2011 GT Course

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Syllabus from 2011

Day Date Lecture Topics Projects Due/Additional Info
August
Tuesday August 23 Class overview
Thursday August 25 lecture 1: Infrastructure of a quant shop.

Policy learning, prediction learning.
see also: Work_Flow_Guide

Tuesday August 30 lecture 2: Why does information impact stock price?

Lecture: What is a company worth?

Thursday August 25 The Capital Assets Pricing Model

Video: Cramer's Guide to the Stock Market
Lecture: Expectation, Risk, and Diversification

Read Chapter 1 & 2 from Grinold & Kahn

Homework 1 due at 11:55PM


Tuesday September 13 Work through setting up environment and tutorial1.py Homework 2 due at 11:55PM
Thursday September 15 Review of hedge fund infrastructure

What goes into an optimizer? And what comes out?
Efficient frontier

Homework 3 due at 11:55PM
Tuesday September 20 Lecture 3: Markets, Data
Thursday September 22 Technical Analysis & Event Profiling Part 1
Tuesday September 27 Technical Analysis & Event Profiling Part 2

paper about event studies Details of Homework 4

Tuesday October 4 Event Profiling Part 3

KNN (statement of problem in terms of regression) Andrew Moore's slides on KNN

Thursday October 6 Anatomy of a trade

KNN intro.

Tuesday October 11 Review of event study code and assignment.

KNN code intro.

Thursday October 13 Jonathan Clarke

Analysis of Spatial Arbitrage

...
Tuesday October 25 Eric Gilbert

Widespread Worry and the Stock Market

Thursday October 27 Full description of KNN project (Project 2)
Tuesday November 1 Assessing learners: Time, Correlation, Error
Thursday November 3 Using and building decision trees (and KD trees)

time complexity of KNN versus decision trees
problems with KNN
features: importance of -1 to 1: normalize by STD
momentum
daily return std
"bollinger number"
52 week high/low
price - moving average
days since crossover
volume / avg(52 week volume)
project 3

Tuesday November 8 Finding the best indicators to use

adding features method
lesioning features method

Thursday November 10 Finding the best indicators to use

adding features method
lesioning features method