{"title":"交易成本和多元化约束下的后验多阶段最优交易","authors":"Mogens Graf Plessen, A. Bemporad","doi":"10.3905/jot.2018.1.064","DOIUrl":null,"url":null,"abstract":"This article presents a simple method for a posteriori (historical) multivariate, multistage optimal trading under transaction costs and a diversification constraint. Starting from a given amount of money in some currency, the authors analyze the stage-wise optimal allocation over a time horizon with potential investments in multiple currencies and various assets. Three variants are discussed: unconstrained trading frequency, a fixed number of total admissible trades, and waiting a specific time period after every executed trade until the next trade. The developed methods are based on efficient graph generation and consequent graph search and are evaluated quantitatively on real-world data. The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning.","PeriodicalId":254660,"journal":{"name":"The Journal of Trading","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A Posteriori Multistage Optimal Trading under Transaction Costs and a Diversification Constraint\",\"authors\":\"Mogens Graf Plessen, A. Bemporad\",\"doi\":\"10.3905/jot.2018.1.064\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article presents a simple method for a posteriori (historical) multivariate, multistage optimal trading under transaction costs and a diversification constraint. Starting from a given amount of money in some currency, the authors analyze the stage-wise optimal allocation over a time horizon with potential investments in multiple currencies and various assets. Three variants are discussed: unconstrained trading frequency, a fixed number of total admissible trades, and waiting a specific time period after every executed trade until the next trade. The developed methods are based on efficient graph generation and consequent graph search and are evaluated quantitatively on real-world data. The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning.\",\"PeriodicalId\":254660,\"journal\":{\"name\":\"The Journal of Trading\",\"volume\":\"13 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Journal of Trading\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3905/jot.2018.1.064\",\"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.2018.1.064","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A Posteriori Multistage Optimal Trading under Transaction Costs and a Diversification Constraint
This article presents a simple method for a posteriori (historical) multivariate, multistage optimal trading under transaction costs and a diversification constraint. Starting from a given amount of money in some currency, the authors analyze the stage-wise optimal allocation over a time horizon with potential investments in multiple currencies and various assets. Three variants are discussed: unconstrained trading frequency, a fixed number of total admissible trades, and waiting a specific time period after every executed trade until the next trade. The developed methods are based on efficient graph generation and consequent graph search and are evaluated quantitatively on real-world data. The fundamental motivation of this work is preparatory labeling of financial time-series data for supervised machine learning.