ML extracts signals from a wide range of market, fundamental, and alternative data, and can be applied at all steps of the algorithmic trading-strategy process. Key applications include:

• Data mining to identify patterns and extract features
• Supervised learning to generate risk factors or alphas and create trade ideas
• Aggregation of individual signals into a strategy
• Allocation of assets according to risk profiles learned by an algorithm
• The testing and evaluation of strategies, including through the use of synthetic data
• The interactive, automated refinement of a strategy using reinforcement learning

We briefly highlight some of these applications and identify where we will demonstrate their use in later chapters.