Feature Engineering for Machine Learning-Based Trading Systems Using Decision Tree, Random Forest, and Gradient Boosting

Machine learning-based trading systems require the selection and creation of features that crucially determine the performance level of the trading system. This study introduces an asset-specific, correlation-based feature selection approach for machine learning–based stock trading models. The research conducts a systematic evaluation of the influence of lookup period, the number of features from technical analysis, and feature selection on the performance of trading systems using tree-based algorithms: Decision Tree, Random Forest, and Gradient Boosting. The performance of the trading system was measured using the backtesting method, with metrics such as total return, win rate ratio, and profit factor. The research steps included selecting stocks with the largest market capitalization in the financial sector, which are included in the banking index. Historical data on the prices of these stocks was obtained from Yahoo! Finance for the years 2014-2025. The historical data was then divided into two parts, namely the in-sample dataset (2014-2024 time period) and the out-of-sample dataset (2025 time period). Each part of the data was supplemented with features from technical analysis and several other additional features. Trading signals are determined based on a profit target of +4% and a loss limit of –2% in a lookup period of 2 to 10 days. The results show that the ML strategy consistently outperforms the buy-and-hold strategy, with Gradient Boosting generating the highest return (37.443%). Spearman correlation-based feature selection per stock improves the performance of the strategy compared to uniform features.

@article{Haryono2025,
  title     = {Feature Engineering for Machine Learning-Based Trading Systems Using Decision Tree, Random Forest, and Gradient Boosting},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Nugroho Agus Haryono and Yuan Lukito and Aditya Wikan Mahastama},
  doi       = {10.14569/IJACSA.2025.0161268},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161268}
}