What you'll learn
Learn to calculate Risk adjusted Portfolio returns
Learn to Optimize portfolio weights
Learn to leverage Matrix Algebra to construct an Optimal Portfolio
Apply Finance Theory to Practice
Requirements
You should have at least basic Python skills
You need a basic understanding of statistics and algebra (not more than High school)
Description
What is this course about?In this 1 hour crash course I am going over the whole process of setting up a Portfolio Optimization with Python step by step. I am doing it hands on showing all calculation steps besides to get the best understanding of all steps involved possible.You will learn:- How stock returns are calculated and why log returns are used- How to pull stock prices and calculate relevant metrics- How to calculate Portfolio Return and Variance (/Portfolio risk)- How to compare a Portfolio of weighted assets with single assets- How to build a whole Optimization by minimizing the Sharpe Ratio (risk adjusted return)- How to build a Optimization from scratch (besides using a solver)- How to split your dataset so that you optimize on seen data and test on unseen dataWhy should I be your constructor?I got years of experience coding in Python both teaching but also several years of actually working in the field.Besides currently working in the field I wrote my Master Thesis on a quantitative Finance topic and got a YouTube channel teaching Algorithmic Trading and Data Science hands-on tutorials with over 75.000 subscribers.Why this course?This course is giving you a non-time wasting hands-on approach on Portfolio Optimization with Python.Any questions coming up?If you got any questions please feel free to reach out! I am happy to hear from you.
Overview
Section 1: Introduction
Lecture 1 Introduction and Disclaimer
Lecture 2 A brief Intro to Returns (Log returns & cumulative Returns)
Section 2: Understanding Matrix operations
Lecture 3 Pulling data & return calculation
Lecture 4 Mean return and Volatility
Lecture 5 Expected Return of a Multi Asset Portfolio
Lecture 6 Portfolio Risk (Portfolio Variance/Standard Deviation)
Lecture 7 Comparing the Portfolio with the single components
Lecture 8 Sharpe Ratio comparison
Lecture 9 Adding more assets to the Portfolio
Section 3: Optimize Portfolio weights using Matrix Algebra
Lecture 10 Recap (Pulling data and weighting assets)
Lecture 11 Optimization objective: Sharpe Ratio function
Lecture 12 Optimization: Constraints
Lecture 13 Optimization: Running and Results
Lecture 14 Instead of using a Solver: Code the optimization from Scratch!
Lecture 15 Optimization on unseen data: Train-Test-Split
Lecture 16 Adding assets, short sell constraints and Outlook
Course is for everyone interested in Portfolio Theory, Algebra, Financial Programming and Portfolio Optimization


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