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15 June 2022 Deep reinforcement learning trading strategy based on lstm-a2c model
Nan Zhang, Jingyuan Wang, Mingzhong Xiao
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Proceedings Volume 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022); 1228519 (2022) https://doi.org/10.1117/12.2637178
Event: International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 2022, Zhuhai, China
Abstract
Stocks are gaining more and more attention as a form of investment. A good stock trading strategy can help investors gain considerable returns in the rapidly changing stock market. In this paper, we use a deep reinforcement learning model combining LSTM deep neural network and A2C reinforcement learning algorithm, construct time-series data, extract the time-series features of the data using LSTM, and adopt reinforcement learning algorithm to let the agent learn by trial and error in the trading environment, and finally get an end-to-end quantitative trading model adapted to the market. The results show that the model returns higher than 30% on both Dow Jones 30 stocks and 30 stocks data of A-shares. the LSTMA2C model works better on 10 days of time-series data than using only 1 day of data, and returns up to 91% on 30 stocks of A-shares in 2020 with a Sharpe ratio of 2.18.
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Nan Zhang, Jingyuan Wang, and Mingzhong Xiao "Deep reinforcement learning trading strategy based on lstm-a2c model", Proc. SPIE 12285, International Conference on Advanced Algorithms and Neural Networks (AANN 2022), 1228519 (15 June 2022); https://doi.org/10.1117/12.2637178
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KEYWORDS
Machine learning
Neural networks
Artificial intelligence
Computer security
Data processing
Feature extraction
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