Writing Production-Grade Python Code for Quant Developers

Writing Production-Grade Python Code for Quant Developers, available at $44.99, has an average rating of 4, with 71 lectures, 2 quizzes, based on 23 reviews, and has 360 subscribers.

You will learn about Learn to write production-grade Python code. Learn how to build high quality Python libraries which will be used by quantitative researchers / algorithmic traders. Crystallise your knowledge in quant developer best practices. Understand the tools at your disposal for creating production-ready code and the processes surrounding them. This course is ideal for individuals who are Aspiring Quant Developers / Algorithmic Traders and Programmers or Quant Traders and Researchers or Data Analysts and Scientists or Tech-Savvy Finance Enthusiasts or Individuals Fascinated by Financial Markets It is particularly useful for Aspiring Quant Developers / Algorithmic Traders and Programmers or Quant Traders and Researchers or Data Analysts and Scientists or Tech-Savvy Finance Enthusiasts or Individuals Fascinated by Financial Markets.

Enroll now: Writing Production-Grade Python Code for Quant Developers

Summary

Title: Writing Production-Grade Python Code for Quant Developers

Price: $44.99

Average Rating: 4

Number of Lectures: 71

Number of Quizzes: 2

Number of Published Lectures: 66

Number of Published Quizzes: 2

Number of Curriculum Items: 79

Number of Published Curriculum Objects: 74

Original Price: $49.99

Quality Status: approved

Status: Live

What You Will Learn

Learn to write production-grade Python code.

Learn how to build high quality Python libraries which will be used by quantitative researchers / algorithmic traders.

Crystallise your knowledge in quant developer best practices.

Understand the tools at your disposal for creating production-ready code and the processes surrounding them.

Who Should Attend

Aspiring Quant Developers / Algorithmic Traders and Programmers

Quant Traders and Researchers

Data Analysts and Scientists

Tech-Savvy Finance Enthusiasts

Individuals Fascinated by Financial Markets

Target Audiences

Aspiring Quant Developers / Algorithmic Traders and Programmers

Quant Traders and Researchers

Data Analysts and Scientists

Tech-Savvy Finance Enthusiasts

Individuals Fascinated by Financial Markets

Embark on a transformative journey into the world of Python programming tailored for the high-octane field of quantitative finance with our university-semester length course, Writing Production-Grade Code for Quantitative Developers. This course is meticulously designed to bridge the gap between academic learning and the dynamic requirements of the quantitative development sector, focusing on the nuances of coding that are vital for success in this challenging field.

Our curriculum is a deep dive into the universe of production-ready Python coding, where every module is an essential building block towards becoming an exceptional quantitative developer. We begin with an exploration of the roles and responsibilities of quantitative developers, delving into the specific skills and tools required in the industry, and how Python plays a pivotal role. You’ll learn about the latest industry trends, the increasing demand for proficient quantitative developers, and the characteristics that make Python code production-ready.

The course covers a broad spectrum of topics, including using Linux in your Python development workflow, techniques in structuring and organizing projects, mastering Git for source control, and best practices in creating quality code and documentation. You’ll gain hands-on experience in working with the wider Python ecosystem, including virtual environments and dependency management.

By the end of this course, you won’t just learn Python; you will have honed a skill set that makes you an invaluable asset in the world of quantitative finance, ready to tackle the challenges faced by top hedge funds around the globe. Join us and transform your understanding of Python in quantitative finance, setting you on a path to career excellence.

