An Algorithmic Trading Approach Merging Machine Learning With Multi-Indicator Strategies for Optimal Performance

被引：1
作者：
Sukma, Narongsak [1 ]
Namahoot, Chakkrit Snae [1 ,2 ]
机构：
[1] Naresuan Univ, Fac Sci, Phitsanulok 65000, Thailand
[2] Naresuan Univ, Fac Sci, Ctr Excellence Nonlinear Anal & Optimizat, Phitsanulok 65000, Thailand
来源：
IEEE ACCESS | 2024年 / 12卷
关键词：
Machine learning algorithms; Machine learning; Prediction algorithms; Adaptation models; Heuristic algorithms; Predictive models; Accuracy; Computational modeling; Analytical models; Decision making; Undervalued stocks; algorithmic approaches; machine learning; predictive modeling; financial markets; INFORMATION FUSION; LSTM; NETWORK; MODEL;
D O I：
10.1109/ACCESS.2024.3516053
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This study investigates the integration of machine learning techniques with multi-indicator strategies in algorithmic trading to overcome the limitations of traditional trading methods. As financial markets become increasingly complex and volatile, innovative approaches are essential to improve predictive accuracy and adaptability. This research aims to develop an algorithmic trading framework that dynamically selects relevant indicators to optimize trading performance. Researchers propose a flexible and computationally efficient model that takes advantage of advanced machine learning algorithms alongside multiple technical indicators, designed to adapt to changing market conditions. An empirical analysis evaluates the effectiveness of this approach against traditional trading indicators. The results demonstrate that the proposed algorithm achieved an impressive total return of 901%, significantly outperforming traditional indicators such as Moving Average (MA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Bollinger Bands, On-Balance Volume (OBV), and Ichimoku. Furthermore, the model maintained a high win rate during the backtesting, showcasing its robustness under various market conditions. These findings highlight the potential of hybrid strategies that combine machine learning with technical analysis. This research emphasizes the need for adaptive trading models that can respond to the dynamic nature of financial markets. The integration of machine learning with multi-indicator strategies is shown to enhance trading performance. Future work should focus on improving the interpretability of the model and exploring diverse datasets to further enhance the effectiveness of algorithmic trading methodologies.
引用
收藏
页码：188154 / 188173
页数：20
相关论文
共 103 条
  • [1] Aguirre R.A.A., Aguirre A.A.A., Behavioral finance: Evolution from the classical theory and remarks, J. Econ. Surv., 38, 2, pp. 452-475, (2024)
  • [2] Xiao Q., Ihnaini B., Stock trend prediction using sentiment analysis, PeerJ Comput. Sci., 9, (2023)
  • [3] Rodriguez-Ibanez M., Casanez-Ventura A., Castejon-Mateos F., Cuenca-Jimenez P.-M., A review on sentiment analysis from social media platforms, Expert Syst. Appl., 223, (2023)
  • [4] Guezzaz A., Asimi Y., Azrour M., Asimi A., Mathematical validation of proposed machine learning classifier for heterogeneous traffic and anomaly detection, Big Data Mining Anal., 4, 1, pp. 18-24, (2021)
  • [5] Wang Z., Xiao X., Rajasekaran S., Novel and efficient randomized algorithms for feature selection, Big Data Mining Anal., 3, 3, pp. 208-224, (2020)
  • [6] Bouazizi M., Ohtsuki T., Multi-class sentiment analysis on Twitter: Classification performance and challenges, Big Data Mining Anal., 2, 3, pp. 181-194, (2019)
  • [7] Shi Y., Zheng Y., Guo K., Ren X., Relationship between herd behavior and Chinese stock market fluctuations during a bullish period based on complex networks, Int. J. Inf. Technol. Decis. Making, 21, 1, pp. 405-421, (2022)
  • [8] Lv D., Gong Y., Chen J., Xiang Y., Recommendation algorithm of industry stock trading model with TODIM, Int. J. Inf. Technol. Decis. Making, 23, 3, pp. 1301-1334, (2024)
  • [9] Yuan X., Yuan J., Jiang T., Ain Q.U., Integrated longterm stock selection models based on feature selection and machine learning algorithms for China stock market, IEEE Access, 8, pp. 22672-22685, (2020)
  • [10] Lee M.-C., Chang J.-W., Yeh S.-C., Chia T.-L., Liao J.-S., Chen X.-M., Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies, Neural Comput. Appl., 34, 16, pp. 13267-13279, (2022)
12345678910