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

The article presents the Mojo SIMD k-d tree for efficient exact nearest-neighbor learning in high-frequency financial time series, addressing the challenge of processing large datasets with real-time latency requirements. This new implementation utilizes variance-based splitting and compile-time vectorized distance computation, achieving a speedup of 17.5–21.6× over scikit-learn's k-d tree on x86 and 28.1–43.5× over brute force on ARM64, while maintaining exact outputs. The results suggest that Mojo can significantly enhance the scalability and efficiency of AI applications in finance, enabling practitioners to handle larger datasets more effectively.