event-driven

Generating trading signals from market news involves analyzing event data in CSV format, applying sentiment scoring from -1.0 to 1.0, and aggregating these signals to inform trading decisions. You can run the 'analyze_events' script with a 'news.csv' file to process market sentiment.

Sentiment analysis for event-driven trading works by scoring news, announcements, and macro events from extremely bearish (-1.0) to extremely bullish (1.0). These scores are then combined with technical signals through weighted aggregation to form the final trading decision.

Yes, you need pandas and numpy to analyze market sentiment from CSV files. The Skill requires these libraries specifically for data manipulation and computation to process event data and aggregate sentiment scores effectively.

The best way to structure CSV data for event sentiment analysis is to include fields for date, event type, score, source, and summary. This format allows the system to parse market events and apply sentiment scoring accurately.

Yes, you can combine event signals with technical signals for trading decisions. The Skill applies weighted aggregation to merge sentiment scores derived from news events with existing technical signals, producing a consolidated final trading decision.

Event-driven market analysis supports any macro events, announcements, or news entries you include in your CSV data. The system applies sentiment scoring to these inputs to generate trading signals based on the aggregated market sentiment.