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Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python

By Sofien Kaabar

Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python Business & Technology

Deep Learning for Finance: Creating Machine & Deep Learning Models for Trading in Python

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Description
Deep learning is rapidly gaining momentum in the world of finance and trading. But for many professional traders, this sophisticated field has a reputation for being complex and difficult. This hands-on guide teaches you how to develop a deep learning trading model from scratch using Python, and it also helps you create and backtest trading algorithms based on machine learning and reinforcement learning. Sofien Kaabar--financial author, trading consultant, and institutional market strategist--introduces deep learning strategies that combine technical and quantitative analyses. By fusing deep learning concepts with technical analysis, this unique book presents outside-the-box ideas in the world of financial trading. This A-Z guide also includes a full introduction to technical analysis, evaluating machine learning algorithms, and algorithm optimization.
  • Understand and create machine learning and deep learning models
  • Explore the details behind reinforcement learning and see how it's used in time series
  • Understand how to interpret performance evaluation metrics
  • Examine technical analysis and learn how it works in financial markets
  • Create technical indicators in Python and combine them with ML models for optimization
  • Evaluate the models' profitability and predictability to understand their limitations and potential
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    Product details
    Binding:
    Paperback
    Edition:
    1
    Number of Pages:
    359
    Release Date:
    2024-02-13
    Publication Date:
    2024-02-13
    Publisher:
    O'Reilly Media
    Languages:
    Published: English, Original: English
    ISBN10:
    1098148398
    GPSR Manufacturer Reference:
    [email protected]
    Weight:
    1050 g
    Height:
    22.2 cm
    Width:
    17.7 cm
    Thickness:
    2.5 cm
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