High-frequency trading requires fast data processing without information lags for precise stock price forecasting. This high-paced stock price forecasting is usually based on vectors that need to be treated as sequential and time-independent signals due to the time irregularities that are inherent in high-frequency trading. A well-documented and tested method that considers these time-irregularities is a type of recurrent neural network, named long short-term memory neural network. This type of neural network is formed based on cells that perform sequential and stale calculations via gates and states without knowing whether their order, within the cell, is optimal. In this paper, we propose a revised and real-time adjusted long short-term memory cell that selects the best gate or state as its final output. Our cell is running under a shallow topology, has a minimal look-back period, and is trained online. This revised cell achieves lower forecasting error compared to other recurrent neural networks for online high-frequency trading forecasting tasks such as the limit order book mid-price prediction as it has been tested on two high-liquid US and two less-liquid Nordic stocks.

翻译：高频交易要求快速处理数据且无信息延迟，以实现精准的股票价格预测。这种高速股票价格预测通常基于向量，由于高频交易固有的时间不规则性，这些向量需被视为时序相关且独立于时间的信号。一种经过充分验证且考虑时间不规则性的方法是循环神经网络，名为长短期记忆神经网络。这类神经网络基于单元构建，通过门控和状态进行序列化且稳定的计算，但单元内各部分的顺序是否最优尚不可知。本文提出一种改进且实时调整的长短期记忆单元，该单元选择最优门控或状态作为最终输出。我们的单元采用浅层拓扑结构，回溯周期最小化，并实现在线训练。与其它循环神经网络相比，改进后的单元在在线高频交易预测任务（如限价订单簿中间价预测）中实现了更低的预测误差，该结论基于对两只高流动性美国股票和两只低流动性北欧股票的测试验证。