Data Quality Checker

Catches data quality issues in market analysis documents before you publish them. Runs five checks: price scale problems (like confusing ETF and futures prices), ticker notation errors, date and weekday mismatches, allocation percentages that don't add up to 100%, and unit inconsistencies. Everything's advisory, so it flags warnings for you to review rather than blocking publication. Works with both English and Japanese content, which is genuinely useful if you're publishing multilingual reports. The digit-count heuristic for price validation is clever because it survives market moves over time. No dependencies beyond Python standard library, and it always exits clean even when it finds issues, so it won't break your CI pipeline.