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)

How to detect objects

Object detection requires the ability to distinguish between several classes of objects, and to decide how many, and which, of these objects are present in an image.

Google Street View house number dataset

A prominent example is Ian Goodfellow's identification of house numbers from Google's Street View dataset. It is required to identify the following:

  • How many of up to five digits make up the house number
  • The correct digit for each component
  • The proper order of the constituent digits

The svhn_preprocessing notebook contains code to produce a simplified, cropped dataset that uses bounding box information to create regularly shaped 32 x 32 images containing the digits; the original images are...