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]