In the competitive world of High-Frequency Trading (HFT) and quantitative finance, software performance is measured in microseconds. Standard application overhead, garbage collection pauses, and unoptimized memory allocation can result in massive latency spikes, leading to lost alpha and missed arbitrage opportunities.
To master systems-level performance, I architected a highly optimized algorithmic trading simulation engine from the ground up. Designed to ingest raw market data feeds and execute simulated orders, the engine prioritized raw speed and deterministic execution over all other factors.
Written entirely in modern C++20, I systematically bypassed standard library overhead by writing custom memory allocators to prevent heap fragmentation during runtime. I leveraged lock-free data structures and extreme multithreading techniques, pinning specific threads to CPU cores on Linux systems to minimize context switching and cache misses.
The resulting engine achieved remarkably consistent sub-millisecond trade execution times under heavy simulated market load. This project served as a deep dive into bare-metal performance tuning, network stack optimization, and advanced computer science principles necessary for institutional-grade quantitative trading environments.