Adversarial Attacks on Deep Algorithmic Trading Policies

TL;DR

This paper explores the vulnerability of deep reinforcement learning trading agents to adversarial attacks, proposing methods to manipulate their performance and demonstrating effectiveness on benchmark and real-world agents.

Contribution

It introduces a threat model for deep trading policies and develops two novel attack techniques to compromise their performance at test-time.

Findings

Proposed attacks significantly degrade trading policy performance.

Attacks are effective against both benchmark and real-world DQN agents.

Demonstrates the need for robustness in deep trading algorithms.

Abstract

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.