Intelligent trading architecture

Freitas, Fábio Daros de; Freitas, Christian Daros; Souza, Alberto F. De

doi:10.1002/cpe.3690

Cited by 5 publications

(7 citation statements)

References 14 publications

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“…Once the rules are established, the computer will intelligently search for trading opportunities according to customer rules and algorithm trading strategies. It will also prepare to automatically generate the order that minimizes the loss [8].…”

Section: Computer Systemmentioning

confidence: 99%

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Exploring Evaluation Factors and Framework for the Object of Automated Trading System

Huang¹,

Cheng²,

Hou³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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Automated trading system (ATS) is a computer program that combines different trading rules to find optimal trading opportunities. The objects of ATS, which are financial assets, need evaluation because that is of great significance for stakeholders and market orders. From the perspectives of dealers, agents, external environment, and objects themselves, this study explored factors in evaluating and choosing the object of ATS. Based on design science research (DSR), we presented a preliminary evaluation framework and conducted semi-structured interviews with twelve trading participants engaged in different occupations. By analyzing the data collected, we validated eight factors from literatures and found four new factors and fifty-four sub-factors. Additionally, this paper developed a relationship model of factors. The results could be used in future work to explore and validate more evaluation factors by using data mining.

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Section: Computer Systemmentioning

confidence: 99%

“…Evidence of software design and its sub-factors are analyzed from interviewees and are listed in Table 10. Software is a carrier of publish/subscribe communication [8] in ATS. During the interview, statements associated with software design are indicated frequently as well.…”

Section: New Factors and Sub-factorsmentioning

confidence: 99%

Exploring Evaluation Factors and Framework for the Object of Automated Trading System

Huang¹,

Cheng²,

Hou³

et al. 2019

Proceedings of the Annual Hawaii International Conference on System Sciences

View full text Add to dashboard Cite

show abstract

“…With the improvement of the underlying technology and the application of machine learning methods, the automated trading system (ATS) becomes a powerful engine that promotes quantitative investment [6]. As a new computer technology to help maximize return in security exchange markets, automated trading [16] has received a lot of attention in developed countries such as the USA. In recent years, the investment environment of China is also about to change due to the application of ATS.…”

Section: Automated Trading System (Ats)mentioning

confidence: 99%

“…These strategies can be defined as a systematic sequence of actions automatically taken by computer programs without human emotion interferences [18]. An advanced ATS is supposed to handle complex tasks [16]: For example, collecting, screening, evaluating, and archiving market data as much as possible; using appropriate analytical models and prediction techniques to help user's decision-making and to improve the performance of funds; adapting to market variations; and reacting as fast as possible, etc.…”

Section: Automated Trading System (Ats)mentioning

confidence: 99%

An Investigation on Factors Affecting Stock Valuation Using Text Mining for Automated Trading

et al. 2019

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Predicted price-to-book value ratios (P/BV) are widely used for the valuation of listed common stocks. However, with the application of automated trading system (ATS), the existing indicators that are applied in the method are losing their effectiveness in the Chinese market. Combining qualitative research with the text mining method, this study explores and validates those ignored factors to improve the accuracy of the stock valuation. On the basis of the principal of the existing valuation method, we clarify the scope of the factors that affects the P/BV ratio prediction. Through semi-structured interviews that are designed with six first-level factors which are taken from the literature, we then excavate some second-level factors. After that, with three corpuses including samples form Sina.com.cn, Xueqiu.com, and CSDN.net, four first-level factors and thirteen second-level factors have been verified step by step through the Latent Dirichlet Allocation (LDA) model. In the process, two other new factors and three sub-factors are also found. Furthermore, based on the factor correlation that was found in a data analysis, a factor relationship model was built. The results can be used in a stock valuation in future work as the basis of the indicator system for the prediction of P/BV ratio.

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“…We encourage the reader to review to gain insight into the breadth and depth of problems and innovative solutions in the multi‐disciplinary field of high‐performance computational finance.…”

Section: Introductionmentioning

confidence: 99%

Workshop on high‐performance computational finance

Dixon

Moreira

Daly

2015

Concurrency and Computation

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EDITORIALWorkshop on high-performance computational finance BACKGROUNDThe purpose of this special issue is to collate a selection of representative research articles that were primarily presented at the Seventh Workshop on High-Performance Computational Finance, held in conjunction with SC'14. This annual workshop brings together practitioners, researchers, vendors, and scholars from the complementary fields of computational finance and high-performance computing in order to promote exchange of ideas, discuss future collaborations and develop new research directions. Financial companies increasingly rely on high performance computers to analyze high volumes of financial data, automatically execute trades, and manage risk.As financial market data continue to grow in volume and complexity, computational capabilities of emerging hardware also increases. Extracting high performance from emerging architectures requires a combination of domain knowledge and specialized technical skills. This special issues presents many examples of how researchers, scholars, vendors, and practitioners are collaborating to address high-performance computing research challenges.The scope of this special issue is broad and is representative of the multi-disciplinary nature of computational finance. In addition to submissions that deal with performance and programmability challenges, theoretical analysis, algorithms, and practical experience in computational finance, this issue also includes articles that address practical challenges with running financial applications in the cloud.The efficient deployment of applications on multi and many-core architectures is a central topic in This issue would be incomplete without coverage of the highly impactful STAC A2 benchmark, which is designed to measure the performance of market risk analysis workloads. A. Nikolaev, B. Ilya, and S. Sania [4] describe how Intel is actively contributing to the specification and provides updated benchmark implementations optimized for the Intel Xeon processor E5 and E7 product families and Intel Xeon Phi coprocessor.Performance versus accuracy of computational methods is a pervasive topic in the financial modeling. C. Brugger, G. Liu, C. De Schryver, and N. Wehn [5] consider the calibration of various models with closed form solutions and propose a hybrid solution.We encourage the reader to review [6][7][8] to gain insight into the breadth and depth of problems and innovative solutions in the multi-disciplinary field of high-performance computational finance.

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Intelligent trading architecture

Cited by 5 publications

References 14 publications

Exploring Evaluation Factors and Framework for the Object of Automated Trading System

Exploring Evaluation Factors and Framework for the Object of Automated Trading System

An Investigation on Factors Affecting Stock Valuation Using Text Mining for Automated Trading

Workshop on high‐performance computational finance

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