What to expect

This book aims to equip you with the strategic perspective, conceptual understanding, and practical tools to add value from applying ML to the trading and investment process. To this end, it covers ML as an important element in a process rather than a standalone exercise.

First and foremost, it covers a broad range of supervised, unsupervised, and reinforcement learning algorithms useful for extracting signals from the diverse data sources relevant to different asset classes. It introduces a ML workflow and focuses on practical use cases with relevant data and numerous code examples. However, it also develops the mathematical and statistical background to facilitate the tuning of an algorithm or the interpretation of the results.

The book recognizes that investors can extract value from third-party data more than other industries. As a consequence, it covers not only how to work with market and fundamental data but also how to source, evaluate, process, and model alternative data sources such as unstructured text and image data.

It relates the use of ML to research and evaluate alpha factors to quantitative and factor-based strategies and introduces portfolio management as the context for the deployment of strategies that combine multiple alpha factors. It also highlights that ML can add value beyond predictions relevant to individual asset prices, for example to asset allocation and addresses the risks of false discoveries from using ML with large datasets to develop a trading strategy.

It should not be a surprise that this book does not provide investment advice or ready-made trading algorithms. Instead, present building blocks required to identify, evaluate, and combine datasets that suitable for any given investment objective, select and apply ML algorithms to this data, and develop and test algorithmic trading strategies based on the results.

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