Python for Finance: Analyze Big Financial Data 1st Edition

Python for Finance: Analyze Big Financial Data 1st Edition
Python+for+Finance%3A+Analyze+Big+Financial+Data+1st+Edition - фото 1
790 грн
12792
ISBN
978-1491945285
Издательство
O'Reilly Media
Автор
Yves Hilpisch
Номер издания
1-е изд.
Год
2014
Страниц
606
Формат
70х100 1/16 (170х240 мм)
Обложка 
Мягкая
Тип бумаги 
Офсет
Язык
Английский
Иллюстрации
С иллюстрациями
Срок поставки
7-10 дней
2 человека
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 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

 Python and Finance

Chapter 1 Why Python for Finance?

What Is Python?

Technology in Finance

Python for Finance

Conclusions

Further Reading

Chapter 2 Infrastructure and Tools

Python Deployment

Tools

Conclusions

Further Reading

Chapter 3 Introductory Examples

Implied Volatilities

Monte Carlo Simulation

Technical Analysis

Conclusions

Further Reading

Financial Analytics and Development

Chapter 4 Data Types and Structures

Basic Data Types

Basic Data Structures

NumPy Data Structures

Vectorization of Code

Conclusions

Further Reading

Chapter 5 Data Visualization

Two-Dimensional Plotting

Financial Plots

3D Plotting

Conclusions

Further Reading

Chapter 6 Financial Time Series

pandas Basics

Financial Data

Regression Analysis

High-Frequency Data

Conclusions

Further Reading

Chapter 7 Input/Output Operations

Basic I/O with Python

I/O with pandas

Fast I/O with PyTables

Conclusions

Further Reading

Chapter 8 Performance Python

Python Paradigms and Performance

Memory Layout and Performance

Parallel Computing

multiprocessing

Dynamic Compiling

Static Compiling with Cython

Generation of Random Numbers on GPUs

Conclusions

Further Reading

Chapter 9 Mathematical Tools

Approximation

Convex Optimization

Integration

Symbolic Computation

Conclusions

Further Reading

Chapter 10 Stochastics

Random Numbers

Simulation

Valuation

Risk Measures

Conclusions

Further Reading

Chapter 11 Statistics

Normality Tests

Portfolio Optimization

Principal Component Analysis

Bayesian Regression

Conclusions

Further Reading

Chapter 12 Excel Integration

Basic Spreadsheet Interaction

Scripting Excel with Python

xlwings

Conclusions

Further Reading

Chapter 13 Object Orientation and Graphical User Interfaces

Object Orientation

Graphical User Interfaces

Conclusions

Further Reading

Chapter 14 Web Integration

Web Basics

Web Plotting

Rapid Web Applications

Web Services

Conclusions

Further Reading

Derivatives Analytics Library

Chapter 15 Valuation Framework

Fundamental Theorem of Asset Pricing

Risk-Neutral Discounting

Market Environments

Conclusions

Further Reading

Chapter 16 Simulation of Financial Models

Random Number Generation

Generic Simulation Class

Geometric Brownian Motion

Jump Diffusion

Square-Root Diffusion

Conclusions

Further Reading

Chapter 17 Derivatives Valuation

Generic Valuation Class

European Exercise

American Exercise

Conclusions

Further Reading

Chapter 18 Portfolio Valuation

Derivatives Positions

Derivatives Portfolios

Conclusions

Further Reading

Chapter 19 Volatility Options

The VSTOXX Data

Model Calibration

American Options on the VSTOXX

Conclusions

Further Reading

Appendix Selected Best Practices

Appendix Call Option Class

Appendix Dates and Times

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