提高市场趋势预测使用卷积神经网络在日本台模式
Edrees Ramadan Mersal1, Kürşat Mustafa Karaoğlan1, Hakan Kutucu2
1Department of Computer Engineering, Karabuk University, Karabuk, Turkey.
PeerJ. Computer science
|March 26, 2025
概括
本研究使用日本台模式和卷积神经网络 (CNN) 来预测金融市场价格变动. 在预测牌趋势方面,CNN模型取得了令人印象深刻的99.3%准确率.
科学领域:
- 量化金融 量化金融
- 金融中的机器学习
- 金融市场分析 金融市场分析
背景情况:
- 日本台图案提供了对市场情绪和趋势方向的见解.
- 金融市场的技术分析依赖于识别预测洞察的模式.
- 预测未来的价格变动对于交易策略至关重要.
研究的目的:
- 用日本台模式预测未来金融市场价格变动.
- 开发和训练一个卷积神经网络 (CNN) 模型,以提高预测准确度.
- 通过严格的测试来验证CNN模型的预测能力.
主要方法:
- 采用了结构化的三步数据准备过程,包括使用Ta-lib库的滑动窗口技术和模式识别.
- 技术指标被用来验证每个数据窗口的趋势方向.
- 开发了一个CNN模型,从子图表中提取特征,以便准确预测.
主要成果:
- 开发的CNN模型表现出高预测准确率,高达99.3%的牌趋势.
- 实施了交叉验证技术,以确保模型可靠性和在未见数据上的性能.
- 该研究成功预测了后续金融台的方向移动.
结论:
- 卷积神经网络是基于日本台模式预测金融市场趋势的有效工具.
- 拟议的方法提供了一个强大的框架,以提高交易中技术分析的准确性.
- 通过先进的机器学习技术,可以准确预测市场情绪和趋势方向.
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