orderbook-sanity

To validate order book data quality, run a structured audit that detects snapshot/diff desync, stale top-of-book, and sequence gaps. This generates a reproducible report with severity levels and concrete remediation steps for your data pipelines.

Unclosed forming bars and timestamp inconsistencies occur due to missing bars, time zone errors, or unit mismatches. A rule-based kline integrity audit validates cross-source alignment and returns severity-graded findings to correct these data quality issues.

To check timestamp alignment and unit correctness in market data feeds, apply deterministic diagnostics that enforce verifiable checks. This identifies timestamp unit errors and cross-source misalignment, returning actionable findings ready for pipeline remediation.

The best way to detect survivorship bias and sequence gaps before backtesting is to run a rule-based audit on your market data. This enforces deterministic diagnostics to reveal data-driven false signals and performance distortion risks.

Yes, you can use this data quality audit on both REST snapshot feeds and incremental WS updates. It applies deterministic diagnostics across both data stream types to ensure verifiable checks for desync, sequence gaps, and timestamp alignment.

Limitations when auditing order book feeds involve diagnosing subtle survivorship bias and snapshot/diff desync across complex pipelines. The audit relies on structured, rule-based checks to return severity-graded findings, requiring proper dataset inputs for accurate remediation.