In this chapter, we will start to illustrate how you can use a broad range of supervised and unsupervised machine learning (ML) models for algorithmic trading. We will explain each model's assumptions and use cases before we demonstrate relevant applications using various Python libraries. The categories of models will include:
- Linear models for the regression and classification of cross-section, time series, and panel data
- Generalized additive models, including non-linear tree-based models, such as decision trees
- Ensemble models, including random forest and gradient-boosting machines
- Unsupervised linear and nonlinear methods for dimensionality reduction and clustering
- Neural network models, including recurrent and convolutional architectures
- Reinforcement learning models
We will apply these models to the market, fundamental, and alternative...
