data-quality-checker

Data quality checks for market analysis documents validate price scale consistency, instrument notation, date/weekday matching, allocation totals, and unit usage. They flag common errors before publication using predefined standards and heuristics.

Financial data validation for market analysis reports requires checking ETF vs futures price scale digit counts, verifying consistent instrument notation, confirming date and weekday matching, and validating allocation percentage totals within document sections.

Yes, you can check allocation totals and price scale consistency using Python's standard library. The validation runs checks against predefined standards and heuristics without requiring any external dependencies.

The best way to ensure unit usage consistency and date accuracy in financial documents is to apply automated document validation checks. These verify weekday matching and flag inconsistent units throughout the document text.

Yes, document validation for market analysis supports checking instrument notation consistency. It verifies that financial instrument notation remains consistent throughout the document to ensure data integrity and reliability.