Mastering Financial and Crypto Market Modelling with Kolmogorov-Arnold Networks (KANs): A Practical Tutorial

Introduction

In the fast changing world of financial and cryptocurrency markets, leveraging advanced machine learning techniques can give you a competitive edge. One such technique is the use of Kolmogorov-Arnold Networks (KANs) for modelling complex market behaviours.

Last week I wrote a book about the subject and how to apply it to the market. This tutorial will introduce you to KANs, walk you through their implementation in Python using PyTorch, and demonstrate their application in financial and crypto market analysis. By the end, you’ll have a solid foundation to apply these powerful tools to your own projects and be eager to explore more through our comprehensive guide available on Kindle.

Understanding Kolmogorov-Arnold Networks (KANs)

KANs are a specialized type of neural network designed to model complex and nonlinear functions. They are rooted in the Kolmogorov-Arnold representation theorem, which asserts that any continuous function can be decomposed into a finite sum of continuous functions of a single variable. This structured approach makes KANs particularly effective for financial modelling, where capturing intricate dependencies…