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.

本文介绍了一种修订的在线长短时记忆细胞，该细胞运行在浅拓扑上，具有最小的回看期，通过选择最佳门或状态作为其最终输出，比其他递归神经网络更适用于在线高频交易预测任务，如限价单簿的中间价格预测。在两只高流动性的美国和两只不太流动的北欧股票上对其进行了测试。