Complete Chapter Outline

This [chapter](02_market_and_fundamental_data) shows how to work with market and fundamental data sources and describes key aspects of the environment in which they are created. Familiarity …

This chapter shows how to work with market and fundamental data sources and describes key aspects of the environment in which they are created. Familiarity …

This chapter outlines categories and describes criteria to assess the exploding number of alternative data sources and providers. It also demonstrates how to create alternative …

If you are already familiar with ML, you know that feature engineering is a key ingredient for successful predictions. This is no different in trading. …

Alpha factors generate signals that an algorithmic strategy translates into trades, which, in turn, produce long and short positions. The returns and risk of the …

This chapter sets the stage by outlining how to formulate, train, tune and evaluate the predictive performance of ML models as a systematic workflow

Linear models are applied to regression and classification problems with the goals of inference and prediction. Numerous asset pricing models developed by academia and industry …

The goal of this chapter is to present an end-to-end perspective on the process of designing, simulating, and evaluating a trading strategy driven by an …

This chapter focuses on models that extract signals from previously observed data to predict future values for the same time series. The time dimension of …

This chapter introduces how Bayesian approaches to machine learning add value when developing and evaluating trading strategies due to their different perspective on uncertainty.

This chapter shows how decision trees and random forests can be used for trading. We will see how decision trees learn rules from data that …

This chapter explores boosting, an alternative tree-based ensemble algorithm that often produces better results. The key difference is that boosting modifies the data that is …

Dimensionality reduction and clustering are the main tasks for unsupervised learning: Dimensionality reduction transforms the existing features into a new, smaller set while minimizing the …

This chapter introduces text feature extraction techniques that focus on individual semantic units, i.e. words or short groups of words called tokens. We will show …

This chapter uses unsupervised learning to model latent topics and extract hidden themes from documents. These themes can produce detailed insights into a large body …

This chapter introduces uses neural networks to learn a vector representation of individual semantic units like a word or a paragraph. These vectors are dense …

This chapter presents feedforward neural networks (NN) to demonstrate how to efficiently train large models using backpropagation, and manage the risks of overfitting. It also …

CNNs are named after the linear algebra operation called convolution that replaces the general matrix multiplication typical of feed-forward networks. Research into CNN architectures has …

The major innovation of RNN is that each output is a function of both previous output and new data. As a result, RNN gain the …

This chapter shows how unsupervised learning can leverage deep learning for trading. More specifically, we’ll discuss autoencoders that have been around for decades but recently …

This chapter introduces a second unsupervised deep learning technique after presenting autoencoders in the last chapter. More specifically, we’ll introduce generative adversarial networks (GANs) that …

Reinforcement Learning (RL) is a computational approach to goal-directed learning performed by an agent that interacts with a typically stochastic environment which the agent has …

In this concluding chapter, we will briefly summarize the key tools, applications, and lessons learned throughout the book to avoid losing sight of the big …

Throughout this book, we emphasized how the smart design of features, including appropriate preprocessing and denoising, is what typically leads to an effective strategy. This …