Course Curriculum

Chapter 1: Introduction to Quantitative Development

Lecture 1: The World of Quant Developers and Researchers

Lecture 2: Demand for Good Quantitative Developers

Lecture 3: What is Production-Grade code

Lecture 4: Course Objectives and Expectations

Lecture 5: Additional Resources

Chapter 2: Implementing Academic Research

Lecture 1: Introduction to Research

Lecture 2: Key Finance Journals and Other Platforms

Lecture 3: Conducting Literature Reviews (1/2)

Lecture 4: Conducting Literature Reviews (2/2) – Obsidian Demo & H&Ts Second Brain

Lecture 5: Introduction to Code Roadmaps

Lecture 6: Resources

Chapter 3: Setting up your workshop

Lecture 1: Transition to Linux – Intro to Linux

Lecture 2: Transition to Linux – Ubuntu

Lecture 3: Ubuntu installation assignment

Lecture 4: Transition to Linux – Terminal refresher

Lecture 5: Transition to Linux – Package management and user privileges

Lecture 6: PyCharm and Jupyter Lab – Intro and IDEs

Lecture 7: PyCharm and Jupyter Lab – PyCharm installation and demo

Lecture 8: PyCharm and Jupyter Lab – PyCharm refactoring demo

Lecture 9: PyCharm and Jupyter Lab – PyCharm debugging demo

Lecture 10: PyCharm and Jupyter Lab – Jupyter Intro

Chapter 4: Mastering Git and Source Control

Lecture 1: Introduction – The basics of version control

Lecture 2: Introduction – Git Essentials

Lecture 3: Introduction – Setting Up Git

Lecture 4: Git workflow fundamentals – The Three States

Lecture 5: Git workflow fundamentals – Common Git Commands

Lecture 6: Git workflow fundamentals – Working with Remote Respositories

Lecture 7: Branching strategies – Branching in Git

Lecture 8: Branching strategies – Popular Branching Strategies

Lecture 9: Handling merge conflicts – Branch Management

Lecture 10: Handling merge conflicts – Merge Conflicts

Lecture 11: Handling merge conflicts – Resolving Conflicts

Lecture 12: Handling merge conflicts – Best Practices for Avoiding Conflicts

Lecture 13: Best practices – Types of Changes to Commit

Lecture 14: Best practices – Writing Good Commit Messages

Lecture 15: Best practices – Organising Commits

Lecture 16: Advanced Git techniques – Stashing

Lecture 17: Advanced Git techniques – Tagging

Lecture 18: Advanced Git techniques – Reverting

Chapter 5: Python Virtual Environments and Dependency Management

Lecture 1: Introduction to Python Virtual Environments

Lecture 2: Creating and Managing Virtual Environments

Lecture 3: Dependency management with pip

Lecture 4: Advanced dependency management with Poetry

Chapter 6: Creating clean code

Lecture 1: Principles of clean code

Lecture 2: Clean Python Code & Style Guides (1/2)

Lecture 3: Clean Python Code & Style Guides (2/2)

Lecture 4: Formatters and Linters (1/3) – Introduction

Lecture 5: Formatters and Linters (2/3) – Black and Pylint CLI demo

Lecture 6: Formatters and Linters (3/3) – Black, Pylint in PyCharm and Configurations

Lecture 7: Type Hints and Annotations

Chapter 7: Refactoring Code

Lecture 1: Introduction to Refactoring

Lecture 2: Inheriting Code

Lecture 3: Code Smells

Lecture 4: Common Refactoring Techniques

Lecture 5: KCA Refactoring Assignment

Chapter 8: Documentation

Lecture 1: The Importance of Good Documentation

Lecture 2: Defining Good Documentation and its Objectives

Lecture 3: Best Practices

Lecture 4: Documentation in Python (1/3) – READMEs

Lecture 5: Documentation in Python (2/3) – Docstrings

Lecture 6: Documentation in Python (3/3) – Introduction to reStructured Text

Lecture 7: Introduction to Sphinx (1/2)

Lecture 8: Introduction to Sphinx (2/2)

Lecture 9: Introduction to Read the Docs

Lecture 10: Sphinx and Read the Docs references

Lecture 11: Changelogs

Instructors

Hudson and Thames Quantitative Research
Developing sophisticated algorithms for quant traders.

Rating Distribution

1 stars: 0 votes

2 stars: 2 votes

3 stars: 5 votes

4 stars: 9 votes

5 stars: 7 votes

Frequently Asked Questions

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