🇯🇵日本語

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

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:

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

7-Level Zone Detail

Each scoring zone is further divided into sub-zones for finer-grained assessment:

Warning System

Active warnings trigger exposure penalties that tighten guidance even when the composite score is high:

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

Weekly Installs

97

Repository

tradermonty/cla…g-skills

GitHub Stars

412

First Seen

Feb 16, 2026

Security Audits

Gen Agent Trust HubPassSocketPassSnykWarn

Installed on

cursor93

gemini-cli93

github-copilot92

amp92

codex92

kimi-cli92