/market-breadth-analyzer

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 |

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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
• Histor