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

Researchers demonstrate a Mojo-based k-d tree algorithm that achieves 17.5-43.5× speedup over existing implementations for nearest-neighbor learning on high-frequency financial time series. The approach enables financial AI systems to process larger datasets while maintaining real-time latency requirements for trading and risk management applications.

The paper addresses a fundamental constraint in financial AI: the tension between learning from increasingly large datasets and maintaining sub-millisecond latency requirements for trading systems. As market data volumes expand across equities, derivatives, and FX products, traditional Python-based solutions create computational bottlenecks that limit model sophistication at scale. The Mojo language—a compiled, SIMD-optimized superset of Python—enables dramatic performance improvements by combining high-level expressiveness with low-level control over memory layout and vectorization.

The k-d tree implementation leverages three key optimizations: variance-based spatial splitting for better pruning efficiency, contiguous flat-buffer storage to maximize CPU cache utilization, and compile-time vectorized distance computation. These technical choices eliminate the interpretability overhead of Python while preserving exact nearest-neighbor results, critical for financial applications where numerical correctness directly impacts trading decisions and regulatory compliance.

The empirical validation across eight datasets demonstrates consistent 17-21× improvements on x86 architectures and even more dramatic 28-43× gains on ARM64 systems, particularly for equity and ETF datasets with moderate dimensionality. Beyond inference speedups, the compiled execution enables training on 10× larger options datasets, reducing implied-volatility pricing errors by 8%. This improvement directly translates to reduced hedging costs and improved option pricing models for financial institutions.

The results position compiled, systems-level AI as a practical solution for production finance infrastructure. Rather than abstract algorithmic contributions, this work demonstrates that appropriate language tooling can unlock order-of-magnitude performance gains in real financial workflows, enabling quantitative teams to scale model complexity without proportional infrastructure costs.

• →Mojo k-d tree achieves 17.5-43.5× speedup over scikit-learn while maintaining exact output compatibility
• →Compiled execution enables training on 10× larger options datasets with 8% improvement in implied-volatility pricing accuracy
• →The approach solves the core financial AI challenge of scaling model learning capacity while preserving real-time latency requirements
• →Performance gains are consistent across x86 and ARM64 architectures, demonstrating broad applicability in production environments
• →Mojo's language-level SIMD and memory optimization capabilities position it as a scalable foundation for data-intensive financial systems