Using AI to Simulate Rival HFT Strategies

The application of LLMs to model high-frequency trading strategies introduces a new layer of abstraction, but the underlying infrastructure's need for raw speed remains unchanged. Kernel bypass techniques are fundamental, allowing trading applications to communicate directly with network hardware, side-stepping the latency-inducing kernel network stack. Technologies like DPDK and OpenOnload are industry standards for achieving this, enabling packet processing in microseconds. This direct hardware access is often paired with Field-Programmable Gate Arrays (FPGAs), which offer deterministic, nanosecond-level latency for critical functions like data feed parsing and order execution. Unlike CPUs, FPGAs are reconfigurable hardware that can be programmed to perform specific tasks in parallel, eliminating OS jitter and context-switching overhead. A 2024 study highlighted an FPGA-based system achieving an average latency of just 480 nanoseconds, processing up to 150,000 orders per second. The strategies being modeled, such as IMC's pre-earnings volatility crush, are designed to exploit predictable market phenomena. Implied volatility (IV) typically rises before an earnings announcement due to uncertainty and collapses afterward—a dynamic known as "IV crush". Traders can capitalize on this by selling options when IV is high and closing the position before the announcement to avoid the subsequent crush. Similarly, Optiver's end-of-day scalping of 0DTE (zero days to expiration) options targets rapid price fluctuations on the contract's final trading day. These strategies rely on high leverage and the accelerated time decay (theta) of options that are about to expire. Scalping these instruments demands ultra-low latency infrastructure to execute a high volume of small, quick trades. While LLMs show promise in financial reasoning, their application in HFT is still nascent and distinct from long-horizon, sentiment-based models. A framework called QuantAgent, for instance, uses specialized LLM agents for short-term signals like technical indicators and chart patterns, achieving high directional accuracy in simulations. The architectural debate between on-premises and cloud deployments continues, with latency as the primary consideration. On-premises and co-located setups have traditionally been favored for ultra-low latency applications, offering complete control over hardware and network configurations. However, major cloud providers are increasingly offering low-latency solutions and scalable compute resources that are becoming viable for less latency-critical, high-volume trading strategies.