Nancirose Piazza

University of New Haven,

Yaser Faghan

CEMAPRE,

Vahid Behzadan

University of New Haven,

and

Ali Fathi

Royal Bank of Canada

Adversarial Attacks on Deep Algorithmic Trading Policies (pdf)

Deep Reinforcement Learning (DRL) has become an appealing solution to algorithmic trading such as high frequency trading of stocks and cyptocurrencies. However, DRL policies are 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 active attack techniques for manipulating the performance of such policies at test-time. Additionally, we explore the exploitation of a passive attack based on adversarial policy imitation. Furthermore, we demonstrate the effectiveness of the proposed attacks against benchmark and real-world DQN trading agents.