scholarly journals Multi-Order Rules Extraction by Genetic Network Programming with Rule Accumulation and its Application to Stock Trading Problems

2011 ◽  
Vol 15 (5) ◽  
pp. 515-524
Author(s):  
Yafei Xing ◽  
◽  
Singo Mabu ◽  
Lian Yuzhu ◽  
Kotaro Hirasawa
Keyword(s):  
Stock Prices ◽  
Stock Price ◽  
Genetic Network ◽  
Rule Extraction ◽  
Network Programming ◽  
Evolutionary Computations ◽  
Stock Trading ◽  
Trading Rules ◽  
Rules Extraction ◽  
Genetic Network Programming

As the effectiveness of the trading rules for stock trading problems has been verified, a method of extracting multi-order rules by Genetic Network Programming (GNP) is proposed using the rule accumulation for improving the efficiency of the trading rules in this paper. GNP is one of the evolutionary computations having a directed graph structure. Because of this special structure, the rule accumulation from GNP individuals is more effective for trading the stock than other methods. In this paper, there are two main points: rule extraction and trading action determination. Rule extraction is carried out in the training period, where the rules including the 1st order rules and multi-order rules, are extracted from the best individual and accumulated into the rule pools generation by generation. In the testing period, the trading action is determined by the matching degree of the stock price information with the rules, and the profits of the trading are evaluated. In the simulations, the stock prices of 16 brands in 2004, 2005 and 2006 are used for the training and those in 2007 for the testing. The simulation results show that the multi-order rules perform better than the 1st order rules. So, it is proved that themulti-order rules extracted by GNP is more effective than the 1st order rules for stock trading.

Generating Trading Rules for Stock Markets Using Robust Genetic Network Programming and Portfolio Beta

2016 ◽  
Vol 20 (3) ◽  
pp. 484-491
Author(s):  
Yan Chen ◽  
◽  
Zhihui Shi
Keyword(s):  
Stock Prices ◽  
Intelligent System ◽  
Fitness Function ◽  
Genetic Network ◽  
Correlated Data ◽  
Network Programming ◽  
Stock Trading ◽  
Trading Rules ◽  
Generalization Ability ◽  
Genetic Network Programming

In this paper, Robust Genetic Network Programming (R-GNP) for generating trading rules for stocks is described. R-GNP is a new evolutionary algorithm, where solutions are represented using graph structures. It has been clarified that R-GNP works well especially in dynamic environments. In the proposed hybrid model, R-GNP is applied to generating stock trading rules with variance of fitness values. The unique point is that the generalization ability of R-GNP is improved by using the robust fitness function, which consists of the fitness functions with the original data and a good number of correlated data. Generally speaking, the hybrid intelligent system consists of three steps: priority selection by the portfolio β, optimization by the Genetic Relation Algorithm (GRA), and stock trading by R-GNP. In the simulations, the trading model is trained using the stock prices of 10 brands on the Tokyo Stock Exchange, and then the generalization ability is tested. From the simulation results, it is clarified that the trading rules created by the proposed R-GNP model obtain much higher profits than the traditional methods even in the world-wide financial crisis of 2007. Hence, its effectiveness has been confirmed.

Trading Rules on Stock Markets Using Genetic Network Programming with Sarsa Learning

2008 ◽  
Vol 12 (4) ◽  
pp. 383-392 ◽  
Author(s):  
Yan Chen ◽  
◽  
Shingo Mabu ◽  
Kaoru Shimada ◽  
Kotaro Hirasawa
Keyword(s):  
Reinforcement Learning ◽  
Stock Prices ◽  
Stock Price ◽  
Memory Function ◽  
Genetic Network ◽  
Network Programming ◽  
Task Execution ◽  
Trading Rules ◽  
Trading Model ◽  
Genetic Network Programming

In this paper, the Genetic Network Programming (GNP) for creating trading rules on stocks is described. GNP is an evolutionary computation, which represents its solutions using graph structures and has some useful features inherently. It has been clarified that GNP works well especially in dynamic environments since GNP can create quite compact programs and has an implicit memory function. In this paper, GNP is applied to creating a stock trading model. There are three important points: The first important point is to combine GNP with Sarsa Learning which is one of the reinforcement learning algorithms. Evolution-based methods evolve their programs after task execution because they must calculate fitness values, while reinforcement learning can change programs during task execution, therefore the programs can be created efficiently. The second important point is that GNP uses candlestick chart and selects appropriate technical indices to judge the timing of the buying and selling stocks. The third important point is that sub-nodes are used in each node to determine appropriate actions (buying/selling) and to select appropriate stock price information depending on the situation. In the simulations, the trading model is trained using the stock prices of 16 brands in 2001, 2002 and 2003. Then the generalization ability is tested using the stock prices in 2004. From the simulation results, it is clarified that the trading rules of the proposed method obtain much higher profits than Buy&Hold method and its effectiveness has been confirmed.

Genetic Network Programming-Sarsa with Subroutines for Trading Rules on Stock Markets

2011 ◽  
Vol 15 (5) ◽  
pp. 488-494 ◽  
Author(s):  
Yang Yang ◽  
◽  
Shingo Mabu ◽  
Jianhua Li ◽  
Kotaro Hirasawa
Keyword(s):  
Natural Selection ◽  
Stock Prices ◽  
Stock Markets ◽  
Genetic Network ◽  
Network Programming ◽  
Main Program ◽  
New Approach ◽  
Trading Rules ◽  
Complex Solutions ◽  
Genetic Network Programming

The purpose of this paper is to propose a new approach to generate effective subroutines, which are automatically discovered by the GNP-Sarsa programs combining evolution and reinforcement learning. We name it asGNP-Sarsa with subroutines (GNPsb-Sarsa) and apply it to the trading rules on stock markets. In the proposal method, GNPsb-Sarsa offers an alternative population, where individuals are represented by subroutines. Each main program of GNPsb-Sarsa can refer to an individual in the subroutine population, after adding a new kind of node, namely subroutine node, in the graph network structure of GNP-Sarsa. GNPsb-Sarsa containing a main program and a subroutine evolves by natural selection and genetic operations, where the gene of GNPsb-Sarsa is the pair of the main GNP and its subroutine. That is, the genetic operations on GNPsb-Sarsa are constrained by the gene structure on which they can operate. In the simulations, the stock prices of different brands from 2001 to 2004 are used to test the effectiveness of the GNPsb-Sarsa. The results show that the proposed approach can provide reasonable opportunities for evolving complex solutions.

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