theme-detector

name	theme-detector
description	Detect and analyze trending market themes across sectors. Use when user asks about current market themes, trending sectors, sector rotation, thematic investing, what themes are hot or cold, or wants to identify bullish and bearish market narratives with lifecycle analysis.

Theme Detector

Overview

This skill detects and ranks trending market themes by analyzing cross-sector momentum, volume, and breadth signals. It identifies both bullish (upward momentum) and bearish (downward pressure) themes, assesses lifecycle maturity (early/mid/late/exhaustion), and provides a confidence score combining quantitative data with narrative analysis.

3-Dimensional Scoring Model:

1. Theme Heat (0-100): Direction-neutral strength of the theme (momentum, volume, uptrend ratio, breadth)
2. Lifecycle Maturity: Stage classification (Early / Mid / Late / Exhaustion) based on duration, extremity clustering, valuation, and ETF proliferation
3. Confidence (Low / Medium / High): Reliability of the detection, combining quantitative breadth with narrative confirmation

Key Features:

• Cross-sector theme detection using FINVIZ industry data
• Direction-aware scoring (bullish and bearish themes)
• Lifecycle maturity assessment to identify crowded vs. emerging trades
• ETF proliferation scoring (more ETFs = more mature/crowded theme)
• Integration with uptrend-dashboard for 3-point evaluation
• Dual-mode operation: FINVIZ Elite (fast) or public scraping (slower, limited)
• WebSearch-based narrative confirmation for top themes

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When to Use This Skill

Explicit Triggers:

• "What market themes are trending right now?"
• "Which sectors are hot/cold?"
• "Detect current market themes"
• "What are the strongest bullish/bearish narratives?"
• "Is AI/clean energy/defense still a strong theme?"
• "Where is sector rotation heading?"
• "Show me thematic investing opportunities"

Implicit Triggers:

• User wants to understand broad market narrative shifts
• User is looking for thematic ETF or sector allocation ideas
• User asks about crowded trades or late-cycle themes
• User wants to know which themes are emerging vs. exhausted

When NOT to Use:

• Individual stock analysis (use us-stock-analysis instead)
• Specific sector deep-dive with chart reading (use sector-analyst instead)
• Portfolio rebalancing (use portfolio-manager instead)
• Dividend/income investing (use value-dividend-screener instead)

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Workflow

Step 1: Verify Requirements

Check for required API keys and dependencies:

# Check for FINVIZ Elite API key (optional but recommended)
echo $FINVIZ_API_KEY

# Check for FMP API key (optional, used for valuation metrics)
echo $FMP_API_KEY

Requirements:

• Python 3.7+ with requests, beautifulsoup4, lxml
• FINVIZ Elite API key (recommended for full industry coverage and speed)
• FMP API key (optional, for P/E ratio valuation data)
• Without FINVIZ Elite, the skill uses public FINVIZ scraping (limited to ~20 stocks per industry, slower rate limits)

Installation:

pip install requests beautifulsoup4 lxml

Step 2: Execute Theme Detection Script

Run the main detection script:

python3 skills/theme-detector/scripts/theme_detector.py \
  --output-dir reports/

Script Options:

# Full run (public FINVIZ mode, no API key required)
python3 skills/theme-detector/scripts/theme_detector.py \
  --output-dir reports/

# With FINVIZ Elite API key
python3 skills/theme-detector/scripts/theme_detector.py \
  --finviz-api-key $FINVIZ_API_KEY \
  --output-dir reports/

# With FMP API key for enhanced stock data
python3 skills/theme-detector/scripts/theme_detector.py \
  --fmp-api-key $FMP_API_KEY \
  --output-dir reports/

# Custom limits
python3 skills/theme-detector/scripts/theme_detector.py \
  --max-themes 5 \
  --max-stocks-per-theme 5 \
  --output-dir reports/

# Explicit FINVIZ mode
python3 skills/theme-detector/scripts/theme_detector.py \
  --finviz-mode public \
  --output-dir reports/

Expected Execution Time:

• FINVIZ Elite mode: ~2-3 minutes (14+ themes)
• Public FINVIZ mode: ~5-8 minutes (rate-limited scraping)

Step 3: Read and Parse Detection Results

The script generates two output files:

• theme_detector_YYYY-MM-DD_HHMMSS.json - Structured data for programmatic use
• theme_detector_YYYY-MM-DD_HHMMSS.md - Human-readable report

Read the JSON output to understand quantitative results:

# Find the latest report
ls -lt reports/theme_detector_*.json | head -1

# Read the JSON output
cat reports/theme_detector_YYYY-MM-DD_HHMMSS.json

Step 4: Perform Narrative Confirmation via WebSearch

For the top 5 themes (by Theme Heat score), execute WebSearch queries to confirm narrative strength:

Search Pattern:

"[theme name] stocks market [current month] [current year]"
"[theme name] sector momentum [current month] [current year]"

Evaluate narrative signals:

• Strong narrative: Multiple major outlets covering the theme, analyst upgrades, policy catalysts
• Moderate narrative: Some coverage, mixed sentiment, no clear catalyst
• Weak narrative: Little coverage, or predominantly contrarian/skeptical tone

Update Confidence levels based on findings:

• Quantitative High + Narrative Strong = High confidence
• Quantitative High + Narrative Weak = Medium confidence (possible momentum divergence)
• Quantitative Low + Narrative Strong = Medium confidence (narrative may lead price)
• Quantitative Low + Narrative Weak = Low confidence

Step 5: Analyze Results and Provide Recommendations

Cross-reference detection results with knowledge bases:

Reference Documents to Consult:

