Scala Trading Systems Development | Low-Latency Trading Engineers | Scala Teams
Scala trading systems

Trading Systems Built for
Speed and Correctness.

We build trading infrastructure on Scala and the JVM where latency is measured in microseconds and incorrect state is not a bug. It is a loss. Order matching engines, market data systems, and risk infrastructure built for firms where performance and correctness are both non-negotiable.

μs.

Latency target

JVM.

Native performance

FP.

Correct by construction

0x

Tolerance for data loss

Order book SCALA/USD Live
Price Size Total
Spread 0.02 VWAP 142.50

Why Scala for trading systems

Trading infrastructure demands a language
built for speed and correctness together.

In trading systems, latency and correctness are not competing concerns. They are both requirements. Scala on the JVM delivers the performance profile of a systems language with the type safety and functional programming model that complex financial logic demands.
01 JVM performance optimized for low latency The JVM's JIT compiler, garbage collection tuning, and memory model can be optimized for the low-latency, high-throughput profile that trading systems require. Scala gives you full access to these optimizations without sacrificing the language features that make complex trading logic maintainable.
02 Type-safe order and position modeling Orders, positions, instruments, and settlement states can be modeled in Scala's type system in a way that makes invalid transitions impossible to compile. The class of bugs that cause incorrect fills and position miscalculations gets eliminated before the code reaches the exchange.
03 Akka and Pekko for actor-based trading systems Actor systems are a natural fit for trading infrastructure. Order matching engines, market data distribution, and risk calculation all benefit from the message-passing concurrency model that Akka and Pekko provide. Location transparency and fault tolerance come with the model.
04 Immutable event sourcing for trade history Immutable event logs are a natural fit for trade history, order audit trails, and regulatory reporting. Every order state transition is a fact that can be replayed, audited, and queried. No reconciliation problems that mutable state creates in high-frequency trading systems.
05 Functional concurrency for market data processing Cats Effect and ZIO give you the structured concurrency and stream processing primitives that high-volume market data feeds require. FS2 pipelines process ticks, order book updates, and trade events with backpressure and resource safety built into the model.
06 Proven at scale in financial production Scala has been running in production at trading firms, exchanges, and financial data platforms for over a decade. The language and the ecosystem have been stress-tested at the latency targets and data volumes that serious trading infrastructure demands.

What we build

Scala trading systems capabilities.

01

Order matching engines

High-performance order matching on the JVM.

Akka Cats Effect Pekko

We build order matching engines that process limit orders, market orders, and complex order types with the throughput and latency profiles that exchange-grade infrastructure requires. Built on Akka and Pekko actor systems with the type-safe order modeling that prevents incorrect fills.

02

Market data infrastructure

Real-time market data feeds and distribution systems.

FS2 Kafka Akka Streams

We build market data ingestion, normalization, and distribution systems that handle high-frequency tick data, order book updates, and trade events at scale. Built on FS2 and Akka Streams with backpressure and exactly-once semantics for the data integrity that downstream systems depend on.

03

Algorithmic trading infrastructure

Execution infrastructure for algorithmic strategies.

ZIO Cats Effect http4s

We build the execution infrastructure that algorithmic trading strategies run on: order routing, smart order routing, execution algorithms, and the low-latency API layers that connect strategies to venues. Built in Scala with the correctness guarantees that algo trading demands.

04

Real-time risk systems

Pre-trade and post-trade risk calculation engines.

Akka Apache Spark ZIO

We build real-time risk engines that calculate pre-trade risk checks, position limits, and exposure metrics with the latency profile that live trading requires. Post-trade risk aggregation on Apache Spark for the regulatory reporting and end-of-day reconciliation that firms depend on.

05

FIX and venue connectivity

FIX protocol and exchange connectivity layers.

Akka Pekko Tapir

We build FIX protocol engines and exchange connectivity layers that handle order routing, execution reports, and market data subscriptions across multiple venues. Built for the reliability and session management that production venue connectivity requires, with failover and reconnection built in.

06

Trade analytics and reporting

Trade history, P&L, and regulatory reporting systems.

Apache Spark Doobie FS2

We build trade history, P&L attribution, and regulatory reporting systems on Apache Spark and Scala. Immutable event logs, reproducible calculations, and the audit trails that compliance teams and regulators require, built for the data volumes that active trading firms generate.

Built for trading

The four properties every
trading system must deliver.

01

Low latency

Order processing, risk checks, and market data updates happen in microseconds. Latency is an engineering constraint, not a target you try to hit after the system is built.

02

Determinism

Given the same inputs, the system produces the same outputs every time. Reproducible order matching, deterministic P&L calculation, and auditable trade history.

03

Fault tolerance

Individual component failures do not cascade into system-wide outages. Supervisor hierarchies, circuit breakers, and graceful degradation built into the architecture from the start.

04

Correctness

Invalid order states, incorrect position calculations, and race conditions in order matching are caught by the type system before they reach production. Correctness is enforced, not tested for.

Common questions

Questions we get before the first call.

If your question isn't here, it takes one conversation to answer it.

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Do your engineers have experience with production trading systems specifically?

Yes. We match clients with engineers who have built trading infrastructure in production: order matching engines, market data systems, FIX connectivity, and risk engines. General Scala experience is a baseline, not the qualification.

Can you work with our existing FIX infrastructure and venue connections?

Yes. We work within your existing connectivity and extend it. Whether you're on a custom FIX engine or a third-party solution, our engineers understand the protocol and the operational requirements well enough to work with what you have.

Our system is latency-sensitive. How do you approach performance optimization?

We start by measuring. Profiling, identifying actual bottlenecks, and optimizing with data rather than assumptions. JVM tuning, GC optimization, and algorithmic improvements all have their place. The right approach depends on where the latency is actually coming from in your system.

We have a Java-based trading system. Can you migrate it incrementally?

Yes. Scala and Java interoperate directly on the JVM. We can migrate component by component, starting with the parts that benefit most from Scala's type system and concurrency model, without taking the whole system offline during the transition.

Do you work on buy-side, sell-side, or both?

Both. Buy-side infrastructure (execution management, strategy engines, portfolio systems) and sell-side infrastructure (order management, market making, connectivity) present different engineering problems. Our engineers have worked across both contexts.

Tell us what you're building.