Comprehensive Guide - Portfolio Optimization Using the Markowitz Model in Python
Portfolio optimization focuses on selecting the optimal combination of assets to maximize return and minimize risk. That involves determining the most appropriate weights for each asset.
The historical performance of the assets, their correlations with each other, and other relevant factors are considered. The goal is to create a well-diversified portfolio with a balanced relationship between risk and return.
In this guide, you’ll learn how portfolio optimization works using the Markowitz model with the Sharpe Ratio. We’ve already explained the Sharpe Ratio in detail in another article, which we highly recommend you read first.
Here’s an overview of the topics:
- How do you optimize a portfolio?
- Data access and creating a demo portfolio
- Technical requirements
- Metrics and Exploratory Data Analysis
- Portfolio Optimization from Scratch
- Portfolio Optimization using SciPy
- Calculate the Efficient Frontier
- Portfolio performance against a benchmark
- Conclusion
Let’s get started to find the optimal portfolio allocation using the Markowitz Model. 😃
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