# Adversarial Attacks on Deep Algorithmic Trading Policies

arXiv: https://arxiv.org/abs/2010.11388 · HTML: https://mlsanity.com/paper/2010.11388/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Red Teaming with Mind Reading: White-Box Adversarial Policies Against RL Agents](https://mlsanity.com/paper/2209.02167/) (2022)
- [Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents](https://mlsanity.com/paper/2309.14615/) (2023)
- [Command-Space Counterfactual Explanations for Pareto-Conditioned Reinforcement Learning](https://mlsanity.com/paper/2608.14963/) (2026)

## Abstract (arXiv)

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.