In the ever-evolving world of financial markets, the ability to adapt machine learning models to different market conditions is crucial. Transfer learning, a technique where a pre-trained model is fine-tuned on a new task, offers a powerful solution. This tutorial will guide you through the process of employing transfer learning to adapt machine learning models for different financial markets using Python.
Introduction
Transfer learning has gained significant traction in recent years, especially in fields like computer vision and natural language processing. However, its application in financial markets is still relatively unexplored. By leveraging pre-trained models, we can significantly reduce the time and computational resources required to develop robust models for different financial markets.
In this tutorial, we will use the yfinance library to download real financial data and the keras library to build and fine-tune our machine learning models. We will focus on a diverse set of financial assets to demonstrate the versatility of transfer learning. Let’s dive in!