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Key benefits

Design, train, and evaluate machine learning algorithms that underpin automated trading strategies

Create a research and strategy development process to apply predictive modeling to trading decisions

Leverage NLP and deep learning to extract tradeable signals from market and alternative data

Description

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This revised and expanded second edition enables you to build and evaluate sophisticated supervised, unsupervised, and reinforcement learning models. This book introduces end-to-end machine learning for the trading workflow, from the idea and feature engineering to model optimization, strategy design, and backtesting. It illustrates this by using examples ranging from linear models and tree-based ensembles to deep-learning techniques from cutting edge research. This edition shows how to work with market, fundamental, and alternative data, such as tick data, minute and daily bars, SEC filings, earnings call transcripts, financial news, or satellite images to generate tradeable signals. It illustrates how to engineer financial features or alpha factors that enable an ML model to predict returns from price data for US and international stocks and ETFs. It also shows how to assess the signal content of new features using Alphalens and SHAP values and includes a new appendix with over one hundred alpha factor examples. By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.

Who is this book for?

If you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. This book is for you if you want to learn how to extract value from a diverse set of data sources using machine learning to design your own systematic trading strategies. Some understanding of Python and machine learning techniques is required.

What you will learn

Leverage market, fundamental, and alternative text and image data

Research and evaluate alpha factors using statistics, Alphalens, and SHAP values

Implement machine learning techniques to solve investment and trading problems

Backtest and evaluate trading strategies based on machine learning using Zipline and Backtrader

Optimize portfolio risk and performance analysis using pandas, NumPy, and pyfolio

Create a pairs trading strategy based on cointegration for US equities and ETFs

Train a gradient boosting model to predict intraday returns using AlgoSeek s high-quality trades and quotes data

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

Frequently bought together

40 Algorithms Every Programmer Should Know

$49.99

Machine Learning for Algorithmic Trading

$57.99

$54.99

Total $ 162.97

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Amazon verified reviews

Akshit shah Aug 15, 2020

As a machine learning practitioner.I always wanted to go into the trading space and apply my knowledge, I have been looking up the internet and had so many overwhelming knowledge. It was really difficult to find an ideal book. Then I came across this book and It helped me understand different ways and how to develop different models and understand the use of it in algorithmic trading. Thanks a lot for this book

Amazon Verified review

Frank S Dec 03, 2022

For example, yes all of the photos are black and white. However in the preface there's very clear instruction on where you can find the color versions (PDFs in the github repo, for those who eschew prefaces) and if you intend to use any of the Python code that goes along with this tome, you'll see the color versions can often also be found in the Jupyter notebooks - a fact frequently referenced in the first two chapters.Second, getting python environments up and running smoothly is, unfortunately, rarely a very easy task. This is certainly not exclusive to this particular use case.If I had any complaint at all about the book, it's that it is overly thorough so you may find yourself slogging through some tedium as you begin. It's not broken up in a way that easily allows for skipping ahead (at least not for my prior knowledge set). In the first couple chapters I've found I've needed - on average - every other paragraph and that the subject matter is the source of the dryness, not the author's use of language; which so far has been smooth and flows far more gracefully than my own.I will update after more extensive use of the author's code.

Jing Zhang Jan 31, 2021

I love all Stefan's books, which are all well written and logically organized - very easy to follow. They not only provide detailed information on the theories behind, but also provide many practical examples and even Jupyter Notebooks that can be used in real life situations. They also covered almost all areas of Machine Learning in trading. They are just like THE bibles of Machine Learning in trading to me. Highly recommended!Got both "Hands-On Machine Learning for Algorithmic Trading" and "Machine Learning for Algorithmic Trading", if you want to master Machine Learning in trading!

James Ma Aug 10, 2020

Gives a comprehensive view of machine learning in trading, asset management, and strategy analysis. Each chapter provides insights, breadth, and concise explaninations more than I can possibly ask for, along with Python codes. I like the fact that almost very page contains diagrams to illustrate complex topics. With over 20 well-written chapters, this is a practical guide for applying machine learning, deep learning, and NLP techniques to algorithmic trading.

Mac Sep 13, 2021

Pretty good

Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python , Second Edition

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Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python , Second Edition

What do you get with Print?

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Description

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What you will learn

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