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  • Hands-On AI Trading with Python, QuantConnect, and AWS
  • Master the art of AI-driven algorithmic trading strategiesJohn Wiley & Sons
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Hands-On AI Trading with Python, QuantConnect, and AWS 1st Edition

by Jiri Pik (Author), Ernest P. Chan (Author), Jared Broad (Author),
4.0 out of 5 stars (44)
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Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance

Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt.

Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks.

The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used:

  • Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab.
  • Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM.
  • Predict market volatility regimes and allocate funds accordingly.
  • Predict daily returns of tech stocks using classifiers.
  • Forecast Forex pairs' future prices using Support Vector Machines and wavelets.
  • Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs.
  • Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications.
  • Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization.
  • Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch.
  • AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation.

Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.

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  1. ISBN-10
    1394268432
  2. ISBN-13
    978-1394268436
  3. Edition
    1st
  4. Publisher
    Wiley
  5. Publication date
    January 29, 2025
  6. Language
    English
  7. Dimensions
    7.1 x 0.9 x 10 inches
  8. Print length
    416 pages
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From the Publisher

1394268432

Modern, Hands-on Approach to AI in Trading

"Hands-On AI Trading with Python, QuantConnect, and AWS" is a practical guide that bridges the gap between AI theory and real-world trading applications. Focusing on intuition and hands-on experience, the book uses QuantConnect's cloud-based platform to streamline AI strategy development by avoiding technical complexities like data management or infrastructure setup. It covers a wide range of techniques, including machine learning, deep learning, and reinforcement learning, applied to practical trading scenarios. Printed in full color, it enhances understanding with detailed charts and code snippets, making advanced concepts accessible to both beginners and professionals in quantitative finance.

1394268432

A Comprehensive Suite of Examples

Our book features over 20 complete AI trading algorithms, addressing key techniques like trend detection, regime classification, and reinforcement learning for optimal hedging. Examples progress from basic concepts to advanced applications using Python with TensorFlow, PyTorch, and scikit-learn, all on the QuantConnect platform for backtesting and live trading.

Technologies covered include Machine Learning (Random Forests, SVMs, Gaussian Processes), Deep Learning (CNNs, RNNs, Transformers), Reinforcement Learning, Natural Language Processing, Time Series Analysis, and Clustering.

Examples include: ML Trend Scanning with MLFinlab, Factor Preprocessing Techniques for Regime Detection, Reversion vs. Trending: Strategy Selection by Classification, Alpha by Hidden Markov Models, FX SVM Wavelet Forecasting, and more. Each includes detailed explanations, code, and discussions on practical considerations, equipping readers to master AI in quantitative finance.

1394268432

Practically Introduces Quantitative Concepts

Our book goes beyond coding exercises, integrating key concepts like risk management, portfolio optimization, and corporate actions directly into AI trading strategies. We explore techniques like Corrective AI and Conditional Parameter Optimization to dynamically adapt strategies to volatile markets and demonstrate how AI can predict and capitalize on events like mergers and dividends. Emphasizing transaction costs, slippage, and real-world frictions, we ensure readers are equipped to transition seamlessly from backtesting to live trading with adaptive, robust systems.

1394268432

Rich Additional Content and Community

To enhance the value of our book, we provide extensive resources, including a GitHub repository with all code examples, actively maintained for compatibility with QuantConnect and Python libraries. Readers can engage with a vibrant community through forums, webinars, and social media, fostering collaboration and continuous learning. Additional online resources, such as extended datasets, articles, and video tutorials, dive into advanced topics and emerging AI trading trends. Our close ties with QuantConnect ensure readers have access to the platform’s latest features, creating a comprehensive learning experience that blends cutting-edge techniques, practical finance concepts, and a supportive ecosystem.

Editorial Reviews

From the Inside Flap

Revolutionize Your Trading with Artificial Intelligence

Hands-On AI Trading with Python™, QuantConnect™, and AWS™ is a comprehensive guide that bridges the gap between cutting-edge artificial intelligence and the dynamic world of quantitative trading. The authors, Jiri Pik, Ernest P. Chan, Jared Broad, Philip Sun, and Vivek Singh, deliver a practical, data-driven roadmap to modern algorithmic trading, featuring over 20 fully implemented real-world examples to ignite your creativity and serve as a launchpad for your ideas.

This book demystifies the complexities of algorithmic trading by leveraging QuantConnect™ to backtest, optimize, and deploy trading strategies. Unlike conventional resources, this book provides fully implemented Python™ examples, empowering you to focus on innovation over infrastructure.

What’s Inside?

The book is packed with practical ways to set up data and use AI models in your trading, including Support Vector Machines for price trend forecasting, Convolutional Neural Networks (CNNs) for pattern recognition in stock prices, Markov Chains for dynamic asset allocation, Gaussian Naive Bayes for risk classification, and Reinforcement Learning for optimal trading strategies.

