theme-detector

To detect trending market themes, you analyze cross-sector momentum, volume, and breadth signals. This approach identifies and ranks both bullish and bearish themes, assigning lifecycle maturity stages like Emerging, Accelerating, or Exhausting to guide thematic investing decisions.

Narrative confirmation in market theme analysis combines quantitative financial data with qualitative web search data. This mechanism generates confidence scores for detected sector trends, ensuring that cross-sector momentum is supported by actual market news and narrative strength.

Assessing the lifecycle maturity of a market sector trend involves evaluating quantitative momentum signals to classify the theme. Themes are categorized into lifecycle stages such as Emerging, Accelerating, Trending, Mature, or Exhausting to indicate their current market position.

Analyzing sector trends requires Python 3.7+ and specific data libraries. You need requests, beautifulsoup4, lxml, pandas, and numpy for core processing, while yfinance, finvizfinance, and PyYAML provide optional financial data retrieval and configuration capabilities.

Yes, you can use yfinance and pandas to identify bullish and bearish market themes. The analysis combines quantitative financial data fetched via these libraries with narrative confirmation to score and rank trending themes across various sectors.

The best way to score market themes is by combining cross-sector quantitative metrics with narrative analysis. This dual approach validates financial signals against qualitative web research, producing confidence scores that reflect both market momentum and storyline strength.