Evaluation of Reinforcement Learning Techniques for Trading on a Diverse Portfolio
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866913230227505152 |
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| author | Khare, Ishan S. Martheswaran, Tarun K. Dassanaike-Perera, Akshana |
| author_facet | Khare, Ishan S. Martheswaran, Tarun K. Dassanaike-Perera, Akshana |
| contents | This work seeks to answer key research questions regarding the viability of reinforcement learning over the S&P 500 index. The on-policy techniques of Value Iteration (VI) and State-action-reward-state-action (SARSA) are implemented along with the off-policy technique of Q-Learning. The models are trained and tested on a dataset comprising multiple years of stock market data from 2000-2023. The analysis presents the results and findings from training and testing the models using two different time periods: one including the COVID-19 pandemic years and one excluding them. The results indicate that including market data from the COVID-19 period in the training dataset leads to superior performance compared to the baseline strategies. During testing, the on-policy approaches (VI and SARSA) outperform Q-learning, highlighting the influence of bias-variance tradeoff and the generalization capabilities of simpler policies. However, it is noted that the performance of Q-learning may vary depending on the stability of future market conditions. Future work is suggested, including experiments with updated Q-learning policies during testing and trading diverse individual stocks. Additionally, the exploration of alternative economic indicators for training the models is proposed. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2309_03202 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Evaluation of Reinforcement Learning Techniques for Trading on a Diverse Portfolio Khare, Ishan S. Martheswaran, Tarun K. Dassanaike-Perera, Akshana Trading and Market Microstructure Machine Learning This work seeks to answer key research questions regarding the viability of reinforcement learning over the S&P 500 index. The on-policy techniques of Value Iteration (VI) and State-action-reward-state-action (SARSA) are implemented along with the off-policy technique of Q-Learning. The models are trained and tested on a dataset comprising multiple years of stock market data from 2000-2023. The analysis presents the results and findings from training and testing the models using two different time periods: one including the COVID-19 pandemic years and one excluding them. The results indicate that including market data from the COVID-19 period in the training dataset leads to superior performance compared to the baseline strategies. During testing, the on-policy approaches (VI and SARSA) outperform Q-learning, highlighting the influence of bias-variance tradeoff and the generalization capabilities of simpler policies. However, it is noted that the performance of Q-learning may vary depending on the stability of future market conditions. Future work is suggested, including experiments with updated Q-learning policies during testing and trading diverse individual stocks. Additionally, the exploration of alternative economic indicators for training the models is proposed. |
| title | Evaluation of Reinforcement Learning Techniques for Trading on a Diverse Portfolio |
| topic | Trading and Market Microstructure Machine Learning |
| url | https://arxiv.org/abs/2309.03202 |