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Key benefits

Implement machine learning algorithms to build, train, and validate algorithmic models

Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions

Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics

Description

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.

Who is this book for?

Hands-On Machine Learning for Algorithmic Trading is for data analysts, data scientists, and Python developers, as well as investment analysts and portfolio managers working within the finance and investment industry. If you want to perform efficient algorithmic trading by developing smart investigating strategies using machine learning algorithms, this is the book for you. Some understanding of Python and machine learning techniques is mandatory.

What you will learn

Implement machine learning techniques to solve investment and trading problems

Leverage market, fundamental, and alternative data to research alpha factors

Design and fine-tune supervised, unsupervised, and reinforcement learning models

Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn

Integrate machine learning models into a live trading strategy on Quantopian

Evaluate strategies using reliable backtesting methodologies for time series

Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow

Work with reinforcement learning for trading strategies in the OpenAI Gym

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

Hands-On Machine Learning for Algorithmic Trading

$65.99

$48.99

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Total $ 158.97

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Amazon verified reviews

Dechauffour May 04, 2019

As I was a near complete beginner, the book really got me started on both topics. Great book, thanks!

Amazon Verified review

M. R. Schormann Apr 16, 2019

I bought a number of books on the subject and this one really approaches the subject in a clear, concise and logical way. For the content of the book and for the way it is presented, this book deserves a five-star rating. The book is excellent. The author is clearly extremely well versed in the field and covers the main topics well. Python code in the text is used to demonstrate how the topic at hand is codified - however, the bulk of the rest of the code is in the Github repository (where it can be kept up-to-date).On the flip side, 5 of the most important chapters of the book were not present in the printed version!!! These chapters covered subjects that are touted on the jacket and were my main reason for buying the book. On this issue, Packt deserves major scorn, as it appears to left the chapters out, in an effort to save on printing costs. Apparently, the chapters are available on the Github repository. In my case, I landed up communicating directly with the author (who has been more than helpful), and the missing chapters have been provided - and they are as good as the rest of the book. Apparently, a reprint of the book will include all the missing material, so later purchasers of the book should get the whole book, which, as mentioned before, is excellent.

Jihao Yu Jan 25, 2019

Great stuff!

Amazon Customer Jan 28, 2019

If you into quant and trading, this is good book to buy.

IntegralBill Aug 20, 2019

I was very excited when I first found this book. For a couple of years, I've been looking for a good book on algorithmic training using Python. Some popular books that I found, prior to this book's release, typically gave examples in MatLab, R, or were just too complex. Better yet, this book is very modern, including chapters on popular Deep Learning (DL), Generative Adversarial Networks (GANs), and Reinforcement Learning (RL)!This book reads well; but, you will need to put in some work. After buying this book, the next thing I recommend is downloading the author's code from GitHub! This will help you understand what is going on while giving you hands-on experience.Some comments mention that there are some missing chapters. If you e-mail the author, Stefan Jansen, he will send you these chapters! This author is very approachable and helpful if you have any questions, suggestions, etc.!Note: I purchased this book direct from the publisher's website, early in 2019 (Packt).

Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

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