Hands-On Machine Learning for Algorithmic Trading

Preface

The availability of diverse data has increased the demand for expertise in algorithmic trading strategies. With this book, you will select and apply machine learning (ML) to a broad range of data sources and create powerful algorithmic strategies.

This book will start by introducing you to essential elements, such as evaluating datasets, accessing data APIs using Python, using Quandl to access financial data, and managing prediction errors. We then cover various machine learning techniques and algorithms that can be used to build and train algorithmic models using pandas, Seaborn, StatsModels, and sklearn. We will then build, estimate, and interpret AR(p), MA(q), and ARIMA (p, d, q) models using StatsModels. You will apply Bayesian ...