Develop a Python Library for Financial Network Analysis and Visualization

Have you ever looked at a portfolio of stocks and wondered how they really influence each other, beyond simple pairwise correlations? I found myself asking this question repeatedly. Standard financial analysis often treats assets in isolation or in simple pairs. But the market is a complex system, an interconnected web where shocks can ripple through unexpected pathways. This realization led me down the path of financial network analysis — a powerful way to model and understand market structure, identify systemic risks and uncover hidden relationships.

This tutorial documents my journey in building a dedicated Python library to tackle these questions. We won’t just run scripts; we’ll construct a reusable, well-structured library from the ground up. We’ll start by laying the foundation, fetching real-world stock data and then dive deep into transforming that data into intricate network graphs using libraries like NetworkX.

Photo by Shahadat Rahman on Unsplash

Table of Contents

• Section 1: Library Foundation and Data Acquisition: Set up the project structure, manage dependencies and implement data fetching and preprocessing using yfinance and pandas.
• Section 2: Constructing Financial Correlation Networks: Define and build correlation-based financial networks using asset returns with…