Deep Learning Models Meet Financial Data Modalities
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Algorithmic TradingDeep LearningPortfolio OptimizationSimilar Papers 제목 키워드 기반
Aspect-based Sentiment Analysis in Document -- FOMC Meeting Minutes on Economic Projection
The Federal Open Market Committee within the Federal Reserve System is responsible for managing inflation, maximizing employment, and stabilizing interest rates. Meeting minutes play an important role for market movement…
Aspect-Based Sentiment AnalysisSentiment AnalysisMulti-Modal Opinion Integration for Financial Sentiment Analysis using Cross-Modal Attention
In recent years, financial sentiment analysis of public opinion has become increasingly important for market forecasting and risk assessment. However, existing methods often struggle to effectively integrate diverse opin…
Sentiment AnalysisResearch on Fast Text Recognition Method for Financial Ticket Image
Currently, deep learning methods have been widely applied in and thus promoted the development of different fields. In the financial accounting field, the rapid increase in the number of financial tickets dramatically in…
Deep LearningRegion ProposalCross-Modal Temporal Fusion for Financial Market Forecasting
Accurate financial market forecasting requires diverse data sources, including historical price trends, macroeconomic indicators, and financial news, each contributing unique predictive signals. However, existing methods…
LLMs Meet Finance: Fine-Tuning Foundation Models for the Open FinLLM Leaderboard
This paper investigates the application of large language models (LLMs) to financial tasks. We fine-tuned foundation models using the Open FinLLM Leaderboard as a benchmark. Building on Qwen2.5 and Deepseek-R1, we employ…
Reinforcement Learning (RL)