English | Size: 310.45 MB
Genre: eLearning
Learn how to construct and optimize a Portfolio using Python
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
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 data
Why 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.
Who this course is for:
Course is for everyone interested in Portfolio Theory, Algebra, Financial Programming and Portfolio Optimization
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