Rust High-Frequency Quantitative Trading System
The article explains why the Linux scheduler’s thread migration hurts deterministic low‑latency trading, outlines three reasons to bind threads to specific cores, demonstrates how to use taskset and Rust’s core_affinity crate for binding, and shows how to verify the binding with htop and command‑line tools.
The article explains why Simultaneous Multi‑Threading (SMT) degrades deterministic low‑latency performance in high‑frequency trading, details the resource contention and unpredictability it introduces, and provides step‑by‑step instructions for disabling SMT on AWS instances and verifying the change.
In quant trading systems, hash maps are on the hot path; replacing Rust's default std::collections::HashMap (which uses SipHash) with hashbrown's foldhash can speed up inserts and lookups by up to five times, and the article explains how, why, and when to make the switch.
This article explains what interviewers expect when they ask about implementing a low‑latency C++ trading system, covering hardware‑level concepts such as cache hierarchy, false sharing, NUMA, kernel bypass, CPU pinning, lock‑free programming, memory‑order semantics, and modern C++ techniques that together achieve microsecond‑ or nanosecond‑scale determinism.
On Linux, a call to std::thread::sleep(Duration::from_micros(2)) typically pauses for about 60 µs—30× longer than requested—because the API only guarantees a lower bound and adds a default timer_slack of 50 µs; the article explains the kernel path, shows benchmark data, and provides practical ways to shrink the floor to ~10 µs or achieve sub‑µs latency with busy‑spin and full system tuning.
The article dissects the cost of obtaining timestamps in Rust, compares chrono::Utc::now(), clock_gettime, std::time::Instant and raw RDTSC, explains Linux vDSO behavior, monotonic vs realtime clocks, and proposes a layered timestamp strategy that uses raw cycles for hot‑path latency measurement, calibrated clock_gettime for wall‑clock alignment, and chrono only at system boundaries.
The article dissects how market‑data parsing becomes the first latency bottleneck in high‑frequency trading, then demonstrates a zero‑copy approach using the Rust logos library that eliminates heap allocations, repeated bounds checks and memory copies, and compares it with other Rust parsing ecosystems.
The article explains SIMD fundamentals, traces the evolution of x86 instruction sets, compares Intel and AMD implementations, examines the controversy around AVX‑512, and shows how Rust leverages SIMD to accelerate critical paths in high‑frequency quantitative trading, offering concrete code examples and performance tips.
The article explains why adding 0.1 and 0.2 in Rust (and other IEEE‑754 languages) yields 0.30000000000000004, explores the binary representation limits of f64, introduces the ULP concept, and details how the Ryū algorithm produces the shortest correct decimal string for floating‑point numbers.
The article explains how Rust’s ustr library implements string interning to store each unique string only once, offering O(1) pointer‑based comparison, pre‑computed hashing, zero‑cost cloning, seamless C FFI, and thread‑safe usage, while also detailing its internal design, usage patterns, suitable scenarios, and trade‑offs.
