Python for Finance Analyze Big Financial Data 1st Edition by Yves Hilpisch 1491945281 9781491945285

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Product details:

ISBN 10: 1491945281 

ISBN 13: 9781491945285

Author: Yves Hilpisch

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 practicesFinancial 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 regressionSpecial topics: performance Python for financial algorithms, such as vectorization and parallelization, integrating Python with Excel, and building financial applications based on Web technologies

Table of contents:

I. Python and Finance
1. Why Python for Finance?
2. Infrastructure and Tools
3. Introductory Examples

II. Financial Analytics and Development
4. Data Types and Structures
5. Data Visualization
6. Financial Time Series
7. Input/Output Operations
8. Performance Python
9. Mathematical Tools
10. Stochastics
11. Statistics
12. Excel Integration
13. Object Orientation and Graphical User Interfaces
14. Web Integration

III. Derivatives Analytics Library
15. Valuation Framework
16. Simulation of Financial Models
17. Derivatives Valuation
18. Portfolio Valuation
19. Volatility Options

A. Selected Best Practices
B. Call Option Class
C. Dates and Times

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Tags: Yves Hilpisch, Python, Finance, Analyze