poster

Linnet: limit order books within switches

Authors:

Changgang Zheng,

Noa ZilbermanAuthors Info & Claims

SIGCOMM '22: Proceedings of the SIGCOMM '22 Poster and Demo Sessions

Pages 37 - 39

https://doi.org/10.1145/3546037.3546057

Published: 25 October 2022 Publication History

Abstract

Financial trading often relies nowadays on machine learning. However, many trading applications require very short response times, which cannot always be supported by traditional machine learning frameworks. We present Linnet, providing financial market prediction within programmable switches. Linnet builds limit order books from high-frequency market data feeds within the switch, and uses them for machine-learning based market prediction. Linnet demonstrates the potential to predict future stock price movements with high accuracy and low latency, increasing financial gains.

References

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Shmuel Baruch. 2005. Who benefits from an open limit-order book? The Journal of Business 78, 4 (2005), 1267--1306.

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Martino Bernasconi-De-Luca, Luigi Fusco, and Ozrenka Dragić. 2021. martinobdl/ITCH: ITCH50Converter.

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Charles Cao, Oliver Hansch, and Xiaoxin Wang. 2009. The information content of an open limit-order book. Journal of Futures Markets: Futures, Options, and Other Derivative Products 29, 1 (2009), 16--41.

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Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. 2002. SMOTE: synthetic minority over-sampling technique. Journal of artificial intelligence research 16 (2002), 321--357.

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Michael A Goldstein, Pavitra Kumar, and Frank C Graves. 2014. Computerized and high-frequency trading. Financial Review 49, 2 (2014), 177--202.

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Michael Kearns and Yuriy Nevmyvaka. 2013. Machine learning for market microstructure and high frequency trading. High Frequency Trading: New Realities for Traders, Markets, and Regulators (2013).

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Adamantios Ntakaris, Martin Magris, Juho Kanniainen, Moncef Gabbouj, and Alexandros Iosifidis. 2018. Benchmark dataset for mid-price forecasting of limit order book data with machine learning methods. Journal of Forecasting 37, 8 (2018), 852--866.

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Christine A Parlour and Duane J Seppi. 2008. Limit order markets: A survey. Handbook of financial intermediation and banking 5 (2008), 63--95.

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Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. the journal of machine Learning research 12 (2011), 2825--2830.

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NASDAQ OMX PSX. 2014. NASDAQ OMX PSX TotalView-ITCH 5.0. (2014). http://www.nasdaqtrader.com/content/technicalsupport/specifications/dataproducts/PSXTVITCHSpecification_5.0.pdf

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Davide Sanvito, Giuseppe Siracusano, and Roberto Bifulco. 2018. Can the network be the AI accelerator?. In Proceedings of the 2018 Morning Workshop on In-Network Computing. 20--25.

Digital Library

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Yuta Tokusashi, Huynh Tu Dang, Fernando Pedone, Robert Soulé, and Noa Zilberman. 2019. The case for in-network computing on demand. In Proceedings of the Fourteenth EuroSys Conference 2019. 1--16.

Digital Library

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Zhaoqi Xiong and Noa Zilberman. 2019. Do switches dream of machine learning? toward in-network classification. In Proceedings of the 18th ACM workshop on hot topics in networks. 25--33.

Digital Library

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Zihao Zhang, Bryan Lim, and Stefan Zohren. 2021. Deep learning for market by order data. Applied Mathematical Finance 28, 1 (2021), 79--95.

[15]

Zihao Zhang, Stefan Zohren, and Stephen Roberts. 2019. Deeplob: Deep convolutional neural networks for limit order books. IEEE Transactions on Signal Processing 67, 11 (2019), 3001--3012.

[16]

Changgang Zheng, Zhaoqi Xiong, Thanh T Bui, Siim Kaupmees, Riyad Bensoussane, Antoine Bernabeu, Shay Vargaftik, Yaniv Ben-Itzhak, and Noa Zilberman. 2022. IIsy: Practical In-Network Classification. arXiv preprint arXiv:2205.08243 (2022).

[17]

Changgang Zheng, Mingyuan Zang, Xinpeng Hong, Riyad Bensoussane, Shay Vargaftik, Yaniv Ben-Itzhak, and Noa Zilberman. 2022. Automating In-Network Machine Learning. arXiv preprint arXiv:2205.08824 (2022).

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Changgang Zheng and Noa Zilberman. 2021. Planter: seeding trees within switches. In Proceedings of the SIGCOMM'21 Poster and Demo Sessions. 12--14.

