24/7 Profit Machine: The Complete Guide to Automated Crypto Trading with Vibe Coding

Build a crypto automated trading bot that makes profits even in a down market?!

"If you don't find a way to make money while you sleep, you will work until you die."

— Warren Buffett

Seeing this quote, one thought came to mind.

'What if we built a trading bot ourselves that could generate profits in both directions amid markets fluctuating up and down even while we sleep?' 🤔

That’s how this course came to be.

Rather than a very high-risk system that gives the AI full authority and entrusts all investments to it,
we will implement the trading strategy we design together in the course as an automated trading system through vibe coding and monetize it!

An automated trading bot that has been operating since the course launched (regular updates planned)

Ripple (XRPUSDT) Short 7x · +77.99%

Dogecoin (DOGEUSDT) Long 5x · +28.81%

In an era when AI trades on its own, how can we automate more safely? 🤔

The answer lies in backtested strategies and vibe coding!

This course does not entrust all investment decisions to LLMs such as ChatGPT and Codex. Instead, people define the trading strategies and risk management criteria, while using LLMs to assist with strategy design, code writing, and troubleshooting.

In this course, you will implement a Binance futures automated trading system using Python and CCXT. You will practice implementing technical indicator strategies, long, short, and bidirectional strategies, averaging-down backtesting, a kimchi premium strategy based on the Upbit API, and even trading strategies using an LLM, then implementing and deploying them as a trading bot.

After adding SNS API notifications and order safety verification to the completed bot, you will manage it with GitHub and deploy it for free to an Ubuntu server on Oracle Cloud.

Aren’t we relying too much on the LLM..?

In the course, the LLM is used as follows.

Human idea → LLM strategy design → backtesting → risk validation → automated trading execution

In other words, LLMs are used as coding agents, not in a way that hands over all of our important permissions to them!

Course Features

📌 You can get started even if you have never coded before.

Starting with Python installation, you will gradually learn everything needed for automated trading, from variables, conditional statements, loops, and functions to pandas. Understanding the basics of Python will make it easier to communicate with an LLM when vibe coding.

Furthermore, through hands-on practice with the Python-based CCXT library, you can understand the logic and processes behind automated trading.

📌 Stock assets can also be traded automatically on cryptocurrency exchanges.

On exchanges like Binance, you can currently use leverage to trade assets such as SK HYNIX (Hynix).

Not only Bitcoin, but stock assets as well can be easily set up for automated trading.

📌 Experience the vibe coding process, not just copying code.

Learn how to write strategy requirements and generate / modify / debug code using ChatGPT and Codex.

📌 Cover everything from backtesting to live operations in one go.

You will directly handle strategy design / performance analysis / live orders / API alerts using SNS / GitHub management / cloud server deployment.

📌 The LLM is not the trader; it is our assistant.

We do not entrust our funds to the spur-of-the-moment decisions of an LLM.

We implement the bot using a Human-in-the-Loop approach, where humans control the strategy and risk management criteria!

In other words, we use artificial intelligence (AI) as a coding and strategy agent, not as an agent that executes trades!

Recommended for people like you

After completing the course, you will be able to

• You can use Python and CCXT to retrieve market prices, balance, and order information.

• You can design your own trading ideas into specific entry and exit conditions.

• You can use ChatGPT and Codex to create new automated trading strategies and code.

• You can analyze ROI / cumulative return / win rate / maximum drawdown / Sharpe Ratio / number of trades / fees.

• You can backtest long / short / two-way strategies and expand them into live trading code.

• You can implement order quantity validation / balance checks / duplicate order prevention / stop-loss / take-profit / error handling features.

• You can receive order execution and error notifications through the SNS used in the course.

• You can manage your automated trading project using Git and GitHub.

• You can operate an automated trading bot 24/7 on an Ubuntu server in Oracle Cloud.

Once you complete the course, you’ll have more than just sample code;

You will have your own automated trading system that you can modify, test, and deploy yourself!!

You’ll learn the following.

The person who created this course

• Graduated from the Department of Computer Science and Engineering at one of the Seongseo-Han universities.

• Currently enrolled in the graduate school of the College of Engineering at Seoul National University

• Time-series data analysis / large language model research

• Performed contract work developing automated trading programs for approximately six months

• Stock/coin data analysis and automated trading algorithm development

Hello. I am Computer Jigi, currently researching time-series data analysis and large language models at Seoul National University’s Graduate School of Engineering.

For about half a year, I have been taking on outsourced automated trading program development projects at specialized outsourcing company K, turning clients’ trading ideas into fully functional programs. In addition to researching LLMs, I have continued analyzing data patterns in the stock and cryptocurrency markets, as well as implementing various trading strategies through backtesting and automated trading algorithms.

I, too, participate in the market by automating various trading strategies with the goal of achieving financial freedom. Along the way, I’ve gone through countless trials and errors—from coming up with strategy ideas to Python, exchange APIs, backtesting, live trading, and server deployment.

Rather than spending a long time explaining complex theories, this course aims to clearly and systematically teach you how to create a real cryptocurrency automated trading program using Python and vibe coding. It is structured around hands-on practice, starting from the basics so that even those who are new to programming can follow along step by step.

Through this course, we hope you gain the ability to go beyond simply running the provided code—to turn your own trading ideas into concrete strategies and test, modify, and deploy them yourself.

Let’s grow together and navigate the ever-changing market with your own system!

Do you have any questions?

Q. Can I take the course even if I know nothing about coding?

Yes, it is possible. The course covers everything from installing Python to basic syntax, pandas, and how to use development tools. By following the Python fundamentals and problem-solving sections, you can practice automated trading even without any coding experience.

Q. Is it okay if I have no experience with cryptocurrency or futures trading?

Yes. We explain the differences between spot and futures trading, long and short positions, technical indicators, and how to use Binance. However, because futures trading can result in significant losses due to leverage, thorough study and small-scale testing are necessary before applying it in real trading.

Q. Does creating an automated trading bot guarantee profits?

No. This course is not an investment course that guarantees profits; it is a technical course on backtesting strategies and implementing them as an automated trading system. Backtest results do not guarantee future profits, and losses may occur depending on market conditions.

Q. Can I follow along on both Windows and macOS?

Yes. The course is recorded using a Windows environment as the basis, but Python / VS Code / Jupyter Notebook / Git / GitHub can also be used on macOS. Some installation screens and commands may differ depending on the operating system.

Q. Are ChatGPT Plus and Codex absolutely necessary?

For smooth vibe coding practice, we recommend using ChatGPT Plus and Codex. If you use other LLMs and coding agents such as Claude or Gemini, you can apply the same strategy design and code generation process.

Q. Can this be used on exchanges other than Binance?

The main exercises will focus on a Kimchi premium strategy based on Binance Futures and Upbit. By understanding CCXT’s common structure and vibe coding approach, you can expand it after the course to work with other exchanges’ APIs and order methods.

Things to Know Before Taking the Course