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With the development of computer science technology and artificial intelligence, quantitative trading attracts more investors due to its efficiency and stable performance. In this paper, we explore the potential of deep reinforcement learning in quantitative trading. A LSTM-based agent is proposed to learn the temporal pattern in data and automatically trades according to the current market condition and the historical data. The input to the agent is the raw financial data and the output of the agent is decision of trading. The goal of the agent is to maximize the ultimate profit. Besides, to reduce the influence of noise in the market and to improve the performance of the agent, we use several technical indicators as an extra input. The proposed system has been back-tested on the stock market. The results demonstrate that our method performs well in most conditions.
Wu et al. (Mon,) studied this question.
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