用K近邻重采样模拟订单簿，高效评估和优化交易策略。
Limit Order Book Simulation and Trade Evaluation with $K$-Nearest-Neighbor Resampling
- 基于历史数据，用K近邻重采样生成真实订单簿动态。
- 相比深度学习方法，在多个关键统计量上表现更优。
- 适用于订单簿校准与高维状态空间的扩展场景。
本文展示如何将一种名为K-近邻（K-NN）重采样的离策略评估方法应用于限价订单簿（LOB）市场的仿真，并用于评估与校准交易策略。利用历史订单簿数据，我们证明该方法能重现真实的订单簿动态，且在仿真中进行的交易行为所导致的市场影响与已有文献一致。相较于其他统计型订单簿仿真方法，该算法在一般条件下具备理论收敛性，无需优化、实现简单且计算高效。此外，在基准对比中，该方法在多个关键统计量上优于基于深度学习的算法。针对采用按比例分配匹配机制的订单簿，我们展示了如何使用该算法校准清算策略中的挂单规模。最后，我们描述了该方法在更高维状态空间下的适用性改进。
原文摘要 · Abstract (English)
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.
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