Book Image

Hands-On Machine Learning for Algorithmic Trading

By : Stefan Jansen
Book Image

Hands-On Machine Learning for Algorithmic Trading

By: Stefan Jansen

Overview of this book

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You’ll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym.
Table of Contents (23 chapters)

Unsupervised Learning

In Chapter 6, Machine Learning Process, we discussed how unsupervised learning adds value by uncovering structures in the data without an outcome variable, such as a teacher, to guide the search process. This task contrasts with the setting for supervised learning that we focused on in the last several chapters.

Unsupervised learning algorithms can be useful when a dataset contains only features and no measurement of the outcome, or when we want to extract information independent of the outcome. Instead of predicting future outcomes, the goal is to study an informative representation of the data that is useful for solving another task, including the exploration of a dataset.

Examples include identifying topics to summarize documents (see Chapter 14, Topic Modeling), reducing the number of features to reduce the risk of overfitting and the computational cost...