论文标题
基于注意机制的BI-LSTM价格预测
Bi-LSTM Price Prediction based on Attention Mechanism
论文作者
论文摘要
随着金融衍生品市场的富集和发展的日益增长，交易的频率也越来越快。由于人类的局限性，算法和自动交易最近已成为讨论的重点。在本文中，我们根据注意机制提出了双向LSTM神经网络，该网络基于两个流行资产，即黄金和比特币。在功能工程方面，一方面，我们添加了传统的技术因素，与此同时，我们结合了时间序列模型来开发因素。在选择模型参数时，我们最终选择了一个两层深度学习网络。根据AUC测量，比特币和黄金的准确性分别为71.94％和73.03％。使用预测结果，我们在两年内获得了1089.34％的回报。同时，我们还将本文中提出的注意力BISTM模型与传统模型进行了比较，结果表明，我们的模型在此数据集中具有最佳性能。最后，我们讨论了模型的重要性和实验结果，以及未来可能的改进方向。
With the increasing enrichment and development of the financial derivatives market, the frequency of transactions is also faster and faster. Due to human limitations, algorithms and automatic trading have recently become the focus of discussion. In this paper, we propose a bidirectional LSTM neural network based on an attention mechanism, which is based on two popular assets, gold and bitcoin. In terms of Feature Engineering, on the one hand, we add traditional technical factors, and at the same time, we combine time series models to develop factors. In the selection of model parameters, we finally chose a two-layer deep learning network. According to AUC measurement, the accuracy of bitcoin and gold is 71.94% and 73.03% respectively. Using the forecast results, we achieved a return of 1089.34% in two years. At the same time, we also compare the attention Bi-LSTM model proposed in this paper with the traditional model, and the results show that our model has the best performance in this data set. Finally, we discuss the significance of the model and the experimental results, as well as the possible improvement direction in the future.
