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

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

 

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.

 

 

📢 Public Presentation Announcement

Join us for a live presentation on:

Data Collection and Staging Process Automation for Machine Learning in Algorithmic Trading

🗓 Wednesday, April 22
⏰ 9:00 AM – 11:00 AM
📍 Okanagan College E-301 / Hybrid

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.

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