In the world of high-performance trading systems, accurate and timely market data is paramount. Yet even in the most robust infrastructures, market data gaps—missing or out-of-sequence messages—are an unavoidable reality. Understanding how they occur, how to detect them, and how to recover is critical for ensuring data integrity and making reliable trading decisions.
In high-frequency trading, conventional software wisdom often breaks down. We're not optimizing for scalability or code elegance - we're optimizing for nanoseconds.
Most systems in HFT are memory-bound, not CPU-bound. Modern CPUs can execute billions of instructions per second, but if your data isn't in the CPU cache, it doesn't matter. Accessing L1 cache takes about 1 nanosecond. Accessing main RAM? Over 100 nanoseconds. That's a 100x penalty for missing the cache.
In many fields - AI, simulations, rendering - GPUs offer massive speedups. But in ultra-low-latency trading, they're rarely the right tool.
When you're optimizing for nanoseconds, runtime behavior matters more than startup time or convenience.
Static linking - especially when combined with native compilation (-march=native, -O3, -mavx, etc.) - offers real runtime performance benefits.
The Nanoconda Trade Simulator enables you to replay historical trading days at real-time speed, enabling you to practice launching algos, monitor their behavior, and review performance on real tick data - all without the need for live market licenses.
This workflow is especially useful for:
This short guide covers two fast and effective ways to find memory leaks:
When optimizing for lowest latencies, small tweaks can make a huge difference. Here are 5 quick wins that everyone should know:
This is a very common question and something you probably need to know right away.
The answer depends on your budget and exchange setup, but if speed matters—and you’re not looking to blow your trading principal on FPGAs—here’s what you should focus on:
When writing code for low-latency trading, many "well-known best practices" must be ignored—or even reversed. What works in theory often collapses in real-world performance.