Mastering Multi-Asset Portfolio Optimization with Constraints and Transaction Costs in Python
In today’s complex and interconnected financial markets, achieving optimal portfolio allocation is a paramount concern for both individual and institutional investors. This comprehensive tutorial delves into the intricacies of multi-asset portfolio optimization, equipping you with the knowledge and practical Python code to construct robust and efficient portfolios tailored to your financial goals.
This tutorial goes beyond the basics by incorporating real-world constraints and transaction costs into the optimization process. We’ll explore how factors like sector exposure limits, position limits, turnover constraints and various transaction cost models can significantly impact portfolio performance and how to account for them effectively.
Table of Contents
- Data Acquisition and Preprocessing: Gathering financial data, cleaning and transforming it for optimization.
- Defining the Optimization Problem: Formulating the objective function, asset universe, constraints and transaction cost models.
- Portfolio Optimization Techniques: Exploring and implementing different optimization algorithms, such as Mean-Variance Optimization, Black-Litterman and Hierarchical Risk Parity.
