Machine Learning and Optimization Techniques for Analyzing Financial Data - QC-126

Preferred Disciplines: Computer Science, Machine Learning, Deep Learning, Statistics (PhD)
Company: Squarepoint Technologies
Project Length: 4-6 months (1 unit)
Desired start date: As soon as possible
Location: Montreal, QC
No. of Positions: 1
Preferences: Language: English. Preference for interns in Montreal

About Company:

Squarepoint is a global investment management firm that utilizes a diversified portfolio of systematic and quantitative strategies across financial markets that seeks to achieve high quality, uncorrelated returns for our clients. We have deep expertise in trading, technology and operations and attribute our success to rigorous scientific research. As a technology and data-driven firm, we design and build our own cutting-edge systems, from high performance trading platforms to large scale data analysis and compute farms. With offices in the Americas, United Kingdom and Asia, we emphasize true, global collaboration by aligning our investment, technology and operations teams functionally around the world.

Project Description:

One of the main tasks of the Data Science team at Squarepoint is to perform research and incorporate Machine Learning techniques to improve or create trading strategies. In this project, the intern’s responsibilities include building tools to help explore data, doing research and running machine learning algorithms to improve existing strategies, presenting research results, and implementing alpha processes for production. The main emphasis of this project is on using state-of-the-art Machine Learning (including Deep Learning) methods as well as Optimization techniques to analyze large amount of data.

Research Objectives/Sub-Objectives:

  • Research and implement strategies within the firm’s automated trading framework
  • Analyze large data sets using advanced statistical methods to identify trading opportunities
  • Develop a strong understanding of market structure of various exchanges and asset classes

Methodology:

  • To be determined

Expertise and Skills Needed:

  • Experience with Machine Learning algorithms and feature generation and processing techniques
  • Familiarity with Deep Learning models
  • Familiarity with Optimization techniques
  • Proficiency with Python
  • Critical thinking: ability to track down complex data and engineering issues, evaluate different algorithmic approaches, and analyze data to solve problems

 

For more info or to apply to this applied research position, please

  1. Check your eligibility and find more information about open projects.
  2. Interested students need to get the approval from their supervisor and send their CV along with a link to their supervisor’s university webpage directly to Noha Gerges at, ngerges(a)mitacs.ca  

Program: