Hands-On Machine Learning for Algorithmic Trading

Making do without a free lunch

No system—computer program or human—has a basis to reliably predict outcomes for new examples beyond those it observed during training. The only way out is to have some additional prior knowledge or make assumptions that go beyond the training examples. We covered a broad range of algorithms from Chapter 7, Linear Models and Chapter 8, Time Series Models, to non-linear ensembles in Chapter 10, Decision Trees and Random Forest and Chapter 11, Gradient Boosting Machines as well as neural networks in various chapters of part 4 of this book.

We saw that a linear model makes a strong assumption that the relationship between inputs and outputs has a very simple form, whereas the models discussed later aim to learn ...