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Enhanced decision making mechanism of rule-based genetic network programming for creating stock trading signals

机译:用于创建股票交易信号的基于规则的遗传网络编程的增强决策机制

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Evolutionary computation generally aims to create the optimal individual which represents optimal action rules when it is applied to agent systems. Genetic Network Programming (CNP) has been proposed as one of the graph-based evolutionary computations in order to create optimal individuals. GNP with rule accumulation is an extended algorithm of GNP, which extracts a large number of rules throughout the generations and stores them in rule pools, which is different from general evolutionary computations. Concretely, the individuals of GNP with rule accumulation are regarded as evolving rule generators in the training phase and the generated rules in the rule pools are actually used for decision making. In this paper, GNP with rule accumulation is enhanced in terms of its rule extraction and classification abilities for generating stock trading signals considering up and down trends and occurrence frequency of specific buying/selling timing. A large number of buying and selling rules are extracted by the individuals evolved in the training period. Then, a unique classification mechanism is used to appropriately determine whether to buy or sell stocks based on the extracted rules. In the testing simulations, the stock trading is carried out using the extracted rules and it is confirmed that the rule-based trading model shows higher profits than the conventional individual-based trading model.
机译:进化计算通常旨在创建代​​表最佳行动规则的最佳个体,并将其应用于代理系统。遗传网络编程(CNP)已被提出作为基于图的进化计算之一,以创建最佳个体。具有规则累积的GNP是GNP的扩展算法,该算法提取了整个世代中的大量规则,并将其存储在规则池中,这与一般的进化计算不同。具体而言,具有规则积累的GNP个体在训练阶段被视为不断发展的规则生成器,并且规则池中生成的规则实际上被用于决策。在本文中,具有规则累积的GNP在规则提取和分类能力方面得到了增强,这些能力考虑到上下趋势和特定买卖时机的发生频率来生成股票交易信号。在培训期间,个人会提取大量的买卖规则。然后,基于提取的规则,使用唯一的分类机制来适当地确定是购买还是出售股票。在测试模拟中,使用提取的规则执行股票交易,并且可以确认基于规则的交易模型显示出比常规的基于个人的交易模型更高的利润。

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