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 concepts of prior, evidence, and posterior, in order to distinguish the concept of uncertainty using PyMC3. We will then utilize NLTK, sklearn, and spaCy to assign sentiment scores to financial news and classify documents to extract trading signals. We will learn to design, build, tune, and evaluate feed forward neural networks, recurrent neural networks (RNNs), and convolutional neural networks (CNNs), using Keras to design sophisticated algorithms. You will apply transfer learning to satellite image data to predict economic activity. Finally, we will apply reinforcement learning for optimal trading results.
By the end of the book, you will be able to adopt algorithmic trading to implement smart investing strategies.
