关键词: 深度强化学习, 双价值网络的深度强化学习（DDQN）, 单步确定性动作值, 量化策略

Abstract:

The stock market has the characteristics of rapid change, many interference factors, and insufficient period data. Stock trading is a game process under incomplete information, and the single-objective supervised learning model is difficult to deal with such serialization decision problems. Reinforcement learning is one of the effective ways to solve this kind of problems. This paper proposes the Intelligent Stock Trader and Gym（ISTG） model based on deep reinforcement learning, which integrates historical data, technical indicators, macroeconomic indicators and other data types. Judging criteria and excellent control strategies, processing long-period data, implementing a replay model that can incrementally expand different types of data, automatically calculating return labels, training intelligent traders, and proposing a method of directly calculating the single-step deterministic action values using market data. Using a stock market of more than 1400 stocks with more than 10 years of data in China, ISTG’s overall revenue has reached 13%, which is better than overall −7% of the buy-and-hold strategy.

Key words: deep reinforcement learning, Deep Reinforcement Learning with Double Q-Learning（DDQN）, one-step deterministic action value, quantization strategy