  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.
