Evolution of online financial trading systems

Security engineering is a new research area in software engineering that covers the definition of processes, plans and designs for security. The researchers are working in this area and however there is a lack in security requirements treatment in this field. Requirements engineering is a major action that begins during the communication activity and continues into the modeling activity. Requirements engineering builds a bridge to design and construction. The security requirements is one of the non functional requirements which acts as constrains on the functions of the system, but our view is that security requirements to be considered as functional requirements and to be analyzed during the earlier phase of software development i.e. Requirements engineering phase. An increasing part of the communication and sharing of information in our society utilizes electronic media. IT security is becoming central to the ability to fulfil business goals, build trustworthy systems, and protect assets. In order to develop systems with adequate security features, it is essential to capture the corresponding security needs and requirements. It is called as the Security requirements engineering, which is emerging as a branch of software engineering, spurred by the realization that security must be dealt with early during requirements phase. In this paper we have proposed a framework for Security Requirements engineering and applied on online trading system. Online trading systems form a critical part of the securities and capital markets today. By using security requirements engineering framework we are able to develop a secure online trading system. The results obtained using Proposed Security Requirements Engineering Framework is simple and better than the Haley and His Colleagues Framework.

The article aims to identify the strategic principles for forming a marketing product innovation policy of an enterprise in the online trading system, taking into account the influence of the digital environment, data on consumer behavior, requirements for service, logistics, and reputation. The object is the process of forming and implementing a marketing product innovation policy of enterprises that sell in the Internet trade system. The relevance of our topic is due to the fact that Internet trade has transformed a product from a simple unit of assortment into a dynamic value proposition that is constantly changing under the influence of consumer behavior, platform algorithms, the speed of logistics and communications in the digital environment, as well as the growth of competition, where the comparison of prices and characteristics occurs instantly. In such conditions, companies cannot limit themselves to tactical decisions such as expanding the line or short-term discounts, since the strategic management of product innovation, its uniqueness, functionality, design, service component, environmental friendliness, safety and compliance with the expectations of specific segments that are formed and change faster than in traditional trade becomes crucial. The need to integrate product policy with demand data, reviews, behavioral signals, reputation management and the quality of customer experience, including information transparency, ease of returns, warranty obligations and after-sales support, becomes even more important, because in online trade these factors often determine repeat purchases and brand trust. The article explores the strategic principles of the formation of marketing product innovation policy in the online trading system and shows its role as the core of the enterprise's competitive strategy in the digital environment, where the consumer makes decisions quickly, has access to an array of alternatives and public reviews, and the quality of service becomes an integral part of the value. It is determined that the product in the online channel exists simultaneously as a material or digital product and as an information offer, which is perceived through the structure of the assortment, page content, comparison of characteristics, rating, availability, delivery terms, warranty obligations and return procedures, therefore, innovation should cover the product, service, information support and customer experience. The levels of innovation in online trading are characterized, in particular, updating functional properties, design, packaging and packaging, expanding the service component through support, exchange and loyalty programs, improving information transparency using high-quality descriptions, visual materials and answers to questions, as well as building convenient purchase and return scenarios.

With the rapid development of the Internet and the continuous progress of computer science and technology, Internet-based online trading has developed rapidly and improved the convenience of everyday life. Increasingly, people conduct business activities through online transactions and payment methods, and the development prospects for online transactions are quite broad. In addition, e-commerce trading systems have attracted considerable attention. Many studies have been conducted to analyze the behaviors of these systems. This paper focuses on analyzing both system behavior and user behavior in e-commerce trading systems. To reflect the characteristics of online trading systems, system behavior reflects the strength of the system, while user behavior reflects various user actions. Consistency, which is a basis for determining whether two objects are similar, provides a reference for designers and modelers. This paper also focuses on analyzing the consistency of system behavior, especially regarding the measurement and diagnosis of e-commerce trading systems. Then, we study the existing user behavior analysis methods for e-commerce trading systems. We divide user behaviors into four groups: web browsing behavior, keystroke behavior, network transaction behavior, and mobile terminal behavior. The performance and correlation analysis techniques of these methods are discussed and compared in detail. Finally, we provide ideas for future research involving behavior analysis for online trading systems.

Abstract: The Algorithmic Trading using Machine Learning is a financial trading system that integrates technical analysis indicators and future predictions with news sentiment analysis through reinforcement learning algorithms. The system trains autonomous trading agents to make effective buy, sell, or hold decisions in complex market environments by combining historical stock data, technical indicators, and sentiment analysis from financial news. It implements and compares three state-of-the-art reinforcement learning algorithms: Proximal Policy Optimization (PPO), Deep Q-Network (DQN), and Advantage Actor-Critic (A2C). The algorithm was tested on diverse market conditions exhibiting remarkable adaptability, including bull, bear, and sideways markets. This research contributes to the field of algorithmic trading by offering a multi-modal approach that considers both market data and news sentiment for more informed trading decisions, paving the way for more sophisticated and adaptable automated trading systems. Keywords: Reinforcement learning, Sentiment scores, Technical Indicators, Market conditions, Financial data.

Blockchain technology is creating triumph all over the world with its presence, and the combination of artificial intelligence and blockchain technology will prove to be more impactful in the coming decades. The various countries of the world are assimilating blockchain technology in the financial trading system because it is robust and secure. It has been observed that after the pandemic, Indian consumers have overcome the fear of using online transaction systems. The usage of smart gadgets, speedy networks, increased product services, and the flood of startups has created a conducive ecosystem for online financial transactions. This study attempts to assess the use of blockchain in financial sectors by various countries. Blockchain is the next generation of technology for any field that needs security, privacy, speed, and accuracy. Financial systems is a field facing problems like financial fraud, data theft, late settlement, and data manipulation. The use of blockchain technology on a large scale will create a secure financial ecosystem in India.

