论文标题
在算法交易问题中使用加固学习
Using Reinforcement Learning in the Algorithmic Trading Problem
论文作者
论文摘要
加强学习方法的开发已将应用程序扩展到包括算法交易在内的许多领域。在本文中，证券交易所的交易被解释为马尔可夫财产，其中包括国家，行动和奖励。提出了用于交易固定量的金融工具的系统，并通过实验测试；这是基于使用几种神经网络体系结构的异步优势参与者批评方法。研究了这种方法在这种方法中的应用。实验是对实际匿名数据进行的。最佳体系结构展示了RTS指数期货（MOEX：RTSI）的交易策略，每年的盈利能力为66％。可以通过以下链接获得项目源代码：http：//github.com/evgps/a3c_trading。
The development of reinforced learning methods has extended application to many areas including algorithmic trading. In this paper trading on the stock exchange is interpreted into a game with a Markov property consisting of states, actions, and rewards. A system for trading the fixed volume of a financial instrument is proposed and experimentally tested; this is based on the asynchronous advantage actor-critic method with the use of several neural network architectures. The application of recurrent layers in this approach is investigated. The experiments were performed on real anonymized data. The best architecture demonstrated a trading strategy for the RTS Index futures (MOEX:RTSI) with a profitability of 66% per annum accounting for commission. The project source code is available via the following link: http://github.com/evgps/a3c_trading.
