Forex Price Movement Prediction Using Stacking Machine Learning Models

被引：1

作者：

Kurujitkosol, Thanapol ^{[1
]}

Takhom, Akkharawoot ^{[2
]}

Usanavasin, Sasiporn ^{[1
]}

机构：

[1] Thammasat Univ, Sirindhorn Int Inst Technol SIIT, Pathum Thani, Thailand

[2] Natl Elect & Comp Technol Ctr, Language & Semant Technol Res Team, Pathum Thani, Thailand

来源：

2022 17TH INTERNATIONAL JOINT SYMPOSIUM ON ARTIFICIAL INTELLIGENCE AND NATURAL LANGUAGE PROCESSING (ISAI-NLP 2022) / 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS (AIOT 2022) | 2022年

关键词：

Forex; Foreign Exchange; Machine Learning; Technical Analysis; Fibonacci Retracements;

D O I：

10.1109/ISAI-NLP56921.2022.9960245

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Forex is an attractive choice for investors who admire any making profit challenges in the fluctuating market. But on the other hand, it means investors can lose money at the same time. Many investors look for ways to reduce the risks by finding price movement prediction tools. Therefore, this paper proposes the Stacking Machine Learning Models to predict the future price direction to help investors to decide and plan strategies. We experimented with comparing baseline models to evaluate the accuracy performance. In addition, we improve the accuracy performance using Technical Analysis and Fibonacci Retracements to gain an accuracy of 90%.

引用

页数：6

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