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Applications of machine learning in quantitative trading
- 1 The University of Queensland, St Lucia QLD 4072, Australia
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Abstract
This paper also addresses quantitative trading where machine learning techniques are applied. Quantitative trading uses historical data and makes future predictions that inform and optimise trading strategies. There are two machine learning techniques used in quantitative trading: supervised and reinforcement learning. In a supervised learning setting, machine learning techniques such as regression analysis, classification models and time series prediction are used to forecast future possible outcomes in the market. Data such as historical prices, volume and financial indicators are used to determine trends that inform and optimise trading decisions. Reinforcement learning is another machine learning technique in quantitative trading where adaptive strategies using a reward system are designed. These strategies then analyse market dynamics and optimise trades through interaction and feedback. The third technique, deep learning, takes neural networks and uses them to process large-scale, complex data. It's particularly useful in quantitative trading as it generates more accurate predictions; for instance, neural networks are capable of learning trading signals from price and volume data and even from news headlines. By enabling computers to analyse large quantities of training data, deep learning improves the predictive accuracy of future price trends. It also helps to generate trading signals that are more robust to market conditions.
Keywords
Machine Learning, Quantitative Trading, Supervised Learning, Reinforcement Learning, Deep Learning.
[1]. Sahu, Santosh Kumar, Anil Mokhade, and Neeraj Dhanraj Bokde. "An overview of machine learning, deep learning, and reinforcement learning-based techniques in quantitative finance: recent progress and challenges." Applied Sciences 13.3 (2023): 1956.
[2]. Alameer, Ali, Haitham Saleh, and Khaled Alshehri. "Reinforcement learning in quantitative trading: A survey." Authorea Preprints (2023).
[3]. Caplar R and Kulisic P 1973 Proc. Int. Conf. on Nuclear Physics (Munich) vol 1 (Amsterdam:North-Holland/American Elsevier) p 517
[4]. Cagliero, Luca, Jacopo Fior, and Paolo Garza. "Shortlisting machine learning-based stock trading recommendations using candlestick pattern recognition." Expert Systems with Applications 216 (2023): 119493.
[5]. Ullah, AKM Amanat, et al. "Combining machine learning and effective feature selection for real-time stock trading in variable time-frames." International Journal of Computational Science and Engineering 26.1 (2023): 28-44.
[6]. Rani, Veenu, et al. "Self-supervised learning: A succinct review." Archives of Computational Methods in Engineering 30.4 (2023): 2761-2775.
[7]. Assran, Mahmoud, et al. "Self-supervised learning from images with a joint-embedding predictive architecture." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2023.
[8]. uarte, José Marcio, and Lilian Berton. "A review of semi-supervised learning for text classification." Artificial intelligence review 56.9 (2023): 9401-9469.
[9]. Ben-Shaul, Ido, et al. "Reverse engineering self-supervised learning." Advances in Neural Information Processing Systems 36 (2023): 58324-58345.
[10]. Cabannes, Vivien, et al. "Active self-supervised learning: A few low-cost relationships are all you need." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023.
Cite this article
Gao,J. (2024). Applications of machine learning in quantitative trading. Applied and Computational Engineering,82,124-129.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 2nd International Conference on Machine Learning and Automation
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