Market Data Time-Series Database
A market data time-series database is a specialized engine for storing and querying high-frequency market data, where each event is indexed by symbol, exchange, and timestamp. It powers trading, analytics, and regulatory workloads on tick and order book data at scale.
What Is a Market Data Time-Series Database?
A market data time-series database is a purpose-built
time-series database optimized for financial
tick, quote, and order book streams. Its primary key is almost always the
triplet (symbol, exchange, timestamp), capturing where and when each event
occurred for a specific instrument.
Unlike generic telemetry stores, it must handle nanosecond-level timestamps, extreme burstiness during auctions or news, and strict ordering guarantees required for trade reconstruction requirements. Typical ingest sources are normalized feeds from market data feed handlers, internal pricing engines, and derived analytics.
Next generation time-series database
QuestDB is an open-source time-series database optimized for market and heavy industry data. Built from scratch in Java and C++, it offers high-throughput ingestion and fast SQL queries with time-series extensions.
Core Data Model and Schema Considerations
The logical row usually represents a single market event: for example, a trade
tick with price, size, trade condition flags, and the
(symbol, exchange, timestamp) key, or an order book update with side, level,
and quantity.
Time-based partitioning (by trading day or smaller buckets) is combined with symbol or exchange partitioning to keep related events physically close, which improves scan performance for symbol- or venue-centric queries.
Systems also track an ingestion timestamp to separate event time from processing time, enabling late-arrival handling and quality checks on upstream infrastructure.
Role in Trading Architecture
In modern architectures, the market data TSDB sits between raw exchange feeds and consuming applications such as algorithmic trading, TCA, and surveillance.
It provides a consistent view of symbol and exchange-normalized data for both real-time queries and historical backtests, often feeding market replay systems and stress simulations.
Performance and Use Cases
Performance priorities include sustained multi-million events-per-second ingestion, time-range and symbol-range scans with low latency, compression tuned for dense tick and order book data storage, and predictable behavior during volatility spikes.
Typical use cases span intraday strategy research on full-depth order books, cross-venue price formation analysis, implementation shortfall and VWAP/TWAP benchmarking, market-impact modeling, and regulatory-grade retention of all events keyed by symbol, exchange, and timestamp for many years.