论文标题
限制订单的深度学习建模：比较观点
Deep Learning modeling of Limit Order Book: a comparative perspective
论文作者
论文摘要
目前的工作解决了高频交易深度学习领域的理论和实用问题。审查并在相同的任务，功能空间和数据集上进行了审查和比较，诸如随机模型，Logistic回归，LSTMS，LSTMS，LSTMS，LSTMS，LSTMS，LSTMS，LSTMS，LSTMS，然后根据成对相似性和性能指标进行了群集，诸如随机模型，LSTMS，LSTM，CNN-LSTMS和MLP等最先进的模型，诸如随机模型，LSTMS，LSTMS，LOGISTIS回归，LSTM，LSTM，然后根据成对的相似性和性能指标进行了比较。因此，对建模技术的基本维度进行了研究，以了解这些技术是否是限制顺序书籍的动态。我们观察到，多层感知器的性能与最先进的CNN-LSTM体系结构相当或更好，这表明动态空间和时间维度是Lob动力学的良好近似值，但不一定是真正的基础维度。
The present work addresses theoretical and practical questions in the domain of Deep Learning for High Frequency Trading. State-of-the-art models such as Random models, Logistic Regressions, LSTMs, LSTMs equipped with an Attention mask, CNN-LSTMs and MLPs are reviewed and compared on the same tasks, feature space and dataset, and then clustered according to pairwise similarity and performance metrics. The underlying dimensions of the modeling techniques are hence investigated to understand whether these are intrinsic to the Limit Order Book's dynamics. We observe that the Multilayer Perceptron performs comparably to or better than state-of-the-art CNN-LSTM architectures indicating that dynamic spatial and temporal dimensions are a good approximation of the LOB's dynamics, but not necessarily the true underlying dimensions.
