The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • GIDP 2026
  • ICONS_BA 2025

Computer Vision Conference (CVC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • RSS Feed

DOI: 10.14569/IJACSA.2024.01504109
PDF

Day Trading Strategy Based on Transformer Model, Technical Indicators and Multiresolution Analysis

Author 1: Salahadin A. Mohammed

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 4, 2024.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Stock prices are very volatile because they are affected by infinite number of factors, such as economical, social, political, and human behavior. This makes finding consistently profitable day trading strategy extremely challenging and that is why an overwhelming majority of stock traders loose money over time. Professional day traders, who are very few in number, have a trading strategy that can exploit this price volatility to consistently earn profit from the market. This study proposes a consistently profitable day trading strategy based on price volatility, transformer model, time2vec, technical indicators, and multiresolution analysis. The proposed trading strategy has eight trading systems, each with a different profit-target based on the risk taken per trade. This study shows that the proposed trading strategy results in consistent profits when the profit-target is 1.5 to 3.5 times the risk taken per trade. If the profit-target is not in that range, then it may result in a loss. The proposed trading strategy was compared with the buy-and-hold strategy and it showed consistent profits with all the stocks whereas the buy-and-hold strategy was inconsistent and resulted in losses in half the stocks. Also three of the consistently profitable trading systems showed significantly higher average profits and expectancy than the buy-and-hold trading strategy.

Keywords: Artificial neural network; saudi stock exchange; machine learning; deep learning; transformer model; stock price prediction; time series analysis; technical analysis; multiresolution analysis

Salahadin A. Mohammed. “Day Trading Strategy Based on Transformer Model, Technical Indicators and Multiresolution Analysis”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.4 (2024). http://dx.doi.org/10.14569/IJACSA.2024.01504109

@article{Mohammed2024,
title = {Day Trading Strategy Based on Transformer Model, Technical Indicators and Multiresolution Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01504109},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01504109},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {Salahadin A. Mohammed}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

Computer Vision Conference (CVC) 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2026

9-10 July 2026

  • London, United Kingdom

Artificial Intelligence Conference 2026

3-4 September 2026

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2026

15-16 October 2026

  • Berlin, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computer Vision Conference
  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

The Science and Information (SAI) Organization Limited is a company registered in England and Wales under Company Number 8933205.