We exploit cutting-edge deep learning methodologies to explore the predictability of high-frequency Limit Order Book mid-price changes for a heterogeneous set of stocks traded on the NASDAQ exchange. In so doing, we release `LOBFrame', an open-source code base, to efficiently process large-scale Limit Order Book data and quantitatively assess state-of-the-art deep learning models' forecasting capabilities. Our results are twofold. We demonstrate that the stocks' microstructural characteristics influence the efficacy of deep learning methods and that their high forecasting power does not necessarily correspond to actionable trading signals. We argue that traditional machine learning metrics fail to adequately assess the quality of forecasts in the Limit Order Book context. As an alternative, we propose an innovative operational framework that assesses predictions' practicality by focusing on the probability of accurately forecasting complete transactions. This work offers academics and practitioners an avenue to make informed and robust decisions on the application of deep learning techniques, their scope and limitations, effectively exploiting emergent statistical properties of the Limit Order Book.

翻译：我们利用前沿深度学习方法，探讨纳斯达克交易所异质股票集合的高频限价订单簿中间价变动的可预测性。为此，我们发布开源代码库`LOBFrame`，用于高效处理大规模限价订单簿数据，并定量评估当前最先进深度学习模型的预测能力。研究结果呈现双重性。我们证明，股票的微观结构特征会影响深度学习方法的有效性，且其高预测能力未必对应可操作的交易信号。我们认为，传统机器学习指标在限价订单簿语境下无法充分评估预测质量。为此，我们提出创新性操作框架，通过聚焦完整交易准确预测的概率，评估预测的实用性。本研究为学术界和从业者提供了关于深度学习技术应用、其适用范围与局限性的知情决策路径，有效利用限价订单簿涌现的统计特性。