validate-data

Validate SQLite data quality by running deterministic checks on congressional trading tables like senate_trades and house_trades. The validator checks table existence, row counts, recency, required fields, ticker formats, duplicate hashes, and date sanity to report integrity issues.

Data validation for AI intelligence signals checks the ai_intelligence_signals table for NULL impact_score values and missing source_name fields. This ensures AI signal integrity alongside standard checks for table existence and record recency.

Find duplicate data_hash entries by running a validation script against your SQLite database. The validator identifies duplicate data_hash values in both senate_trades and house_trades tables to help maintain data quality and prevent redundant records.

Yes, you can check for NULL values in required SQLite fields including politician_name, filing_date, ptr_link, and data_hash. The validator reports any NULL values found in NOT NULL columns across congressional trading pipeline tables.

Flag invalid ticker formats by checking values against the ^[A-Z]{1,5}$ regex pattern, and flag future transaction dates by checking if transaction_date occurs in the future or before 2020. The validator reports both issues for SQLite trading data.

Yes, this data validation approach works for institutional_holdings and ocr_queue tables alongside senate_trades, house_trades, and ai_intelligence_signals. It validates table existence and row counts across all congressional trading pipeline tables using Python's sqlite3 module.