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Hands-On Machine Learning for Algorithmic Trading
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Hands-On Machine Learning for Algorithmic Trading
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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.
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Current Title:
Hands-On Machine Learning for Algorithmic Trading
Table of Contents (23 chapters)
Preface
Free Chapter
Machine Learning for Trading
Market and Fundamental Data
Alternative Data for Finance
Alpha Factor Research
Strategy Evaluation
The Machine Learning Process
Linear Models
Time Series Models
Bayesian Machine Learning
Decision Trees and Random Forests
Gradient Boosting Machines
Unsupervised Learning
Working with Text Data
Topic Modeling
Word Embeddings
Deep Learning
Convolutional Neural Networks
Recurrent Neural Networks
Autoencoders and Generative Adversarial Nets
Reinforcement Learning
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