Limit Order Book Simulation and Trade Evaluation with $K$-Nearest-Neighbor Resampling
This provides a computationally efficient and theoretically grounded method for financial market simulation and strategy calibration, though it is incremental as it adapts an existing off-policy evaluation technique to a specific domain.
The paper tackles the problem of simulating limit order book markets and evaluating trading strategies by applying K-nearest neighbor resampling, demonstrating that it recreates realistic dynamics and aligns with market impact literature while outperforming a deep learning-based algorithm in benchmarks.
In this paper, we show how $K$-nearest neighbor ($K$-NN) resampling, an off-policy evaluation method proposed in \cite{giegrich2023k}, 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.