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)

Gradient Boosting Machines

In the previous chapter, we learned about how random forests improve the predictions made by individual decision trees by combining them into an ensemble that reduces the high variance of individual trees. Random forests use bagging, which is short for bootstrap aggregation, to introduce random elements into the process of growing individual trees.

More specifically, bagging draws samples from the data with replacement so that each tree is trained on a different but equal-sized random subset of the data (with some observations repeating). Random forests also randomly select a subset of the features so that both the rows and the columns of the data that are used to train each tree are random versions of the original data. The ensemble then generates predictions by averaging over the outputs of the individual trees.

Individual trees are usually grown deep...