In C's high-frequency trading (HFT) system development, reducing dynamic memory allocation, avoiding lock competition and thread blocking, optimizing performance with CPU architecture features, and careful handling of logs and exceptions are key best practices. 1. Reduce latency jitter caused by dynamic memory through object pools, stack allocations, and custom allocators; 2. Reduce lock competition and context switching using lock-free data structures, thread binding and independent queues; 3. Improve computing efficiency with SIMD instructions, data alignment and cache optimization; 4. Asynchronous logging, disabling expensive log levels, and using return code instead of exception mechanisms to reduce overhead. These methods jointly ensure the low latency and high stability of the HFT system.

In the field of high-frequency trading (HFT), C is one of the preferred languages ??because it provides fine-grained control of hardware, low latency, and efficient memory management capabilities. If you are using C for HFT system development, the following best practices can help you write more stable, faster and more controllable code.

Reduce dynamic memory allocation

HFT applications are extremely latency-sensitive, while dynamic memory allocations such as new or malloc introduce unpredictable latency and performance jitter. To increase certainty, it is recommended:

• Use an object pool or a preallocated memory pool to reuse objects.
• Try to use stack allocation instead of heap allocation.
• Replace the default std::allocator as a custom efficient allocator, such as a slab-based allocation strategy.

For example, you can pre-allocate an array of order structures of a fixed size, and only make index references at runtime to avoid frequent requests to free memory.

Avoid lock competition and thread blocking

Multithreading is the foundation of HFT systems, but lock competition and thread switching can cause delays to soar. Optimization methods include:

• Use lock-free data structures such as atomic variables ( std::atomic ) and CAS operations.
• Thread affinity is used to reduce context switching.
• Try to use single-thread processing for critical paths, and use multiple independent queues to isolate task flow.

For example, a high-frequency order processing module can bind each symbol to a separate thread, so that sequential execution is guaranteed without locking.

Optimize performance with CPU architecture features

C allows you to directly utilize the features of modern CPUs to improve performance:

• Use SIMD instructions (such as SSE/AVX) to speed up batch numerical operations.
• Control data alignment to avoid false sharing.
• Put hotspot functions and key data into the L1/L2 cache to reduce cache miss.

For example, if you are processing a large amount of tick data and need to quickly calculate the moving average, you can use the AVX instructions to process multiple floating-point numbers at once to greatly improve throughput.

Be careful when handling logs and exceptions

In HFT scenarios, logging and exception handling may become performance bottlenecks or even system crashes if improperly handled:

• Do not use expensive log operations in critical paths, asynchronously write or lower log levels.
• Avoid using try-catch structure to wrap high-performance code paths, because exception throws are expensive.
• The return code can be used instead of the exception mechanism for error transmission.

For example, for a market processing loop that is tens of thousands of times per second, the debug level log output should be closed and only the fatal error message should be retained.

Basically these are the more practical methods. HFT has extremely high performance requirements, so many details have to be polished repeatedly, such as cache line alignment, instruction reordering, system calls, etc. Although it doesn’t seem complicated, each item is easily overlooked, especially if the stress test is not sufficient.

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