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Python for Finance : Analyze Big Financial Data by Yves Hilpisch
US $29.90
ApproximatelyRM 123.77
Condition:
“Some wear to cover and corner dings”
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eBay item number:326151665703
Item specifics
- Condition
- Good
- Seller Notes
- “Some wear to cover and corner dings”
- ISBN
- 9781491945285
About this product
Product Identifiers
Publisher
O'reilly Media, Incorporated
ISBN-10
1491945281
ISBN-13
9781491945285
eBay Product ID (ePID)
201735058
Product Key Features
Number of Pages
603 Pages
Publication Name
Python for Finance : Analyze Big Financial Data
Language
English
Publication Year
2015
Subject
Finance / Financial Engineering, Finance / General, General, Programming Languages / Python
Type
Textbook
Subject Area
Computers, Business & Economics
Format
Trade Paperback
Dimensions
Item Height
1.2 in
Item Weight
36.3 Oz
Item Length
9.2 in
Item Width
7.1 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2015-460491
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
005.13/3
Synopsis
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
LC Classification Number
HG173
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