# Limit Order Book Simulation and Trade Evaluation with K-Nearest-Neighbor Resampling

arXiv: https://arxiv.org/abs/2409.06514 · HTML: https://mlsanity.com/paper/2409.06514/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 48 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 11 of 11)

- [Universal features of price formation in financial markets: perspectives from Deep Learning](https://mlsanity.com/paper/1803.06917/) (2018)
- [The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation](https://mlsanity.com/paper/2009.04266/) (2020)
- [Neural networks for option pricing and hedging: a literature review](https://mlsanity.com/paper/1911.05620/) (2019)
- [ABIDES: Towards High-Fidelity Market Simulation for AI Research](https://mlsanity.com/paper/1904.12066/) (2019)
- [Generating Realistic Stock Market Order Streams](https://mlsanity.com/paper/2006.04212/) (2020)
- [Combining Parametric and Nonparametric Models for Off-Policy Evaluation](https://mlsanity.com/paper/1905.05787/) (2019)
- [Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness](https://mlsanity.com/paper/2306.12806/) (2023)
- [Model-based gym environments for limit order book trading](https://mlsanity.com/paper/2209.07823/) (2022)
- [K-Nearest-Neighbor Resampling for Off-Policy Evaluation in Stochastic Control](https://mlsanity.com/paper/2306.04836/) (2023)
- [The Limit Order Book Recreation Model (LOBRM): An Extended Analysis](https://mlsanity.com/paper/2107.00534/) (2021)
- [Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow](https://mlsanity.com/paper/2109.13905/) (2021)

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

- [Mean-Field Generalisation Bounds for Learning Controls in Stochastic Environments](https://mlsanity.com/paper/2508.16001/) (2025)

## Abstract (arXiv)

In this paper, we show how K-nearest neighbor (K-NN) resampling, an off-policy evaluation method proposed in citegiegrich2023k, can be applied to simulate limit order book (LOB) markets and how it can be used to evaluate and calibrate trading strategies. Using historical LOB data, we demonstrate that our simulation method is capable of recreating realistic LOB dynamics and that synthetic trading within the simulation leads to a market impact in line with the corresponding literature. Compared to other statistical LOB simulation methods, our algorithm has theoretical convergence guarantees under general conditions, does not require optimization, is easy to implement and computationally efficient. Furthermore, we show that in a benchmark comparison our method outperforms a deep learning-based algorithm for several key statistics. In the context of a LOB with pro-rata type matching, we demonstrate how our algorithm can calibrate the size of limit orders for a liquidation strategy. Finally, we describe how K-NN resampling can be modified for choices of higher dimensional state spaces.