The methods of Rough set and Genetic Algorithms in the Intelligent Hybrid Trading System for Disclosure of Futures Trading Rules

Subject Areas : Financial Knowledge of Securities Analysis

Mohammadreza Vatanparast ¹ , Abbas Babaei ² , Shaban Mohammadi ³

1 - Assistant Professor of Accounting, Rasht Branch, Islamic Azad University, Rasht, Iran
2 - Ph.D. student of financial engineering, Rasht Branch, Islamic Azad University, Rasht, Iran
3 - MSC. Accounting, Shahid Rajaee Faculty, Vocational University of Khorasan, Iran

Received: 2018-11-20 Accepted : 2019-07-15 Published : 2020-09-22

Keywords: Intelligent hybrid trading sys, Technical Analysis, Rough series, Genetic algorithm,

Abstract :

The discovery of intelligent technical sales rules from the complex and making systems for buying and selling is a difficult task. The purpose of this study is to develop an intelligent mixing system for buying and selling to discover the rules of technical sales through the analysis of the Rough series and the genetic algorithm. The datasets used included 30 open, up, down, closing and volume futures contracts of stock indexes in the stock market in the period from 2011 to 2017. For this purpose, it is recommended that when discovering technical rules for future markets and solving optimization problems, discretization and data reduction, analyzing the Ruff series, and ultimately, for making optimal decisions about buying and selling the approach of the genetic algorithm. To test the proposed model and compare it with corresponding approaches, randomizations, correlations and approaches to genetic algorithm interventions were designed. Also, these comprehensive interventions, many issues of the existing buying and selling system, the use of slider windows, the number of sales laws, and the duration of the training course. In order to evaluate the intelligent mixing system, interventions were carried out on historical data of the stock index of Tehran Stock Exchange. Specifically, the analysis of sales performance was performed according to decision sets and volumes of training courses to discover the rules for buying and selling the test period. The results showed that the proposed model had better performance in terms of average returns and adjusted risk scale compared to the benchmark model.

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