cs.LGJun 8, 2026

Fast Exact Nearest-Neighbor Learning for High-Frequency Financial Time Series

Authors: Henry Han, Diane Li

Organizations: Data Science and Artificial Intelligence Innovation Laboratory, School of Engineering and Computer Science, Baylor University, Waco, TX 76798, USA · Department of Business, Management and Accounting, University of Maryland Eastern Shore, USA

Abstract

AI efficiency at scale is becoming critical in finance as market data volumes surge across equities, ETFs, FX, options, and high-frequency trading streams. This growth creates a core challenge for mature financial AI systems: models must learn from larger historical corpora while still meeting real-time latency constraints in trading, risk management, and derivative pricing. We use exact nearest-neighbor learning for high-frequency financial time series as a concrete case study to show that Mojo-based financial AI can address this challenge. We introduce a Mojo SIMD k-d tree with variance-based splitting, contiguous flat-buffer storage, and compile-time vectorized distance computation. We also provide a runtime result showing that, under standard pruning and implementation-cost assumptions, the Mojo SIMD k-d tree asymptotically dominates Mojo SIMD brute force and scikit-learn's k-d tree in the fixed-stock, large-nn, moderate-dimensional regime. Empirically, across eight financial datasets on x86 and ARM64 with up to 277K training samples, the method achieves 17.5--21.6×\times speedup over scikit-learn's k-d tree on x86 and 28.1--43.5×\times over scikit-learn brute force on ARM64 equity/ETF datasets, while preserving exact outputs. Beyond nearest-neighbor inference, Mojo's compiled execution enables an Extra Trees-based implied-volatility pricing model to train on 10×10\times more options data, reducing put-IV RMSE by 8.0%. These results position Mojo as a scalable, production-ready stack for financial AI and a promising foundation for efficient AI in other data-intensive fields. \keywords{Financial AI \and AI Efficiency \and Mojo \and SIMD \and K-D Trees \and KNN \and High-Frequency Trading \and Financial Time Series \and Scaling}

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