check-signals

To query AI intelligence signals from a SQLite database, you read the ai_intelligence_signals table in data/data.db to retrieve and display fields like source type, ticker, impact score, and sentiment.

AI intelligence signals are AI-discovered trading activity records containing source, ticker, impact score, and sentiment data. Reviewing these signals helps inform trading-related decisions by highlighting high-priority market events.

You can filter trading signals by applying optional filters for politician, ticker, date range, or minimum impact score when querying the ai_intelligence_signals table to focus on specific high-priority results.

Yes, you can review impact scores for AI-discovered trading activity by querying the SQLite-based ai_intelligence_signals store and formatting results to display key fields like impact_score and timestamp.

The best way to audit recent AI trading signals is to query the ai_intelligence_signals table and present key fields such as source_name, ticker, sentiment, and timestamp to support monitoring and decision-making.