news-sector-intelligence

You can identify sector hotspots from financial news without an external market API by running an offline sentiment analysis on a local aggregated news pool. This approach clusters sector heat, extracts driving logic like policy changes or price moves, and highlights leading stocks using locally stored data.

Extracting driving logic and evidence excerpts from unstructured financial news involves analyzing text to identify policy shifts, price movements, and events. The process outputs supporting evidence snippets including titles, timestamps, and sources to ensure full traceability for manual validation.

Sector sentiment analysis works with offline data sources like latest-24h.json by processing the local file directly. It scans the aggregated offline news pool to rank sector popularity and pinpoint emotional leaders without connecting to live external data APIs.

Finding lead stocks and sector momentum from daily news coverage requires clustering unstructured articles to rank sector heat. It then mines and labels potential leading stocks within those clusters, attaching traceable news excerpts and uncertainty labels for verification.

Validating stock sector radar outputs generated from offline news pools requires checking the provided scan time, source lists, and evidence excerpts. Each conclusion includes uncertainty labels and traceable titles with timestamps to facilitate reliable manual verification of the sentiment analysis.