Summer 2018 Tentative Detailed Schedule (subject to change)

Wk
Out
Date
Topics Notes/Assignment
1
HI
05/15
  1. Tue: May 15 - First Day of our Class
  2. Course Overview
  3. Python Introduction
  4. Pandas, NumPy, SciPy, Matplotlib (Ch 4)
  5. Working with many stocks at once
  6. Statistical analysis of time series (Ch 6)

Georgia Tech Summer Academic Calendar 2018

Python Book
* Ch 04: Data types and structures
* Ch 05: Financial time series
* Ch 06: Data visualization

Class Resources
* Udacity videos ( here )
* slides (000 ,001, 002)
*set 002 - will be modified to include Th class notes.
Class code snippets
* code/

Before Lecture Thursday:
* Install Python framework that includes
----> pandas, numpy, scipy [anaconda recommended]
on your laptop - bring laptop to class -
we will use this framework for first set of
in-class activities at 2nd half of class

Core core track both UG/GRAD. Projects will be assiged from this site ( here ).

2
05/22
  1. Portfolio Statistics: Sharpe Ratio, Bollinger band, and daily return, cuulative return.
  2. Histograms and scatter plots (Ch 5)
  3. Reading, Slicing and plotting stock data (Ch 6)
  4. Incomplete Data
  5. Project 1: Assessing a portfolio (Due June 4) MC1-P1 (assigned).

Python Book
* Ch 11: Statistics-Portfolio optimization

codewk2/
slides are updated

P1 assigned.

3
05/29
  1. Sharpe Ratio: Intuition to implementation.
  2. Portfolio construction
  3. Optimizers: Building a parameterized model
  4. Optimizers: How to optimize a portfolio (Ch 11)

slides

Piazza link ( here ) Note this is for both grad and undergrad part of class, it may be confusing at first but it will be arich resource in the end.

Video reference link (text file here) may enable you to navigate through the videos easier. Hint: Lesson 01-07: Lesson 8: Sharpe Ratio & Other portfolio staticistics is related to P1.

4
P2
06/05
Module 2: Learning Begin:
  1. Optimizing a portfolio - wrapup

  2. How Machine Learning is used in a hedge fund
  3. Regression
  4. Decision Tree
  5. Project 2 : Due Monday June 18 (Assess & Defeat learners - see links on the right).

--->P1 due. Monday June 4

slides-set4
slides-set5

 

Mitchell Book:
* Ch 01: Introduction Machine Learning,
* Ch 08: Instance-based Learning
* Ch 03: Decision Tree Learning

Links (Assess and Defeat Learners)

5
06/12
  1. Decision Tree to Random Forest
  2. Assessing A Machine Learning Algorithm
  3. Ensemble Learners, Bagging and Boosting
  4. Project 2 Discussion - Build and assess a random forest learner.


slides( pdf )

Mitchell Book:

Random Forest: Read: Paper: "Perfect Random Tree Ensembles"
by Adele Cutler (pdf)

Video of Mechanics of Building a DT ( here ), and in a table ( here )
Video slides/spreadsheet ( here.pdf, tabular .xlsx )
Quinlan's paper ( here )

slides ( summary review pdf)

6
E1
06/19
  1. So you want to be a hedge fund manager (01)
  2. Market Mechanics (02)
  3. Th: Exam 1.

---> P2 Due 11:55 PM: Monday June 18 Build & Assess a random forest learner. [ note tree must be implemented in a table ]

Exam 1 : Material -- up to and including Week 5 (i.e., ensemble learners, bagging and boosting).

study guides - are available here ( link ) {MC2 is not on exam]

Romero/Balch:
Ch 02: So you want to be a hedge fund manager?
Ch 04: Market mechanics

slides ( pdf ) set 007

exam1.txt (some notes for studying)


7
P3
06/26
  1. Go over Exam 1 ( pdf ). P3 Assigned.
  2. Market Mechanics ( pdf )
  3. The Capital Assets Pricing Model (CAPM) ( pdf )

Ch 05 : Introduction to company evaluation

Romero/Balch:
Ch 07: Framework for investing: The Capital Assets Pricing Model (CAPM)
Sat - June 30 - Withdrawal Deadline

bonus assignment ( here )

https://www.youtube.com/watch?v=ibQmtYrTEDQ

8
07/03
  1. Tu: School Break
  2. The Capital Assets Pricing Model (CAPM)
  3. How hedge funds use CAPM ( pdf )
  4. Project 3 Discussion. Due this upcoming Monday
  5. HW: Movie: Black-Scholes (MIDAS Formula) please watch.

Jul 03-Jul 04: School Break/Holiday

Romero/Balch:
Ch 12: Overcoming data quirks to design trading strategies
Ch 08: The efficient market hypothesis

Compact schedule with video key reference ( here )

9
07/10
  1. What is your company worth? ( pdf )
  2. Technical Analysis ( pdf )
  3. Dealing with Data ( pdf )
  4. Efficient Market Hypothesis ( pdf )
  5. HW: The Big Short watch.

Study guide exam 2 ( here )

Project 3: Build a market simulator due (should be available for submission now 7/05).

Ch 09: The fundamental law of active portfolio management

10
07/17
  1. The Fundamental Law of Active Portfolio Management ( pdf )
  2. Portfolio Optimization and the efficient frontier
  3. Project 4 Discussion
  4. Th: Exam 2:
 
11
P5
07/24
  1. Project 4: Strategy Learner ( here) with Random Forests is Due Monday (July 30, same date as online class - bonus project, Q-Learning/a reinforcement learning strategy will be due Thursday, Aug 2). Manual strategy is not required.
  2. Portfolio Optimization and the efficient frontier
  3. Wrapup.
  4. Tue: Last day of our Class - No final exam.

Tue - Jul 24 Final Day of Class
Wed - Jul 25 - Reading Day
Final Exams: Jul 26-Aug 02 ( no final in our class )

 
07/31
  1. Grades Due

Jul 25-Aug 06 Grade Entry Deadline Ends at 12:00 pm.

Aug 07: Grades Available at 6 pm