theme-detector

What this skill does

# 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 (Emerging/Accelerating/Trending/Mature/Exhausting), 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 (Emerging / Accelerating / Trending / Mature / Exhausting) based on duration, extremity clustering, valuation, and ETF proliferation
3. **Confidence** (Low / Medium / High): Reliability of the detection, combining quantitative breadth with narrative confirmation. Script output is capped at Medium; Claude's WebSearch narrative confirmation step can elevate to High.

**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

---

## 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)

---

## Prerequisites

**Required:**
- Python 3.7+ with core dependencies:
  ```bash
  pip install requests beautifulsoup4 lxml pandas numpy yfinance
  ```

**Optional API Keys:**

FINVIZ Elite (recommended for full industry coverage and speed):
```bash
export FINVIZ_API_KEY=your_finviz_elite_api_key_here
```

FMP API (optional, for P/E ratio valuation data):
```bash
export FMP_API_KEY=your_fmp_api_key_here
```

**Optional Python packages:**
- `finvizfinance` - Required for FINVIZ Elite mode
- `PyYAML` - Required for `--themes-config` custom themes

Without FINVIZ Elite, the skill uses public FINVIZ scraping (limited to ~20 stocks per industry, slower rate limits).

---

## Workflow

### Step 1: Verify Environment

Check that API keys are configured (see Prerequisites):

```bash
# Verify FINVIZ Elite API key (optional but recommended)
echo $FINVIZ_API_KEY

# Verify FMP API key (optional)
echo $FMP_API_KEY
```

### Step 2: Execute Theme Detection Script

Run the main detection script:

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

**Script Options:**
```bash
# 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 10 \
  --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:

```bash
# 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 (Emerging = opportunity, Mature/Exhausting = 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 Mature/Exhausting

For **Emerging Themes** (Heat 40-69, Lifecycle = Emerging):
- These may represent early rotation signals
- Recommend monitoring with watchlist
- Identify catalyst events that could accelerate the theme

For **Exhausted Themes** (Heat >= 60, Lifecycle = Exhausting):
- 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:

```markdown
# 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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