Five Factors That Determine Latency in Trading Systems | Trading Engineering #5

Five Factors That Determine Latency in Trading Systems

Trading Engineering Series — 05

 

When developing a Low Latency Trading System, the first things that often come to mind are CPU performance and network speed.

However, trading system latency is not determined by a single factor.

From the moment market data enters the system to the moment an order reaches the exchange, many small operations contribute to the overall latency.

These include Memory Copy, Process Context Switching, Locking, Network Processing, and Message Parsing.

In environments such as Algorithmic Trading, DMA, LP, MM, and HFT, where fast order processing is critical, these seemingly small differences can accumulate.

This article examines five key factors that can affect latency in trading systems.

---

1. Memory Copy

One of the often-overlooked factors in high-performance systems is Memory Copy.

Copying data from one memory location to another can be extremely fast.

However, when data is repeatedly copied as it moves between systems and processes, the overhead can become significant.

For example:

Market Data
     ↓
Buffer A
     ↓ copy
Buffer B
     ↓ copy
Process C
     ↓ copy
Strategy
     ↓ copy
Order Buffer
     ↓
FEP

Each individual copy may be inexpensive.

But repeated copying can increase CPU usage and add unnecessary latency.

For this reason, Low Latency systems should minimize unnecessary Memory Copy operations and consider efficient ways of sharing data.

Depending on the architecture, technologies such as Shared Memory and approaches that minimize copying can be useful.

---

2. Process Context Switching

The second factor is Process Context Switching.

Operating systems switch between processes and threads so that multiple tasks can share CPU resources.

For general-purpose applications, the overhead may not be significant.

In latency-sensitive systems, however, it can become an important consideration.

For example, an order-processing pipeline may be divided across multiple processes:

Process A
    ↓
Process B
    ↓
Process C
    ↓
Process D

Data transfer between these processes can introduce IPC overhead and additional scheduling or context-switching costs.

This does not mean that all processes should be combined into a single process.

In financial systems, fault isolation, stability, and operational manageability are also critical.

The important principle is to design the process architecture according to the system's purpose and minimize unnecessary Context Switching and IPC overhead on latency-critical paths.

---

3. Lock & Synchronization

When multiple threads or processes access shared data, synchronization is often required to maintain data consistency.

One common approach is the use of locks.

Thread A
    ↓
   LOCK
    ↓
Shared Data
    ↓
  UNLOCK

The problem occurs when multiple threads need to wait for the same lock.

Thread A ──→ LOCK ──→ Processing
Thread B ──→ WAIT
Thread C ──→ WAIT

This waiting time can increase latency.

High-Performance Trading Systems therefore need to carefully consider how synchronization is implemented.

Depending on the system, techniques such as reducing lock scope, lock-free or wait-free approaches, single-writer designs, and efficient ring buffers may be considered.

The goal is not to eliminate every lock.

The important thing is to understand where synchronization overhead actually occurs in the data flow and optimize the areas that matter.

---

4. Network Processing

Network latency is another important factor in trading systems.

However, network latency should not simply be considered a matter of Internet speed.

A network path may involve several stages:

Application
    ↓
Socket
    ↓
Kernel
    ↓
Network Interface
    ↓
Switch
    ↓
Network
    ↓
Exchange

In addition to physical transmission time, factors such as Socket Processing, Kernel Overhead, Packet Handling, and Network Buffering can affect the overall processing path.

For FEPs and DMA Gateways connected to exchanges, both reliable connectivity and efficient Network I/O architecture are important.

---

5. Message Parsing

The fifth factor is Message Parsing.

Financial systems receive a wide variety of messages from exchanges and external institutions.

These messages need to be parsed so that the required information can be extracted and processed.

For example:

Message
 ├─ Message Type
 ├─ Sequence
 ├─ Account
 ├─ Symbol
 ├─ Price
 ├─ Quantity
 └─ Order Information

In a typical business application, parsing overhead may not be particularly important.

In a system processing thousands of messages per second, however, the parsing method itself can affect performance.

Unnecessary string conversions, repeated memory allocation, and excessive object creation can all introduce overhead.

Depending on the protocol, efficient parsing strategies should be designed around the characteristics of fixed-length messages, binary protocols, or other financial message formats.

---

6. Latency Is More Than a Single Number

Another important concept in Low Latency systems is End-to-End Latency.

A fast network does not necessarily mean that the entire trading system is fast.

Consider the following processing path:

Market Data
    ↓
Network
    ↓
Message Parsing
    ↓
Strategy
    ↓
Risk Check
    ↓
Order Processing
    ↓
FEP
    ↓
Network
    ↓
Exchange

The total latency is influenced by every stage along this path.

Therefore, optimizing only one component may not produce a meaningful improvement.

A better approach is to measure the entire Critical Path, identify the actual bottleneck, and optimize the area that contributes most to the latency.

---

7. Performance vs. Stability

Reducing latency should not be the only goal when building a trading system.

An aggressive optimization may improve performance while making the system more difficult to maintain or recover from failures.

Financial systems need to consider several factors together:

Performance

Fast data processing and order execution.

Stability

Reliable operation throughout the trading session.

Reliability

Safe handling of data and order states even when failures occur.

Maintainability

The ability to adapt to changing market environments and system requirements.

A good Low Latency Architecture is therefore not necessarily the fastest possible system.

It is a system that achieves an appropriate balance between performance, stability, reliability, and maintainability.

---

8. FontesFintech's Approach to Low Latency

At FontesFintech, we analyze latency from the perspective of the entire data flow rather than focusing on a single component.

We pay particular attention to:

• Minimizing Memory Copy
• Efficient Inter-Process Communication
• Minimizing Context Switching
• Optimizing Lock and Synchronization
• Efficient Message Parsing
• Network I/O Optimization
• Simplifying Critical Paths
• Measuring End-to-End Latency

Low Latency is not achieved by applying one particular technology.

It requires looking at the entire architecture:

Architecture → Data Flow → Memory → Process → Network → Message Processing

Based on practical experience in C-based financial system development, FontesFintech designs and develops High-Performance Trading Systems, DMA, FEP, and Algorithmic Trading Infrastructure tailored to customer requirements.

---

Conclusion

Trading system latency is not determined by CPU or network performance alone.

Memory Copy, Context Switching, Locking, Network Processing, and Message Parsing can all contribute to the overall performance of a trading system.

Most importantly, optimization should not begin with assumptions.

The system should first be measured, the Critical Path should be identified, and the actual bottlenecks should be optimized.

 

A Low Latency Trading System is not simply about using faster hardware.

It is about designing a better architecture, data flow, and execution path.

Fast. Efficient. Stable. Maintainable.

That is the approach FontesFintech takes when building high-performance financial trading infrastructure.