Numerical Techniques for Portfolio Optimization
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Numerical Techniques for Portfolio Optimization

Optimization in Excel, Python and R
New
0.0 (0 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
106 students enrolled
Created by Loony Corn
Last updated 8/2017
English
Current price: $10 Original price: $50 Discount: 80% off
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Includes:
  • 4.5 hours on-demand video
  • 1 Article
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • Implement optimization techniques in Python, R and Excel
  • Understand the Simplex algorithm and its extensions
  • Use integer programming and avoid pitfalls of misusing the LP-relaxation problem
View Curriculum
Requirements
  • Basic understanding of math and statistics at a high school level. No real programming background required. Some understanding of finance would help
Description

Optimization techniques are used everywhere, but until recently they were not that important in software. With the rising importance of machine learning that is changing, because training ML models requires optimization in parameter training.

This course focuses on the theory and implementation of optimization in Python, R and Excel.

  • Understand the classic linear programming problem setup and the primal and dual problems
  • Really understand the simplex method - intuition, mechanics and implementation
  • Study various extensions to the Simplex method, including K-of-N constraints and quadratic objective functions
  • Understand what integer programming is, and how the LP-relaxation problem can be helpful
  • Be aware of the dangers of blindly using the LP-relaxation and then rounding off the solutions
  • Implement portfolio optimization for risk minimization in Python, Excel and R


Who is the target audience?
  • Anyone looking to use optimization - in finance, or elsewhere
Compare to Other Finance Courses
Curriculum For This Course
45 Lectures
04:24:21
+
Introduction
1 Lecture 01:52
+
Introducing Numerical Optimisation
9 Lectures 54:31
Optimisation - Slides and Source Code
00:02



Framing the Problem
08:25

Solving the problem
10:17

Applications
06:44

PortfolioAllocation
05:55

Regression
06:55

Gradient Descent
05:52
+
Linear Programming and the Simplex Method
7 Lectures 52:24
Wyndor
07:25

Standard Dual
07:02

Micro Econ
06:11

Graphical
07:34

Simplex Intuition
07:45

Simplex Mechanics
08:46

Simplex Extensions
07:41
+
Implementing Linear Programming in Excel
6 Lectures 33:22
Outlining our Approach
03:54

Assembling Data
03:52

Linear Estimations
06:51

Solver
05:27

VBA for Covariance
05:49

Quadratic Optimization
07:29
+
Implementing Linear Programming In R
5 Lectures 29:32
Introducing R
03:29

Data frames
05:14

Linear Estimates
07:41

Quadratic Estimates
06:22

Quadratic Programming in R
06:46
+
Implementing Linear Programming in Python
5 Lectures 24:21
Python for optimization
05:21

Pandas
03:13

Linear Estimates
05:45

Quadratic Estimates
06:05

Quadratic Optimization
03:57
+
Understanding Integer Programming
6 Lectures 38:07
Integer Programming
06:01

LP Relaxation
04:51

Flaws Naive LP
06:58

Applications
07:21

Either Or Constraints
05:40

Unusual Forms
07:16
+
Implementing Integer Programming in Excel
3 Lectures 12:38
Integer Constraints
04:27

Leverage and Long-bias Constraints
03:28

Solver for Integer Programming
04:43
+
Implementing Integer Programming in R
1 Lecture 06:42
Implementing Integer Programming in R
06:42
+
Implementing Integer Programming in Python
2 Lectures 10:52
Integer Constraints
03:45

Solving for Leverage in Python
07:07
About the Instructor
Loony Corn
4.3 Average rating
5,481 Reviews
42,669 Students
75 Courses
An ex-Google, Stanford and Flipkart team

Loonycorn is us, Janani Ravi and Vitthal Srinivasan. Between us, we have studied at Stanford, been admitted to IIM Ahmedabad and have spent years  working in tech, in the Bay Area, New York, Singapore and Bangalore.

Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft

Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too

We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!

We hope you will try our offerings, and think you'll like them :-)