theme-detector

这段内容是审核时保存的快照。外部来源才是完整且最新的版本。

---
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 (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.
4. **Stock Leadership**: Optional daily scan-hit evidence from 5D+20%, EP9M, range expansion, new highs, and high-RS stocks. When supplied, this is blended into Theme Heat v2; when absent, it lowers confidence coverage but does not force leadership to zero.
5. **Theme Match Quality**: Specificity of the theme match from industry participation, explicit stock-basket hits, proxy ETF confirmation, and optional offline narrative scores. This is evidence quality, not a trade recommendation.

**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
- Stock-level leadership evidence via `--scan-hits`
- Theme match quality via explicit stock baskets and proxy ETF confirmation
- Leader candidate evidence ranked by abnormal move/volume/range/RS metrics, with market cap shown only as a risk bucket
- Theme history and acceleration metrics via `--history-file`
- 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 3.9+ with core dependencies.
  ```bash
  pip install requests beautifulsoup4 lxml pandas numpy yfinance
  ```

**Cron / mixed-Python fallback:** If the active `python3` is older than 3.10, or a newer Hermes venv lacks the data-science dependencies, run the detector through `uv` with an explicit modern interpreter and temporary dependencies instead of editing the environment mid-cron:
```bash
uv run --python 3.12 \
  --with requests --with beautifulsoup4 --with lxml \
  --with pandas --with numpy --with yfin