Commodity Trading · Pre-Trade Data
Building a Market Data Pipeline for Commodity Trading
A cross-commodity trading operation relied on manual spreadsheet uploads for several market data sources, creating delays and periodic data-entry errors that reached the risk system.
The Challenge
A trading operation covering multiple commodity classes had grown its market data sources organically - some automated, several still manually downloaded and uploaded by an operations analyst each morning. The manual step introduced both delay and occasional transcription errors that propagated into valuation.
Existing Environment
A CTRM platform for trade capture and risk, fed by a mix of automated API feeds and manual file uploads, with no centralized validation layer.
Root Cause
No single team owned data quality end-to-end. Each source had been integrated independently over several years, with validation logic - where it existed - built directly into ad-hoc upload scripts rather than a shared framework.
Solution
We built a centralized ingestion layer that normalized every data source - manual and automated - into a common format, added validation and outlier-detection rules at the point of ingestion, and automated the remaining manual feeds via API or scheduled file retrieval where the provider supported it.
Market data pipeline
Implementation
- Audited all existing market data sources and their integration method
- Designed a common ingestion schema across automated and manual sources
- Automated remaining manual feeds via API or scheduled retrieval
- Added validation and outlier-detection rules at ingestion
- Built a monitoring dashboard for pipeline health and data completeness
Result
- Manual, error-prone uploads were eliminated for all but one legacy source
- Data quality issues were caught at ingestion rather than discovered downstream in risk reports
- The operations team regained the time previously spent on manual data handling
Technologies
Lessons Learned
- Market data pipelines that grow organically over years tend to accumulate inconsistent validation - a periodic audit is worth the investment.
- Automating a manual feed is often less about the API integration itself and more about agreeing on ownership and escalation when the feed fails.
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