Low-Latency Machine Learning for Options Pricing: High-Speed Models and Trading Performance

Abstract

Low-latency machine learning presents a transformative approach to options pricing and trading, addressing fundamental challenges in computational finance where traditional models struggle with market realities. This article introduces an end-to-end machine learning framework engineered specifically for high-speed options trading environments, integrating specialized neural network architectures with advanced system infrastructure. The framework incorporates recurrent neural networks optimized for temporal dependencies in options data alongside domain-specific signals, including momentum indicators, mean-reversion metrics, and autoencoder anomaly detection. Performance is enhanced through a multi-faceted optimization strategy encompassing model quantization, kernel fusion, magnitude-based pruning, and batching optimization. The system architecture features a robust data pipeline for multi-source ingestion, distributed task scheduling for parallel computation, and a tiered API serving layer. Hardware acceleration through GPUs, FPGAs, and vectorized CPU operations complements comprehensive memory and I/O optimizations. The resulting trading strategy demonstrates exceptional risk-adjusted returns compared to traditional approaches, with superior performance, particularly evident during market stress periods. This integrated approach effectively balances the dual requirements of pricing accuracy and execution speed, establishing a compelling case for machine learning applications in competitive financial markets where milliseconds determine trading success.