Python for Finance: Analyze Big Financial Data Book (PDF - Summary - Review - Online Reading - Download)

Python for Finance: Analyze Big Financial Data Book by Yves Hilpisch The financial industry has adopted Python at a dizzying pace recently, with some of the largest investment banks and hedge funds that use it to build commercial and risk management systems. This practical guide helps developers and quantitative analysts to start using Python and guides you through the most important aspects of using Python for quantitative finance.

Using practical examples throughout the book, author Yves Hilpisch also shows you how to develop a complete framework for Monte Carlo derivatives based on risk simulation and analysis, based on a large and realistic case study. Much of the book uses interactive IPython laptops, with themes that include:

Fundamentals: Python data structures, NumPy matrix management, time series analysis with pandas, matplotlib visualization, high-performance I / O operations with PyTables, date/time information management and selected best practices
Financial topics: mathematical techniques with NumPy, SciPy, and SymPy as regression and optimization; Stochastic for Monte Carlo simulation, calculations of value at risk and value at credit risk; statistics for normality tests, portfolio optimization of medium variance, principal component analysis (PCA) and Bayesian regression
Special topics: Python performance for financial algorithms, such as vectorization and parallelization, Python integration with Excel and creation of financial applications based on web technologies

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About The Author
Yves Hilpisch is the founder and managing partner of The Python Quants, an analytics software provider and financial engineering group. The Python Quants offer, among others, the Python Quant Platform (http://quant-platform.com) and DX Analytics (http://dx-analytics.com). Yves also lectures on mathematical finance and organizes meetups and conferences about Python for Quantitative Finance in New York and London.

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