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. Speaker(s): , , Room: E-301, Bldg: 1000 K. L. O. Rd, 1000 K. L. O. Rd, Kelowna, British Columbia, Canada, V1Y 4X8, Virtual: https://events.vtools.ieee.org/m/555692