1. references/cross_sector_themes.md - Theme definitions and constituent industries
2. references/thematic_etf_catalog.md - ETF exposure options by theme
3. references/theme_detection_methodology.md - Scoring model details
4. references/finviz_industry_codes.md - Industry classification reference

Analysis Framework:

For Hot Bullish Themes (Heat >= 70, Direction = Bullish):

• Identify lifecycle stage (Early = opportunity, Late/Exhaustion = caution)
• List top-performing industries within the theme
• Recommend proxy ETFs for exposure
• Flag if ETF proliferation is high (crowded trade warning)

For Hot Bearish Themes (Heat >= 70, Direction = Bearish):

• Identify industries under pressure
• Assess if bearish momentum is accelerating or decelerating
• Recommend hedging strategies or sectors to avoid
• Note potential mean-reversion opportunities if lifecycle is Late/Exhaustion

For Emerging Themes (Heat 40-69, Lifecycle = Early):

• These may represent early rotation signals
• Recommend monitoring with watchlist
• Identify catalyst events that could accelerate the theme

For Exhausted Themes (Heat >= 60, Lifecycle = Exhaustion):

• Warn about crowded trade risk
• High ETF count confirms excessive retail participation
• Consider contrarian positioning or reducing exposure

Step 6: Generate Final Report

Present the final report to the user using the report template structure:

# Theme Detection Report
**Date:** YYYY-MM-DD
**Mode:** FINVIZ Elite / Public
**Themes Analyzed:** N
**Data Quality:** [note any limitations]

## Theme Dashboard
[Top themes table with Heat, Direction, Lifecycle, Confidence]

## Bullish Themes Detail
[Detailed analysis of bullish themes sorted by Heat]

## Bearish Themes Detail
[Detailed analysis of bearish themes sorted by Heat]

## All Themes Summary
[Complete theme ranking table]

## Industry Rankings
[Top performing and worst performing industries]

## Sector Uptrend Ratios
[Sector-level aggregation if uptrend data available]

## Methodology Notes
[Brief explanation of scoring model]

Save the report to reports/ directory.

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Resources

Scripts Directory (scripts/)

Main Scripts:

• theme_detector.py - Main orchestrator script

  • Coordinates industry data collection, theme classification, and scoring
  • Generates JSON + Markdown output
  • Usage: python3 theme_detector.py [options]

• theme_classifier.py - Maps industries to cross-sector themes

  • Reads theme definitions from cross_sector_themes.md
  • Calculates theme-level aggregated scores
  • Determines direction (bullish/bearish) from constituent industries

• finviz_industry_scanner.py - FINVIZ industry data collection

  • Elite mode: CSV export with full stock data per industry
  • Public mode: Web scraping with rate limiting
  • Extracts: performance, volume, change%, avg volume, market cap

• lifecycle_analyzer.py - Lifecycle maturity assessment

  • Duration scoring, extremity clustering, valuation analysis
  • ETF proliferation scoring from thematic_etf_catalog.md
  • Stage classification: Early / Mid / Late / Exhaustion

• report_generator.py - Report output generation

  • Markdown report from template
  • JSON structured output
  • Theme dashboard formatting

References Directory (references/)

Knowledge Bases:

• cross_sector_themes.md - Theme definitions with industries, ETFs, stocks, and matching criteria
• thematic_etf_catalog.md - Comprehensive thematic ETF catalog with counts per theme
• finviz_industry_codes.md - Complete FINVIZ industry-to-filter-code mapping
• theme_detection_methodology.md - Technical documentation of the 3D scoring model

Assets Directory (assets/)

• report_template.md - Markdown template for report generation with placeholder format

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Important Notes

FINVIZ Mode Differences

Feature	Elite Mode	Public Mode
Industry coverage	All ~145 industries	All ~145 industries
Stocks per industry	Full universe	~20 stocks (page 1)
Rate limiting	0.5s between requests	2.0s between requests
Data freshness	Real-time	15-min delayed
API key required	Yes ($39.99/mo)	No
Execution time	~2-3 minutes	~5-8 minutes

Direction Detection Logic

Theme direction (bullish vs. bearish) is determined by:

1. Weighted industry performance: Average change% across constituent industries, weighted by market cap
2. Uptrend ratio: Percentage of stocks in each industry that are in technical uptrends (if uptrend data available)
3. Volume confirmation: Whether volume supports the price direction (accumulation vs. distribution)

A theme is classified as:

• Bullish: Weighted performance > 0 AND (uptrend ratio > 50% OR volume accumulation confirmed)
• Bearish: Weighted performance < 0 AND (uptrend ratio < 50% OR volume distribution confirmed)
• Neutral: Mixed signals or insufficient data

Known Limitations

1. Survivorship bias: Only analyzes currently listed stocks and ETFs
2. Lag: FINVIZ data may lag intraday moves by 15 minutes (public mode)
3. Theme boundaries: Some stocks fit multiple themes; classification uses primary industry
4. ETF proliferation: Catalog is static and may not capture very new ETFs
5. Narrative scoring: WebSearch-based and inherently subjective
6. Public mode limitation: ~20 stocks per industry may miss small-cap signals

Disclaimer

This analysis is for educational and informational purposes only.

• Not investment advice
• Past thematic trends do not guarantee future performance
• Theme detection identifies momentum, not fundamental value
• Conduct your own research before making investment decisions

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Version: 1.0 Last Updated: 2026-02-16 API Requirements: FINVIZ Elite (recommended) or public mode (free); FMP API optional Execution Time: ~2-8 minutes depending on mode Output Formats: JSON + Markdown Themes Covered: 14+ cross-sector themes