Deep Learning Models Meet Financial Data Modalities

2025-04-18

AI Summary

Modeling multi-source heterogeneous financial data—such as candlestick charts, order flow, trading volume, limit order book (LOB) snapshots, and news—poses significant challenges due to their disparate structures and temporal dynamics. To address this, we propose a unified multimodal deep learning framework. Our key innovation is the first-ever encoding of LOB snapshot sequences as multi-channel images, coupled with a dedicated embedding scheme that enables visual representation learning for structured time-series data. We further design a hybrid CNN–RNN architecture to jointly model cross-modal features, support inter-modal alignment, and learn dynamic modality-specific weights. Evaluated on high-frequency trading strategy tasks, our method achieves state-of-the-art performance: it improves price direction prediction accuracy by +3.2% and portfolio Sharpe ratio by +0.41. This work establishes a scalable, multimodal deep learning paradigm for financial time-series modeling.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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.

Problem

• Applying deep learning to structured financial data challenges
• Enhancing trading strategies with limit order book analysis
• Improving predictive performance in algorithmic trading systems

Innovation

• Deep learning models analyze diverse financial data
• Embedding techniques process limit order book snapshots
• Image-based representation enhances trading algorithm performance