In the field of high-frequency trading, transaction speed and latency directly impact a strategy's ability to outperform the market. Even millisecond or even microsecond latency differences can affect a high-frequency strategy's optimal buy and sell points, resulting in order slippage and reduced returns. Therefore, building a trading network with the lowest possible latency is a key goal for high-frequency traders when deploying servers and infrastructure. While achieving true zero latency is theoretically impossible, systematic optimization of the entire network can minimize latency and maximize trade execution effectiveness.

Order slippage in high-frequency trading is often caused by two factors: delays in sending orders from the trading terminal to the exchange gateway, and insufficient server and system performance when processing trading logic. The first is a network optimization issue, while the second involves optimizing server hardware and operating systems. The core of network optimization lies in reducing intermediate hops and link congestion, thereby improving routing stability and consistency. Server performance optimization primarily involves tuning kernel parameters, memory allocation, disk I/O throttling, and affinity binding for multi-core CPUs.

Choosing a trading server location is the first step. If the server is far from the target exchange's data center, the physical distance will cause additional latency, which cannot be compensated even by increasing bandwidth. Therefore, many high-frequency trading firms choose to place their servers directly in data centers near the exchange, or even use co-location services within the same data center, connecting the servers directly to the exchange's gateway via internal fiber optic cables, thereby reducing latency to nanoseconds. For scenarios where co-location is not possible, low-latency optimized links, such as financial dedicated lines and CN2 GIA, can be used to avoid unnecessary forwarding nodes.

When optimizing server systems, the first priority is to ensure that the operating system kernel handles network transmissions efficiently. Taking Linux as an example, kernel parameters can be adjusted to reduce packet forwarding and queue latency:

sysctl -w net.core.netdev_max_backlog=250000
sysctl -w net.core.somaxconn=65535
sysctl -w net.ipv4.tcp_fin_timeout=10
sysctl -w net.ipv4.tcp_tw_reuse=1

Adjusting these parameters can make TCP connection establishment and release more efficient, while reducing system bottlenecks under high-concurrency conditions. At the data transmission level, enabling large page memory and NUMA node optimization can improve data processing throughput.

Network-level optimization can also be achieved through protocol selection. For example, high-frequency trading systems often bypass the standard TCP protocol and adopt UDP or custom protocols to reduce handshake latency and flow control overhead. Although UDP does not guarantee data reliability, in financial trading scenarios, servers typically ensure data accuracy through application-layer mechanisms rather than relying on protocol retransmissions. To further improve performance, some companies even employ FPGA hardware accelerators to directly process data packets, offloading some logic to the hardware layer and shortening the software processing chain.

For transmission path optimization, intelligent routers or SDN control systems can be used to lock in the optimal path. For example, when an international link experiences jitter, the system can automatically switch to a lower-latency backup path, ensuring that trade orders are not affected by network fluctuations. This requires BGP optimization to avoid excessively long detours during path selection. In large-scale trading clusters, load balancing and redundant switching are also important considerations to prevent single points of failure from causing latency spikes.

Application-layer optimization is reflected in the processing speed of the trading engine. For high-frequency trading servers, using in-memory databases instead of traditional disk-based databases can reduce I/O latency, and multi-threaded asynchronous processing can accelerate order matching and risk control calculations. Code optimization is equally important, as redundant logic and excessive system calls can introduce significant latency. Therefore, during the development phase, many teams use specialized performance analysis tools to analyze the system line by line to eliminate any links that may slow execution.

Furthermore, order slippage is also related to the speed at which market data is transmitted. Latency in market data means trading systems are receiving outdated market data. Even a delay of just a few tens of milliseconds can lead to ineffective order placement decisions. Therefore, when implementing network optimization, it's crucial not only to optimize the order transmission link but also to ensure that market data reaches the server as quickly as possible, which often relies on high-speed, direct market data connections.

In actual deployments, many high-frequency trading firms employ a layered optimization strategy. The first layer is the hardware layer, including high-speed CPUs, low-latency memory, NVMe SSDs, and high-speed network cards to ensure server hardware can complete computations and responses in microseconds. The second layer is the system layer, which achieves efficient kernel scheduling through Linux kernel parameter optimization, network protocol stack tailoring, and IRQ affinity binding. The third layer is the network layer, which reduces link latency through optimized routing, dedicated lines, and intelligent routing control. The fourth layer is the application layer, which improves software processing efficiency through algorithm optimization, in-memory matching engines, and asynchronous concurrent design.

The ultimate goal is to ensure that the entire trading process, from market data reception and strategy calculation to order placement, is completed in the shortest possible time, thereby reducing the risk of slippage. In certain scenarios, if sub-millisecond or even nanosecond optimization can be achieved, the advantages of the trading system will be very obvious. This is why top high-frequency trading firms are willing to invest huge amounts of money to build dedicated networks and hardware systems.