Digital Library

Cited By

Zang MZheng CKoziak TZilberman NDittmann L(2024)Federated In-Network Machine Learning for Privacy-Preserving IoT Traffic AnalysisACM Transactions on Internet Technology10.1145/369635424:4(1-24)Online publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1145/3696354
Zheng CXiong ZBui TKaupmees SBensoussane RBernabeu AVargaftik SBen-Itzhak YZilberman N(2024)IIsy: Hybrid In-Network Classification Using Programmable SwitchesIEEE/ACM Transactions on Networking10.1109/TNET.2024.336475732:3(2555-2570)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1109/TNET.2024.3364757
Zang MZheng CDittmann LZilberman N(2024)Toward Continuous Threat Defense: in-Network Traffic Analysis for IoT GatewaysIEEE Internet of Things Journal10.1109/JIOT.2023.332377111:6(9244-9257)Online publication date: 15-Mar-2024
https://doi.org/10.1109/JIOT.2023.3323771
Show More Cited By

Index Terms

Linnet: limit order books within switches
1. Computing methodologies
  1. Machine learning
2. Networks
  1. Network services
    1. In-network processing
    2. Programmable networks

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cover image ACM Conferences

SIGCOMM '22: Proceedings of the SIGCOMM '22 Poster and Demo Sessions

August 2022

69 pages

ISBN:9781450394345

DOI:10.1145/3546037

Copyright © 2022 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 25 October 2022

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SIGCOMM '22

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SIGCOMM '22: ACM SIGCOMM 2022 Conference

August 22 - 26, 2022

Amsterdam, Netherlands

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Overall Acceptance Rate 92 of 158 submissions, 58%

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Cited By

Zang MZheng CKoziak TZilberman NDittmann L(2024)Federated In-Network Machine Learning for Privacy-Preserving IoT Traffic AnalysisACM Transactions on Internet Technology10.1145/369635424:4(1-24)Online publication date: 18-Nov-2024
https://dl.acm.org/doi/10.1145/3696354
Zheng CXiong ZBui TKaupmees SBensoussane RBernabeu AVargaftik SBen-Itzhak YZilberman N(2024)IIsy: Hybrid In-Network Classification Using Programmable SwitchesIEEE/ACM Transactions on Networking10.1109/TNET.2024.336475732:3(2555-2570)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1109/TNET.2024.3364757
Zang MZheng CDittmann LZilberman N(2024)Toward Continuous Threat Defense: in-Network Traffic Analysis for IoT GatewaysIEEE Internet of Things Journal10.1109/JIOT.2023.332377111:6(9244-9257)Online publication date: 15-Mar-2024
https://doi.org/10.1109/JIOT.2023.3323771
Zheng CHong XDing DVargaftik SBen-Itzhak YZilberman N(2024)In-Network Machine Learning Using Programmable Network Devices: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.334435126:2(1171-1200)Online publication date: Oct-2025
https://doi.org/10.1109/COMST.2023.3344351
Zang MZheng CKoziak TZilberman NDittmann L(2023)Federated Learning-Based In-Network Traffic Analysis on IoT Edge2023 IFIP Networking Conference (IFIP Networking)10.23919/IFIPNetworking57963.2023.10186438(1-6)Online publication date: 12-Jun-2023
https://doi.org/10.23919/IFIPNetworking57963.2023.10186438
Zheng CTang HZang MHong XFeng ATassiulas LZilberman N(2023)DINC: Toward Distributed In-Network ComputingProceedings of the ACM on Networking10.1145/36291361:CoNEXT3(1-25)Online publication date: 28-Nov-2023
https://dl.acm.org/doi/10.1145/3629136
Parizotto RCoelho BNunes DHaque ISchaeffer-Filho A(2023)Offloading Machine Learning to Programmable Data Planes: A Systematic SurveyACM Computing Surveys10.1145/360515356:1(1-34)Online publication date: 26-Aug-2023
https://dl.acm.org/doi/10.1145/3605153
Hong XZheng CZohren SZilberman N(2023)LOBIN: In-Network Machine Learning for Limit Order Books2023 IEEE 24th International Conference on High Performance Switching and Routing (HPSR)10.1109/HPSR57248.2023.10147958(159-166)Online publication date: 5-Jun-2023
https://doi.org/10.1109/HPSR57248.2023.10147958

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