Hot News Financial Analysis

Problem

Hot lists are noisy: entertainment items often rank high because they appear on multiple platforms, while financial signals that matter for rates, FX, futures, and supply chains can be buried. Cross-platform duplication also distorts simple weighted scoring.

How It Works and Limits

The skill runs an engineering-oriented pipeline for sentiment-to-market analysis:
- Fetch five hot-list APIs in parallel with Python urllib, extracting title, rank, and heat while filtering ad items;
- Use compute.py prepare for exact and substring deduplication, then emit pending.json for semantic merging;
- Group events by same entity, action, and time window, and assign finance and exclusivity coefficients;
- Score items by rank plus normalized heat, using heat / max_heat, and apply marginal decay across platforms: S1 + 0.8*S2 + 0.6*S3 + 0.3*S4+;
- Produce a TOP 30 ranking, an HTML report, and market analysis across rate bonds, credit spreads, Shenwan sectors, related listed stocks, and futures.

It is suited for signal filtering and directional assessment, not exact level forecasts or trade certainty. Results depend on API quality, LLM judgment, and sector mapping, with indirect inferences marked lower confidence.