Research on Portfolio Optimization Based on Deep Reinforcement Learning

Research on Portfolio Optimization Based on Deep Reinforcement Learning

연구 분야: Artificial Intelligence

학회: 2022 4th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI)

Due to the complexity and high cost in the actual stock market, it is often difficult to achieve. Deep reinforcement learning has many advantages and can already outperform human players in many challenging video games. This paper defines the Markov decision process (MDP) model of stock portfolio trading. In order to diversify the weight of investment, on the basis of introducing entropy to improve the deep deterministic strategy gradient (DDPG) algorithm, this paper proposes a portfolio strategy based on technical indicators. Based on deep reinforcement learning, the strategy is studied from two aspects: technical indicators and covariance of portfolio stocks to further improve returns. It calculates technical indicators based on the stock’s closing, opening, high, and low prices, and then calculates the covariance of the portfolio. Input the closing price, technical indicators, covariance, stock share, and fund balance as state input, and output the portfolio value. The constituent stocks of the CSI 300 Index are selected as the stocks to be traded in the portfolio for experiments, and the results show that the strategy proposed in this paper has higher returns in terms of yield and Sharpe ratio.