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

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Albert Wong, Gaétan Hains, Ajitesh Parihar
University Paris East Creteil, Langara College and Computer Science, Okanagan College

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

Time & Date: 9:30 a.m. – 12:30 pm, Monday, July 29th, 2024
Location: Room E-310 and online in Zoom, Okanagan College, Kelowna, 1000 KLO Rd, Kelowna V1Y 4X8
Registration (is open now): https://events.vtools.ieee.org/m/428090 

Abstract:

The increase in popularity of machine learning models and algorithms has brought up methods for increasing the efficiency of processes in various industries and new use cases. From corporations to individuals, machine learning is applicable in a wide range of settings. One such application is using machine learning models for stock price forecasting. This area is often studied. However, it still needs to solve problems due to the high complexity and volatility that technical factors and sentiment-analysis models try to capture. Based on this premise, we suggested you build up our previous experience building short-term forecasting models using machine learning models. The research project aims to develop practical algorithmic trading algorithms based on accurate short-term forecasts for financial time series using machine learning models. The project focuses on these research activities:

  1. Building a database containing the targeted financial and other time series is valuable input to the machine learning models. This streamlines the process of acquiring financial data required for training the machine learning model by adding a web UI for configuring data collection automation and extracting required data.
  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 networks, random forecasts, support vector regression, XGBoost, and long short-term memory (LSTM) are evaluated with several performance criteria to identify the most accurate model from a short-term trading perspective. After the performance analysis based on several metrics, XGBoost was chosen for further development.

A mini workshop agenda:
1. Welcome from the IEEE Okanagan Subsection, IEEE Okanagan College Student Branch and Computer Science Department: Dr. Youry Khmelevsky, Chair (9:30 am – 9:35 am)
2. Invited speaker from the University of Paris East Creteil: Dr. Gaétan Hains (9:35 am – 10:15 am)
3. Invited speakers from Langara College: Dr. Albert Wong & Andres C. Viloria Garcia (10:15 am – 11:00 am)
Machine Learning algorithms for stock price forecasting
Model building process and methodology
Normalization and performance evaluation
Iterations and results
4. Coffee Break (11:00 am – 11:15 am)
5. Student research project presentation and demonstration (11:15 am – 12:30 pm)
Database design
Data collection process automation
Extraction, Transformation and Loading Process
UI development for data configurations and extraction
Integration prototype using Jupyter notebooks

We will demonstrate how to evaluate the quality of the collected data, the metrics of the machine learning model’s prediction, and their integration. 

Bios:

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, Dr. Wong sets the technical direction for projects proposed within several applied research applications and liaises 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.

Gaétan Hains is an expert in software safety and performance and holds a PhD in Computer Science from the University of Oxford. He is currently a Professor of Computer Science and Vice President for Digital Affairs at the University of Paris-Est Creteil (UPEC). He is also a member of the Laboratory of Algorithmics, Complexity, and Logic (LACL), where he has previously served as the head and held academic positions at the University of Montreal and the University of Orleans. He has extensive experience in R&D project leadership and doctoral supervision in parallel-distributed computing, software safety, privacy, and formal methods, with applications in finance, telecom, Al, automotive, and automated trading.

Ajitesh Parihar is a fourth-year Computer Science student and a Research Assistant at Okanagan College. He is a tech enthusiast who enjoys learning about innovations and technologies and is passionate about software engineering, cyber security, and algorithms. Ajitesh is excited to get more involved as a Board Member of the IEEE Student Branch and to meet and work with talented individuals.

Andres C. Viloria Garcia has a post-degree Diploma in Data Analytics from Langara College. He currently works as a Research Associate at Langara College, participating in an NSERC-funded project to assist companies in developing decision-support capabilities and using machine learning techniques to solve business problems. He has experience building machine learning and artificial intelligence models for companies in energy, manufacturing, and healthcare.

For further information, please get in touch with Youry Khmelevsky (email: Youry at IEEE.org) and subscribe to the news at okanagan@listserv.ieee.org
Pizza and Refreshments will be provided