uptrend-analyzer

name: uptrend-analyzerdescription: 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

Prerequisites

• Python 3.9+ with the requests library (CSV parsing uses the stdlib csv/io modules)
• Internet connection to fetch CSV data from GitHub (no API key required)
• No paid API subscriptions needed

Difference from Market Top Detector

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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

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5-Component Scoring System

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.

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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

Same-run provenance

Pass --run-id daily-20261017-unique to both required market-regime-daily producers. Without it each producer generates its own unique ID. JSON includes schema 1.0 data_provenance with actual primary timeseries business date, UTC retrieval time, source and run ID. Unknown adjustment, point-in-time, survivorship and corporate-action flags are false. Auxiliary summary dates are unverified; this does not certify the freshness of every composite input. Missing or malformed primary dates stop report generation.

Pass the exact JSON paths printed by these invocations to Exposure Coach's --require-provenance --run-id … --as-of … mode. Run IDs are caller-provided consistency markers, not authenticity or point-in-time proof.