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

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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.

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

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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

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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

Same-run provenance

Pass --run-id daily-20261017-unique to both required market-regime-daily producers. Without it each producer generates its own unique ID. JSON includes schema 1.0 data_provenance with actual primary timeseries business date, UTC retrieval time, source and run ID. Unknown adjustment, point-in-time, survivorship and corporate-action flags are false. Auxiliary summary dates are unverified; this does not certify the freshness of every composite input. Missing or malformed primary dates stop report generation.

Pass the exact JSON paths printed by these invocations to Exposure Coach's --require-provenance --run-id … --as-of … mode. Run IDs are caller-provided consistency markers, not authenticity or point-in-time proof.