Uptrend Analyzer Agent Skill
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.
Install
one command, takes just this skill from the repositorynpx skills add https://github.com/BaggaT236/AI-Trading-Skills --skill uptrend-analyzerWhat comes with it
184 989 bytes besides the instructionThe instruction itself
19 sections, as written by the authorUptrend 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
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How to use it
Take baggat236/uptrend-analyzer from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.