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Item #:
45158780
Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies
87% of respondents would recommend this to a friend
Item #:
45158780
VUV 14739
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What Stands Out
Advanced Predictive Models
This book covers cutting-edge predictive models that help traders identify market signals, empowering them to make informed decisions and enhance profitability in their trading strategies.
Python Focused
Utilizing Python, a dominant language in data science, this book provides practical examples and coding techniques, making it accessible for both beginners and experienced programmers in algorithmic trading.
Market & Alternative Data
It emphasizes using both market and alternative data, ensuring traders can leverage diverse datasets for comprehensive analysis, thereby improving their systematic trading approaches and decision-making processes.
Product Details
- Design, train, and assess machine learning algorithms for automated trading strategies
- Create a research process to utilize predictive modeling in trading decisions
- Utilize NLP and deep learning to extract tradeable signals from market and alternative data
- Learn to work with market, fundamental, and alternative data to generate tradeable signals
- Implement machine learning techniques for investment and trading problem-solving
- Target audience: data analysts, Python developers, investment analysts, and portfolio managers
| Publisher | Packt Publishing |
| Publication date | 31 July 2020 |
| Edition | 2nd edition |
| Language | English |
| Print length | 820 pages |
| ISBN-10 | 1839217715 |
| ISBN-13 | 978-1839217715 |
| Item weight | 1.47 kg |
| Dimensions | 19.05 x 4.72 x 23.5 cm |
Who Should Buy?
-
Aspiring Traders
Ideal for those looking to integrate machine learning techniques into their trading strategies for improved performance.
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Data Scientists
Beneficial for individuals with data science backgrounds wanting to apply machine learning in finance and trading contexts.
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Python Developers
Great for programmers familiar with Python who want to learn about financial applications and algorithmic trading.
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Beginners in Trading
Not suitable for novice traders without prior knowledge of trading concepts or programming skills in Python.
Product Description
About This Item
Introducing the "Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition" 2nd Edition. Are you looking to take your algorithmic trading strategies to the next level? Look no further! This comprehensive guide is packed with valuable insights and techniques that will help you harness the power of machine learning to make informed trading decisions. With the rise of big data and advancements in technology, traditional methods of trading are being replaced by more sophisticated approaches. This book dives deep into the world of machine learning and its applications in algorithmic trading, providing you with the tools and knowledge needed to develop successful systematic trading strategies. Using Python, a popular programming language in the world of finance, you'll learn how to build predictive models that can extract signals from market and alternative data.
This will enable you to identify profitable trading opportunities and make data-driven decisions with confidence. Whether you're a seasoned trader or just starting out, this book is suitable for all levels of expertise. It covers a wide range of topics, including quantitative trading models, algorithmic trading algorithms, and systematic trading techniques. You'll also discover how to implement machine learning algorithms for trading and use predictive analytics to optimize your trading strategies. What sets this 2nd edition apart is its updated content and examples.
The author has included the latest developments in the field of algorithmic trading, ensuring that you have access to the most up-to-date information. Additionally, the book provides practical exercises and real-world case studies to reinforce your learning and help you apply the concepts in a practical setting. So, if you're ready to take your algorithmic trading strategies to new heights, "Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition" is the essential guide you've been waiting for. Don't miss out on this opportunity to gain a competitive edge in the financial markets.
Grab your copy today and start making smarter trading decisions.
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Statements regarding dietary supplements have not been evaluated by the Food and Drug Administration and are not intended to diagnose, treat, cure, or prevent any disease or health condition.
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Customer Questions & Answers
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Question:
What kind of data is used in this book for generating tradeable signals?
Answer: This book covers market, fundamental, and alternative data such as tick data, minute and daily bars, SEC filings, earnings call transcripts, and financial news, among others. -
Question:
What kind of strategies can be created using the techniques discussed in this book?
Answer: This book covers end-to-end machine learning for the trading workflow, from idea and feature engineering to model optimization and backtesting. It shows how to create supervised, unsupervised, and reinforcement learning-based strategies. -
Question:
Is this book suitable for beginners in machine learning?
Answer: This book assumes basic familiarity with Python and machine learning libraries such as pandas, scikit-learn, and TensorFlow. However, it does provide an introduction to these topics for those who need it.
E-business Editorial Review
Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition is a comprehensive guide to algorithmic trading. The book covers a wide range of topics and includes various predictive models for extracting market signals. The first section of the book provides a solid foundation in electronic trading basics and economic/portfolio fundamentals. The book assumes a certain level of knowledge and proficiency in Python, Pandas, and third-party libraries. It also expects readers to have a familiarity with regression, gradient boosting, FFNN, convolutional networks, NLP, time series analysis, GANs, linear algebra, calculus, and statistics. While the book is introductory in nature, it is not dumbed-down and requires readers to read around and do additional research as needed. One of the strengths of the book is its extensive coverage and the wealth of information it provides. Readers can gain significant momentum and go beyond their expectations by using the book in conjunction with online references. However, there are a few drawbacks to be aware of. The book heavily relies on a large number of libraries, which can lead to conflicts in versioning and require significant time to resolve. Some of the code provided may not work as intended, and readers may need to make modifications or hacks to get it to work. Additionally, the book makes use of the now-defunct Quantopian resources and libraries, which limits its practical usefulness. The author may Consider updating future editions of the book to use alternative resources, such as QuantConnect's LEAN APIs and trading engine. Despite these limitations, the book is highly recommended for those with the necessary experience and skills to navigate the challenges it presents. It offers a wealth of valuable information and stands out as a pragmatic resource in the field of algorithmic trading.
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Pros
- Comprehensive coverage of algorithmic trading topics
- Extensive information and valuable insights
- Provides a solid foundation in electronic trading basics and economic/portfolio fundamentals
Cons
- Heavy reliance on libraries may cause conflicts and require extra effort to resolve
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Features & Benefits
- Design, train, and evaluate machine learning algorithms to create trading strategies
- Work with market, fundamental, and alternative data to generate tradeable signals and alpha factors
- Backtest and evaluate machine learning-based trading strategies using pandas, NumPy, and pyfolio
- Optimize portfolio risk and performance analysis using various tools and techniques
- Train models to predict intraday returns based on high-quality trades and quotes data
- Ideal for data analysts, scientists, developers, investment analysts, and portfolio managers interested in learning machine learning for trading
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