  Algorithmic trading systems are often completely automated, and deep learning
is increasingly receiving attention in this domain. Nonetheless, little is
known about the robustness properties of these models. We study valuation
models for algorithmic trading from the perspective of adversarial machine
learning. We introduce new attacks specific to this domain with size
constraints that minimize attack costs. We further discuss how these attacks
can be used as an analysis tool to study and evaluate the robustness properties
of financial models. Finally, we investigate the feasibility of realistic
adversarial attacks in which an adversarial trader fools automated trading
systems into making inaccurate predictions.
