预测日内交易量和交易量百分比

V. Satish, Abhay Saxena, Max Palmer
{"title":"预测日内交易量和交易量百分比","authors":"V. Satish, Abhay Saxena, Max Palmer","doi":"10.3905/jot.2018.13.4.107","DOIUrl":null,"url":null,"abstract":"This article discusses recent techniques and results in the area of forecasting intraday volume and intraday volume percentages. By exploring ways to predict volume, the authors seek to improve the performance of trading algorithms, many of which depend upon the volume that will trade while the order is active. Traditionally, algorithms use historical averages to predict volume over the lifetime of an order. The authors show that improving the prediction of volume boosts the performance of algorithms.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Predicting Intraday Trading Volume and Volume Percentages\",\"authors\":\"V. Satish, Abhay Saxena, Max Palmer\",\"doi\":\"10.3905/jot.2018.13.4.107\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article discusses recent techniques and results in the area of forecasting intraday volume and intraday volume percentages. By exploring ways to predict volume, the authors seek to improve the performance of trading algorithms, many of which depend upon the volume that will trade while the order is active. Traditionally, algorithms use historical averages to predict volume over the lifetime of an order. The authors show that improving the prediction of volume boosts the performance of algorithms.\",\"PeriodicalId\":254660,\"journal\":{\"name\":\"The Journal of Trading\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Journal of Trading\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3905/jot.2018.13.4.107\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Journal of Trading","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3905/jot.2018.13.4.107","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

本文讨论了预测日内交易量和日内交易量百分比的最新技术和结果。通过探索预测交易量的方法,作者试图提高交易算法的性能,其中许多算法依赖于订单活跃时的交易量。传统上,算法使用历史平均值来预测订单生命周期内的交易量。作者表明,改进体积预测可以提高算法的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Predicting Intraday Trading Volume and Volume Percentages
This article discusses recent techniques and results in the area of forecasting intraday volume and intraday volume percentages. By exploring ways to predict volume, the authors seek to improve the performance of trading algorithms, many of which depend upon the volume that will trade while the order is active. Traditionally, algorithms use historical averages to predict volume over the lifetime of an order. The authors show that improving the prediction of volume boosts the performance of algorithms.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Phantom Liquidity and High-Frequency Quoting COMMENTARY: Commentary on “If Best Execution Is a Process, What Does That Process Look Like?”1 Editor’s Letter Machine Learning for Algorithmic Trading and Trade Schedule Optimization COMMENTARY: A Market Structure That Fits the Needs of Portfolio Managers
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1