Market Data Replay System
A market data replay system is trading infrastructure that plays back recorded exchange feeds as if they were live. It preserves tick-by-tick sequencing, timestamps, and message semantics so algorithms, risk controls, and monitoring can be tested against realistic historical conditions.
What Is a Market Data Replay System?
A market data replay system ingests historical tick data, quotes, and order book updates, then emits them in original or modified time, recreating the behavior of live feeds.
Unlike generic historical data replay, which may drive offline analytics or batch simulations, a market data replay system targets low-latency trading stacks: algorithms, OMS/SOR, risk engines, and market surveillance systems.
It is narrower than general market replay systems, focusing specifically on the feed layer rather than full end-to-end trade lifecycle simulation.
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.
How It Works in Trading Infrastructure
Typical components include capture, storage, and replay:
Feed handlers normalize raw exchange protocols and write into a market data time-series schema implemented on a specialized market data time-series database.
The replay engine reads this history, controls a virtual clock, and schedules messages so downstream systems cannot distinguish replay from live traffic, apart from controlled speed-up or slow-down factors.
Primary Use Cases
For quant teams, replay enables realistic backtesting of execution and market-making algorithms, including interactions with fragmented venues and microstructure noise.
Operations and performance engineers use replay to benchmark new hardware, codecs, and latency-sensitive market data analytics pipelines without risking production.
Compliance and surveillance groups rely on tick-accurate replay for trade reconstruction, best-execution reviews, and investigation of spoofing or quote stuffing patterns.
Performance and Data Management Considerations
To be credible for high-frequency trading, replay must honor original inter-arrival times at microsecond or millisecond precision, preserve per-venue sequencing, and sustain production-level throughput. This places heavy demands on tick data storage architecture and order book data storage.
Systems often provide selective symbol/venue filters, session-based scenarios, and parameterized time dilation, turning the replay layer into a deterministic, repeatable “time machine” for the entire trading stack.