CS7646 Fall 2016

Overview

Please visit the main course page for an overall course introduction and policies: [Machine_Learning_for_Trading_Course]. The information on this page is specific to this semester.

2016 Fall Schedule

• [2016 Fall Schedule]

Assignments & Grading

• [MC1-Project-1: Assess portfolio] 5%
• [MC1-Project-2: Optimize a portfolio] 5%
• MC3-Project-1: Implement and assess a regression learner using decision trees and random forests 15%
• MC2-Project-1: Build a market simulator 15%
• MC2-Homework-1: Create two midterm questions 5%
• Midterm 20%
• MC3-Project-2: Implement your own "manual" quant strategy, then do it with decision tree classification, compare 10%
• MC3-Project-3: Q-learning maze navigation 10%
• MC3-Project-4: Q-learning trader 15%
• Piazza participation, up to 2% bonus for the most helpful contributors.

Thresholds:

• A: 90% and above
• B: 80% and above
• C: 70% and above
• D: 60% and above
• F: below 60%

The projects linked to below are from previous semesters. We keep them here so you can peek ahead, but please keep in mind that they will be revised.

• [MC2-Project-1: Build a market simulator]
• [MC2-Project-2: Implement bollinger bands, and create a simple trading strategy]
• [MC3-Homework-1: Create a Finance midterm question]
• Midterm Study Guide
• [MC3-Project-1]
• [MC3-Project-2]
• [MC3-Project-3]

Minimum technical requirements

• Browser and connection speed: An up-to-date version of Chrome or Firefox is strongly recommended. We also support Internet Explorer 9 and the desktop versions of Internet Explorer 10 and above (not the metro versions). 2+ Mbps recommended; at minimum 0.768 Mbps download speed.

• Hardware: A computer with at least 4GB of RAM and CPU speed of at least 2.5GHz.

• OS:
  • PC: Windows XP or higher with latest updates installed
  • Mac: OS X 10.6 or higher with latest updates installed
  • Linux: Any recent distribution that has the supported browsers installed

Office hours

To be determined.

Plagiarism

In most cases I expect that all submitted code will be written by you. I will present some libraries in class that you are allowed to use (such as pandas and numpy). Otherwise, all source code, images and write-ups you provide should have been created by you alone.

Late Policy & Absences

Assignments are due at 11:55PM Eastern Time on the assignment due date. Assignments turned in after 11:55PM are considered late. Assignments may be turned in up to one day late with a 10% penalty.

Exams will be held on specific days at specific times. If there is an emergency or other issue that requires changing the date of an exam for you, you will need to have it approved by the Dean of Students. You can apply for that here:

• http://www.deanofstudents.gatech.edu (under Resources -> Class Absences)

Legacy

• Legacy: [old schedule]