Algorithmic Trade Forecasting DBMS and Data Warehouse Implementation within Digital Research Alliance of Canada Advanced Research Computing (ARC) Platforms

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Authors: Kristina Cormier, Helana Jaraiseh, Justin Drenka, Minami Sato, Brandon Hay, James Midtdal, Gaétan Hains, Youry Khmelevsky, Emilio Iturbide Gonzalez
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Abstract

Short-term stock market predictions using machine learning (ML) and artificial intelligence (AI) reduce the risk of human error and bias. They can also detect patterns and work with large and complex datasets. In this research paper, a new Database Management System (DBMS) implementation that uses Digital Research Alliance of Canada (DRAC) computational resources for Algorithmic Trade with Staging and Data Cleaning is presented. The paper explores data staging and cleaning for machine learning (ML) using the Database Management System (DBMS) and the cloud and computational resources provided by the Digital Research Alliance of Canada’s Alliance Cloud Connect Pilot [1].

Fields of Study & Subjects

Stock Market Forecasting Methods Cloud Computing and Resource Management Advanced Database Systems and Queries

Publication details

Publication date
11 February 2026
Language
EN
Document type
Preprint