Building Automated Pricing Data Systems for Quantitative Trading
Summary
This article explains the pricing developer’s role in a systematic hedge fund and how market data is prepared for research and trading. It divides the trading pipeline into pricing and feeds, signal research, and execution, then focuses on building the pricing-data layer across equities, fixed income, derivatives, foreign exchange, commodities, and indices.
The described workflow maintains a deduplicated security master and maps differing vendor identifiers, retrieves data through automated source connections, stores it in a tuned relational database, and checks for missing, inconsistent, or anomalous prices. It also adjusts prices for corporate actions so researchers can work with return series, then distributes the data through internal interfaces and database replication. The account describes automated operations, with people reviewing error logs and maintaining sources and interfaces. It is a practitioner’s overview rather than a strategy study: it gives no performance evidence and deliberately omits the fund’s trading algorithms.
Key ideas
- A systematic trading pipeline depends on reliable pricing data alongside signal research and trade execution.
- A security master and identifier mapping help unify instruments that vendors label differently.
- Data quality checks can compare sources, flag unexplained price spikes, and identify missing data.
- Corporate action adjustments turn price histories into more useful return series.
- Automating collection, storage, and distribution still leaves ongoing source maintenance and error review.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.