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Hands-On Machine Learning for Algorithmic Trading

Hands-On Machine Learning for Algorithmic Trading

By : Yau, Stefan Jansen
4.1 (20)
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Hands-On Machine Learning for Algorithmic Trading

Hands-On Machine Learning for Algorithmic Trading

4.1 (20)
By: Yau, 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)
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Alpha factor resources

The research process requires designing and selecting alpha factors with respect to the predictive power of their signals. An algorithmic trading strategy will typically build on multiple alpha factors that send signals for each asset. These factors may be aggregated using an ML model to optimize how the various signals translate into decisions about the timing and sizing of individual positions, as we will see in subsequent chapters.

Alternative algorithmic trading libraries

Additional open-source Python libraries for algorithmic trading and data collection include (see links on GitHub):

  • QuantConnect is a competitor to Quantopian
  • WorldQuant offers online competition and recruits community contributors...
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