event-driven

To convert news events into quantitative trading signals, you need automated sentiment scoring of news articles combined with time-decay computation and weighted blending with technical indicators. This process eliminates manual sentiment analysis and ad-hoc CSV handling.

Time-decay computation for event-driven trading signals calculates the diminishing impact of older news events on current prices. It applies a configurable decay rate and look-back window to weighted event scores before blending them with technical indicators.

Generating event CSV data requires structuring news articles, earnings announcements, and macro-economic events into a strict schema for downstream processing. The Skill automates this with standardized LLM prompts to ensure consistent sentiment scoring inputs.

Yes, you can combine sentiment scores with technical indicators for equity analysis using configurable signal weighting parameters. This allows flexible strategy tuning by adjusting the blend ratio between event-driven sentiment and technical signals.

Event-driven sentiment scoring works with macro-economic data and social sentiment by processing announcements, earnings reports, and social media events through standardized LLM prompts. It transforms unstructured inputs into quantitative signals for equity price movement analysis.

Automated sentiment scoring limitations include dependency on event CSV data quality and the accuracy of standardized LLM prompts. It requires proper configuration of decay rate, look-back window, and signal weighting to avoid misinterpreting unstructured news events.