Market Data

Every trading system ultimately depends on one thing: market data. Get it late, get it incomplete, or get it wrong, and everything downstream — order routing, risk checks, pricing models, backtesting — is compromised. Yet market data infrastructure is the least glamorous and most under-invested part of most trading platforms. Firms spend millions on execution algorithms and risk models while running market data through a decade-old, single-threaded ticker plant that drops packets when volume spikes. Market data pipelines at trading firms exhibit a consistent pattern: the existing system works at normal volumes, fails silently during market events, and data gaps go undetected until a trade breaks. The fix is not a faster machine. It is an architecture designed for the scale, diversity, and reliability requirements of modern trading. ...

Financial data platforms must handle workloads that few other industries contend with: market data ingestion at millions of messages per second, risk calculations over hundreds of millions of positions, regulatory queries that must return results in seconds, and quantitative research that scans decades of tick data. This guide covers the database choices, pipeline architectures, and operational patterns we have used in production at global banks and hedge funds. ...

If you are building a financial data platform, the database choice determines what your quants and risk analysts can do — and how fast they can do it. Pick kdb+ and your time-series queries execute in microseconds, but your infrastructure bill runs six figures. Pick ClickHouse and you get analytical power at a fraction of the cost, but you trade off the specialised financial operations language that your quant team has been using for a decade. Pick TimescaleDB and your PostgreSQL-skilled engineers are productive immediately, but you hit query performance walls at petabyte scale. We have deployed all three in production at tier-one banks and hedge funds — kdb+ for real-time market data analytics, ClickHouse for regulatory reporting and risk aggregation, and TimescaleDB for back-office and treasury workloads. Here is what we learned about where each one fits. ...