Data Collection and Staging Process Automation Precision, Speed and Scalability for Machine Learning Modelling of Algorithmic Trading Stocks-Price Prediction
April 22 @ 9:00 am - 11:00 am
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 <a href="http://utilization.
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📢 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 <a href="http://prediction.
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Learn how we designed a scalable pipeline using distributed computing and XGBoost, covering system architecture, data engineering, and real-world ML applications in algorithmic <a href="http://trading.
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This work also establishes a foundation for ongoing research and extended large-scale <a href="http://evaluation.
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Open to students, faculty, and anyone interested in machine learning, data systems, or <a href="http://fintech.
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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