Algotradingdesk maps HFT layers

On May 22, Algotradingdesk published an X thread that described modern high-frequency trading as a stack of time horizons rather than a single race for the lowest latency. The post said FPGAs sit on the fastest layer, handling narrowly defined reactions at what it called “physics-speed,” while richer decision systems operate across longer windows from microseconds to hours. The thread argued that the shortest path should stay simple because each added branch or model increases latency and operational complexity. It framed the core design choice as separation: minimal logic on the hot path, heavier inference and coordination off it. ### Why does the thread split HFT into layers instead of one speed contest? The May 22 post described HFT as a hierarchy of decisions with different deadlines, not a single monolithic engine. At the bottom are hardware paths that must react in tens of nanoseconds; above them are software and model layers that can absorb more data, compare more venues and hold more state because they have more time. (x.com) That framing matters because it separates execution mechanics from strategy. A system can be extremely fast at packet handling and still derive most of its edge from how it combines signals, risk rules and inventory decisions across longer horizons, according to the thread. ### What exactly are FPGAs doing in the sub-100-nanosecond layer? Algotradingdesk said FPGAs are best used for simple arithmetic and tightly bounded reaction logic. (x.com) In that setup, the hardware is not running a broad, adaptive model; it is taking a very small number of inputs, applying fixed rules and emitting a response with minimal delay. The thread said those responses can come in under 100 nanoseconds when the logic is kept sparse. (x.com) That is the part of the stack where firms care about propagation delay, serialization, wire distance and gate-level simplicity, because every extra step costs time. ### If the hardware is fastest, why did the post say most alpha is elsewhere? The thread said most trading alpha does not come from cramming a complex prediction model into the fastest hardware path. (x.com) It said the larger source of edge is orchestration across horizons: deciding what to quote, where to quote, how to hedge, when to back away and how to combine short-term signals with inventory and market-state information. That means the fastest layer is often acting on instructions shaped upstream. The FPGA may execute a narrow reaction first, but the broader system design — model training, parameter updates, venue selection and risk budgets — is what determines whether that reaction is profitable over time, according to the thread. ### Why keep the hot path simple? The May 22 post argued that lower latency usually requires simpler models. (x.com) A path designed for nanosecond decisions cannot tolerate the branching, memory access and maintenance burden that come with richer logic, the thread said. That is also an engineering choice, not only a speed choice. By keeping Tier 0 logic small, firms can test it more directly, reason about failure modes more easily and change higher-level models without constantly disturbing the most latency-sensitive path, as the thread described it. (x.com) ### What does this say about how firms should build trading stacks? The post’s framework points to a layered architecture in which hardware handles deterministic micro-reactions and software layers handle context, adaptation and model richness. (x.com) In practice, that means putting only the most time-critical math in FPGA or adjacent hardware and leaving broader optimization problems to systems with more latency budget. The next reference point is the original May 22 thread on Algotradingdesk’s X account, where the post lays out the timing ladder from nanoseconds through longer trading horizons. (x.com)