论文标题
扩展加密货币市场中的深入加强学习框架
Extending Deep Reinforcement Learning Frameworks in Cryptocurrency Market Making
论文作者
论文摘要
最近，人们对人工智能在自动交易中的应用产生了兴趣。强化学习已应用于单件和多仪器用例，例如市场制造或投资组合管理。本文提出了一种新的方法，通过引入基于事件的环境来构架加密货币市场的制作，作为强化学习挑战，其中事件的定义是价格的变化或更大或低于给定的阈值，而不是通过tick或基于时间的事件（例如，每分钟，小时，小时，日，等等）。对两名基于政策的代理商进行了培训，可以使用八天的培训数据学习市场制作交易策略，并使用30天的测试数据评估其绩效。从BITMEX Exchange记录的限制订单数据数据用于验证这种方法，与两种代理的基于时间的方法相比，它在使用简单的多层感知器神经网络进行功能近似和七个不同的奖励函数时，证明了利润和稳定性的提高。
There has been a recent surge in interest in the application of artificial intelligence to automated trading. Reinforcement learning has been applied to single- and multi-instrument use cases, such as market making or portfolio management. This paper proposes a new approach to framing cryptocurrency market making as a reinforcement learning challenge by introducing an event-based environment wherein an event is defined as a change in price greater or less than a given threshold, as opposed to by tick or time-based events (e.g., every minute, hour, day, etc.). Two policy-based agents are trained to learn a market making trading strategy using eight days of training data and evaluate their performance using 30 days of testing data. Limit order book data recorded from Bitmex exchange is used to validate this approach, which demonstrates improved profit and stability compared to a time-based approach for both agents when using a simple multi-layer perceptron neural network for function approximation and seven different reward functions.
