analyzer/scripts/market_breadth_analyzer.py \ --detail-url "https://tradermonty.github.io/market-breadth-analysis/market_breadth_data.csv" \ --summary-url "https://tradermonty.github.io/market-breadth-analysis/market_breadth_summary.csv" \ --output-dir reports/<routine-or-date> ``` For a simple ad-hoc run, omit `--output-dir` or use an existing directory. In scheduled cron runs from the repository root, prefer a repo-relative output directory such as `reports/after-close-YYYY-MM-DD` rather than an absolute path. If an absolute nested `--output-dir` unexpectedly fails at the history-writing step despite the directory existing, rerun once with the equivalent repo-relative path before treating the breadth analysis as unavailable. The script will: 1. Fetch detail CSV (~2,500 rows, 2016-present) and summary CSV (8 metrics) 2. Validate data freshness (warn if > 5 days old) 3. Calculate all 6 component scores (with automatic weight redistribution if any component lacks data) 4. Generate composite score with zone classification 5. Track score history and compute trend (improving/deteriorating/stable) 6. Output JSON and Markdown reports ### Phase 2: Present Results Present the generated Markdown report to the user, highlighting: - Composite score and health zone - Strongest and weakest components - Recommended equity exposure level - Key breadth levels to watch - Any data freshness warnings --- ## 6-Component Scoring System | # | Component | Weight | Key Signal | |---|-----------|--------|------------| | 1 | Breadth Level & Trend | **25%** | Current 8MA level + 200MA trend direction + 8MA direction modifier | | 2 | 8MA vs 200MA Crossover | **20%** | Momentum via MA gap and direction | | 3 | Peak/Trough Cycle | **20%** | Position在 GitHub 阅读完整来源 (打开外部页面)