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