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Support resistance levels towards profitability in intelligent algorithmic trading models

Past studies showed that more advanced model architectures and techniques are being developed for intelligent algorithm trading, but the input features of the models across these studies are very similar. This justifies the increasing need for new meaningful input features to better explain price mo...

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Main Authors: Chan, Jireh Yi-Le, Phoong, Seuk Wai, Cheng, Wai Khuen, Chen, Yen-Lin
Format: Article
Published: MDPI 2022
Subjects:
QA Mathematics
Online Access:http://eprints.um.edu.my/40840/
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spelling my.um.eprints.408402023-09-26T06:53:12Z http://eprints.um.edu.my/40840/ Support resistance levels towards profitability in intelligent algorithmic trading models Chan, Jireh Yi-Le Phoong, Seuk Wai Cheng, Wai Khuen Chen, Yen-Lin QA Mathematics Past studies showed that more advanced model architectures and techniques are being developed for intelligent algorithm trading, but the input features of the models across these studies are very similar. This justifies the increasing need for new meaningful input features to better explain price movements. This study shows that the inclusion of Support Resistance input features engineered from the proposed novel methodology increased the machine learning model's aggregate profitability performance by 65% across eight currency pairs when compared to an identical machine learning model without the Support Resistance input features. Moreover, the results also showed that the profitability distribution is statistically significantly different between two identical intelligent models with and without the Support Resistance input features, respectively. Therefore, the objective of this study is 3-fold: (1) to propose a novel methodology to automate meaningful Support Resistance price levels identification; (2) to propose a methodology to engineer Support Resistance features for Machine Learning Models to improve algorithmic trading profitability; (3) to provide empirical evidence towards the significant incremental contribution of Support Resistance (Psychological Price Levels) input features towards profitability in algorithmic trading models. MDPI 2022-10 Article PeerReviewed Chan, Jireh Yi-Le and Phoong, Seuk Wai and Cheng, Wai Khuen and Chen, Yen-Lin (2022) Support resistance levels towards profitability in intelligent algorithmic trading models. Mathematics, 10 (20). ISSN 2227-7390, DOI https://doi.org/10.3390/math10203888 <https://doi.org/10.3390/math10203888>. 10.3390/math10203888
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA Mathematics
spellingShingle QA Mathematics
Chan, Jireh Yi-Le
Phoong, Seuk Wai
Cheng, Wai Khuen
Chen, Yen-Lin
Support resistance levels towards profitability in intelligent algorithmic trading models
description Past studies showed that more advanced model architectures and techniques are being developed for intelligent algorithm trading, but the input features of the models across these studies are very similar. This justifies the increasing need for new meaningful input features to better explain price movements. This study shows that the inclusion of Support Resistance input features engineered from the proposed novel methodology increased the machine learning model's aggregate profitability performance by 65% across eight currency pairs when compared to an identical machine learning model without the Support Resistance input features. Moreover, the results also showed that the profitability distribution is statistically significantly different between two identical intelligent models with and without the Support Resistance input features, respectively. Therefore, the objective of this study is 3-fold: (1) to propose a novel methodology to automate meaningful Support Resistance price levels identification; (2) to propose a methodology to engineer Support Resistance features for Machine Learning Models to improve algorithmic trading profitability; (3) to provide empirical evidence towards the significant incremental contribution of Support Resistance (Psychological Price Levels) input features towards profitability in algorithmic trading models.
format Article
author Chan, Jireh Yi-Le
Phoong, Seuk Wai
Cheng, Wai Khuen
Chen, Yen-Lin
author_facet Chan, Jireh Yi-Le
Phoong, Seuk Wai
Cheng, Wai Khuen
Chen, Yen-Lin
author_sort Chan, Jireh Yi-Le
title Support resistance levels towards profitability in intelligent algorithmic trading models
title_short Support resistance levels towards profitability in intelligent algorithmic trading models
title_full Support resistance levels towards profitability in intelligent algorithmic trading models
title_fullStr Support resistance levels towards profitability in intelligent algorithmic trading models
title_full_unstemmed Support resistance levels towards profitability in intelligent algorithmic trading models
title_sort support resistance levels towards profitability in intelligent algorithmic trading models
publisher MDPI
publishDate 2022
url http://eprints.um.edu.my/40840/
_version_ 1781704532196065280
score 13.211869

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