Algorithmic Trading: XGBoost Machine Learning Model - Student Presentation
Join us for an exciting student research presentation!
Come support and learn from students as they share their work, there will be multiple presentations happening throughout the day.
Snacks and lunch will be provided, so bring your curiosity (and your appetite)!
We’d love to see you there!
12:00 pm - 12:30 pm | AlgoTrade & XGBoost: Harsh
12:30 pm - 12:45 pm | AlgoTrade & XGBoost Q&A: Harsh
12:45 pm - 1:00 pm | Snacks & Refreshments
Abstract:
Stock markets generate enormous amounts of data every day, yet predicting how a stock will move throughout a trading session remains a difficult problem. This paper presents MarketSight, a system that learns from years of historical market data to forecast how individual stock prices will evolve across an entire trading day — before that day begins. By identifying patterns in past price behavior, the system produces a predicted price path for each stock, giving traders an early picture of likely intraday movement. The system runs automatically each night, updating its predictions with the latest available data. Experiments across approximately 500 US-listed stocks demonstrate meaningful forecasting accuracy, with predictions consistently outperforming naive baselines.
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- 1000 K. L. O. Rd
- Kelowna, British Columbia
- Canada V1Y 4X8
- Building: C
- Room Number: 130
- Click here for Map
- Starts 06 May 2026 07:00 AM UTC
- Ends 08 May 2026 12:00 AM UTC
- No Admission Charge
Speakers
Dolcy Sareen
Activity Diagrams Analysis for Visually Impaired Students
Diagrams are a very important part of education,learning and understanding , allowing the transmission of complex ideas in a visual format.
However, current accessibility technologies introduce substantial limitations for visually impaired students in accessing these materials. Traditional approaches to fix this problem, such as audio descriptions and tactile graphics, either lack detail or are prohibitively expensive to scale. This review will focus on the current status of accessible diagram technologies, such as tactile graphics, 3D printing, AI-driven recognition applications and methods proposed till now . We identify the major technical challenges and propose an integrated approach using multi-modal interaction by combining audio, and AI-enhanced diagram interpretation. The approach offers equal access to STEM visual content as part of the road to inclusive education.
Biography:
Dolcy Sareen is a fourth-year Computer Science student at Okanagan College. Dolcy is also the webmaster of the Okanagan College IEEE Student Branch. She is passionate about learning new technologies, with a strong focus on data. Dolcy enjoys connecting with people and continuously learning from new experiences.
Harsh Saw
Algorithmic Trading: XGBoost Machine Learning Model - Student Presentation
Stock markets generate enormous amounts of data every day, yet predicting how a stock will move throughout a trading session remains a difficult problem. This paper presents MarketSight, a system that learns from years of historical market data to forecast how individual stock prices will evolve across an entire trading day — before that day begins. By identifying patterns in past price behavior, the system produces a predicted price path for each stock, giving traders an early picture of likely intraday movement. The system runs automatically each night, updating its predictions with the latest available data. Experiments across approximately 500 US-listed stocks demonstrate meaningful forecasting accuracy, with predictions consistently outperforming naive baselines.
Biography:
Harsh Saw is a fourth-year Computer Science student with hands-on experience spanning software engineering and AI/ML — from writing production code to deploying real-world systems. He has worked across multiple domains in industry, building technology that goes beyond the classroom. Passionate about turning ideas into working products, Harsh enjoys the full journey from early development to live deployment. He is driven by curiosity about what technology can do when it is built to actually work in the real world.