LLM Finetuning : RunPod and Multi-GPU Practice
danielyouk
Packed with LLM Fine-Tuning Know-how, learned from Silicon Valley LLM Project Practitioners.
초급
LLM, RunPod, openAI API
This course covers the process of automating algorithmic trading in local and cloud environments, and focuses on hands-on practice.
GitHub Action
Windows Scheduler
Crontab
Windows registry
IBC (Interactive Brokers Controller)
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.
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.
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.
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.
If I am logged out of the system for security reasons in automated trading, can I still trade? 🤔
Can I continue trading if the Internet environment is temporarily unstable ? ❓
Can Algorithmic Automated Trading Be Realized in the Cloud ? ❓
Can we schedule our cloud computers to turn ON and OFF to minimize costs ? ❓
What are the best specifications for a cloud computer for trading ? ❓
Can I create a quant portfolio for employment ? ❓
Are terms like MLOps or GitHub Actions unfamiliar to you ? ❓
...
Most trading platforms do not allow long-term logged-on status for security reasons. This is also the case with the Interactive Brokers (IBKR) API, which is a major obstacle to automated trading.
You will learn how to maintain long-term logon status on the TWS API (trading system) by applying the Interactive Brokers Controller (IBC).

In recent years, the importance of Ops has been increasingly highlighted in the data field. Now, even traditional data scientists have difficulty being competitive without understanding and utilizing automation and MLOps .
In this course, you will learn how to control and manage virtual machines using github-actions . This will enable you to automate more efficient data workflows and enhance your operational capabilities in cloud environments.
Below is how to start a cloud computer either by scheduling or manually using github-actions .

Computing specifications for cloud automated trading are tailored to the minimum specifications (cost optimization).
We also set up an analysis environment using Anaconda in the cloud.
You can set up automated script execution using Task Scheduler on Windows and crontab on Mac. In this lesson, you will learn how to automate Python scripts and trading processes using the scheduler for each operating system. This will help you automate repetitive tasks and increase the efficiency of your trading workflow.
✅ 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
Remove RDP : Use VS Code Remote Tunnels instead of RDP connections for better security
Reduce security vulnerabilities : Minimize attack vectors by not opening RDP ports
Encrypted Connection : Provides secure, encrypted connection with GitHub authentication.
Simplify access management : Easy permission management with GitHub-based authentication
Additional Security : Can be used with additional security methods such as VPN
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.

Anyone who wants to implement quantitative automated trading in the cloud

Anyone who wants to implement MLOps or DataOps through a practical portfolio

Python Algorithm Trading Part 2 Basic Course
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.
All lecture materials are attached to the course materials and the main script notebook is also accessible via the Kaggle platform.
Who is this course right for?
Selection: Those who have completed Python Algorithm Trading Part 1
Required: Completion of Python Algorithmic Trading Part 2
Need to know before starting?
How to use Python and GitHub
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24 lectures ∙ (3hr 43min)
Course Materials:
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