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event-driven

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Event-driven strategy based on sentiment-scored signals from news, announcements, and macro events. The LLM acts as the NLP engine, and event data follows a CSV schema.

agent/src/skills/event-driven/SKILL.md

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# 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.**