Trade Ease With Machine Learning and AWS
Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by crea...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10068512/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846128571411595264 |
|---|---|
| author | Kamurthi Ravi Teja Chuan-Ming Liu |
| author_facet | Kamurthi Ravi Teja Chuan-Ming Liu |
| author_sort | Kamurthi Ravi Teja |
| collection | DOAJ |
| description | Global trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research. |
| format | Article |
| id | doaj-art-db055413b63d4c27ade1cd26fba1545c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-db055413b63d4c27ade1cd26fba1545c2024-12-11T00:03:58ZengIEEEIEEE Access2169-35362023-01-0111258932590510.1109/ACCESS.2023.325703710068512Trade Ease With Machine Learning and AWSKamurthi Ravi Teja0https://orcid.org/0000-0001-9544-7478Chuan-Ming Liu1https://orcid.org/0000-0001-9005-57151International Program of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei, TaiwanDepartment of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, TaiwanGlobal trading is undergoing significant changes, necessitating modifications to the trading strategies. This study presents a newly developed cloud-based trading strategy that uses Amazon Web Services (AWS), machine learning (ML), and data science to automate trading tasks. The study begins by creating a machine learning trading strategy using a customized deep neural network (DNN). The strategy was then tested using a trading station before deployment on a cloud machine. The execution of the strategy resulted in a total profit of 42.74526 USD, with a test accuracy score of 87.45% and a training accuracy score of 89.15% over 6047 epochs. The roadmap provides a step-by-step overview of the entire process from strategy development to execution. In addition, this study offers insights into related issues and solutions that can enhance the effectiveness of trading strategies. Overall, this study contributes significantly to the field of cloud-based trading strategies and opens avenues for future research.https://ieeexplore.ieee.org/document/10068512/AWSdeep neural network (DNN)machine learning (ML)trade strategyfinancial marketcloud computing |
| spellingShingle | Kamurthi Ravi Teja Chuan-Ming Liu Trade Ease With Machine Learning and AWS IEEE Access AWS deep neural network (DNN) machine learning (ML) trade strategy financial market cloud computing |
| title | Trade Ease With Machine Learning and AWS |
| title_full | Trade Ease With Machine Learning and AWS |
| title_fullStr | Trade Ease With Machine Learning and AWS |
| title_full_unstemmed | Trade Ease With Machine Learning and AWS |
| title_short | Trade Ease With Machine Learning and AWS |
| title_sort | trade ease with machine learning and aws |
| topic | AWS deep neural network (DNN) machine learning (ML) trade strategy financial market cloud computing |
| url | https://ieeexplore.ieee.org/document/10068512/ |
| work_keys_str_mv | AT kamurthiraviteja tradeeasewithmachinelearningandaws AT chuanmingliu tradeeasewithmachinelearningandaws |