论文标题
使用连续的动作空间深入增强学习算法交易
Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning
论文作者
论文摘要
价格运动预测一直是交易者在金融市场交易中的关注之一。为了增加利润，他们可以分析历史数据并预测价格变动。它们之间的大尺寸数据和复杂关系使我们使用算法交易和人工智能。本文旨在使用双延迟的DDPG（TD3）和每日关闭价格提供一种方法，以实现股票和加密货币市场的交易策略。与以前使用离散动作空间增强学习算法的研究不同，TD3是连续的，既可以提供头寸和交易份额的数量。在本研究中介绍了股票（亚马逊）和加密货币（比特币）市场，以评估拟议算法的性能。使用技术分析，增强学习，随机和确定性策略通过两个标准指标，返回和夏普比进行了将使用TD3实现的策略与某些算法进行比较。结果表明，使用头寸和交易股数量可以根据上述指标提高交易系统的绩效。
Price movement prediction has always been one of the traders' concerns in financial market trading. In order to increase their profit, they can analyze the historical data and predict the price movement. The large size of the data and complex relations between them lead us to use algorithmic trading and artificial intelligence. This paper aims to offer an approach using Twin-Delayed DDPG (TD3) and the daily close price in order to achieve a trading strategy in the stock and cryptocurrency markets. Unlike previous studies using a discrete action space reinforcement learning algorithm, the TD3 is continuous, offering both position and the number of trading shares. Both the stock (Amazon) and cryptocurrency (Bitcoin) markets are addressed in this research to evaluate the performance of the proposed algorithm. The achieved strategy using the TD3 is compared with some algorithms using technical analysis, reinforcement learning, stochastic, and deterministic strategies through two standard metrics, Return and Sharpe ratio. The results indicate that employing both position and the number of trading shares can improve the performance of a trading system based on the mentioned metrics.
