作者：Yves Hilpisch著
页数：18,691页
出版社：东南大学出版社
出版日期：2019
ISBN：9787564183721
电子书格式：pdf/epub/txt
内容简介
Python凭借其简单、易读、可扩展性以及拥有巨大而活跃的科学计算社区，在需要分析、处理大量数据的金融行业得到了广泛而迅速的应用，并且成为该行业开发核心应用的编程语言。《Python金融大数据分析》提供了使用Python进行数据分析，以及开发相关应用程序的技巧和工具。
作者简介
Yves Hilpisch，The Python Quans的创始人和任事股东，该集团专注于用于金融数据科学、人工智能、算法交易和计算金融的开源技术的使用。他也是The AI Machine的创始人和CEO，该公司专注于利用通过专有策略执行平台进行的算法交易的人工智能的力量。Yves还是最终成为University Certificatein Python for Algorithmic Trading的首位在线培训计划的负责人。
本书特色
Python凭借其简单、易读、可扩展性以及拥有巨大而活跃的科学计算社区，在需要分析、处理大量数据的金融行业得到了广泛而迅速的应用，并且成为该行业开发核心应用的编程语言。《Python金融大数据分析》提供了使用Python进行数据分析，以及开发相关应用程序的技巧和工具。
目录
Preface
Part 1．Python and Finance
1． Why Python for Finance
The Python Programming Language
A Brief History of Python
The Python Ecosystem
The Python User Spectrum
The Scientific Stack
Technology in Finance
Technology Spending
Technology as Enabler
Technology and Talent as Barriers to Entry
Ever-Increasing Speeds， Frequencies， and Data Volumes
The Rise of Real-Time Analytics
Python for Finance
Finance and Python Syntax
Efficiency and Productivity Through Python
From Prototyping to Production
Data-Driven and AI-First Finance
Data-Driven Finance
AI-First Finance
Conclusion
Further Resources
2． Python Infrastructure
conda as a Package Manager
Installing Miniconda
Basic Operations with conda
conda as a Virtual Environment Manager
Using Docker Containers
Docker Images and Containers
Building an Ubuntu and Python Docker Image
Using Cloud Instances
RSA Public and Private Keys
Jupyter Notebook Configuration File
Installation Script for Python and Jupyter Notebook
Script to Orchestrate the Droplet Setup
Conclusion
Further Resources
Part II．Mastering the Basics
3． Data Types and Structures
Basic Data Types
Integers
Floats
Booleans
Strings
Excursion： Printing and String Replacements
Excursion： Regular Expressions
Basic Data Structures
Tuples
Lists
Excursion： Control Structures
Excursion： Functional Programming
Dicts
Sets
Conclusion
Further Resources
4． Numerical Computing with NumPy
Arrays of Data
Arrays with Python Lists
The Python array Class
Regular NumPy Arrays
Part III． Financial data science
Part IV． Algorithmic Trading
Part V． Derivatives Analytics
Part 1．Python and Finance
1． Why Python for Finance
The Python Programming Language
A Brief History of Python
The Python Ecosystem
The Python User Spectrum
The Scientific Stack
Technology in Finance
Technology Spending
Technology as Enabler
Technology and Talent as Barriers to Entry
Ever-Increasing Speeds， Frequencies， and Data Volumes
The Rise of Real-Time Analytics
Python for Finance
Finance and Python Syntax
Efficiency and Productivity Through Python
From Prototyping to Production
Data-Driven and AI-First Finance
Data-Driven Finance
AI-First Finance
Conclusion
Further Resources
2． Python Infrastructure
conda as a Package Manager
Installing Miniconda
Basic Operations with conda
conda as a Virtual Environment Manager
Using Docker Containers
Docker Images and Containers
Building an Ubuntu and Python Docker Image
Using Cloud Instances
RSA Public and Private Keys
Jupyter Notebook Configuration File
Installation Script for Python and Jupyter Notebook
Script to Orchestrate the Droplet Setup
Conclusion
Further Resources
Part II．Mastering the Basics
3． Data Types and Structures
Basic Data Types
Integers
Floats
Booleans
Strings
Excursion： Printing and String Replacements
Excursion： Regular Expressions
Basic Data Structures
Tuples
Lists
Excursion： Control Structures
Excursion： Functional Programming
Dicts
Sets
Conclusion
Further Resources
4． Numerical Computing with NumPy
Arrays of Data
Arrays with Python Lists
The Python array Class
Regular NumPy Arrays
Part III． Financial data science
Part IV． Algorithmic Trading
Part V． Derivatives Analytics
