Adversarial Attacks on Deep Algorithmic Trading Policies
This addresses a security problem for financial institutions and traders using DRL-based algorithmic trading, but it is incremental as it applies known adversarial attack concepts to a new domain.
The paper tackles the vulnerability of deep reinforcement learning (DRL) agents in algorithmic trading to adversarial attacks, demonstrating that proposed attack techniques can manipulate policy performance at test-time with effectiveness shown against benchmark and real-world DQN agents.
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.