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

Design Of Intelligent Financial System Based On Adaptive Learning Algorithm-Intelligent Optimization Of High Frequency Trading System

Algorithmic trading represents a pivotal area of inquiry within the financial sphere, denoting the execution of commands by computers based on predefined trading strategies devoid of human intervention (Azzutti, 2022). Central to this paradigm is the algorithmic model of trading, with high-frequency trading (HFT) standing as a subtype, seeking to capitalize on marginal price differentials through frequent trading (Arnoldi, 2016). It can be conceptualized as an online decision-making conundrum comprising two primary facets: the perception of environmental features and online accurate decision-making. These constitute the principal challenges confronting high-frequency trading. Moreover, the elevated trading fees linked to frequent trading significantly influence investment returns.

In the financial realm, environmental feature perception primarily entails the extraction of efficacious feature representations from nonstationary and noisy financial time series data. Existing manual extraction methodologies heavily hinge on historical experiences and are subject to subjective influences. Concerning online accurate decision-making, algorithmic models must not only leverage extant experiences but also autonomously navigate uncharted terrains, partake in dynamic interactions, and execute precise trading actions at opportune moments.

With the incessant evolution of financial markets and the rapid strides in information technology, HFT systems have become indispensable components of financial landscapes (Haferkorn, 2017; Dutta et al., 2023). These systems harness sophisticated algorithms and high-velocity data processing techniques to execute myriad trades within exceedingly brief timeframes, with the objective of seizing upon minute price differentials and capitalizing on opportunities amid market fluctuations. However, in tandem with the heightened market competition and the perpetual evolution of market landscapes, traditional high-frequency trading strategies may confront increasingly formidable hurdles (Wu & Duan, 2024).

Currently, an array of methods is dedicated to information feature extraction. This encompasses the deployment of convolutional neural networks (CNNs) for feature extraction, alongside the application of support vector machines (SVMs) and random forests for the classification processing of the extracted features, facilitating information feature extraction (Lee et al., 2023). Nonetheless, this approach encounters hindrances due to the limited adaptive capacity of convolutional neural networks, resulting in diminished efficacy and a lack of insight in feature extraction. In this context, the authors delve into feature extraction methodologies employing rough set reduction and apply this framework to network intrusion detection (Prasad et al., 2023). Through the computation of the gain ratio of rough sets and subsequent reduction, optimal clustering clusters are derived to extract information features. However, this technique entails substantial initial information requisites and is unable to conduct feature extraction on heterogeneous information stemming from diverse sources, thereby constraining its practicality.

Adaptive learning methods possess the capacity to autonomously adjust during computation, ensuring that their computational outputs align with prevailing constraints, thereby attaining computational equilibrium. Hence, to tackle the aforementioned challenges pertaining to information feature extraction, this study introduces adaptive learning methods and entropy projection clustering into high-frequency trading systems.

We propose a methodology for deep information feature extraction within financial systems predicated on cross-adaptive learning entropy projection clustering. Initially, we establish a composite distribution model of financial data, capturing sequences of financial activities. Leveraging this sequence, we employ a particle swarm cross-adaptive optimization learning approach to identify correlated financial information features, with the objective of achieving a more precise extraction of financial data attributes.