Designing efficient online trading systems

It has been determined that online trading is associated with a significant degree of risk, which can be reduced through a thorough and systematic approach to the choice of exchange trading strategies. The strategy (system) of exchange trading is a personal trading rules that take into account the market situation, knowledge and understanding of the trader of this situation, as well as the trader’s wishes regarding the profitability of trading, taking into account the risks. Trading systems based on technical analysis of stock charts are considered: trend (based on the assumption that the price will rise or fall according to market trends), flat (assumes that prices change within a corridor that has clearly defined borders, supported by levels of resistance or support), counter-trend (involves determining the turning point of the price movement), trading on forex patterns (involves determining the figures of graphical analysis of stock charts, resulting in, according to statistics, there is a high probability predict price movements), wave analysis (assumes that market behavior depends on the psychology of participants, which is expressed in the impulsiveness of behavior), breakthrough volatility (assumes that a significant event is the actual exit of the price from the established channel, creating opportunities for trends), session trade (involves work in the market within a specific trade session), trading at Fibonacci levels (assumes that the price should create adjustments to the trend, reflecting the special levels, which are based on the numerical sequence of Fibonacci), scalping (provides trading within the trading day and is characterized by small levels of take profits and relatively large levels of stop-losses) and universal (provide a different combination of the above systems depending on the preferences and experience of the trader). It is concluded that all types of trading systems can be profitable, but first of all it all depends on the knowledge and skills of the person who carries out trading operations.

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MULTI-AGENT SYSTEM ARCHITECTURE TO TRADING SYSTEMS

Journal of Interconnection Networks ◽

10.1142/s0219265905001435 ◽

2005 ◽

Vol 06 (03) ◽

pp. 283-302 ◽

Cited By ~ 1

Author(s):

ELHADI SHAKSHUKI ◽

SAAD ABU-DRAZ

Keyword(s):

System Architecture ◽

Multi Agent System ◽

Distributed Resources ◽

Agent System ◽

Interface Agents ◽

Trading Systems ◽

Online Trading ◽

Single Attribute ◽

Multi Agent ◽

Trading Agent

Agents for online trading purpose can be seen as a tool that helps computer users to purchase products from distributed resources based on their interests and preferences. One of the major features that determine the success of trading agent is the ability to negotiate with other agents, because most trading tasks involve interaction among agents. This paper presents a peer-to-peer multi-agent system architecture for online trading. The main objective of this system is to address some of the shortcomings that are present in contemporary online trading systems that focused on providing solutions for specific trading issues, such as single attribute-based negotiation, the requirement of an electronic marketplace and variations and status changes within the network. The proposed system architecture is a multi-tier, multi-agent architecture. The system architecture consists of three types of agents that are classified based on their functionality: interface, resource and retrieval agents. The interface agents are the front-end of the system and able to interact with different users to fulfill their needs. At the middle-tier, the resource agents access and capture the contents and the changes of the local information database. The retrieval agents are the back-end of the system and able to travel and interact with other agents at remote host machines. A prototype of this system is implemented using the IBM Aglet SDK.

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Metode Alternatif dalam Pencarian Peringkat E-Commerce di Indonesia Berdasarkan Rating Pelanggan

Xplore Journal of Statistics ◽

10.29244/xplore.v10i1.280 ◽

2020 ◽

Vol 10 (1) ◽

pp. 27-40

Author(s):

Azira Irawan ◽

Aam Alamudi ◽

Septian Rahardiantoro

Keyword(s):

