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

arXiv: https://arxiv.org/abs/2312.12774 · HTML: https://mlsanity.com/paper/2312.12774/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2023.

## Built on by (most cited first, 1 of 1)

- [SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science](https://mlsanity.com/paper/2503.23314/) (2025)

## Abstract (arXiv)

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.