论文标题
对机器学习系统的对抗性攻击，用于高频交易
Adversarial Attacks on Machine Learning Systems for High-Frequency Trading
论文作者
论文摘要
算法交易系统通常是完全自动化的，并且深度学习越来越受到该领域的关注。尽管如此，对于这些模型的鲁棒性特性知之甚少。我们从对抗机器学习的角度研究算法交易的估值模型。我们引入了针对该领域的新攻击，并具有尺寸约束，以最大程度地减少攻击成本。我们进一步讨论如何将这些攻击用作研究和评估财务模型的鲁棒性特性的分析工具。最后，我们研究了现实的对抗攻击的可行性，在这种攻击中，对抗性交易者愚弄了自动交易系统来做出不准确的预测。
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.
