MileniumTick/market-breadth-analyzer
skills/market-breadth-analyzer/SKILL.md
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.
tools
Updated Apr 17, 2026
$ install --global
skillsauthnpx skillsauth add MileniumTick/skills market-breadth-analyzerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- 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 3.8+ 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:
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"
The script will:
- Fetch detail CSV (~2,500 rows, 2016-present) and summary CSV (8 metrics)
- Validate data freshness (warn if > 5 days old)
- Calculate all 6 component scores (with automatic weight redistribution if any component lacks data)
- Generate composite score with zone classification
- Track score history and compute trend (improving/deteriorating/stable)
- 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 in breadth cycle | | 4 | Bearish Signal | 15% | Backtested bearish signal flag | | 5 | Historical Percentile | 10% | Current vs full history distribution | | 6 | S&P 500 Divergence | 10% | Multi-window (20d + 60d) price vs breadth divergence |
Weight Redistribution: If any component lacks sufficient data (e.g., no peak/trough markers detected), it is excluded and its weight is proportionally redistributed among the remaining components. The report shows both original and effective weights.
Score History: Composite scores are persisted across runs (keyed by data date). The report includes a trend summary (improving/deteriorating/stable) when multiple observations are available.
Health Zone Mapping (100 = Healthy)
| Score | Zone | Equity Exposure | Action | |-------|------|-----------------|--------| | 80-100 | Strong | 90-100% | Full position, growth/momentum favored | | 60-79 | Healthy | 75-90% | Normal operations | | 40-59 | Neutral | 60-75% | Selective positioning, tighten stops | | 20-39 | Weakening | 40-60% | Profit-taking, raise cash | | 0-19 | Critical | 25-40% | Capital preservation, watch for trough |
Data Sources
Detail CSV: market_breadth_data.csv
- ~2,500 rows from 2016-02 to present
- Columns: Date, S&P500_Price, Breadth_Index_Raw, Breadth_Index_200MA, Breadth_Index_8MA, Breadth_200MA_Trend, Bearish_Signal, Is_Peak, Is_Trough, Is_Trough_8MA_Below_04
Summary CSV: market_breadth_summary.csv
- 8 aggregate metrics (average peaks, average troughs, counts, analysis period)
Both are publicly hosted on GitHub Pages - no authentication required.
Output Files
- JSON:
market_breadth_YYYY-MM-DD_HHMMSS.json - Markdown:
market_breadth_YYYY-MM-DD_HHMMSS.md - History:
market_breadth_history.json(persists across runs, max 20 entries)
Reference Documents
references/breadth_analysis_methodology.md
- Full methodology with component scoring details
- Threshold explanations and zone definitions
- Historical context and interpretation guide
When to Load References
- First use: Load methodology reference for framework understanding
- Regular execution: References not needed - script handles scoring
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