论文标题
兼容深度神经网络框架与财务时间序列数据，包括数据预处理，神经网络模型和交易策略
Compatible deep neural network framework with financial time series data, including data preprocessor, neural network model and trading strategy
论文作者
论文摘要
经验表明，股票和加密货币市场的交易有可能获得高度盈利。从这个角度来看，最近已致力于研究如何应用机器学习和深入学习来解释和预测市场行为。这项研究介绍了一种新的深神经网络架构，以及在将财务数据喂入模型之前如何准备财务数据的新颖想法。在数据准备部件中，第一步是使用技术指标生成许多功能，然后将XGBoost模型应用于功能工程。将数据分为三类，并使用单独的自动编码器，在第二步中提取高级混合功能。引入了此数据预处理以预测价格变动。关于建模，不同的卷积层，一个长的短期记忆单元和几个完全连接的层已设计用于执行二进制分类。这项研究还介绍了一种利用训练有素的模型输出的交易策略。三个不同的数据集用于评估此方法，结果表明该框架可以为我们提供有利可图且健壮的预测。
Experience has shown that trading in stock and cryptocurrency markets has the potential to be highly profitable. In this light, considerable effort has been recently devoted to investigate how to apply machine learning and deep learning to interpret and predict market behavior. This research introduces a new deep neural network architecture and a novel idea of how to prepare financial data before feeding them to the model. In the data preparation part, the first step is to generate many features using technical indicators and then apply the XGBoost model for feature engineering. Splitting data into three categories and using separate autoencoders, we extract high-level mixed features at the second step. This data preprocessing is introduced to predict price movements. Regarding modeling, different convolutional layers, an long short-term memory unit, and several fully-connected layers have been designed to perform binary classification. This research also introduces a trading strategy to exploit the trained model outputs. Three different datasets are used to evaluate this method, where results indicate that this framework can provide us with profitable and robust predictions.
