Python for Finance
Analyze Big Financial Data
Publisher: O'Reilly Media
Final Release Date: December 2014
Pages: 606

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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oreillyPython for Finance
 
4.0

(based on 2 reviews)

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(1 of 1 customers found this review helpful)

 
5.0

I think it is great book

By Jason

from Toronto Canada

Pros

  • Accurate
  • Helpful examples
  • Well-written

Cons

    Best Uses

    • Intermediate

    Comments about oreilly Python for Finance:

    As someone working in finance it is tough to find a book that truly illustrates the application of python for finance. I understand the above review about it being indecisive if advanced or beginner text but I would consider myself very basic as my only other experience with python was basic code academy and I found the examples good to learn from and experiment with changing code. I would definitely recommend if you want to use Python for finance.

    (7 of 8 customers found this review helpful)

     
    3.0

    Not as good as Python for Data Analysis

    By Matt Cullen-Meyer

    from Denver, CO

    About Me Financial Analyst

    Verified Buyer

    Pros

      Cons

      • Not comprehensive enough

      Best Uses

        Comments about oreilly Python for Finance:

        The author provides many interesting examples, but does not provide sufficient background or support to, say, modify the code for custom purposes. At other times the author gives overviews of very basic Python topics, such as data types and structures. In this respect the book suffers from an indecision on whether it is an introductory or advanced text. Most of the concepts considered herein are covered more in depth in Python for Data Analysis. This has been a nice refresher text, but I do not recommend it for the beginner or for anyone hoping to learn the pandas library for the first time.

        (Disclaimer: I'm writing this having only gone through the first early release.)

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