Performance Analysis of Trading Strategy using Gradient Boosting Machine Learning and Genetic Algorithm (그래디언트 부스팅 기계학습 및 유전 알고리즘을 적용한 증권 트레이딩 전략의 성능분석)

Phil-Sik Jang (장필식)

doi:10.9708/jksci.2022.27.11.147

Performance Analysis of Trading Strategy using Gradient Boosting Machine Learning and Genetic Algorithm

Journal of The Korea Society of Computer and Information
Abbr : JKSCI
2022, 27(11), pp.147-155
DOI : 10.9708/jksci.2022.27.11.147
Publisher : The Korean Society Of Computer And Information
Research Area : Engineering > Computer Science
Received : October 18, 2022
Accepted : November 8, 2022
Published : November 30, 2022

Phil-Sik Jang ¹

¹세한대학교

Accredited

ABSTRACT

In this study, we developed a system to dynamically balance a daily stock portfolio and performed trading simulations using gradient boosting and genetic algorithms. We collected various stock market data from stocks listed on the KOSPI and KOSDAQ markets, including investor-specific transaction data. Subsequently, we indexed the data as a preprocessing step, and used feature engineering to modify and generate variables for training. First, we experimentally compared the performance of three popular gradient boosting algorithms in terms of accuracy, precision, recall, and F1-score, including XGBoost, LightGBM, and CatBoost. Based on the results, in a second experiment, we used a LightGBM model trained on the collected data along with genetic algorithms to predict and select stocks with a high daily probability of profit. We also conducted simulations of trading during the period of the testing data to analyze the performance of the proposed approach compared with the KOSPI and KOSDAQ indices in terms of the CAGR (Compound Annual Growth Rate), MDD (Maximum Draw Down), Sharpe ratio, and volatility. The results showed that the proposed strategies outperformed those employed by the Korean stock market in terms of all performance metrics. Moreover, our proposed LightGBM model with a genetic algorithm exhibited competitive performance in predicting stock price movements.

KEYWORDS

Algorithmic trading, Stock market, Gradient boosting, Machine learning, Genetic algorithm

Citation status

* References for papers published after 2023 are currently being built.

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Performance Analysis of Trading Strategy using Gradient Boosting Machine Learning and Genetic Algorithm

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

Performance Analysis of Trading Strategy using Gradient Boosting Machine Learning and Genetic Algorithm

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KEYWORDS

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* References for papers published after 2023 are currently being built.