论文标题
通过在加密货币市场上使用深厚的加强学习来盈利的策略设计
Profitable Strategy Design by Using Deep Reinforcement Learning for Trades on Cryptocurrency Markets
论文作者
论文摘要
深厚的增强学习解决方案已应用于不同的控制问题，其表现优于和有希望的结果。在这项研究工作中，我们应用了近端政策优化，软批评和生成的对抗性模仿学习，以设计三个加密货币市场的策略设计问题。我们的输入数据包括价格数据和技术指标。我们已经基于加密货币市场实施了健身房环境，以与算法一起使用。我们对看不见的数据的测试结果表明，这种方法具有巨大的潜力，可以帮助使用专家系统利用市场并获利的投资者。看不见的66天跨度的最高收益是每10000美元投资4850美元。我们还讨论了如何使用环境设计中的特定超参数来调整生成的策略中的风险。
Deep Reinforcement Learning solutions have been applied to different control problems with outperforming and promising results. In this research work we have applied Proximal Policy Optimization, Soft Actor-Critic and Generative Adversarial Imitation Learning to strategy design problem of three cryptocurrency markets. Our input data includes price data and technical indicators. We have implemented a Gym environment based on cryptocurrency markets to be used with the algorithms. Our test results on unseen data shows a great potential for this approach in helping investors with an expert system to exploit the market and gain profit. Our highest gain for an unseen 66 day span is 4850 US dollars per 10000 US dollars investment. We also discuss on how a specific hyperparameter in the environment design can be used to adjust risk in the generated strategies.
