基于规则的股票交易策略:一种新的深度强化学习方法

Badr Hirchoua, B. Ouhbi, B. Frikh
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引用次数: 2

摘要

自动交易完全表现为一个在线决策问题,代理人希望以较高的价格卖出,以较低的价格买入。在金融理论中,金融市场交易产生了一种包含高度不完全信息的嘈杂和随机行为。因此,在动态和复杂的股票市场环境中,制定盈利策略是非常复杂的。提出了一种基于激励窗口策略的深度强化学习(DRL)自动股票交易方法。该方法在优势函数的激励下,训练一个DRL代理来处理交易环境的动态性并产生巨大的利润。一方面,优势函数试图估计当前状态下所选动作的相对值。它由奖励的贴现和基线估计组成。另一方面,鼓励窗口仅基于最后的奖励,提供密集的综合体验,而不是嘈杂的信号。这个过程通过平衡行动选择和状态的不确定性,逐步提高了行动的质量。自学习规则驱动代理的策略选择在整个环境中产生高成就的生产性行为。四种实际股票的实验结果证明了该系统的有效性。准确地说,它产生了出色的表现,通过少量交易执行了更多创造性的交易,并且表现优于其他基准。
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Rules Based Policy for Stock Trading: A New Deep Reinforcement Learning Method
Automated trading is fully represented as an online decision-making problem, where agents desire to sell it at a higher price to buy at a low one. In financial theory, financial markets trading produces a noisy and random behavior involving highly imperfect information. Therefore, developing a profitable strategy is very complicated in dynamic and complex stock market environments.This paper introduces a new deep reinforcement learning (DRL) method based on the encouragement window policy for automatic stock trading. Motivated by the advantage function, the proposed approach trains a DRL agent to handle the trading environment’s dynamicity and generate huge profits. On the one hand, the advantage function tries to estimate the relative value of the current state’s selected actions. It consists of the discounted sum of rewards and the baseline estimate. On the other hand, the encouragement window is based only on the last rewards, providing a dense synthesized experience instead of a noisy signal. This process has progressively improved actions’ quality by balancing the action selection versus states’ uncertainty. The self-learned rules drive the agent’s policy to choose productive actions that produce a high achievement across the environment. Experimental results on four real-world stocks have proven the proposed system’s efficiency. Precisely, it has produced outstanding performances, executed more creative trades by a small number of transactions, and outperformed different baselines.
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