Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python
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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
Build and evaluate supervised, unsupervised, and reinforcement learning models for trading strategies using machine learning and alternative data.
Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python
Nº de artículo: 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

Nº de artículo: 45158780

PEN 462

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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.

Detalles de producto

Learn how to extract signals from market data and develop systematic trading strategies using machine learning and Python. Shop at Ubuy Peru
  • 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?

Suitable For
  • Aspiring Traders

    Ideal for those looking to integrate machine learning techniques into their trading strategies for improved performance.

  • Data Scientists

    Beneficial for individuals with data science backgrounds wanting to apply machine learning in finance and trading contexts.

  • Python Developers

    Great for programmers familiar with Python who want to learn about financial applications and algorithmic trading.

Not Suitable For
  • Beginners in Trading

    Not suitable for novice traders without prior knowledge of trading concepts or programming skills in Python.

DESCRIPCIÓN DEL PRODUCTO

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

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.

Dietary Supplement Disclaimer

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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Preguntas y respuestas de los clientes

  • Pregunta: What kind of data is used in this book for generating tradeable signals?

    Respuesta: 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.
  • Pregunta: What kind of strategies can be created using the techniques discussed in this book?

    Respuesta: 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.
  • Pregunta: Is this book suitable for beginners in machine learning?

    Respuesta: 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 is a comprehensive resource for both quantitative trading and machine learning enthusiasts. This book, published by Packt Publishing, offers 820 pages of insightful content, delving into electronic trading basics and economic principles while guiding readers through numerous models and ideas. Although some readers appreciated the depth of coverage, they noted that the extensive use of multiple libraries can lead to conflicts that may require troubleshooting. The book effectively lays a solid foundation for implementing strategies using ML algorithms and is filled with valuable information, making it a remarkable reference despite some minor concerns about physical quality and coding challenges.

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ventajas

  • In-depth coverage of trading and machine learning basics
  • Extensive models and strategies for practical application
  • Free data sources and open tools introduced
  • Good for both beginners and experienced users
  • Enhances understanding of advanced ML algorithms

Contras

  • Physical book quality could be better with pages coming apart

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características y beneficios

  • 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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