data-quality-checker

You can validate data quality in a market analysis document by checking price scale consistency, instrument notation, date and weekday accuracy, allocation totals, and unit usage. This process flags identified issues as warnings for human review before publication.

Price scale consistency ensures that prices for instruments like ETFs and futures use uniform scales throughout a document. Validating this consistency prevents misleading data interpretations and ensures accuracy in market analysis reports before they are published.

Yes, you can check date and weekday accuracy in both English and Japanese formats. The validation process supports mixed-language content to ensure chronological data correctness across international market analysis documents.

You verify allocation totals by ensuring percentage allocations sum correctly, and check unit usage for consistency throughout the document. This validation highlights discrepancies as warnings for human review.

No, you do not need external Python libraries to check instrument notation consistency. The validation uses only Python's standard library to perform checks for notation consistency and other document validation tasks.

You should use a document validation skill for market analysis immediately before publication. Running it on documents like a weekly market analysis report ensures data accuracy by catching common issues in price scales, dates, and units before the report reaches readers.