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MarketHawk

Market Data You Can Trust: Signal, Not Noise

Separate signal from noise: Source feed, Data validation, Usable signal.
Centrien insights / MarketHawk

Check what the feed means before using the number

A market-data acceptance test should establish the instrument, observation time, market session, and calculation behind each value. More fields and faster delivery do not resolve a disagreement about those definitions.

Preserve source and receipt timestamps, currency, adjustments, and price or total-return basis. Test stale values, missing intervals, corrections, and downstream handling. Check permitted uses and entitlements before distributing the data into another application or report.

Put it into practice

Choose one instrument and compare provider timestamps, receipt timestamps, exchange session, currency, adjustment rules, and missing intervals. Preserve the original observation and document every correction. Do not interpret a delayed feed as a live trading signal.

A feed acceptance record with source entitlement, timestamp definitions, reconciliation results, and exception ownership: scope, rehearse, prove, and decide.
Operational playbook. A feed acceptance record with source entitlement, timestamp definitions, reconciliation results, and exception ownership. Open full-size graphic

Implementation steps

  1. Specify the data contractDefine the source, permitted uses, schema, identifiers, timestamps, refresh expectations, and correction policy. Identify the systems and decisions that depend on each field.
  2. Preserve the raw observationRetain the original record and transformation lineage. Check duplicates, missing intervals, units, time zones, adjustment logic, and late arrivals before interpreting an apparent business or security trend.
  3. Test against a referenceReconcile a bounded sample with an authoritative source or agreed control totals. For prediction work, keep later-period evaluation separate from development data and compare with a simple baseline.
  4. Make quality actionableSet an owner and response for stale, incomplete, or conflicting data. Distinguish unknown coverage from a clean result. Version corrections so a reviewer can reproduce the report that informed a decision.

Track completeness and freshness

Measures that support an operational decision
Measure Calculation or definition How to use it
Completeness Valid expected observations received / observations expected Define the observation window and known exclusions.
Freshness Receipt time minus source event time Separate source latency, transport latency, and processing delay.
Reconciliation accuracy Records matching the agreed reference / records checked Report material mismatches and transformation assumptions.

Example data-quality acceptance

Worked example. Figures are illustrative.

Example data-quality acceptance. Expected: 1000; Received: 960; Validated: 900. Illustrative data.
Example data-quality acceptance. 960 / 1,000 = 96% arrived; 900 / 1,000 = 90% passed validation. Received records and validated records answer different questions. Illustrative calculation. Open full-size graphic
Worked example — inputs
Observation Records
Expected 1,000
Received 960
Validated 900

Evaluate data quality and model performance separately.

The record to keep

The deliverable: A feed acceptance record with source entitlement, timestamp definitions, reconciliation results, and exception ownership.

Reconcile a representative session from source observation to published output. Accept the feed for a defined use when the checks can be reproduced and its remaining limitations are understood.

References

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