解决高频交易中收益预测的标签不平衡问题，提升模型准确性。
Label Unbalance in High-frequency Trading
- 采用端到端深度学习框架并整合多种标签不平衡处理方法。
- 在中文期货市场高频收益预测任务中取得显著效果。
- 适合关注金融时序建模与不平衡数据处理的研究者。
在金融交易中，收益预测是成功交易系统的基础。随着深度学习在图像处理、自然语言等领域的发展，其在处理金融数据方面也展现出显著优势。然而，深度学习的成功依赖于大量标注样本，即对每个时间点或事件标记为盈利或亏损，这在考虑交易成本的高频交易场景下，面临严重的标签不平衡问题。本文采用严谨的端到端深度学习框架，并结合全面的标签不平衡调整方法，在中国期货市场的高频收益预测任务中取得了成功。相关代码已公开于 https://github.com/RS2002/Label-Unbalance-in-High-Frequency-Trading。
原文摘要 · Abstract (English)
In financial trading, return prediction is one of the foundation for a successful trading system. By the fast development of the deep learning in various areas such as graphical processing, natural language, it has also demonstrate significant edge in handling with financial data. While the success of the deep learning relies on huge amount of labeled sample, labeling each time/event as profitable or unprofitable, under the transaction cost, especially in the high-frequency trading world, suffers from serious label imbalance issue.In this paper, we adopts rigurious end-to-end deep learning framework with comprehensive label imbalance adjustment methods and succeed in predicting in high-frequency return in the Chinese future market. The code for our method is publicly available at https://github.com/RS2002/Label-Unbalance-in-High-Frequency-Trading .
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