By Yves Hilpisch

The monetary has followed Python at an important price lately, with the various greatest funding banks and hedge cash utilizing it to construct center buying and selling and probability administration structures. This hands-on consultant is helping either builders and quantitative analysts start with Python, and publications you thru an important points of utilizing Python for quantitative finance.

Using functional examples during the booklet, writer Yves Hilpisch additionally exhibits you ways to advance a full-fledged framework for Monte Carlo simulation-based derivatives and hazard analytics, in response to a wide, practical case examine. a lot of the ebook makes use of interactive IPython Notebooks, with issues that include:

basics: Python facts constructions, 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 subject matters: mathematical innovations with NumPy, SciPy and SymPy equivalent to regression and optimization; stochastics for Monte Carlo simulation, Value-at-Risk, and Credit-Value-at-Risk calculations; information for normality assessments, mean-variance portfolio optimization, imperative part research (PCA), and Bayesian regression

distinctive subject matters: functionality Python for monetary algorithms, akin to vectorization and parallelization, integrating Python with Excel, and construction monetary purposes in line with internet applied sciences

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Extra info for Python for Finance: Analyze Big Financial Data

Example text

She wants to find evidence that volatility, in contrast to some typical model assumptions, fluctuates over time and is far from being constant. The results should also be visualized. She mainly has to do the following: • Download Google stock price data from the Web. • Calculate the rolling standard deviation of the log returns (volatility). • Plot the stock price data and the results. These tasks are complex enough that not too long ago one would have considered them to be something for professional financial analysts.

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