data-engineer

Validate point-in-time market data by enforcing universe correctness and timeline gap analysis to prevent survivorship bias in backtests. This skill applies strict quality gates to ensure auditable, snapshot-based data integrity across historical simulations.

The best way to handle corporate actions and OHLCV data quality is through a validation ledger that applies explicit dividend and split adjustments. This prevents double-counting and ensures price-volume integrity across ingestion pipelines.

Yes, you can normalize trading calendars and timezones across B3 and US market feeds. The skill performs gap analysis and calendar validation to align market sessions, ensuring consistent timeline data across different regional exchanges.

Timeline gaps and point-in-time versioning errors occur when ingestion pipelines lack strict data quality gates. Implementing PIT versioning with snapshot IDs and checksums ensures auditable, survivorship-free data by validating timeline continuity.

Yes, this data quality validation supports point-in-time universe checks specifically for backtesting. It enforces strict data quality gates and validates PIT versioning with snapshot IDs and checksums to guarantee survivorship-free historical simulations.

Limitations of running OHLCV integrity checks across markets include requiring explicit timezone normalization and accurate corporate actions ledgers. Without proper calendar handling and anti-double-count policies, multi-market validation may produce misaligned timelines.