Python With its simple, easy to read, scalability, and has a large and active scientific computing community, the needs analysis, processing large amounts of data in the financial sector has been widely and quickly applied, and the programming language of choice for the industry to develop core applications . "Python Great Financial Data Analysis" provides tips and tools for data analysis using Python, as well as the development of related applications. Total is divided into three parts, 19 chapters, Part 1 introduces the application of Python in finance, which covers the reasons for the financial industry Python, Python infrastructure and tools, and Python in Quantitative Finance in some introductory examples; part 2 describes the financial analysis and application development of the most important Python libraries, techniques and methods, which covers the data type and structure of Python, data visualizing matplotlib, financial time series data processing, high performance input / output operations, a variety of mathematical tools and technical performance Python library required in finance, a random number generator and a random process simulation, statistical applications Python, Python and Excel integration, Python programming and object-oriented GUI development, integration, and the development of Web-based applications and Web services Python and Web technology; part 3 is concerned that development of the practical application of Monte Carlo simulation options and derivatives pricing, which covers the introduction of the valuation framework financial model simulations, derivatives valuation, portfolio valuation, volatility options and other knowledge.
"Financial Python big data analysis" on the use of Python for large data analysis, processing of interest to the financial sector developers to read.
Learning Reference:
"Python Great Financial Data Analysis" in the English version of the two comparative studies. Complete source code.
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