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): 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.md for full framework understanding Regular execution: References not needed - script handles scoring