Real-time market data
Market data infrastructure that keeps every tick.
We build the systems that collect, normalise and store order books and trades, live and historical, so traders, quants and surveillance teams work from the same complete data.
Signs you need this
- Your algo traders cannot backtest because historical order book data is incomplete or scattered.
- Tick capture drops messages or falls behind when the market gets busy.
- Every team pulls prices from a different source, and nobody trusts the numbers.
- Your time-series database is fast to write to but slow to query, or the other way round.
What we do
Historical collection at volume
High-volume collectors for order books and trades from venue and vendor APIs such as Trayport, with retries, gap detection and backfills.
Low-latency tick capture
In-process pipelines built on the Disruptor pattern, with lock-free hand-offs and back-pressure, so a burst does not become a backlog.
Multi-target ingestion
One feed written to several stores at once, such as Kx Insights over MQTT, TimescaleDB, QuestDB and Parquet on object storage, each serving the workload it suits best.
Storage and query design
Schemas, partitioning and retention tuned for both the ingest rate and the queries your quants actually run.
Operations
Brokers and databases deployed on Kubernetes, with CI/CD for installs, upgrades and licence renewals.
What you get
- Production collectors and capture services, covered by tests
- Storage schemas and retention policies
- Monitoring for lag, gaps and throughput
- Runbooks and architecture decision records
Related work
Collecting 120 million market data records a day
A high-volume collection system for order books and trades that gives algo traders the history they need to design and backtest strategies.
Read the case studyUltra-low-latency tick capture with the Disruptor pattern
Real-time capture of orders and trades, delivered to four storage targets without slowing the hot path.
Read the case studyQuestions
Which venues and data vendors have you integrated?
Most recently Trayport for European energy markets and Trading Technologies for audit and fills data. The same patterns apply to other REST, streaming and message-queue sources.
Do we need Kx, or will PostgreSQL do?
It depends on your query patterns and volumes. TimescaleDB and QuestDB cover many use cases at lower cost, while kdb-based stores excel at heavy analytics on tick data. We benchmark on your data before recommending one.
Can you extend our existing platform instead of replacing it?
Yes. Most engagements extend what is already in production.