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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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