<p><em>This research aims to predict the effect of financial literacy, ease of entry into the capital market and the role of university to academic community’s investment decisions on the online trading system. This empirical research was carried out by taking academic community of State Islamic University (PTKIN) in Central Java and Yogyakarta as the subjects of the research. The novelty of this research is the formulation of indicators of ease of entry to the capital market and the role of university, which are formulated through not only literature review, but also field observations. Using Partial Least Square (PLS) as the analysis tools, this research finds that financial literacy and ease of entry are being able to predict the influence the academic community's investment decisions. Role of universities variable, although it is empirically proven that four PTKIN samples have been maximized, can not been able to influence the investment decisions of the academic community.</em></p>

Q-Learning-Based Financial Trading Systems with Applications

Reinforcement Learning for Automatic Financial Trading: Introduction and Some Applications

Testing Different Reinforcement Learning Configurations for Financial Trading: Introduction and Applications

A financial trading system with optimized indicator setting, trading rule definition, and signal aggregation through Particle Swarm Optimization

Capital markets represent an important component of the economy of any country. Yet, it has been found that in a majority of countries, participation of the general public in capital markets is at a low level even with the availability of online trading platforms. In this study, we integrate constructs of diffusion of innovation, technology acceptance and trust models to form a context specific model in order to identify the factors influencing the general public in using online trading systems. Implications of a survey study carried out among a diverse group of investors are presented in this paper.

Purpose – The purpose of this paper is to test an augmented technology acceptance model (TAM) in the online financial trading context. This research aims to investigate how e‐investors are influenced by perceived trust, security, and privacy jointly with traditional TAM constructs. Design/methodology/approach – The research examines e‐investors' behavioral intention to use online dealers' and stockbrokers' services. The model suggests that perceived trust jointly with perceived usefulness and perceived ease of use are important antecedents of intentions; the hypotheses are statistically tested using structural modeling. Findings – The results from this study suggest that perceived trust, usefulness and ease of use are important issues in online trading systems. The findings suggest that online financial dealers and stockbrokers must improve the security of the online system since e‐investors form perceptions about its perceived security and when these perceptions are confirmed, their trust is enhanced and consequently they are more likely to use these online services particularly if the financial information is useful for their purposes. Research limitations/implications – The findings of the present study have various implications for research as well as practice. First, perceived trust, perceived usefulness and perceived ease of use are critical to the success of an online trading system. Second, perceived privacy did not influence users' beliefs in trust. Since perceived trust and perceived usefulness are the most important antecedents of behavioral intention, managers can increase e‐investors' usage intention by improving their beliefs in how the online trading system can enhance their performance and effectiveness using a system with enough security mechanisms. The major limitation is that trust is examined as a single‐dimension construct. Originality/value – This paper is one of the first that has empirically tested the link between trust, security, privacy, usefulness, ease of use and behavioral intention in the online trading context.

The purpose of this study is to measure the service quality of online trading system implemented by PT KDB Daewoo Securities Indonesia. The study is a part of the company efforts to provide the best solution services. The study is performed to determine the influence of service quality factors on the customer satisfaction by means of ServQual method. The method takes into account five independent variables, namely, reliability (X1 ), responsiveness (X2 ), assurance (X3 ), empathy (X4 ), and tangibility (X5 ). Meanwhile, the cus- tomer satisfaction is considered as the dependent variable (Y ). The population of of the study is all 67 900 online traders at the company and the sample size is 265 traders. The data are collected via questionnaires and interviews. From the study results, we conclude that the five variables have significant influence on the customer satisfaction on the online trading system application at PT KDB Daewoo Securities Indonesia. To improve the customer satisfaction, the company should prioritize the quality- related factors, and expand and upgrade the existing facilities and infrastructures.

This study examines retail investors’ trading behaviour and its determinants in the Indonesian Shari’ah stock market by mainly focusing on the religious practice-related factors in the form of sadaqah or charitable giving on individual investors’ trading behaviour. Contextually, the Islamic moral economy (IME) assumes a direct relationship between religiosity and sadaqah giving due to the falah (salvation) oriented individual objective function, which can be reached through doing ihsan (beneficence for equilibrium). The findings based on a questionnaire survey distributed to individual investors on Shari’ah Online Trading System (SOTS) delineate that religiosity, accounting information, neutral information, personal financial needs, and the sadaqah feature have significantly affected investors’ trading behaviour in which the sadaqah feature is positively correlated, while religiosity factors are negatively correlated. Thus, despite the theoretical expectation through IME, this study evidences that Islamic logic is not the main determining factor, as market logic related factors seem to be more dominant in the behaviour of investors in the Indonesian capital market.

FPGA technology is widely used in the finance domain. We describe the design of a financial trading system order processing component using FPGAs, implemented with high-level synthesis (HLS) flow. The order processing component is the major contributor to increased delays and low throughput in the current software implementation of trading systems. The objective of FPGA implementation is to reduce the latency of order processing and increase the throughput of trading systems as compared to software implementation. Our design is one of the first attempts to speed up order processing in a trading system using FPGA technology and HLS flow. HLS was used in implementing the design for higher productivity and faster turnaround time. The design shows orders of magnitude of improvement in performance indicating that more complex FPGA systems could be designed using HLS. We obtained more than 2X of an advantage in order processing speed and a reduction in latency with FPGA technology. Moreover, we gained a 4X advantage in terms of productivity using HLS.