Market Breadth Analyzer
Quantifies market breadth health using TraderMonty's public CSV data.
Install
npx skills add tradermonty/claude-trading-skills --skill market-breadth-analyzer -g -a claude-code -y- Repository
- tradermonty/claude-trading-skills3.5k
- Category
- Engineering
- Updated
- 3d ago
- License
- MIT
- Context cost
- Mediumabout 1,649 tokens
- Runs scripts
- Yes, review before use
What it does
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
requestslibrary (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.
