Develop an integrated candlestick technical analysis model using meta-heuristic algorithms

Armin Mahmoodi

doi:10.1108/EMJB-02-2022-0034

Develop an integrated candlestick technical analysis model using meta-heuristic algorithms

2023-11-21

8

PRE

AI

DOI：10.1108/EMJB-02-2022-0034

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PurposeIn this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.Design/methodology/approachFurther as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.FindingsAs per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.Research limitations/implicationsIn this study, the data for stock market of the years 2013-2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.Originality/valueIn this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

Keyword:

Machine learning

Stock market predicting

Candlestick technical analysis

Support vector machine

Meta-heuristic algorithms

Develop an integrated candlestick technical analysis model using meta-heuristic algorithms

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