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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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The multiple linear regression model

We will introduce the model's specification and objective function, methods to learn its parameters, statistical assumptions that allow for inference and diagnostics of these assumptions, as well as extensions to adapt the model to situations where these assumptions fail.

How to formulate the model

The multiple regression model defines a linear functional relationship between one continuous outcome variable and p input variables that can be of any type but may require preprocessing. Multivariate regression, in contrast, refers to the regression of multiple outputs on multiple input variables.

In the population, the linear regression model has the following form for a single instance...

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