Python for Finance: Analyze Big Financial Data

ISBN-13: 9781491945285

ISBN-10: 1491945281

Author: Yves Hilpisch

Edition: 1

Publication date:
2014
Publisher:
O'Reilly Media
Format:
Paperback 606 pages
Category:
Accounting, Business, Economics, Macroeconomics, Computers, Finance
 
Get it directly from us
$23.29
$23.29

eBook
$38.99
FREE shipping on ALL orders

Summary

Acknowledged author Yves Hilpisch wrote Python for Finance: Analyze Big Financial Data comprising 606 pages back in 2014. Textbook and etextbook are published under ISBN 1491945281 and 9781491945285. Since then Python for Finance: Analyze Big Financial Data textbook was available to sell back to BooksRun online for the top buyback price or rent at the marketplace.


Description

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