Ant Colony Optimization ◽

Analytical Hierarchy Process ◽

Ant Colony ◽

Ahp Method ◽

Trading System ◽

Trading Systems ◽

Several Variables ◽

Online Trading ◽

Feasibility Test ◽

Hierarchy Process

The existence of the internet raises an online trading system using applications. The rise of online trading systems has triggered the emergence of various e-commerce in Indonesia that provide various kinds of customer needs. This also causes problems for customers, namely the difficulty in choosing quality e-commerce. The effort to overcome this problem is to rank e-commerce in Indonesia based on customer ratings. The method commonly used for ranking is the analytical hierarchy process (AHP) method, but in practice there are several variables that are not found in e-commerce so the AHP method cannot be used. The alternative method chosen is the ant colony optimization (ACO) method. The feasibility test of the ACO method in searching rankings for e-commerce data needs to be done because not all variables are in e-commerce. Simulations for ranking search are carried out using 2 generated data scenario with analytical hierarchy process (AHP) and ant colony optimization (ACO) method. The simulation results show that the ACO method is feasible to be used for ranking with blank data, then the application of the ACO method for e-commerce data in Indonesia. The best taboo results are based on the highest opportunity value and the highest correlation coefficient, namely in the second taboo, with three major ratings, namely JD, SP, and TP

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User Satisfaction Affecting Factors in the Online Trading Systems

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c4118.118419 ◽

2019 ◽

Vol 8 (4) ◽

pp. 639-642

Keyword(s):

User Satisfaction ◽

Information Quality ◽

Factor Model ◽

Primary Data ◽

System Quality ◽

Affecting Factors ◽

Trading Systems ◽

Performance Quality ◽

Online Trading ◽

And Function

The purpose of this paper is to show how to find a new factor model that affects the satisfaction of online trading system users which is done by analyzing the factors in finding the factors that most influence the online user satisfaction system trading. of the 8 variables that have been determined namely System Quality, Service Quality, Information Quality, Intended use, User Satisfaction, Interface Quality, Performance Quality, and Function Quality which are the basic concepts of the underlying theory. The technique for processing data begins with building a research instrument by constructing and following up on existing concepts and breaking them down into factors that exist in the theory. Through the distribution of questionnaires to obtain primary data, then the data is processed using factor analysis and a number of new factors are obtained, namely application system features, information and knowledge, and comfortable to use that affect the satisfaction of users of online trading systems. It is hoped that this model can help organizational management to improve services better and find out what is desired from users of online trading systems.

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User Satisfaction of Online Trading Systems: An Empirical Study

Soft Computing in Management and Business Economics - Studies in Fuzziness and Soft Computing ◽

10.1007/978-3-642-30457-6_20 ◽

2012 ◽

pp. 313-326

Author(s):

Juan J. García-Machado ◽

Juan C. Roca ◽

Juan J. de la Vega

Keyword(s):

Empirical Study ◽

User Satisfaction ◽

Trading Systems ◽

Online Trading

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The profitability of algorithmic trading systems based on football sentiment

International Sports Studies ◽

10.30819/iss.42-1.04 ◽

2020 ◽

Vol 42 (1) ◽

pp. 33-46

Author(s):

Raúl Gómez-Martínez ◽

Camila Marqués-Bogliani ◽

Jessica Paule-Vianez

Keyword(s):

Financial Markets ◽

Investment Decisions ◽

Futures Market ◽

Algorithmic Trading ◽

Investment Strategies ◽

Measurable Effect ◽

Behavioural Finance ◽

Trading Systems ◽

European Football

Behavioural finance has shown that investment decisions are the result of not just rational but also emotional brain processes. On the assumption that emotions affect financial markets, it would seem likely that football results might have a measurable effect on financial markets. To test this, this study describes three algorithmic trading systems based exclusively on the results of three top European football teams (Juventus, Bayern München and Paris St Germain) opening long or short positions in the next market season of the futures market of the index of each country (MIB (Milano Italia Borsa), DAX (Deutscher Aktien Index) and CAC (Cotation Assistée en Continu). Depending on the outcome of the last game played a long position was taken after a victory and a short position after a draw or defeat. The results showed that the algorithmic systems were profitable in the case of Juventus and Bayern whereas in the case of PSG, the system was profitable, but in an inverse way. This study shows that investment strategies that take account of sports sentiment could have a profitable outcome.

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