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  • 标题:Forex Data Analysis Using Weka
  • 本地全文:下载
  • 作者:Luciana Abednego ; Cecilia Esti Nugraheni
  • 期刊名称:Computer Science & Information Technology
  • 电子版ISSN:2231-5403
  • 出版年度:2020
  • 卷号:10
  • 期号:12
  • 页码:143-155
  • DOI:10.5121/csit.2020.101215
  • 出版社:Academy & Industry Research Collaboration Center (AIRCC)
  • 摘要:This paper conducts some experiments with forex trading data. The data being used is from kaggle.com, a website that provides datasets for machine learning and data scientists. The goal of the experiments is to know how to design many parameters in a forex trading robot. Some questions that want to be investigated are: How far the robot must set the stop loss or target profit level from the open position? When is the best time to apply for a forex robot that works only in a trending market? Which one is better: a forex trading robot that waits for a trending market or a robot that works during a sideways market? To answer these questions, some data visualizations are plotted in many types of graphs. The data representations are built using Weka, an open-source machine learning software. The data visualization helps the trader to design the strategy to trade the forex market.
  • 关键词:forex trading data ;forex data experiments ;forex data analysis ;forex data visualization ;weka.
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