用深度学习处理订单簿数据，提升高频交易预测能力
Deep Learning Models Meet Financial Data Modalities
- 将订单簿快照转为图像通道，用嵌入技术建模
- 在高频交易中达到当前最优性能
- 适合量化交易与金融时序分析研究者
算法交易依赖从多种金融数据源中提取有效信号，包括蜡烛图、看跌与撤单的订单统计、成交成交量、限价订单簿及新闻流。尽管深度学习在处理非结构化数据方面表现卓越，并显著推动了自然语言处理的发展，但其在结构化金融数据中的应用仍面临挑战。本研究探讨深度学习模型与金融数据模态的融合，旨在提升交易策略与投资组合优化的预测性能。提出一种新方法，通过开发嵌入技术，将限价订单簿分析融入算法交易，将连续的订单簿快照视为图像型输入通道。该方法在高频交易算法中实现当前最优性能，验证了深度学习在金融应用中的有效性。
原文摘要 · Abstract (English)
Algorithmic trading relies on extracting meaningful signals from diverse financial data sources, including candlestick charts, order statistics on put and canceled orders, traded volume data, limit order books, and news flow. While deep learning has demonstrated remarkable success in processing unstructured data and has significantly advanced natural language processing, its application to structured financial data remains an ongoing challenge. This study investigates the integration of deep learning models with financial data modalities, aiming to enhance predictive performance in trading strategies and portfolio optimization. We present a novel approach to incorporating limit order book analysis into algorithmic trading by developing embedding techniques and treating sequential limit order book snapshots as distinct input channels in an image-based representation. Our methodology for processing limit order book data achieves state-of-the-art performance in high-frequency trading algorithms, underscoring the effectiveness of deep learning in financial applications.
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