对外汇市场趋势的基于深度学习的预测模型:实际实施和绩效评估
Phuong Dong Nguyen1, Nguyen Ngoc Thao2, Duong Thi Kim Chi3
1CIRTech Institute, HUTECH University, Ho Chi Minh City, Vietnam.
Science progress
|August 22, 2024
概括
本研究介绍了外汇 (外汇) 市场预测的实用深度学习模型,重点关注利回报而不是准确性. 三值标签提高了性能,并减少了对现实世界应用的交易订单.
科学领域:
- * 计算金融学
- * 机器学习应用程序
- * 金融市场分析
背景情况:
- * 对金融市场趋势预测对现实应用的兴趣日益增长.
- * 外汇 (外汇) 市场的复杂性简化为二元分类.
- * 在外汇交易中需要实用的深度学习模型.
研究的目的:
- * 提出实际的基于深度学习的外汇交易预测模型.
- *通过尽量减少损失和预测风险,提高交易者的决策能力.
- *强调利回报作为一个关键的评估指标,而不是准确性.
主要方法:
- * 实施深度学习模型用于外汇市场预测.
- * 利用现实的雅虎金融数据集进行实验验证.
- * 传统的两值标签与拟议的三值标签的比较.
主要成果:
- * 实施深度学习预测机制的证明有效性.
- * 通过对现实的财务数据进行广泛的实验研究来验证.
- *与两价值标签相比,与三价值标签观察到更好的性能.
结论:
- * 深度学习模型为外汇市场预测提供了实际解决方案.
- * 优先考虑利回报对于评估交易模式至关重要.
- *三值标签可以提高预测准确度,优化交易策略.
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