Data Extraction, Transformation, and Loading (ETL) Process Automation and Data Warehouse Implementation for Algorithmic Trading Machine Learning Modelling*

This research intends to implement a data warehouse (DW) and ETL (Extract, Transform, Load) process to handle time-series data used by an XGBoost model to predict stock market prices. The ETL pipeline automates data ingestion from Financial Modelling Prep (FMP) API [2], performs necessary transformation, and stores it for future use. Furthermore, it leverages its design to optimize resource usage. The paper also explores the DW performance and estimations for growing workloads.