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

arXiv: https://arxiv.org/abs/2606.10219 · HTML: https://mlsanity.com/paper/2606.10219/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 24 references that are in the map.
- First posted on arXiv 8 Jun 2026.

## Builds on (most cited first, 2 of 2)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)

## Built on by (most cited first, 1 of 1)

- [Mojo: A Promising Tool for Scalable Financial AI Efficiency](https://mlsanity.com/paper/2606.16059/) (2026)

## Abstract (arXiv)

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-n, 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× speedup over scikit-learn's k-d tree on x86 and 28.1--43.5× 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× 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. keywordsFinancial AI and AI Efficiency and Mojo and SIMD and K-D Trees and KNN and High-Frequency Trading and Financial Time Series and Scaling