Download Python for Finance: Analyze Big Financial Data by Yves Hilpisch PDF

By Yves Hilpisch

ISBN-10: 1491945281

ISBN-13: 9781491945285

The monetary has followed Python at an enormous fee lately, with the various biggest funding banks and hedge money utilizing it to construct center buying and selling and danger administration structures. This hands-on advisor is helping either builders and quantitative analysts start with Python, and courses you thru an important points of utilizing Python for quantitative finance.

Using sensible examples in the course of the ebook, writer Yves Hilpisch additionally exhibits you the way to enhance a full-fledged framework for Monte Carlo simulation-based derivatives and threat analytics, in line with a wide, practical case examine. a lot of the publication makes use of interactive IPython Notebooks, with issues that include:

basics: Python information buildings, NumPy array dealing with, time sequence research with pandas, visualization with matplotlib, excessive functionality I/O operations with PyTables, date/time details dealing with, and chosen top practices

monetary themes: mathematical concepts with NumPy, SciPy and SymPy similar to regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; facts for normality exams, mean-variance portfolio optimization, relevant part research (PCA), and Bayesian regression

detailed issues: functionality Python for monetary algorithms, corresponding to vectorization and parallelization, integrating Python with Excel, and construction monetary functions in accordance with internet applied sciences

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Extra resources for Python for Finance: Analyze Big Financial Data

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It is not possible to give even a somewhat exhaustive overview. 5 Python For completeness, let us first consider using the standard Python interpreter itself. 1 (Apple Inc. build 5493)] on darwin 5. For Windows users and developers, the full integration of Python in Visual Studio is a compelling alter‐ native. There is even a whole suite of Python tools for Visual Studio available (cf. com). 34 | Chapter 2: Infrastructure and Tools Type "help", "copyright", "credits" or "license" for more information.

Screenshot of Spyder Conclusions If you are a beginner or casual Python developer or an expert coming from a different programming background, getting started with Python is generally pretty easy in that only a couple of simple steps are required. To begin, you should install an appropriate Python distribution, like Anaconda, to have a consistent Python environment available and also to simplify the regular updating procedures. With a distribution like Anaconda you have available the most important tools to inter‐ actively practice data and financial analytics, like with IPython, or to develop larger applications in a more traditional implement-test-debug fashion, like with Spyder.

A list of all available magic commands can be generated in an IPython session as follows: In [2]: %lsmagic In interactive computing, magic commands can, for example, be used for simple profil‐ ing tasks. 7 ms Wall time: 39 ms Out[5]: array([ 0. 5 ms per loop This function executes a number of loops to get more reliable estimates for the duration of a function call or a snippet of code. It is not possible to explain in detail all the magic functions that IPython provides. However, IPython itself strives to make it as easy as possible to interactively look up information about IPython and its commands.

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