Abstract
In this study, Reinforcement Learning (RL) techniques are used to develop trading strategies for the stock market. Conventional trading strategies rely on human intuition and the examination of historical data to make forecasts, whereas RL agents can automatically learn the best trading strategies through market interaction. The Proximal Policy Gradient (PPO) agent is employed to produce trading policies using real-world stock market data. The tests covered in the paper are conducted on the notoriously turbulent Indian intraday market. The findings of this study can be helpful to financial institutions, RL researchers, and those interested in the use of RL approaches for stock trading.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Montague, P.R.: Reinforcement learning: an introduction, by Sutton, RS and Barto, AG. Trends Cogn. Sci. 3(9), 360 (1999)
Schulman, J., et al.: Proximal policy optimization algorithms. ar**v preprint ar**v:1707.06347 (2017)
Bahdanau, D., et al.: An actor-critic algorithm for sequence prediction. ar**v preprint ar**v:1607.07086 (2016)
Haarnoja, T., et al.: Soft actor-critic algorithms and applications. ar**v preprint ar**v:1812.05905 (2018)
Yang, H., et al.: Deep reinforcement learning for automated stock trading: an ensemble strategy. In: Proceedings of the First ACM International Conference on AI in Finance (2020)
Lillicrap, T.P., et al.: Continuous control with deep reinforcement learning. ar**v preprint ar**v:1509.02971 (2015)
Mnih, V., et al.: Asynchronous methods for deep reinforcement learning. In: International Conference on Machine Learning. PMLR (2016)
Pricope, T.-V.: Deep reinforcement learning in quantitative algorithmic trading: a review. ar**v preprint ar**v:2106.00123 (2021)
Mnih, V., et al.: Playing atari with deep reinforcement learning. ar**v preprint ar**v:1312.5602 (2013)
Chen, L., Gao, Q.: Application of deep reinforcement learning on automated stock trading. In: 2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS). IEEE (2019)
Huang, Y.: Deep Q-networks. In: Deep Reinforcement Learning: Fundamentals, Research and Applications, pp. 135–160 (2020)
Hausknecht, M., Stone, P.: Deep recurrent Q-learning for partially observable MDPs. ar**v preprint ar**v:1507.06527 (2015)
Azhikodan, A.R., Bhat, A.G.K., Jadhav, M.V.: Stock Trading Bot Using Deep Reinforcement Learning. In: Saini, H.S., Sayal, R., Govardhan, A., Buyya, R. (eds.) Innovations in Computer Science and Engineering. LNNS, vol. 32, pp. 41–49. Springer, Singapore (2019). https://doi.org/10.1007/978-981-10-8201-6_5
Lee, C.-Y., Soo, V.-W.: Predict stock price with financial news based on recurrent convolutional neural networks. In: 2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI). IEEE (2017)
Cervelló-Royo, R., Guijarro, F., Michniuk, K.: Stock market trading rule based on pattern recognition and technical analysis: forecasting the DJIA index with intraday data. Expert Syst. Appl. 42(14), 5963–5975 (2015)
Liu, X.-Y., et al.: FinRL: a deep reinforcement learning library for automated stock trading in quantitative finance. ar**v preprint ar**v:2011.09607 (2020)
Bajpai, S.: Application of deep reinforcement learning for Indian stock trading automation. ar**v preprint ar**v:2106.16088 (2021)
Ghosh, P., Neufeld, A., Sahoo, J.K.: Forecasting directional movements of stock prices for intraday trading using LSTM and random forests. Finan. Res. Lett. 46, 102280 (2022)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)
Parambalath, G., et al.: Big data analytics: a trading strategy of NSE stocks using Bollinger bands analysis. In: Balas, V.E., Sharma, N., Chakrabarti, A. (eds.) Data Management, Analytics and Innovation. AISC, vol. 839, pp. 143–154. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-1274-8_11
Brockman, G., et al.: OpenAI gym. ar**v preprint ar**v:1606.01540 (2016)
Python Package Index – PyPI: Python Software Foundation (n.d.). Accessed https://pypi.org/project/stockstats/
Yazdi, S.H.M., Lashkari, Z.H.: Technical analysis of Forex by MACD Indicator. Int. J. Human. Manag. Sci. (IJHMS) 1(2), 159–165 (2013)
Bollinger, J.: Using Bollinger bands. Stocks Commodities 10(2), 47–51 (1992)
Gumparthi, S.: Relative strength index for develo** effective trading strategies in constructing optimal portfolio. Int. J. Appl. Eng. Res. 12(19), 8926–8936 (2017)
Python Package Index – PyPI: Python Software Foundation (n.d.). Accessed https://pypi.org/project/yfinance/
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pandit, R., Nerkar, N., Walunj, P., Tank, R., Kolhe, S. (2023). Stock Market Intraday Trading Using Reinforcement Learning. In: Morusupalli, R., Dandibhotla, T.S., Atluri, V.V., Windridge, D., Lingras, P., Komati, V.R. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2023. Lecture Notes in Computer Science(), vol 14078. Springer, Cham. https://doi.org/10.1007/978-3-031-36402-0_35
Download citation
DOI: https://doi.org/10.1007/978-3-031-36402-0_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36401-3
Online ISBN: 978-3-031-36402-0
eBook Packages: Computer ScienceComputer Science (R0)