Algorithmic Trading and Short-term Forecast for Financial Time Series with Machine Learning Models

#Data #Warehouse #DBMS #Big #Cloud #Machine #Learning #AI #NN

A profitable algorithmic stock trading algorithm benefits from a forecasting system that can produce accurate short-term forecasts. Based on this premise, we proposed to build up our previous experience in building short-term forecasting models using machine learning models. The research project aims to develop effective algorithmic trading algorithms based on accurate short-term forecasts for financial time series using machine learning models. The project focuses on three research activities: (1) building a data warehouse containing the targeted financial time series and other time series considered valuable as input to the machine learning models. Data acquisition, processing, and staging routines that are required to feed the data warehouse dynamically are evaluated and developed. A data visualization and business intelligence layer is also built on top of the data warehouse; (2) a short-term forecasting model based on machine learning is developed using the various time-series data from the data warehouse. Machine learning algorithms such as neural network, random forecast, support vector regression, XGBoost, and long short-term memory (LSTM) are evaluated in conjunction with several performance criteria to identify the most accurate model from a short-term trading perspective; (3) an automated evaluation system is developing to assess the effectiveness of existing algorithmic short-term trading strategies. Novel algorithms are also developed and evaluated using the evaluation system and the short-term forecast machine learning model.

  Date and Time




  • Date: 29 Mar 2022
  • Time: 04:00 PM to 05:00 PM
  • All times are (GMT-08:00) US/Pacific
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Topic: IEEE
Time: Mar 29, 2022 04:00 PM Pacific Time (US and Canada)
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  • Starts 26 March 2022 04:58 PM
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Dr. Youry Khmelevsky Dr. Youry Khmelevsky


Algorithmic Trading and Short-term Forecast for Financial Time Series with Machine Learning Models


Youry Khmelevsky received his Ph.D. degree in computer science and MSc in Electrical Engineering. His current research interests include software engineering; cloud and high-performance computing; enterprise-wide information systems; no programming paradigm, blind computing; and interdisciplinary applied computer science research. Dr. Khmelevsky had served as a postdoctoral fellow at Harvard University; was a Visiting Scientist in the Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology (MIT); was an Invited Researcher at Database Management and Machine Learning Department, Sorbonne University, Paris, France; held engineering and R&D positions in Industry in Europe and North America for about 15 years, including at Alberta Energy, Government of Alberta, Canada.

Albert Wong, Ph.D. Albert Wong, Ph.D.


Algorithmic Trading and Short-term Forecast for Financial Time Series with Machine Learning Models


  • Albert Wong, Ph.D., teaches at the Post-degree Diploma Program in Data Analytics at Langara (the “DA Program”). Drawing on experience and skills developed over a long career in the field, Dr. Wong sets the technical direction for projects proposed within several applied research applications and liaise closely with the partners’ senior leadership teams to ensure that the projects align with each firm’s strategic and financial imperatives. Dr. Wong’s academic expertise includes sampling, multivariate statistical analysis, and ML. He has spent decades working and consulting with organizations of various sizes on the use of statistics, DA, and information technology to solve strategic and/or tactical problems.

Address:British Columbia, Canada