Python Algorithmic Trading Part 3: Cloud Trading Automation

Notes before taking the course 📢

Lecture Purpose and Instructions

This course is mainly aimed at learning investment strategies and automation through simulation . It is a training course for developers and data analysts who focus on developing quantitative trading strategies using programming and algorithms , not for actual investment purposes.

caution:

• This course does not cover the actual procedures required to execute investments, such as opening investment accounts, legal procedures, and tax-related procedures.

• This lecture also does not cover legal issues related to investing in Korea or other countries , such as the legality of certain strategies such as Pairs Trading .

• All simulations in the course are provided for learning purposes only and do not contain any advice or recommendations regarding real asset investments .

Students should be aware that the content of this course does not cover actual investments , and if you need advice or legal advice on investment execution, please contact a relevant professional.

[Python Algorithm Trading Lecture] is a three-part series , and this lecture is 'Part 3'.

• The content in Part 2 is a prerequisite for taking this course.

• Part 1 is recommended , but not required.

• Even if you haven't taken the previous Quant lectures, you can focus only on cloud automation, such as GitHub Actions . However, in this case, some of the content may be difficult.

Course Structure:

• Part 1 : 'Python Data Analysis for Algorithmic Trading'

• Part 2 : 'Real-time Algorithmic Trading with Interactive Brokers API'

• Part 3 : 'Cloud Transaction Automation' (this lecture)

In Part 3 , you will learn how to automatically run virtual machines according to your stock trading schedule using cloud automation.

Course Review Event

• There is a course review event. Please leave a course review for Part 2 and contact us by email (daniel@datarian.education ) and we will issue you a discount coupon.

  
  

✅ Set up automatic login :: Learn how to set up automatic login when starting a virtual machine

✅ Improved convenience : Automatically prepares the analysis environment without manually logging in every time

✅ Key takeaways :

• Modify Windows Registry

• Enter key value for automatic login setting

  
  

💡 What sets it apart from other Python data analysis courses

• MLOps/DataOps Practical Applications : This course goes beyond simple conceptual explanations to cover how to apply MLOps and DataOps in real-world settings.

• Controlling Virtual Machines : Learn how to take practical control of your virtual machines in the cloud and on-premises.

• Real Project-Based Lectures : This course is based on portfolio projects submitted to actual quant firms, and covers real-world applications rather than just theory.

Practice environment

• In this lecture, we will create a Windows OS-based virtual machine in Azure and use Anaconda to build a Python analysis environment and proceed with hands-on practice.

• We also provide guides to enable automation implementation in Mac and Windows local environments.

  
  

Learning Materials

• All lecture materials are attached to the course materials and the main script notebook is also accessible via the Kaggle platform.