Technologies are illustrated with real-world examples, including mean-reversion pairs trading strategies, momentum-based equity trading strategies, volatility-based options strategies, dynamic hedging, portfolio optimization, and asset class selection using Principal Component Analysis (PCA).

Accompanied by a GitHub repository with source code and strategy results, readers can rapidly test, refine, and experiment with strategies.

Who Should Read This Book?

Whether you’re a seasoned hedge fund professional, an asset manager, or a graduate student in finance, Hands-On AI Trading with Python™, QuantConnect™, and AWS™ equips you with actionable tools to integrate AI into your trading workflows. This book is essential for anyone aiming to excel in today’s competitive financial markets.

Take control of your trading future today― get your copy and leverage AI to transform your strategies.

From the Back Cover

Praise for HANDS-ON AI TRADING

“A must-have for algorithmic traders and students, this book emphasizes designing trading strategies with QuantConnect™. Featuring Python™ examples and advanced AI/ML models, it offers a clear and accessible presentation ideal for anyone in quantitative finance.”
―PETTER N. KOLM, Professor, Courant Institute of Mathematical Sciences, New York University; Awarded “Quant of the Year” in 2021

“This concise guide provides a gentle introduction with hands-on examples and expert insights into dissecting and evaluating trades from seasoned traders. The code will make otherwise complex or confusing examples clear. It is an excellent springboard for developing your own strategies.”
―MICHAEL ROBBINS, Author of Quantitative Asset Management

“This is the book I wish I had when starting out, it would have saved me years! It offers rare insights and practical tutorials, allowing the next generation of quants to stand on the shoulders of giants.”
―JACQUES JOUBERT, Quant Researcher and Developer, Co-Founder and CEO of Hudson and Thames Quantitative Research

“The book ties both theory and industry together while providing code, output, and a platform to implement AI models in a trading environment. Cookbook style makes it a great book for those new to machine learning and AI in quantitative finance.”
―DIMITRI BIANCO, Head of Quant Risk and Research, Agora Data, Inc.

“As a novice trader myself, I have been looking for ways to apply AI in real-world trading scenarios. This book does an excellent job in explaining trading concepts and mapping these to AI concepts to build trading strategies. A must-read if you want to use AI for building wealth.”
―RAJNEESH SINGH, Director, Amazon SageMaker

“This book is an excellent resource for learning machine learning and AI for quantitative trading. The authors’ practical guidance helps in creating strategies, building portfolios, and managing risks with QuantConnect’s™ support.”
―JASON JIE SHENG LIM, CFA, FRM, Risk Data Scientist

“This comprehensive guide masterfully bridges the gap between AI technology and practical trading applications, offering finance professionals valuable insights for developing robust, data-driven trading strategies.”
―CHRIS BARTLETT, CEO, Algoseek.com

Product details

  • Publisher ‏ : ‎ Wiley
  • Publication date ‏ : ‎ January 29, 2025
  • Edition ‏ : ‎ 1st
  • Language ‏ : ‎ English
  • Print length ‏ : ‎ 416 pages
  • ISBN-10 ‏ : ‎ 1394268432
  • ISBN-13 ‏ : ‎ 978-1394268436
  • Item Weight ‏ : ‎ 2.3 pounds
  • Dimensions ‏ : ‎ 7.1 x 0.9 x 10 inches
  • Best Sellers Rank: #146,629 in Books (See Top 100 in Books)
  • Customer Reviews:
    4.0 out of 5 stars (44)

About the authors

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  1. Ernest P. Chan

    Ernest P. Chan

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    Full content visible, double tap to read brief content.

    Ernest Chan (Ernie) is the founder and chief scientific officer of Predictnow dot ai, a machine learning SaaS and consultancy for risk management and adaptive optimization. He started his career as a machine learning researcher at IBM’s T.J. Watson Research Center’s Human Language Technologies group, which produced some of the best-known quant fund managers. He was also one of the first few employees of Morgan Stanley’s AI group. He is the founder and non-executive chairman of QTS Capital Management, a quantitative CPO/CTA, and the acclaimed author of several books on quantitative trading, all published by Wiley. He obtained his PhD in physics from Cornell University and his BS in physics from the University of Toronto.

    See more on the author's page
  2. Jiri Pik

    Jiri Pik

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    Full content visible, double tap to read brief content.

    Jiri Pik is a leading innovator in the field of algorithmic trading. With extensive experience in the financial industry, Jiri has established himself as a true expert in developing and implementing cutting-edge trading strategies. He is the author of two highly acclaimed books, "Hands-On Financial Trading with Python" and "Hands-On AI Trading with Python, QuantConnect, and AWS," both of which have become indispensable resources for traders and developers seeking to master the art of automated trading. Jiri's passion for sharing his knowledge and empowering others has made him a sought-after speaker and educator in the field.

