Algorithmic Trading Machine Learning Modelling and Forecasting Subsystems Integration and Data Transformation Process Automation for a Data Warehouse

A DW is needed to efficiently and continuously prepare data for practical real-time data analysis and forecasting with machine learning (ML) algorithms. This paper discusses extraction, transformation, and loading process (ETL) automation and the critical subsystems integration for algorithmic trading ML modelling and stock market forecasting. Additionally, we discuss a new API designed for direct access to the DW. It opens vast opportunities for performance and data processing time to become critical nonfunctional requirements.