Xinpeng Hong
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.
- Shmuel Baruch. 2005. Who benefits from an open limit-order book? The Journal of Business 78, 4 (2005), 1267--1306.Google Scholar
Cross Ref
- Martino Bernasconi-De-Luca, Luigi Fusco, and Ozrenka Dragić. 2021. martinobdl/ITCH: ITCH50Converter. Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- Michael A Goldstein, Pavitra Kumar, and Frank C Graves. 2014. Computerized and high-frequency trading. Financial Review 49, 2 (2014), 177--202.Google ScholarCross Ref
- 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).Google Scholar
- 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.Google ScholarCross Ref
- Christine A Parlour and Duane J Seppi. 2008. Limit order markets: A survey. Handbook of financial intermediation and banking 5 (2008), 63--95.Google Scholar
- 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.Google Scholar
- NASDAQ OMX PSX. 2014. NASDAQ OMX PSX TotalView-ITCH 5.0. (2014). http://www.nasdaqtrader.com/content/technicalsupport/specifications/dataproducts/PSXTVITCHSpecification_5.0.pdfGoogle Scholar
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- 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.Google ScholarDigital Library
- Zihao Zhang, Bryan Lim, and Stefan Zohren. 2021. Deep learning for market by order data. Applied Mathematical Finance 28, 1 (2021), 79--95.Google ScholarCross Ref
- 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.Google ScholarCross Ref
- 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).Google Scholar
- 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).Google Scholar
- Changgang Zheng and Noa Zilberman. 2021. Planter: seeding trees within switches. In Proceedings of the SIGCOMM'21 Poster and Demo Sessions. 12--14.Google ScholarDigital Library
Index Terms
- Linnet: limit order books within switches
Recommendations
Using Long Short-Term Memory to Predict Cash Dividend
ICEBT '21: Proceedings of the 2021 5th International Conference on E-Education, E-Business and E-TechnologyInvesting in stocks has been very popular in recent years. Investors hope to make a profit by investing in stocks. However, stock prices are highly volatile. Many investors judge whether to invest in stocks based on historical stock prices, technical ...
Deep Reinforcement Learning for Market Making
AAMAS '20: Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent SystemsMarket Making is high frequency trading strategy in which an agent provides liquidity simultaneously quoting a bid price and an ask price on an asset. Market Makers reaps profits in the form of the spread between the quoted price placed on the buy and ...
Boosting Trading Strategies performance using VIX indicator together with a dual-objective Evolutionary Computation optimizer
This work proposes a new EC approach to boost Trading Strategies (TS) performance.TSs combining Volatility Index (VIX) and other Technical Indicators is proposed.A dual-objective EC adaptive approach is used to reduce human intervention.The approach is ...
Comments