论文标题
对深度算法交易政策的对抗性攻击
Adversarial Attacks on Deep Algorithmic Trading Policies
论文作者
论文摘要
深厚的增强学习（DRL）已成为算法交易的一种吸引人的解决方案，例如股票和塞克托货币的高频交易。但是，DRL已被证明容易受到对抗攻击的影响。因此，算法交易DRL代理也可能因这种对抗技术而损害，从而导致政策操纵。在本文中，我们为深层交易政策开发了一个威胁模型，并提出了两种攻击技术，以操纵测试时间的性能。此外，我们证明了针对基准和现实世界DQN贸易代理商的拟议攻击的有效性。
Deep Reinforcement Learning (DRL) has become an appealing solution to algorithmic trading such as high frequency trading of stocks and cyptocurrencies. However, DRL have been shown to be susceptible to adversarial attacks. It follows that algorithmic trading DRL agents may also be compromised by such adversarial techniques, leading to policy manipulation. In this paper, we develop a threat model for deep trading policies, and propose two attack techniques for manipulating the performance of such policies at test-time. Furthermore, we demonstrate the effectiveness of the proposed attacks against benchmark and real-world DQN trading agents.
