Deep Reinforcement Learning based Multi-Objective Systems for Financial Trading

Because of the risky nature of stock market, most people do not feel a secure option to invest their money in financial trading. Focusing on this basic concern of investors much research efforts are devoted to develop automated trading systems that make intelligent decisions according to the market situation and help investor to make profit beside risk. In contrast, in this paper we proposed multi-objective systems based on deep reinforcement learning for stock trading. Target of the multi-objective systems is to get maximum profit by adjusting risk. We design the whole structure of systems consisting two deep neural networks first is LSTM autoencoder for robust feature extraction and second deep reinforcement learning with LSTM recurrent neural network for decision making in order to achieve the investor's goal. We conduct an experiment on real historic data for verification of the systems and compare them with conventional trading systems.