Event-Driven Strategy

Purpose

Uses event information such as news, announcements, and macro policy updates. The LLM analyzes sentiment and impact magnitude to generate event-driven trading signals. Event data is managed in CSV format, and technical signals are combined with event signals through weighted aggregation to form the final trading decision.

Workflow

1. Data collection: use the read_url tool to fetch the full text of news and announcements
2. LLM analysis: the LLM reads the news and scores it from -1.0 to 1.0 with a standardized prompt (extremely bearish to extremely bullish)
3. Generate the event CSV: write data in the date,event_type,score,source,summary schema
4. Signal aggregation: signal_engine.py reads the event CSV, applies time decay, and combines it with the technical signal

Key principle: the event CSV is the data layer, and signal_engine.py is the logic layer. Keep them decoupled.

Event CSV Schema