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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

Product Details

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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 : 9781789342710
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Languages :
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What do you get with eBook?

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Product Details

Publication date : Dec 31, 2018
Length: 684 pages
Edition : 1st
Language : English
ISBN-13 : 9781789342710
Category :
Languages :
Tools :

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Frequently bought together


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Total $ 158.97
Machine Learning for Finance
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Learn Algorithmic Trading
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Hands-On Machine Learning for Algorithmic Trading
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Total $ 158.97 Stars icon
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Table of Contents

16 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
Next Steps 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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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
Mike Jan 06, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Good book
Amazon Verified review Amazon
MH Oct 24, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Quite puzzled by some of the negative comments given here as in general this is a field with an ever changing landscape and to dive-in and demonstrate some of the nuances experienced in the field is quite a daring move to say the least!Ignoring the nay sayers, for a book of this size and nature, you'd have to adopt an iterative approach. Specifically, do an initial pass at one or more chapters of interest to get a feel of the challenges, google on topics of interest to gain more supporting background, and then follow through with a final pass to appreciate the related material.Stefan Jansen (the author) does not shy away from sharing his insights nor detailed level codes on just about every practical challenges encountered in machine/deep learning, pertaining to algo-trading; so all kudos for retaining 'walk the talk' approach throughout the book.In all, Stefan is among those few authors (5%) who actually know what they're talking about!Matthew
Amazon Verified review Amazon
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