Data Extraction, Transformation, and Loading Process Automation for Algorithmic Trading Machine Learning Modelling and Performance Optimization

TL;DR

This paper explores automating the ETL process in data warehouses and lakes to enhance machine learning modeling and performance in algorithmic trading, emphasizing integration for improved research opportunities.

Contribution

It discusses existing ETL automation solutions and highlights the potential benefits of integrating data warehouses and lakes with machine learning for trading.

Findings

Enhanced data processing efficiency for trading algorithms

Potential for improved model performance through integrated data systems

Future research opportunities in automated data management

Abstract

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.