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Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition
Master advanced financial modeling, visualization, and trading infrastructure using Python
Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition
Item #: 19531845

Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition

Item #: 19531845

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What Stands Out

Advanced Techniques
This edition covers state-of-the-art financial statistical applications, making it ideal for finance professionals who want to leverage Python for advanced data analysis and model building.
Hands-On Learning
Through practical examples and exercises, readers gain real-world experience in financial analysis, ensuring a deeper understanding of financial data manipulation and algorithmic trading strategies.
Comprehensive Resource
As an updated edition, it includes the latest trends and tools in finance, providing readers with a complete guide to mastering Python in the context of financial analysis.

Product Details

Discover state-of-the-art financial statistical applications using Python. Learn advanced Python techniques for finance with the 2nd edition of Mastering Python for Finance from Ubuy Bulgaria.
  • Master advanced financial models and methodologies using Python
  • Explore machine learning and deep learning techniques for financial applications
  • Understand time series data and its applications in finance
  • Build high-frequency algorithmic trading platforms and backtesting systems
  • Apply regression-based and classification-based machine learning for prediction
  • Suitable for financial or data analysts and software developers in finance industry
Publisher Packt Publishing
Publication date April 30, 2019
Edition 2nd ed.
Language English
Print length 426 pages
ISBN-10 1789346460
ISBN-13 978-1789346466
Item Weight 1.77 pounds (800 grams)
Dimensions 7.5 x 0.96 x 9.25 inches (19.1 x 2.4 x 23.5 cm)

Who Should Buy?

Suitable For
  • Finance Professionals

    Ideal for finance professionals seeking to enhance their analytical skills using advanced Python applications for financial data.

  • Data Scientists

    Great for data scientists looking to apply statistical methods and machine learning techniques in financial contexts.

  • Students and Learners

    Beneficial for students studying finance or data analysis, providing practical Python skills in finance applications.

Not Suitable For
  • Beginners in Python

    Not suitable for beginners as it assumes prior knowledge of Python programming and financial concepts.

Product Description

Mastering Python for Finance: Implement advanced state-of-the-art financial statistical applications using Python, 2nd Edition

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Python Editorial Review

The second edition of "Mastering Python for Finance" seems to have received mixed reviews from customers. While some appreciated the decent coverage of financial concepts and supporting mathematics, others found the book inadequate for learning Python for finance. One major issue noted by customers is the reliance on data providers that no longer offer data used in the examples, with the author directing readers to paid data sources. Additionally, the author seems to lack a good understanding of financial time series, and there are critical program coding missing for API access to popular financial data sources. The book comes with code files that are of type "ipynb" and require the use of Jupyter Notebook, which might be difficult for some readers. Databases such as MySQL or 'kdb' are also missing from the book. However, some recommend using other references to fill in the missing pieces. The book was delivered quickly and arrived in perfect shape, according to one customer.

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Pros

  • Decent coverage of financial concepts and supporting mathematics
  • Effort to cover the entire world of Finance Computing is appreciated

Cons

  • Reliance on data providers that no longer offer data used in the examples

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