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Python for Finance: Analyze Big Financial Data

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The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through 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, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies


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The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. The financial industry has adopted Python at a tremendous rate recently, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. This hands-on guide helps both developers and quantitative analysts get started with Python, and guides you through the most important aspects of using Python for quantitative finance. Using practical examples through 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, with topics that include: Fundamentals: Python data structures, NumPy array handling, time series analysis with pandas, visualization with matplotlib, high performance I/O operations with PyTables, date/time information handling, and selected best practices Financial topics: mathematical techniques with NumPy, SciPy and SymPy such as regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; statistics for normality tests, mean-variance portfolio optimization, principal component analysis (PCA), and Bayesian regression Special topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies

30 review for Python for Finance: Analyze Big Financial Data

  1. 4 out of 5

    Matthieu Brucher

    My full review is on my blog: http://blog.audio-tk.com/2018/01/30/b... My full review is on my blog: http://blog.audio-tk.com/2018/01/30/b...

  2. 4 out of 5

    Alex

    This makes for a very good "grand tour" of a lot of the practical financial uses of a handful of important Python libraries, and illustrates well how to combine the functionality they offer into a fairly coherent financial application library for derivatives valuation. If one doesn't already have at least a bit of background in both Python and some aspects of financial valuation, there may be a little less value for the reader here -- most chapters touch on topics that are worthy of books in the This makes for a very good "grand tour" of a lot of the practical financial uses of a handful of important Python libraries, and illustrates well how to combine the functionality they offer into a fairly coherent financial application library for derivatives valuation. If one doesn't already have at least a bit of background in both Python and some aspects of financial valuation, there may be a little less value for the reader here -- most chapters touch on topics that are worthy of books in their own right, so some preexisting knowledge (or willingness to go find and read those other books to answer questions) is assumed. Generally, though, a useful resource, and while some of the underlying libraries have evolved since this was published, the presentation is general enough that a motivated reader could figure out how to make the appropriate updates for similar applications of their own.

  3. 4 out of 5

    JDK1962

    My June "work book" in my work-book-a-month effort, that obviously took me far longer to get through than I thought it would. Useful intro to Python to someone in the financial field, especially with regard to identifying the 3rd party libraries of interest. Some of the specific financial topics were less interesting to me (e.g., option pricing) than others, and some of the examples--early on especially--would have benefited from immediate use of concrete examples, rather than using random number My June "work book" in my work-book-a-month effort, that obviously took me far longer to get through than I thought it would. Useful intro to Python to someone in the financial field, especially with regard to identifying the 3rd party libraries of interest. Some of the specific financial topics were less interesting to me (e.g., option pricing) than others, and some of the examples--early on especially--would have benefited from immediate use of concrete examples, rather than using random number sets, since the motivation was sometimes unclear. But I was grateful for the amount of code...I always learn better from examples. Not quite sure where the reference to "big [financial] data" was coming from in the subtitle. The term "big data" typically refers to using a data set that can't fit in memory. I don't think any of the examples actually addressed big data (or the topics that come along with it, such as distributed servers or databases, multiple cores, or explicit parallelization of code).

  4. 4 out of 5

    Lizi Zhu

    The book is awfully written. I have not read a worse written book on a language. Imagine you write something in English and let google translate it into Hawaiian, and then translate that back into English, and then...back and forth for 100 times. This book has to be written in a way aforementioned. It doesn't feel like it's written by a human. I trust a program can write a better book. Ironically, it's a book about language written a computer scientist. Awful. My suggestion is to ignore the expl The book is awfully written. I have not read a worse written book on a language. Imagine you write something in English and let google translate it into Hawaiian, and then translate that back into English, and then...back and forth for 100 times. This book has to be written in a way aforementioned. It doesn't feel like it's written by a human. I trust a program can write a better book. Ironically, it's a book about language written a computer scientist. Awful. My suggestion is to ignore the explanations and simply type the code provided in the book. So work through the book by literally typing the code. What you see is what you believe. You will see what he fails to convey in his own words.

  5. 5 out of 5

    Robert

    I love the quant software that comes with this. Its great that they share all of the data and scripts for you to play along while reading through this well written and informative introduction to pythons never ending usefulness in finance.

  6. 4 out of 5

    Victor

    Interesting introduction to Pandas library for finance related programs

  7. 4 out of 5

    Michal

  8. 5 out of 5

    Matt

  9. 5 out of 5

    Richard

  10. 5 out of 5

    Justin

  11. 4 out of 5

    Michaƫl

  12. 5 out of 5

    Jack Ryan

  13. 4 out of 5

    Matt Howland

  14. 4 out of 5

    Nathan Thomas

  15. 4 out of 5

    Dan CaJacob

  16. 5 out of 5

    Rex

  17. 5 out of 5

    Hayden

  18. 4 out of 5

    Tom Glaser

  19. 4 out of 5

    Daniel

  20. 4 out of 5

    Ali

  21. 5 out of 5

    Christine

  22. 5 out of 5

    Amul Shah

  23. 4 out of 5

    Gleb

  24. 5 out of 5

    Dejan

  25. 4 out of 5

    Don Goin

  26. 5 out of 5

    Brian Davey

  27. 4 out of 5

    Thomas Cormen

  28. 5 out of 5

    Eric Brown

  29. 4 out of 5

    Eric Nichols

  30. 5 out of 5

    Rick Galbo

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