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)

Autoencoders and Generative Adversarial Nets

In this chapter, we present two unsupervised learning techniques that leverage deep learning: autoencoders, which have been around for decades, and Generative Adversarial Networks (GANs), which were introduced by Ian Goodfellow in 2014 and which Yann LeCun has called the most exciting idea in AI in the last ten years. They complement the methods for dimensionality reduction and clustering introduced in Chapter 12, Unsupervised Learning.

Unsupervised learning addresses machine learning (ML) challenges such as the limited availability of labeled data and the curse of dimensionality that requires exponentially more samples for successful learning from complex, real-life data with many features. At a higher level, unsupervised learning resembles human learning and the development of common sense much more closely than supervised and reinforcement...