Building a Robo-Advisor Platform with Python: From Portfolio Optimization to Backtesting

In today’s tech-driven financial landscape, robo-advisors have emerged as powerful tools, democratizing investment management through sophisticated algorithms and user-friendly interfaces. This tutorial will guide you through constructing your own robo-advisor platform using Python, covering key aspects from portfolio optimization to rigorous backtesting.

This tutorial is structured to provide a practical and comprehensive understanding of building a robo-advisor, assuming basic familiarity with Python and financial concepts. We’ll employ real-world examples and delve into the logic behind each step, empowering you to customize the platform to your own investment strategies and risk preferences.

Photo by Jakub Żerdzicki on Unsplash

Table of Contents

• Architecting the Robo-Advisor: Defining user risk profiles and their implications on investment strategies.
• Portfolio Optimization Techniques: Implementing Modern Portfolio Theory (MPT) with Python using libraries like cvxpy or PyPortfolioOpt.
• Data Acquisition and Preprocessing: Sourcing financial data, handling missing values and conducting exploratory data analysis (EDA) with visualization libraries like matplotlib or seaborn.
• Backtesting and Performance…