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| + | **[https://arxiv.org/abs/1904.12066 ABIDES arXiv paper] | ||
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Revision as of 13:53, 16 August 2019
Contents
Quantitative Software Research Group at Georgia Tech
The Quantitative Software Research Group investigates systematic algorithms for trading and investing. Our focus is on Machine Learning, but we are also interested in other types of algorithms that inform us about markets and trading.
- Teaching
- Machine Learning for Trading Course (grad)
- Undergrad ML4T (obsolete - now same as grad class)
- ML4T Software Installation
- Undergrad Intro AI Course
- Research
- ABIDES Agent-Based Interactive Discrete Event Simulator
- ABIDES arXiv paper
- HMM Toolkit
- Memoization (Beta)
- ML4Art
- DQ4T
Members of Our Group
Tucker Balch, Ph.D., Director, Quant Software Research Group
Professor, Interactive Computing, Georgia Tech
Instructor for CS 7646
Chief Scientist, Lucena Research, Inc.
website
Maria Hybinette, Ph.D.
Associate Professor, Computer Science, University of Georgia
David Byrd, Ph.D. Student
Research Scientist, Interactive Media Technology Center, Georgia Tech
Instructor for CS 7646, Summer 2016, Summer 2017, Fall 2019
Instructor for CS 4646, Spring 2018
Brian Hrolenok, Ph.D. Student and Head TA
Multiagent Robotics and Systems Lab
website
Jianling Wang, TA for CS 7646
Graduate Student, College of Computing, Ga Tech
Vivek George, TA for CS 7646
Graduate Student, College of Computing (CSE), Ga Tech
2016 Summer Intern, Electronic Arts
website: https://www.linkedin.com/in/vivekjohn
Alumni
Sourabh Bajaj, MSCS, Georgia Tech
Software Engineer, Coursera Inc
website
Devpriya Dave, MSCS, Georgia Tech
Analyst, Data Division, Morgan Stanley
website
Jayita Bhattacharya, MSCS, Georgia Tech
Software Engineer (Playlist), Pandora Media
Alexander Moreno, MSCS, Georgia Tech
Ph.D. student, Georgia Tech
website
Rohit Sharma, MS QCF, Georgia Tech
Blackrock Capital
Vishal Shekhar, MS QCF, Georgia Tech
Software Engineer, Axioma Inc.
website
Publications
- Moreno, Alexander, and Tucker Balch. "Speeding up large-scale financial recomputation with memoization." Proceedings of the 7th Workshop on High Performance Computational Finance. IEEE Press, 2014. (conference)
- Moreno, Alexander, and Tucker Balch. "Improving financial computation speed with full and subproblem memoization." Concurrency and Computation: Practice and Experience (2015). (Journal) [1]
