Hands-On Machine Learning for Algorithmic Trading

Part I – the framework – from data to strategy design

The first part provides a framework for the development of algorithmic trading strategies. It focuses on the data that power the ML algorithms and strategies discussed in this book, outlines how ML can be used to derive trading signals, and how to deploy and evaluate strategies as part of a portfolio.

The remainder of this chapter summarizes how and why ML became central to investment, describes the trading process and outlines how ML can add value. Chapter 2, Market and Fundamental Data, covers sources and working with original exchange-provided tick and financial reporting data, as well as how to access numerous open-source data providers that we will rely on throughout this book.