Data Extraction, Transformation, and Loading Process Automation for Algorithmic Trading Machine Learning Modelling and Performance Optimization
A data warehouse efficiently prepares data for effective and fast data analysis and modelling using machine learning algorithms. This paper discusses existing solutions for the Data Extraction, Transformation, and Loading (ETL) process and automation for algorithmic trading algorithms. Integrating t...
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creator | Ebadifard, Nassi Parihar, Ajitesh Khmelevsky, Youry Hains, Gaetan Wong, Albert Zhang, Frank |
description | A data warehouse efficiently prepares data for effective and fast data
analysis and modelling using machine learning algorithms. This paper discusses
existing solutions for the Data Extraction, Transformation, and Loading (ETL)
process and automation for algorithmic trading algorithms. Integrating the Data
Warehouses and, in the future, the Data Lakes with the Machine Learning
Algorithms gives enormous opportunities in research when performance and data
processing time become critical non-functional requirements. |
doi_str_mv | 10.48550/arxiv.2312.12774 |
format | Article |
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analysis and modelling using machine learning algorithms. This paper discusses
existing solutions for the Data Extraction, Transformation, and Loading (ETL)
process and automation for algorithmic trading algorithms. Integrating the Data
Warehouses and, in the future, the Data Lakes with the Machine Learning
Algorithms gives enormous opportunities in research when performance and data
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analysis and modelling using machine learning algorithms. This paper discusses
existing solutions for the Data Extraction, Transformation, and Loading (ETL)
process and automation for algorithmic trading algorithms. Integrating the Data
Warehouses and, in the future, the Data Lakes with the Machine Learning
Algorithms gives enormous opportunities in research when performance and data
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analysis and modelling using machine learning algorithms. This paper discusses
existing solutions for the Data Extraction, Transformation, and Loading (ETL)
process and automation for algorithmic trading algorithms. Integrating the Data
Warehouses and, in the future, the Data Lakes with the Machine Learning
Algorithms gives enormous opportunities in research when performance and data
processing time become critical non-functional requirements.</abstract><doi>10.48550/arxiv.2312.12774</doi><oa>free_for_read</oa></addata></record> |
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source | arXiv.org |
subjects | Computer Science - Distributed, Parallel, and Cluster Computing |
title | Data Extraction, Transformation, and Loading Process Automation for Algorithmic Trading Machine Learning Modelling and Performance Optimization |
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