DEEP REINFORCEMENT LEARNING-BASED STOCK OPTIMIZATION: A STUDY OF DEEP QNETWORK ALGORITHM
The algorithmic trading trend has been gaining significant attention recently. The rapid development of artificial intelligence algorithms and the allure of investing in the stock market have made algorithmic trading appealing, both from an academic perspective, such as algorithm development, and...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/87212 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The algorithmic trading trend has been gaining significant attention recently. The rapid
development of artificial intelligence algorithms and the allure of investing in the stock
market have made algorithmic trading appealing, both from an academic perspective,
such as algorithm development, and an investment perspective, such as business
applications. This thesis aims to implement the Deep Q-Network algorithm, which is
part of Deep Reinforcement Learning, and compare the results with conventional
]optimization methods: the DJIA method and the Sharpe ratio method. Unlike
supervised and unsupervised learning, reinforcement learning operates by simulating
the system into two main components: the interaction between the agent and the
environment. The data is sourced from the Dow Jones Industrial Average, sorted by
market capital, and divided into three periods: training (January 1, 2009, to December
31, 2019), validation (January 1, 2020, to December 31, 2021), and testing (January 1,
2022, to December 14, 2023). Simulation results show that the portfolio returns
achieved outperform the two conventional methods. However, it is important to note
that these results are not absolute, as the environment created must be continuously
updated to reflect stock market fluctuations.
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