uptrend-analyzer git:20260215.97140fdA

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---
name: uptrend-analyzer
description: Analyzes market breadth using Monty's Uptrend Ratio Dashboard data to diagnose the current market environment. Generates a 0-100 composite score from 5 components (breadth, sector participation, rotation, momentum, historical context). Use when asking about market breadth, uptrend ratios, or whether the market environment supports equity exposure. No API key required.
---

# Uptrend Analyzer Skill

## Purpose

Diagnose market breadth health using Monty's Uptrend Ratio Dashboard, which tracks ~2,800 US stocks across 11 sectors. Generates a 0-100 composite score (higher = healthier) with exposure guidance.

Unlike the Market Top Detector (API-based risk scorer), this skill uses free CSV data to assess "participation breadth" - whether the market's advance is broad or narrow.

## When to Use This Skill

**English:**
- User asks "Is the market breadth healthy?" or "How broad is the rally?"
- User wants to assess uptrend ratios across sectors
- User asks about market participation or breadth conditions
- User needs exposure guidance based on breadth analysis
- User references Monty's Uptrend Dashboard or uptrend ratios

**Japanese:**
- 「市場のブレドスは健全？」「上昇の裾野は広い？」
- セクター別のアップトレンド比率を確認したい
- 相場参加率・ブレドス状況を診断したい
- ブレドス分析に基づくエクスポージャーガイダンスが欲しい
- Montyのアップトレンドダッシュボードについて質問

## Difference from Market Top Detector

| Aspect | Uptrend Analyzer | Market Top Detector |
|--------|-----------------|-------------------|
| Score Direction | Higher = healthier | Higher = riskier |
| Data Source | Free GitHub CSV | FMP API (paid) |
| Focus | Breadth participation | Top formation risk |
| API Key | Not required | Required (FMP) |
| Methodology | Monty Uptrend Ratios | O'Neil/Minervini/Monty |

---

## Execution Workflow

### Phase 1: Execute Python Script

Run the analysis script (no API key needed):

```bash
python3 skills/uptrend-analyzer/scripts/uptrend_analyzer.py
```

The script will:
1. Download CSV data from Monty's GitHub repository
2. Calculate 5 component scores
3. Generate composite score and reports

### Phase 2: Present Results

Present the generated Markdown report to the user, highlighting:
- Composite score and zone classification
- Exposure guidance (Full/Normal/Reduced/Defensive/Preservation)
- Sector heatmap showing strongest and weakest sectors
- Key momentum and rotation signals

---

## 5-Component Scoring System

| # | Component | Weight | Key Signal |
|---|-----------|--------|------------|
| 1 | Market Breadth (Overall) | **30%** | Ratio level + trend direction |
| 2 | Sector Participation | **25%** | Uptrend sector count + ratio spread |
| 3 | Sector Rotation | **15%** | Cyclical vs Defensive balance |
| 4 | Momentum | **20%** | Slope direction + acceleration |
| 5 | Historical Context | **10%** | Percentile rank in history |

## Scoring Zones

| Score | Zone | Exposure Guidance |
|-------|------|-------------------|
| 80-100 | Strong Bull | Full Exposure (100%) |
| 60-79 | Bull | Normal Exposure (80-100%) |
| 40-59 | Neutral | Reduced Exposure (60-80%) |
| 20-39 | Cautious | Defensive (30-60%) |
| 0-19 | Bear | Capital Preservation (0-30%) |

---

## API Requirements

**Required:** None (uses free GitHub CSV data)

## Output Files

- JSON: `uptrend_analysis_YYYY-MM-DD_HHMMSS.json`
- Markdown: `uptrend_analysis_YYYY-MM-DD_HHMMSS.md`

## Reference Documents

### `references/uptrend_methodology.md`
- Uptrend Ratio definition and thresholds
- 5-component scoring methodology
- Sector classification (Cyclical/Defensive/Commodity)
- Historical calibration notes

### When to Load References
- **First use:** Load `uptrend_methodology.md` for full framework understanding
- **Regular execution:** References not needed - script handles scoring