Data Collection and Staging Process Automation Precision, Speed and Scalability for Machine Learning Modelling of Algorithmic Trading Stocks-Price Prediction

Time & Date: 9:00am – 11:00am , Wednesday, April 22, 2026
Location: Room E-301 and via Zoom (see email and registration information), Computer Science Department, Okanagan College
Registration is open now: https://events.vtools.ieee.org/m/555692

This project brings together three teams: Data Collection, Data Warehousing, and Machine Learning into a unified, end-to-end system for high-frequency stock price prediction.
Learn how we designed a scalable pipeline using distributed computing and XGBoost, covering system architecture, data engineering, and real-world ML applications in algorithmic trading.
This work also establishes a foundation for ongoing research and extended large-scale evaluation.
Open to students, faculty, and anyone interested in machine learning, data systems, or fintech.

Abstract:

This presentation discusses an automated data collection and staging pipeline for high-frequency stock price prediction using machine learning. The system integrates scalable ELT processes, data deduplication, and distributed training with XGBoost on high-performance computing infrastructure. Designed for precision, speed, and scalability, the framework enables efficient handling of large financial time-series datasets while maintaining robust predictive performance and optimized resource utilization.

Contributors:

This project was developed through a collaborative effort across three specialized teams:

Data Collection Team

Responsible for sourcing, aggregating, and preprocessing raw financial and market data.

Andrew Johnson
Emilio Iturbide
Reilly Mager
Lian Heckrodt
Cade Dempsey
Kristina Cormier

Data Warehouse Team

Designed and implemented the data storage architecture, ETL/ELT pipelines, and database systems.

Alex Anthony
Hayden Nikkel
Daemon Lewis
John Cortez
Jackson Rosco

Machine Learning Team (XGBoost)

Developed, trained, and evaluated machine learning models for predictive analytics and trading strategies.

Harsh Saw
Zane Tessmer
Kavaljeet Singh
Dante Bertolutti
Guntash Brar
Parag Jindal

Acknowledgements

We thank all contributors and collaborators who supported the development, testing, and deployment of this system.

Photographs

For further information please contact: Youry Khmelevsky (email: Youry at IEEE.org)
Refreshments will be provided

Data Warehouse for Algorithmic Trading Stocks-Price Forecasting on Digital Research Infrastructure using Machine Learning Modelling

Time & Date: 9:00am – 10:00am , Wednesday, April 21, 2026
Location: Room HS107 and via Zoom (see email and registration information), Computer Science Department, Okanagan College
Registration is open now: https://events.vtools.ieee.org/m/555684

Join this presentation to explore how a Data Warehouse system is built to manage and process large-scale stock market data. The project demonstrates how raw financial data is transformed into structured formats that can be efficiently queried and used for predictive modelling.

You’ll learn:

How Data Warehouses store and organize large datasets
Star schema design in a real system
How data pipelines automate transformation of financial data
How machine learning models use prepared datasets for predictions
How large datasets are processed efficiently

Abstract:
This presentation explains how large volumes of stock market data are organized and processed using a structured database system. The system takes raw financial data and converts it into a format that is easier to store, search, and analyze.

The data is processed through an automated pipeline that calculates key financial values such as price changes and trading volume summaries. The structured data is then stored in an optimized system that allows fast access and efficient analysis.

This approach helps make large financial datasets more usable and easier to work with.

Speakers Bio:
Emilio Iturbide Gonzalez is a graduating Computer Science student at Okanagan College with interests in data systems, software engineering, and artificial intelligence.
He has worked on building a Data Warehouse system designed to manage and process large volumes of stock market data. His work focuses on creating efficient data pipelines, organizing complex datasets, and supporting data-driven applications.
He is interested in building practical systems that connect software engineering, data processing, and real-world applications in technology.

Photographs: