Creating stock trading rules using graph-based estimation of distribution algorithm

Li, Xianneng; Wen, He; Hirasawa, Kotaro

doi:10.1109/cec.2014.6900421

Cited by 5 publications

(2 citation statements)

References 46 publications

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“…So far, EDAs have been widely used to solve different optimization problems such as route planning for vehicles (Pérez-Rodríguez and Hernández-Aguirre 2019), harvesting agricultural fields (Utamima et al 2019), energy-efficient robots manufacturing (Sun et al 2020), permutation-based combinatorial problems (Irurozki et al 2018), and public transport driver scheduling (Shen et al 2017). EDAs are also used coupled with reinforcement learning (RL) approaches as they demand a large amount of data, and EDAS can satisfy this needness by working as a generative model of valid data (Li et al 2014).…”

Section: Introductionmentioning

confidence: 99%

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

2022

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Many real-world optimization problems involve two different subsets of variables: decision variables, and those variables which are not present in the cost function but constrain the solutions, and thus, must be considered during optimization. Thus, dependencies between and within both subsets of variables must be considered. In this paper, an estimation of distribution algorithm (EDA) is implemented to solve this type of complex optimization problems. A Gaussian Bayesian network is used to build an abstraction model of the search space in each iteration to identify patterns among the variables. As the algorithm is initialized from data, we introduce a new hyper-parameter to control the influence of the initial data in the decisions made during the EDA execution. The results show that our algorithm improves the cost function more than the expert knowledge does.

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Section: Introductionmentioning

confidence: 99%

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

2022

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“…GNP is a fairly simple algorithm that directly applies the traditional crossover and mutation to evolve the directed graph for global search. It has been successfully utilized to solve many different kinds of complicated problems, such as the problems of controlling the agents' behavior [16], robot control [17], [18], [19], data mining [20], [21], financial node 1 node 2 node 3 node 4 node 5 [22], [23], and so on.…”

Section: Introductionmentioning

confidence: 99%

Evolving directed graphs with artificial bee colony algorithm

Gao

Hirasawa

2014

2014 14th International Conference on Intelligent Systems Design and Applications

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Artificial bee colony (ABC) algorithm is a rela tively new optimization technique that simulates the intelligent foraging behavior of honey bee swarms. It has been applied to several optimization domains to show its efficient evolution ability. In this paper, ABC algorithm is applied for the first time to evolve a directed graph chromosome structure, which derived from a recent graph-based evolutionary algorithm called genetic network programming (GNP). Consequently, it is explored to new application domains which can be efficiently modeled by the directed graph of GNP. In this work, a problem of controlling the agents's behavior under a well known benchmark testbed called Tileworld are solved using the ABC-based evolution strategy. Its performance is compared with several very well-known methods for evolving computer programs, including standard GNP with crossover/mutation, genetic programming (GP) and reinforcement learning (RL).

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Building Intelligent Moving Average-Based Stock Trading System Using Metaheuristic Algorithms

Kuo¹,

Chou²

2021

IEEE Access

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Many studies have been proposed to prove that technical analysis can help investors make trading decisions. The moving average (MA) is a widely used technical indicator that plays an important role in this field since it directly reflects stock fluctuations. However, most studies ignore the parameter settings of the MA, which leads to underestimation of the potential of the MA. Therefore, this paper is the first attempt to remove all restrictions and extend the limits of the MA to take advantage of the MA's capability well. It also uses different kinds of MA, such as the weighted moving average (WMA) and the exponential moving average (EMA), to compose trading strategies. Our system proposes the global best-guided quantum-inspired tabu search algorithm (GQTS), which is better at searching than traditional algorithms, to optimize trading strategies based on the MA. Furthermore, an innovative 2-phase sliding window is invented to consider more investment situations in changeable stock markets. In summary, this paper intended to investigate the ability of MA and proposed dynamic and intelligent trading strategies based on MA, GQTS, and 2-phase sliding window to assist investors to make trading decisions. The experiments show that the proposed system flexibly discovers better trading points. Our system outperforms traditional methods and beats the buy-and-hold strategy to yield significant profits both in developed and emerging stock markets.

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Creating stock trading rules using graph-based estimation of distribution algorithm

Cited by 5 publications

References 46 publications

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

Evolving directed graphs with artificial bee colony algorithm

Building Intelligent Moving Average-Based Stock Trading System Using Metaheuristic Algorithms

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