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.