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书名 Python金融大数据分析(影印版第2版)
分类 经济金融-金融会计-金融
作者 (德)伊夫·希尔皮斯科
出版社 东南大学出版社
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简介
内容推荐
Python已成为数据驱动和AI优先的金融界的首选编程语言。一些最大的投资银行和对冲基金现在都使用Python及其生态系统来构建核心交易和风险管理系统。在本书的第二版中,Yves Hilpisch向开发人员和定量分析师展示了如何使用Python包和工具进行金融数据科学、算法交易和计算金融学。第二版针对Python 3进行了更新,其中的大部分代码都采用了Jupyter Notebooks的形式,为几乎所有的示例提供了可执行交互式版本。在共5部分内容中,你将学习到Python及其生态系统是如何为从事金融业务的公司和个人提供技术框架的。
作者简介
伊夫·希尔皮斯科,The Python Quants是一个专注于Python与开源软件在量化金融中应用的团队,而Yves Hilpisch是The Python Quants的创始人与股东。Yves也是CQF项目的计算金融学讲师。他的客户遍及全球金融界,本身也积累了10年Python经验。Yves同时是Python and Open Source for EquantFinance这个会议在法兰克福、伦敦和纽约的组织者。
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
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