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Crafting a targeted universal adversarial perturbation against the alpha model of an algorithmic trading system. Crafting a targeted universal adversarial perturbation against the alpha model of an algorithmic trading system.
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<img width=80% src="https://github.com/nehemya/Algo-Trade-Adversarial-Examples/blob/main/git_logo.jpeg">
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## About the Project ## About the Project
This repository contains the code for the ECML PKDD 2021 manuscript [Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders](https://arxiv.org/abs/2010.09246). Our study presents a realistic scenario in which an attacker influences algorithmic trading systems using adversarial learning techniques to manipulate the input data stream in real-time. This repository contains the code for the ECML PKDD 2021 manuscript [Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders](https://arxiv.org/abs/2010.09246). Our study presents a realistic scenario in which an attacker influences algorithmic trading systems using adversarial learning techniques to manipulate the input data stream in real-time.
The attacker creates a targeted universal adversarial perturbation (TUAP) that is agnostic to the target model and time of use, which remains imperceptible when added to the input stream. The attacker creates a targeted universal adversarial perturbation (TUAP) that is agnostic to the target model and time of use, which remains imperceptible when added to the input stream.
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## Acknowledgments
The logo was created using [Lorenzo's image from Pexels](https://www.pexels.com/photo/turned-on-monitor-displaying-frequency-graph-241544/).