    See more on the author's page
  3. Philip Sun

    Philip Sun

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    Full content visible, double tap to read brief content.

    Philip Sun is a fintech entrepreneur, teacher of mathematical finance, quant trader and hedge fund manager and leader of research and investment teams with over 27 years of professional experience. Philip currently is the CEO and cofounder of Adaptive Investment Solutions, LLC; and an adjunct professor, teaching Algorithmic and High-Frequency Trading in the Master of Science in the Master of Science in Mathematical Finance & Financial Technology program at Boston University.

    Philip holds an MBA from the Wharton School of University of Pennsylvania, PhD in Physics from Carnegie Mellon University, and Dual Bachelor Degree in Mathematics and Physics from Stony Brook University.

    See more on the author's page
  4. Jared Broad

    Jared Broad

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    Full content visible, double tap to read brief content.

    New Zealand biomedical engineer living in Miami. CEO and founder of QuantConnect. QuantConnect empowers quants, independent investors, and trading firms to build institutional caliber quantitative trading strategies for 1% of the cost.

    We embrace a radical, fully open-source philosophy - building an ecosystem of 300,000 engineers and funds who leverage our technology to quickly and affordably do sophisticated analysis. Our open-source engine, LEAN, will be the operating system powering quantitative investment funds.

    See more on the author's page
  5. Vivek Singh

    Vivek Singh

    Brief content visible, double tap to read full content.
    Full content visible, double tap to read brief content.

    Vivek Singh is a Product leader at Amazon Web Services (AWS). He leads the development and growth of large language models (LLMs) and Generative AI application evaluation services, at AWS, to enable enterprises build scalable generative AI applications and improve AI safety, trust and responsible use. His area of expertise in technology, lies in LLM architectures, model evaluation, machine learning, pre-training and fine-tuning techniques. Prior to AWS, Vivek built his investment experience working at a large hedge fund, performing fundamental stock analysis, and covering multiple sectors including aerospace and defense, online retail, and travel and lodging. Vivek is passionate about using technology to democratize finance by spreading awareness and education on financial concepts and the power of investing in improving financial health and security for everyone. His field of interest in investing, lies in macroeconomics, fundamental stock analysis and value investing.

    See more on the author's page
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Customer reviews

4 out of 5 stars
44 global ratings
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It is a great book with a lot of real-life trading strategy scenarios
5 out of 5 stars
It is a great book with a lot of real-life trading strategy scenarios
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Top reviews from the United States

  • Ashutosh
    5.0 out of 5 stars A must-have for every aspiring quant!
    Reviewed in the United States on March 10, 2025
    Format: HardcoverVerified Purchase
    One of the most practical books I've come across in the quant trading space! It provides a hands-on approach to learning QuantConnect, Python, AI, and real-world trading strategies.
    One person found this helpful
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  • Douglas Goullet
    5.0 out of 5 stars Hands-On AI Trading with Python, QuantConnect, and AWS
    Reviewed in the United States on March 12, 2025
    Format: HardcoverVerified Purchase
    Shows the infinite possibilities in trading today with a platform like Quantconnect.
    One person found this helpful
    Helpful
     Report
  • S Prasad
    5.0 out of 5 stars It is a great book with a lot of real-life trading strategy scenarios
    Reviewed in the United States on March 10, 2025
    Format: HardcoverVerified Purchase
    Great book
    Customer image
    S Prasad
    5.0 out of 5 stars
    It is a great book with a lot of real-life trading strategy scenarios

    Reviewed in the United States on March 10, 2025
    Great book
    Images in this review
    Customer image
    One person found this helpful
    Helpful
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  • Blu Shrk
    1.0 out of 5 stars little to no value for AI trading or to build a RAG LLM
    Reviewed in the United States on March 7, 2025
    Format: KindleVerified Purchase
    This was such a disappointment after page 2, once I realized it was a sales pitch for the QuantConnect platform. The chapter on AWS was trivial and worthless. I loved their cost estimate of running cost $8500 per month. No one that buys this book would have that kind of money to spend.
    10 people found this helpful
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  • Amer
    4.0 out of 5 stars Great so far
    Reviewed in the United States on February 26, 2025
    Format: HardcoverVerified Purchase
    So far it is great but a bit disappointed with print quality of figures. Hard to read.
    Customer image
    Amer
    4.0 out of 5 stars
    Great so far

    Reviewed in the United States on February 26, 2025
    So far it is great but a bit disappointed with print quality of figures. Hard to read.
    Images in this review
    Customer image
    2 people found this helpful
    Helpful
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  • SG
    2.0 out of 5 stars Not very interesting book, language and content could have been much better
    Reviewed in the United States on October 29, 2025
    Format: HardcoverVerified Purchase
    A book like this should focus on giving you some basic strategies first to whet the appetite while introducing the important concepts. Rather this book begins by telling you everything less interesting, the kind of things that you would count as noise towards achieving your goal.

