Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Generating trading rules on the stock markets with genetic programming
Computers and Operations Research
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
A fusion model of HMM, ANN and GA for stock market forecasting
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Genetic network programming with actor-critic and its application to stock trading model
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Resource allocation neural network in portfolio selection
Expert Systems with Applications: An International Journal
Using genetic algorithm to support portfolio optimization for index fund management
Expert Systems with Applications: An International Journal
Portfolio algorithm based on portfolio beta using genetic algorithm
Expert Systems with Applications: An International Journal
A study of evolutionary multiagent models based on symbiosis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
International Journal of Business Intelligence and Data Mining
Information Systems Frontiers
Hi-index | 12.05 |
In this paper, an enhancement of stock trading model using Genetic Network Programming (GNP) with Sarsa Learning is described. There are three important points in this paper: First, we use GNP with Sarsa Learning as the basic algorithm while both Technical Indices and Candlestick Charts are introduced for efficient stock trading decision-making. In order to create more efficient judgment functions to judge the current stock price appropriately, Importance Index (IMX) has been proposed to tell GNP the timing of buying and selling stocks. Second, to improve the performance of the proposed GNP-Sarsa algorithm, we proposed a new method that can learn the appropriate function describing the relation between the value of each technical index and the value of the IMX. This is an important point that devotes to the enhancement of the GNP-Sarsa algorithm. The third point is that in order to create more efficient judgment functions, sub-nodes are introduced in each node to select appropriate stock price information depending on the situations and to determine appropriate actions (buying/selling). To confirm the effectiveness of the proposed method, we carried out the simulation and compared the results of GNP-Sarsa with other methods like GNP with Actor Critic, GNP with Candlestick Chart, GA and Buy&Hold method. The results shows that the stock trading model using GNP-Sarsa outperforms all the other methods.