BaggaT236 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
Prerequisites
- Python 3.9+ with the
requestslibrary (CSV parsing uses the stdlibcsv/iomodules) - Internet connection to fetch CSV data from GitHub (no API key required)
- No paid API subscriptions needed
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):
python3 skills/uptrend-analyzer/scripts/uptrend_analyzer.py
The script will:
- Download CSV data from Monty's GitHub repository
- Calculate 5 component scores
- 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%) |
7-Level Zone Detail
Each scoring zone is further divided into sub-zones for finer-grained assessment:
| Score | Zone Detail | Color |
|---|---|---|
| 80-100 | Strong Bull | Green |
| 70-79 | Bull-Upper | Light Green |
| 60-69 | Bull-Lower | Light Green |
| 40-59 | Neutral | Yellow |
| 30-39 | Cautious-Upper | Orange |
| 20-29 | Cautious-Lower | Orange |
| 0-19 | Bear | Red |
Warning System
Active warnings trigger exposure penalties that tighten guidance even when the composite score is high:
| Warning | Condition | Penalty |
|---|---|---|
| Late Cycle | Commodity avg > both Cyclical and Defensive | -5 |
| High Spread | Max-min sector ratio spread > 40pp | -3 |
| Divergence | Intra-group std > 8pp, spread > 20pp, or trend dissenters | -3 |
Penalties stack (max -10) + multi-warning discount (+1 when ≥2 active). Applied after composite scoring.
Momentum Smoothing
Slope values are smoothed using EMA(3) (Exponential Moving Average, span=3) before scoring. Acceleration is calculated by comparing the recent 10-point average vs prior 10-point average of smoothed slopes (10v10 window), with fallback to 5v5 when fewer than 20 data points are available.
Historical Confidence Indicator
The Historical Context component includes a confidence assessment based on:
- Sample size: Number of historical data points available
- Regime coverage: Proportion of distinct market regimes (bull/bear/neutral) observed
- Recency: How recent the latest data point is
Confidence levels: High, Medium, Low.
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.mdfor full framework understanding - Regular execution: References not needed - script handles scoring