How We Reduce Latency in Automated Trading Systems

How We Reduce Latency in Automated Trading Systems

In automated trading, speed is not simply a matter of using faster hardware.

A trading system must process market data, evaluate trading logic, generate orders, and communicate with external systems — often within a very short time window.

Even a few microseconds of unnecessary latency can become meaningful when a strategy is competing for execution priority.

At FontesFintech, we design high-performance trading systems with a simple principle:

Minimize unnecessary work on the critical path.

This article introduces some of the engineering approaches we use to reduce latency in automated trading systems.

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1. Understanding the Latency Path

A typical automated trading flow can be simplified as:

Market Data → Data Processing → Strategy → Order Generation → Order Transmission

Each stage can introduce latency.

For example, unnecessary memory copies, synchronization between threads, system calls, network communication, and inefficient data structures can all contribute to the total execution time.

Therefore, optimizing a trading system is not about optimizing a single function.

It is about optimizing the entire critical path.

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2. Minimize Data Copies

One of the first principles we consider is reducing unnecessary data movement.

A conventional architecture may pass market data through several layers:

Network → Buffer → Parser → Queue → Strategy → Order Module

If every layer creates another copy of the message, CPU cycles and memory bandwidth are consumed unnecessarily.

In latency-sensitive systems, we prefer architectures that allow components to access data with minimal copying.

This can be achieved through techniques such as:

• Shared memory
• Pre-allocated buffers
• Ring buffers
• Zero-copy or copy-minimized data paths
• Direct access to structured market data

The objective is simple:

Move less data, and move it fewer times.

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3. Shared Memory for Inter-Process Communication

Inter-process communication is another important consideration.

TCP/IP or other socket-based communication is convenient and flexible, but it also introduces additional processing overhead.

For components running on the same server, shared memory can provide a significantly more efficient communication mechanism.

For example:

Process A

Market Data Handler

↓

Shared Memory

↓

Process B

Strategy Engine

Instead of serializing and transmitting a message through a network stack, the processes can exchange data through a shared memory region.

This approach can reduce unnecessary context switching, memory copying, and protocol processing.

However, shared memory itself does not automatically make a system fast.

The synchronization mechanism and memory-access pattern must also be carefully designed.

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4. Minimize Lock Contention

Synchronization is essential in concurrent systems, but excessive locking can become a significant source of latency.

A simple architecture might use a mutex whenever multiple threads access shared data.

While this approach is straightforward, it can create problems when the system becomes highly concurrent.

A thread may have to wait for another thread to release a lock.

In a latency-sensitive trading system, this waiting time can become part of the critical path.

We therefore try to minimize locking wherever possible through techniques such as:

• Single-producer / single-consumer queues
• Ring buffers
• Thread-local data
• Lock-free or low-lock data structures
• Clear ownership of data
• Separation of read and write paths

The goal is not necessarily to eliminate every lock.

The goal is to avoid unnecessary synchronization on the latency-critical path.

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5. Keep the Critical Path Short

One of the most important design principles is to keep the latency-critical path as simple as possible.

For example:

Market Data Received

↓

Parse

↓

Update State

↓

Evaluate Strategy

↓

Generate Order

↓

Send Order

Operations that are not essential to this path should be moved away from it whenever possible.

Logging, monitoring, persistence, statistics, and other secondary operations should not unnecessarily block the execution of the trading strategy.

This separation allows the system to focus CPU resources on the operations that directly affect order latency.

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6. Efficient Data Structures Matter

High-performance trading software often processes a large number of small messages.

This makes data structure design particularly important.

General-purpose abstractions can sometimes introduce overhead that is insignificant in ordinary applications but meaningful in a latency-sensitive environment.

We prefer predictable memory access patterns and pre-allocated structures where appropriate.

For example:

• Fixed-size structures
• Pre-allocated memory
• Contiguous memory layouts
• Ring buffers
• Avoiding unnecessary dynamic allocation

Predictability is often as important as raw speed.

A system that is consistently fast is easier to optimize and operate than one with unpredictable latency spikes.

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7. Why We Use C

For latency-sensitive components, we often choose C because it provides direct control over memory, data structures, and execution behavior.

C allows us to carefully control:

• Memory allocation
• Data layout
• Pointer-based data access
• Buffer management
• Thread synchronization
• CPU-oriented optimization

This does not mean that every component of a trading system must be written in C.

Rather, we believe that the most latency-sensitive components should use technologies that provide the level of control required by their workload.

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8. Hardware and Software Must Be Designed Together

Software optimization alone is not enough.

CPU architecture, memory hierarchy, NUMA topology, network interfaces, and operating-system behavior can all affect latency.

For this reason, we consider the execution environment as part of the system architecture.

Depending on the requirements, optimization may involve:

• CPU affinity
• NUMA-aware design
• Cache-friendly data structures
• Memory pre-allocation
• Network optimization
• Kernel and OS configuration
• Dedicated processing threads

The right optimization depends on the actual workload.

There is no single technique that makes every trading system faster.

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9. Measure Before Optimizing

Perhaps the most important principle is measurement.

Latency should not be estimated simply by looking at source code.

We measure the actual execution path and identify where time is being spent.

For example:

Market Data Received

T0

↓

Data Processing

T1

↓

Strategy Evaluation

T2

↓

Order Generation

T3

↓

Order Transmission

T4

By measuring each stage, we can identify the actual bottleneck rather than optimizing based on assumptions.

In latency-sensitive systems, optimization should be driven by measurement.

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10. Our Approach

At FontesFintech, we approach automated trading systems from the perspective of the entire execution path.

Our focus is not simply to make individual functions faster.

We design the architecture to minimize:

Data Movement

Synchronization

Memory Allocation

Context Switching

Unnecessary Processing

and ultimately:

Time on the Critical Path.

This philosophy is applied across our trading-related solutions, from automated trading engines to high-performance market data and order-processing components.

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Conclusion

Low-latency trading is not achieved by a single optimization technique.

It is the result of many small architectural decisions made consistently throughout the system.

Efficient data structures, shared memory, reduced synchronization, predictable memory access, careful CPU utilization, and continuous measurement all contribute to reducing end-to-end latency.

At FontesFintech, we believe that high-performance trading systems should be designed from the latency path outward.

Less copying.
Less waiting.
Less unnecessary work.

More time for the strategy to compete.

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About FontesFintech

FontesFintech develops high-performance software solutions for financial markets, including automated trading systems, low-latency trading infrastructure, market data processing, risk management, and trading-related technology.

Our engineering focus is on building systems that are fast, predictable, and designed for the requirements of real-world financial markets.