Xinpeng Hong, Changgang Zheng, S. Zohren, Noa Zilberman
{"title":"红雀:在交换机内限制订单","authors":"Xinpeng Hong, Changgang Zheng, S. Zohren, Noa Zilberman","doi":"10.1145/3546037.3546057","DOIUrl":null,"url":null,"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.","PeriodicalId":351682,"journal":{"name":"Proceedings of the SIGCOMM '22 Poster and Demo Sessions","volume":"141 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Linnet: limit order books within switches\",\"authors\":\"Xinpeng Hong, Changgang Zheng, S. Zohren, Noa Zilberman\",\"doi\":\"10.1145/3546037.3546057\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":351682,\"journal\":{\"name\":\"Proceedings of the SIGCOMM '22 Poster and Demo Sessions\",\"volume\":\"141 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the SIGCOMM '22 Poster and Demo Sessions\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3546037.3546057\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the SIGCOMM '22 Poster and Demo Sessions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3546037.3546057","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.