Application of expert systems

Carter, Ian M.

doi:10.1016/0954-1810(89)90025-3

Cited by 6 publications

(1 citation statement)

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“…This is especially important since it enables traders who are not proficient in programming to design complex trading strategies that are suited to their own tastes and the state of the market. Furthermore, our experiment demonstrates how sophisticated trading methods, when combined with software for automated trading such as FXDreema, may improve trading performance, increase efficiency, and even yield steady profits in the Forex market [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Revolutionizing Forex Trading: Harnessing AI for Seamless Market Automation

Raswanth S S,

Roshan M,

Sanjit S

et al. 2024

Int Res J Adv Engg Hub

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The article explores the integration of AI and automated trading in the Forex market, focusing on the development of a custom Expert Advisor using FXDreema. To improve trading success, the approach includes a custom indicator and a martingale mechanism for hedging. Backtesting results show clear trade opportunities and substantial profits. Iterative testing procedures are highlighted while discussing optimization and integration challenges. The significance of automated trading methods and the part FXDreema plays in democratizing the creation of expert advisors are emphasized in the references. The potential for consistent profitability and future optimization tactics of the Expert Advisor are emphasized in the conclusion.

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Section: Introductionmentioning

confidence: 99%

Revolutionizing Forex Trading: Harnessing AI for Seamless Market Automation

Raswanth S S,

Roshan M,

Sanjit S

et al. 2024

Int Res J Adv Engg Hub

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A Description Length-Based Decision Criterion for Default Knowledge in the Ripple Down Rules Method

Wada

Horiuchi

Motoda

et al. 2001

Knowledge and Information Systems

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The 'Ripple Down Rules (RDR)' method is a promising approach to directly acquiring and encoding knowledge from human experts. It requires data to be supplied incrementally to the knowledge base being constructed, each new piece of knowledge being added as an exception to the existing knowledge base. Because of this patching principle, the knowledge acquired depends strongly on what is given as the default knowledge, used as an implicit outcome when inference fails. Therefore, it is important to choose good default knowledge for constructing an accurate and compact knowledge base. Further, real-world data are often noisy and we want the RDR to be noise resistant. This paper reports experimental results about the effect of the selection of default knowledge and the amount of noise in data on the performance of RDR, using a simulated expert in place of a human expert. The best default knowledge is characterized as the class knowledge that maximizes the description length of encoding rules and misclassified cases. We confirmed by extensive experimentation that this criterion is indeed valid and useful in constructing an accurate and compact knowledge base. We also ascertained that the same criterion holds when the data are noisy.

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