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Python for Finance: Mastering Data-Driven Finance
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Python for Finance: Mastering Data-Driven Finance
Master the financial industry's most in-demand programming language and elevate your analytics with practical examples and real-world case studies.
Python for Finance: Mastering Data-Driven Finance
Item #: 9482901

Python for Finance: Mastering Data-Driven Finance

90% of respondents would recommend this to a friend

Item #: 9482901

TWD 1861

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What Stands Out

Comprehensive Coverage
Explores essential finance topics with Python, aiding both beginners and professionals in understanding data-driven financial practices comprehensively.
Real-World Applications
Includes practical examples and case studies, empowering users to apply Python skills effectively in real financial scenarios, enhancing their employability.
Updated Content
The second edition features the latest tools and techniques in data-driven finance, ensuring users stay current with industry standards and advancements.

Product Details

Master financial analysis using Python with Python for Finance: Mastering Data-Driven Finance 2nd Edition. Explore data-driven strategies and techniques. Shop at Ubuy Taiwan
  • The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.
Publisher O'Reilly Media
Publication date January 8, 2019
Edition 2nd
Language English
Print length 711 pages
ISBN-10 1492024333
ISBN-13 978-1492024330
Item Weight 2.45 pounds (1.11 kg)
Dimensions 7 x 1.5 x 9.25 inches (17.8 x 3.8 x 23.5 cm)

Who Should Buy?

Suitable For
  • Finance Professionals

    Individuals in finance roles can leverage Python for effective data analysis and financial modeling techniques.

  • Data Analysts

    This book provides insights on using Python for analyzing financial data, enhancing data analysis skills.

  • Developers Transitioning

    Developers seeking to shift into finance will benefit from practical applications of Python in this field.

Not Suitable For
  • Beginners in Python

    Complete novices may struggle with foundational concepts before applying Python techniques in finance.

Product Description

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Customer Questions & Answers

  • Question: What is 'Python For Finance: Mastering Data-Driven Finance' about?

    Answer: 'Python For Finance: Mastering Data-Driven Finance' is a comprehensive guide that teaches you the intricacies of using Python programming for financial analysis and data-driven decision-making. The book covers various concepts like quantitative finance, portfolio optimization, and financial modeling, making it suitable for both beginners and experienced practitioners. Through practical examples and case studies, readers can apply Python skills to real-world financial issues, facilitating a deeper understanding of complex financial topics.
  • Question: Who is the target audience for this book?

    Answer: The book is designed for finance professionals, data analysts, and anyone interested in merging finance with programming. It caters to readers who possess basic knowledge of finance and wish to enhance their skill set with Python. Whether you are an analyst seeking to automate financial calculations or an aspiring quant looking to apply algorithms to trading, this book equips you with essential tools to navigate the field of finance effectively.
  • Question: What programming skills are needed before reading this book?

    Answer: While prior experience with Python is beneficial, it is not mandatory. The initial chapters introduce fundamental Python programming concepts and basic data structures, making the book accessible even for those new to coding. As the book progresses, it builds on these skills with more complex financial applications, ensuring readers can follow along comfortably and apply what they learn to practical finance challenges.
  • Question: Can this book help with algorithmic trading?

    Answer: Yes, 'Python For Finance' delves into algorithmic trading techniques and demonstrates how to implement them using Python. The book covers various strategies such as backtesting and developing trading algorithms, providing readers with insights into how financial markets operate. By applying the concepts learned in this book, readers can develop their own trading strategies and gain an edge in competitive financial environments.
  • Question: Does this book cover data visualization techniques?

    Answer: Absolutely! The book emphasizes the importance of data visualization in finance and provides practical tools and libraries, such as Matplotlib and Seaborn, to aid in creating insightful financial graphics. By mastering these visualization techniques, readers can present complex financial data in a more digestible format, facilitating better decision-making and analysis. This skill is essential for analysts who report findings to stakeholders or create dashboards.
  • Question: Are there real-world case studies included in the book?

    Answer: Yes, the book features multiple real-world case studies that illustrate the application of Python in actual financial scenarios. These case studies highlight challenges faced by finance professionals and how Python can provide solutions. This approach gives readers concrete examples to work from, making the lessons more relatable and applicable to their own financial projects or careers.
  • Question: How does this book address risk management in finance?

    Answer: Risk management is a crucial aspect of finance, and 'Python For Finance' dedicates significant coverage to this topic. The book explores various risk assessment models and methods to calculate Value at Risk (VaR) using Python. Readers learn how to implement these models, allowing them to assess and mitigate potential risks in investment portfolios, which is invaluable for making informed financial decisions.
  • Question: Is this book suitable for self-study?

    Answer: Yes, this book is excellent for self-study due to its structured approach and clear explanations. Each chapter builds on the last, allowing readers to progress at their own pace. Additionally, it includes exercises and practical applications to reinforce learning, making it easier for individuals to engage deeply with the material and apply it directly in their finance-related tasks.
  • Question: What libraries or tools are utilized in this book?

    Answer: The book prominently features popular libraries such as Pandas, NumPy, and Scikit-learn, which are crucial for data manipulation, statistical analysis, and machine learning in finance. It guides readers on how to effectively use these libraries to process financial data, analyze trends, and automate tasks, streamlining workflows in day-to-day financial operations and research tasks.
  • Question: Where can I buy 'Python For Finance: Mastering Data-Driven Finance' in Taiwan?

    Answer: You can purchase 'Python For Finance: Mastering Data-Driven Finance' on Ubuy. Ubuy is a reliable online store that offers a variety of books and resources including this title. By shopping on Ubuy, you can ensure a convenient purchasing process, along with various payment options and potential perks tailored for customers in Taiwan.

Data Modeling & Design Editorial Review

Python for Finance: Mastering Data-Driven Finance 2nd Edition is a comprehensive book that provides an introduction to the scientific stack of Python, focusing on the core functionality of the tools and packages available. The first two-thirds of the book offer quick overviews and examples of numpy, pandas, scipy, scikit-learn, and other packages, making it a useful reference for those in the finance industry. However, the book has several flaws, including a lack of explanation of actual finance concepts, requirements for paid platforms, and code that does not work properly. Overall, the book is a decent introduction to the scientific stack but falls short in some areas.

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Pros

  • Provides an introduction to the scientific stack of Python
  • Offers quick overviews and examples of core functionality
  • Useful reference for those in the finance industry

Cons

  • Little-to-no explanation of actual finance concepts

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Features & Benefits

  • Discover the power of Python in the financial sector.
  • Learn to build core trading and risk management systems.
  • Updated for Python 3 with hands-on examples.
  • Includes a full-fledged framework for Monte Carlo simulation.
  • Utilizes interactive IPython notebooks for practical learning.
  • Ideal for developers and quantitative analysts looking to enhance their skills.

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