FinTDA: Python package for estimating market change through persistent homology diagrams

Abstract

This paper presents a user-friendly version of Persistent Homology (PH) graph code to model financial market structures and changes. By leveraging Topological Data Analysis (TDA), the code offers an effective approach for analyzing high-dimensional stock data, enabling the identification of persistent topological features indicative of market changes. The code’s potential applications in financial stability prediction, investment strategy development, and educational advancement are discussed. This contribution aims to facilitate the adoption of PH techniques in finance, promising significant implications for academic research and practical market analysis.