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：高频交易需要快速处理数据且无信息滞后，以实现精确的股票价格预测。这种高速的股价预测通常基于向量，由于高频交易固有的时间不规则性，这些向量需要被视为序列性和时间无关的信号。一种经过充分文献记载和测试的考虑时间不规则性的方法是循环神经网络的一种变体，即长短期记忆神经网络。这种神经网络基于单元构建，这些单元通过门和状态执行序列性和陈旧的计算，但不知道它们在单元内的顺序是否为最优。在本文中，我们提出一种改进且实时调整的长短期记忆单元，该单元选择最佳的门或状态作为其最终输出。我们的单元在浅层拓扑下运行，具有最小的回溯周期，并且进行在线训练。与其他循环神经网络相比，这种改进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，该单元已在两只高流动性美国股票和两只低流动性北欧股票上进行了测试。