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Hands-On Machine Learning for Algorithmic Trading
Hands-On Machine Learning for Algorithmic Trading

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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Hands-On Machine Learning for Algorithmic Trading

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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
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 31, 2018
Length: 684 pages
Edition : 1st
Language : English
ISBN-13 : 9781789346411
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Estimated delivery fee Deliver to Sweden

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€17.95
(Includes tracking information)

Product Details

Publication date : Dec 31, 2018
Length: 684 pages
Edition : 1st
Language : English
ISBN-13 : 9781789346411
Category :
Languages :
Concepts :
Tools :

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Table of Contents

22 Chapters
Machine Learning for Trading Chevron down icon Chevron up icon
Market and Fundamental Data Chevron down icon Chevron up icon
Alternative Data for Finance Chevron down icon Chevron up icon
Alpha Factor Research Chevron down icon Chevron up icon
Strategy Evaluation Chevron down icon Chevron up icon
The Machine Learning Process Chevron down icon Chevron up icon
Linear Models Chevron down icon Chevron up icon
Time Series Models Chevron down icon Chevron up icon
Bayesian Machine Learning Chevron down icon Chevron up icon
Decision Trees and Random Forests Chevron down icon Chevron up icon
Gradient Boosting Machines Chevron down icon Chevron up icon
Unsupervised Learning Chevron down icon Chevron up icon
Working with Text Data Chevron down icon Chevron up icon
Topic Modeling Chevron down icon Chevron up icon
Word Embeddings Chevron down icon Chevron up icon
Deep Learning Chevron down icon Chevron up icon
Convolutional Neural Networks Chevron down icon Chevron up icon
Recurrent Neural Networks Chevron down icon Chevron up icon
Autoencoders and Generative Adversarial Nets Chevron down icon Chevron up icon
Reinforcement Learning Chevron down icon Chevron up icon
Next Steps Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.1
(20 Ratings)
5 star 70%
4 star 0%
3 star 10%
2 star 10%
1 star 10%
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Dechauffour May 04, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
As I was a near complete beginner, the book really got me started on both topics. Great book, thanks!
Amazon Verified review Amazon
M. R. Schormann Apr 16, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Jihao Yu Jan 25, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great stuff!
Amazon Verified review Amazon
Amazon Customer Jan 28, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you into quant and trading, this is good book to buy.
Amazon Verified review Amazon
IntegralBill Aug 20, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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).
Amazon Verified review Amazon
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Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

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Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
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  9. Iran
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  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
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  16. Ukraine
  17. Venezuela