SCALING TEMPORAL FEATURE EXTRACTION FOR HIGHFREQUENCY CRYPTO TRADING VIA HARDWARE-AWARE STATE SPACE DESIGN

High-frequency trading in cryptocurrency markets demands sub-millisecond temporal feature extraction from order book dynamics. Traditional recurrent architectures face inherent sequential bottlenecks that prevent real-time deployment in latency-critical environments. This paper proposes a hardware-aware state space model framework explicitly designed for field programmable gate array (FPGA) execution, enabling parallel temporal computation through structured matrix parameterizations that map efficiently to systolic array architectures. Theframework incorporates diagonal-plus-low-rank decomposition to reduce computational complexity from O(N²) to O(N+Nr), combined with quantization-aware training that maintains directional accuracy while enabling fixed-point arithmetic. Empirical validation on Bitcoin andEthereum tick data from 2022-2024 demonstrates 15.2× throughput improvement versus graphics processing unit (GPU)-optimized long short-term memory (LSTM) implementations, achieving 62.7% directional accuracy for next-tick price movement prediction with 247 microsecond end to-end latency for 100-dimensional order book state vectors. The system processes 4.2 million feature updates per second on Stratix V FPGA hardware, establishing practical viability for ultra low-latency algorithmic trading applications where microsecond advantages translate to significant competitive edges.