Mandates
Market Data

Design ingestion layers that reduce vendor lock-in and silent data risk.

We design ingestion layers that normalize fragmented feeds, detect latency and conflict anomalies, preserve precision through every transformation, and keep raw data separate from processed data. Institutions that understand their data sources can trust what the data tells them.

Signals
Pipeline Failure Integration Breakdown
Standards
Accounts Environments Pipelines Logic Releases Blocks

Scope

We cover the full market data supply chain: real-time and end-of-day feeds from exchanges, brokers, and data vendors; derived and calculated market data including curves, surfaces, and indices; and distribution to the pricing, risk, and reporting systems that consume it. The scope includes both the technical ingestion layer and the data quality controls around it.

  • Live and settlement prices, curves, volatility surfaces, and reference data from multiple vendors and exchanges.
  • Internally calculated curves, composite indices, and model-derived inputs.
  • Managed distribution to consuming systems with access controls, latency monitoring, and delivery confirmation.

Approach

We treat every vendor feed as an external input with an explicit contract, not a trusted source. Feeds are normalized into consistent internal representations at the point of ingestion. Latency, staleness, and conflict anomalies are detected before data reaches downstream consumers. Original precision is preserved through every transformation, and raw data remains accessible alongside processed data so the institution always has access to the original record.

  • Vendor-specific formats mapped to a canonical internal schema at the ingestion boundary.
  • Automated quality gates that flag stale quotes, crossed markets, and unexpected gaps before data enters the pipeline.
  • Immutable raw storage alongside the processed layer so that any transformation can be verified against the original.

Outcomes

The institution gains control over its own market data story. Vendor abstraction layers reduce lock-in, making it possible to switch or add vendors without redesigning downstream systems. Data quality is validated before it reaches calculations and reports, so that confidence in the numbers starts at the source rather than at the point of consumption.

  • Clean abstraction layers that make vendor transitions a configuration change rather than a replatforming project.
  • Downstream consumers receive data that has already passed quality checks, reducing defensive coding and manual overrides.
  • A single source of truth for market data that all pricing, risk, and reporting systems share.

Where we've applied this

We applied this mandate at BatteryOS, where market data from multiple energy exchanges and price reporting agencies had to be normalized, validated, and distributed to downstream calculation engines with full auditability. The signals that drive this mandate are Pipeline Failure and Integration Breakdown.