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.

翻译：分析交易系统往往是完全自动化的,在这一领域深层次的学习日益受到注意。然而,对这些模型的稳健性特性知之甚少。我们从对抗性机器学习的角度研究算法交易的估价模型。我们引入了这一领域特有的新攻击,其规模限制使攻击成本降至最低。我们进一步讨论了如何将这些攻击作为分析和评价金融模型稳健性特性的分析工具。最后,我们调查了现实的对抗性攻击的可行性,在这种攻击中,一个对抗性交易员愚昧的对交易系统自动作出不准确的预测。