An intelligent algorithmic trading based on a risk-return reinforcement learning algorithm

2022/08/23 by Boyi Jin, Jin, Boyi
Decision Sciences · Economics, Econometrics and Finance · #FOS: Computer and information sciences #FOS: Economics and business #Financial Markets and Investment Strategies #Machine Learning (cs.LG) #Risk Management (q-fin.RM) #Risk and Portfolio Optimization #Stock Market Forecasting Methods

paper · pdf · doi:10.48550/arxiv.2208.10707

openalex publication_date 2022/08/23 · openalex created_date 2022/08/27 · openalex updated_date 2026/07/28

Abstract

This scientific paper propose a novel portfolio optimization model using an improved deep reinforcement learning algorithm. The objective function of the optimization model is the weighted sum of the expectation and value at risk(VaR) of portfolio cumulative return. The proposed algorithm is based on actor-critic architecture, in which the main task of critical network is to learn the distribution of portfolio cumulative return using quantile regression, and actor network outputs the optimal portfolio weight by maximizing the objective function mentioned above. Meanwhile, we exploit a linear transformation function to realize asset short selling. Finally, A multi-process method is used, called Ape-x, to accelerate the speed of deep reinforcement learning training. To validate our proposed approach, we conduct backtesting for two representative portfolios and observe that the proposed model in this work is superior to the benchmark strategies.

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