Skip to content

Portfolio optimization using python: using simple concepts from Modern Portfolio Theory (MPT) to pick stocks based on past performance. This partial version is intended as an exercise for CSE 440 students.

Notifications You must be signed in to change notification settings

sari-saba-sadiya/Portfolio-Optimization-with-Python

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

24 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Stock Portfolio Optimization with Python

Author: Sari Saba-Sadiya

For students in cse440 introduction for artificial intellegence, spring 2019.

Modern portfolio theory (MPT) was formulated by the economist Harry Markowitz at 1952, and later won him a Nobel Prize. The key concept of MPT is that by calculating the risk, expected returns, and correlations between stocks one can choose a portfolio (a group of holdings in different stocks) to maximize his expected returns. His methods, now standard at every hedge-fund, rely on optimizing a complex multi variable function. Hence, many economists focus on coming up with ways to convert the equations to convex optimizations problems that can be easily approximated numerically.

This assignment will walk you through some of the classical and cutting edge techniques in portfolio optimization using the python convex optimization packadge CVXPY.

Contents:
|-> stock_table.csv : A stock table for the excersize.
|-> cse440PortOpt.pdf : The assignment guide

To view the code use: https://nbviewer.jupyter.org/github/sari-saba-sadiya/portfolioOptPython/blob/master/portOpt_censored.ipynb

About

Portfolio optimization using python: using simple concepts from Modern Portfolio Theory (MPT) to pick stocks based on past performance. This partial version is intended as an exercise for CSE 440 students.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published