    In other words, this book is a manual, not an interesting lecture by a popular professor. Hence its hard to read for beginners. ChatGPT will do a better job instead of this book in achieveing one's goals.

    There is also lot of unnecessary information in the first few chapters which I do not understand why it was even mentioned in the book.

    Lastly, the language is hard to absorb. I had to read many sentences multiple times and yet I could notnunderstand what they meant.

    All in all, I decided to return the book after reaching end of second chapter and glancing over the next few ones.
    Helpful
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  • Marie
    5.0 out of 5 stars AI
    Reviewed in the United States on February 10, 2025
    Format: HardcoverVerified Purchase
    The information shared to learn techniques.
    3 people found this helpful
    Helpful
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  • Eric Nichols
    3.0 out of 5 stars Some good ideas, but discussion of results for presented algos is lacking
    Reviewed in the United States on March 15, 2025
    Format: HardcoverVerified Purchase
    I have mixed feelings on this one. I like Ernest Chan's work, but he's just a coauthor on this with several others, and I'm not sure what each author's contribution was, since it's no indicated in the text.

    I agree with other reviewers that this is too focused on quantconnect, but that's not necessarily a bad thing if you're ok using that platform.

    In Chapter 6, which contains a bunch of example strategies and analysis, I'm left disappointed with the discussion. Many times the results are just presented in a graph, without actual discussion in text of the results. Moreover, many of the strategies actually are losers, but it's not mentioned in the text; instead you can only tell by looking at the figures. I've fine with authors presenting negative results, but there should be discussion at the end of each strategy regardless of outcome. Similarly, I don't find the analysis to be very rigorous. Sometimes the analysis is run just on a single stock symbol, no idea how it does on other symbols. Similarly, they don't show good choices of comparison baseline in many instances. There's one algorithm that does stock selection from the S&P500 universe based on volume, and then weights a portfolio of the selected stocks. The S&P500 is used as the baseline, but also they should have plotted another baseline of an equally-weighted portfolio to separate the effect of stock-selection from weight-selection. Missing this sort of thing is pretty sloppy in my view.
    12 people found this helpful
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Top reviews from other countries

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  • Minh Nguyen
    5.0 out of 5 stars A very useful reference book for quickly getting up to speed with QuantConnect
    Reviewed in the United Kingdom on November 24, 2025
    Format: HardcoverVerified Purchase
    This is a very useful reference book for those beginning to learn about trading strategies and looking to test them on QuantConnect like myself. I appreciate its lightweight approach, focusing on practical application rather than delving deeply into complex mathematical details—details that readers can easily explore on their own.
    Report
  • K.Binder
    5.0 out of 5 stars High level trading and programming with python
    Reviewed in Germany on January 30, 2026
    Format: HardcoverVerified Purchase
    dies Buch vermittelt eine wunderbare Ideenlandschaft fürs trading und programmieren
    Report
  • Raf
    1.0 out of 5 stars Useless, not rigorous. Wasted money (just nice a sexy title for a book on the personal library)
    Reviewed in Italy on June 27, 2025
    Format: HardcoverVerified Purchase
    The worst and useless book ever. I try to not provide negative reviews in general but I must in this case. Unfortunately is too late to send it back, but this book is not worth the money I spent for it. The only nice stuff is its title and nothing else. The quality of the figures is extremely low and the book content is just copy paste of some python codes without any real explanation or rational. There is also no explanation about how use QC for trading (the main reason why I buy it), only the link to QC website with few sentences. The content is not rigorous and very bad explained. It is clear that this text has never been peer reviewed. In sum, it is useless to both experts and beginners. 300 pages just because big figures and copy paste of codes with very large font size. Almost zero content. Don’t waste your money, it is very sad to see practitioners writing so low quality book. It is surprising that Wiley allowed this publication in general.
    Report
  • Amazon Customer
    5.0 out of 5 stars Quant trading
    Reviewed in India on October 31, 2025
    Format: KindleVerified Purchase
    Excellent write with practical orientation.
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  • Jason
    5.0 out of 5 stars A must-read book for all interested in using AI for trading and portfolio construction
    Reviewed in Singapore on April 2, 2025
    Format: HardcoverVerified Purchase
    This book is a must-read for anyone looking to master AI-driven trading strategies. It offers clear, step-by-step examples with complete code reference. The book provides practical insights that are both innovative and immediately applicable. Highly recommended for professionals and enthusiasts alike.
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