Design of Intelligent Financial System Based on Adaptive Learning Algorithm: Intelligent Optimization of High Frequency Trading System

• Published in: International journal of information technologies and systems approach 2024-01, Vol.17 (1), p.1-20
• Main Authors: Zhang, Zhaozhe, Ahmad, Shahbaz
• Format: Article
• Language: English
• Subjects: Accuracy; Adaptive algorithms; Adaptive systems; Algorithms; Artificial intelligence; Clustering; Data mining; Decision making; Deep learning; Design optimization; Electronic trading systems; Entropy; Feature extraction; Feature selection; High frequency trading; Machine learning; Neural networks; Optimization; Securities markets; Sequences; Stochastic models; Support vector machines; Systems design; Time series; Volatility
• ISSN: 1935-570X; 1935-5718
• DOI: 10.4018/IJITSA.350300

The high-frequency trading system in the financial domain has long been a focal point of investigation. This study posits an intelligent financial system design framework predicated on a cross-adaptive self-entropy projection clustering model, aimed at enhancing the efficacy of high-frequency trading systems. A composite distribution model of financial data is formulated to derive sequences of financial data activities. And cross-adaptive learning algorithm is employed to ascertain the interrelated attributes of financial data. Following this, the support vector machine algorithm is applied for the classification processing of these interrelated features, yielding a set of financial data feature vectors, which are then fed into the gray correlation-based information feature extraction model. Through extensive empirical evaluations with authentic trading data, the proposed intelligent financial system design framework exhibits commendable performance, furnishing a viable solution for the intelligent optimization of high-frequency trading systems.