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**BaDshaH** · 05-29-2023, 11:03 AM

[Image: 9-HId-D5-Ro-Ebu-Jq5-Bj-Js-CN9-N9-V7-Qeuk-FNY.jpg]

Published 5/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 310.45 MB | Duration: 0h 35m

Learn how to construct and optimize a Portfolio using Python

[b]What you'll learn[/b]
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

[b]Requirements[/b]
You should have at least basic Python skills
You need a basic understanding of statistics and algebra (not more than High school)

[b]Description[/b]
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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