Deep Learning Models Meet Financial Data Modalities
This work addresses the problem of enhancing predictive performance in algorithmic trading for financial professionals, representing an incremental advance by adapting existing deep learning techniques to a specific domain.
The study tackled the challenge of applying deep learning to structured financial data by developing a novel approach that treats limit order book snapshots as image channels, achieving state-of-the-art performance in high-frequency trading algorithms.
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.