Practical Deep Reinforcement Learning Approach for Stock Trading

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Explore the potential of deep reinforcement learning to optimize stock trading strategies and maximize investment returns. This study evaluates the performance of a deep reinforcement learning agent in comparison to traditional strategies using 30 selected stocks. Results demonstrate outperformance in terms of Sharpe ratio and cumulative returns.

  • Reinforcement Learning
  • Stock Trading
  • Investment
  • Deep Learning
  • Strategy
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  1. Practical Deep Reinforcement Learning Approach for Stock Trading Zhuoran Xiong, Xiao-Yang Liu, Shan Zhong, Hongyang (Bruce) Yang, and Anwar Walid arXiv:1811.07522v2 [cs.LG] 2 Dec 2018 Presenter: Cheng-Han Wu Date:2018/12/10

  2. Abstract Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent s performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns

  3. Q-learning ??+1?(??+1,??+1)] ????,?? = ???+1[? ??,??,??+1 + ?max ?3 ?3 ?1 ?2 ?2 ?2 ?1 ?1 ?1 ?2 ?3 ?1 ? ?1,?1 ? ?1,?2 ? ?1,?3

  4. Deep Deterministic Policy Gradient (DDPG) Actor Network ?(?) ?? ?? R ??,??,??+1,?? Critic Network ?(?,?) ?(?,?)

  5. Update critic network R ??= ??+ ?? ??+1,? ??+1?? ,?? ,? = 1, ,? ???? =1 2 ?? ? ??,???? ? ? 2] ?(??) = ???,??,??,??+1~??????[ ??+ ?? ??+1,? ??+1??|?? ? ??,???? Critic Network update

  6. Policy gradient ?3 Actor Network ?(?) ?1 ?2 ?1 ??? = ???,??,??,??+1~??????[ ?? ??,? ?? ?? ???(??|??)]

  7. Data Stocks : Dow Jones 30 stocks

  8. Result

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