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)

Designing and training autoencoders using Python

In this section, we illustrate how to implement several of the autoencoder models introduced in the preceding section using Keras. We first load and prepare an image dataset that we use throughout this section because it makes it easier to visualize the results of the encoding process.

We then proceed to build autoencoders using deep feedforward nets, sparsity constraints, and convolutions and then apply the latter to denoise images.

Preparing the data

For illustration, we'll use the Fashion MNIST dataset, a modern drop-in replacement for the classic MNIST handwritten digit dataset popularized by Yann LeCun with LeNet in the 1990s. We also relied on this dataset in Chapter...