The second part covers the fundamental supervised and unsupervised learning algorithms and illustrates their application to trading strategies. It also introduces the Quantopian platform where you can leverage and combine the data and ML techniques developed in this book to implement algorithmic strategies that execute trades in live markets.
Chapter 6, The Machine Learning Process, sets the stage by outlining how to formulate, train, tune and evaluate the predictive performance of ML models as a systematic workflow.
Chapter 7, Linear Models, it shows how to use linear and logistic regression for inference and prediction and how to use regularization to manage the risk of overfitting. It presents the Quantopian trading platform and demonstrates how to build factor models and predict asset prices.
Chapter 8, Time Series Models, covers univariate and multivariate time series, including vector autoregressive models and cointegration tests, and how they can be applied to pairs trading strategies. Chapter 9, Bayesian Machine Learning, presents how to formulate probabilistic models and how Markov Chain Monte Carlo (MCMC) sampling and Variational Bayes facilitate approximate inference. It also illustrates how to use PyMC3 for probabilistic programming to gain deeper insights into parameter and model uncertainty.
Chapter 10, Decision Trees and Random Forests, shows how to build, train and tune non-linear tree-based models for insight and prediction. It introduces tree-based ensemble models and shows how random forests use bootstrap aggregation to overcome some of the weaknesses of decision trees. Chapter 11, Gradient Boosting Machines ensemble models and demonstrates how to use the libraries xgboost, lightgbm, and catboost for high-performance training and prediction, and reviews in depth how to tune the numerous hyperparameters.
Chapter 12, Unsupervised Learning introduces how to use dimensionality reduction and clustering for algorithmic trading. It uses principal and independent component analysis to extract data-driven risk factors. It presents several clustering techniques and demonstrates the use of hierarchical clustering for asset allocation.