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

Salahadin A. Mohammed*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)1077-1089
Number of pages13
JournalInternational Journal of Advanced Computer Science and Applications
Volume15
Issue number4
DOIs
StatePublished - 2024

Bibliographical note

Publisher Copyright:
© (2024), (Science and Information Organization). All Rights Reserved.

Keywords

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

ASJC Scopus subject areas

  • General Computer Science

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