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.

Chapter 3, Alternative Data for Finance, provides categories and criteria to assess the exploding number of sources and providers. It also demonstrates how to create alternative data sets by scraping websites, for example to collect earnings call transcripts for use with natural language processing (NLP) and sentiment analysis algorithms in the second part of the book.

Chapter 4, Alpha Factor Research, provides a framework for understanding how factors work and how to measure their performance, for example using the information coefficient (IC). It demonstrates how to engineer alpha factors from data using Python libraries offline and on the Quantopian platform. It also introduces the zipline library to backtest factors and the alphalens library to evaluate their predictive power. 

Chapter 5, Strategy Evaluation, introduces how to build, test and evaluate trading strategies using historical data with zipline offline and on the Quantopian platform. It presents and demonstrates how to compute portfolio performance and risk metrics using the pyfolio library. It also addresses how to manage methodological challenges of strategy backtests and introduce methods to optimize a strategy from a portfolio risk perspective.