mirror of
https://github.com/nehemya/Algo-Trade-Adversarial-Examples
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47 lines
2.2 KiB
Markdown
47 lines
2.2 KiB
Markdown
<h1 align="center">
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Taking Over the Stock Market: </br>
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Adversarial Perturbations Against Algorithmic Traders
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</h1>
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<h3 align="center">
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<a href="https://arxiv.org/abs/2010.09246">
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Paper
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</a>
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<span> | </span>
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<a href="https://github.com/nehemya/Algo-Trade-Adversarial-Examples">
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Code
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</a>
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<span> | </span>
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<a href="https://www.kaggle.com/nickdl/snp-500-intraday-data">
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Data
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</a>
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</h3>
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<p align="center">
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Crafting a targeted universal adversarial perturbation against the alpha model of an algorithmic trading system.
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</p>
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<p align="center">
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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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</p>
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## About the Project
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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.
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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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Our code reads and processes the data from the [S&P 500 Intraday](https://www.kaggle.com/nickdl/snp-500-intraday-data) dataset and divides it into a set for training the alpha models, a set for crafting TUAPs, and six test sets to evaluate the attack. The training set is used to train three alpha models. Then, we use the TUAP set to craft a universal adversarial perturbation that can fool the target alpha models and evaluate the perturbations' performance. Finally, we also explore various mitigation methods. Additional information is available in the paper.
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## Citation
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```
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@article{nehemya2020bots,
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title={When Bots Take Over the Stock Market: Evasion Attacks Against Algorithmic Traders},
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author={Nehemya, Elior and Mathov, Yael and Shabtai, Asaf and Elovici, Yuval},
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journal={arXiv preprint arXiv:2010.09246},
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year={2020}
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}
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```
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## Acknowledgments
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The logo was created using [Lorenzo's image from Pexels](https://www.pexels.com/photo/turned-on-monitor-displaying-frequency-graph-241544/).
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