2010SPR8803
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CS 8803 FIN: Machine Learning in Finance
Important Announcements
2010SPR8803 Project 6 Final Project: Use your event profiler to look at survivor-biased data.
Course Overview
This course focuses on the development of quantitative models for equity trading. The course will center on projects in which students develop and test quantitative models using historical pricing data and data feeds.
The course is open to and intended for graduate students in Computing, ISYE, Math & Management. Students should be have strong coding skills and some familiarity with equity markets.
Course Logistics
- Instructor: Associate Professor Tucker Balch
- Time/Location: Tuesday & Thursday 1:35 to 2:55, Bunger Henry 311
- Course Website: http://wiki.quantsoftware.org, Online Forums: forums.quantsoftware.org
- Text: Active Portfolio Management by Grinold & Kahn. If you are a GT faculty or student, you may be able to read the book online [here] at no cost.
- Prerequisites: Machine learning and portfolio management experience is not assumed; the course is designed to provide students with the necessary background they will need on these topics. Programming will be in the Python language. Students are expected to be strong programmers (or willing to invest significant effort in learning to program in Python).
- People in the class 2010SPR8803 People
- GT T-Square site for the class. I don't know for sure if this works for others, or if the URL is just for me.
- Resources
Grading
- Project 1, 100 Primes: 10%
- Project 2, Implement KNN Regression: 10%
- Project 3, Improve your KNN algorithm: 10%
- Project 4, Decision Trees: 15%
- Project 5, Implement An Event Profiler: 15%
- Final Project: 15%
- Test 1: 10%
- Class Participation & Homeworks: 5%
- No Final
Projects & Homework
2010SPR8803 Homework 1: Add information about yourself to the wiki.
2010SPR8803 Homework 2: Get a paper trading account at an online broker.
2010SPR8803 Homework 3: Trade with your broker and provide screenshot evidence.
2010SPR8803 Project 1: Write a Python program to print the first 100 prime numbers.
2010SPR8803 Project 2: Implement KNN in Python.
2010SPR8803 Project 3: Improve your KNN learner.
2010SPR8803 Project 4: Implement a Random Forest learner.
2010SPR8803 Project 5: Implement an Event Profiler.
2010SPR8803 Project 6: Use Event Profiler to assess impact of survivor-biased data.
Other Resources
See Resources on this wiki.
