用TensorFlow长短期记忆(LSTM)神经网络改进交易技术分析

Q1 Mathematics
Chenjie Sang, Massimo Di Pierro
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引用次数: 46

摘要

在本文中,我们利用长短期记忆神经网络来学习和改进技术分析中使用的传统交易算法。我们研究背后的基本原理是,网络可以学习市场行为,并能够预测给定策略何时更有可能成功。我们利用Google的TensorFlow在Python中实现了我们的算法。我们表明,我们的策略,基于神经网络预测和传统的技术分析相结合,比后者单独表现更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving trading technical analysis with TensorFlow Long Short-Term Memory (LSTM) Neural Network

In this paper we utilize a Long Short-Term Memory Neural Network to learn from and improve upon traditional trading algorithms used in technical analysis. The rationale behind our study is that the network can learn market behavior and be able to predict when a given strategy is more likely to succeed. We implemented our algorithm in Python pursuing Google's TensorFlow. We show that our strategy, based on a combination of neural network prediction, and traditional technical analysis, performs better than the latter alone.

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来源期刊
Journal of Finance and Data Science
Journal of Finance and Data Science Mathematics-Statistics and Probability
CiteScore
3.90
自引率
0.00%
发文量
15
审稿时长
30 days
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