Design and execution of a trading strategy

ML can add value at multiple steps in the lifecycle of a trading strategy, and relies on key infrastructure and data resources. Hence, this book aims to addresses how ML techniques fit into the broader process of designing, executing, and evaluating strategies.

An algorithmic trading strategy is driven by a combination of alpha factors that transform one or several data sources into signals that in turn predict future asset returns and trigger buy or sell orders. Chapter 2Market and Fundamental Data and Chapter 3Alternative Data for Finance cover the sourcing and management of data, the raw material and the single most important driver of a successful trading strategy.  

Chapter 4Alpha Factor Research outlines a methodologically sound process to manage the risk of false discoveries that increases with the amount of data. Chapter 5, Strategy Evaluation provides the context for the execution and performance measurement of a trading strategy:

Let's take a brief look at these steps, which we will discuss in depth in the following chapters.

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