Building a US Stock AI Automated Analysis System with Vibe Coding and Claude Code

How long are you going to manually look through 2,500 stocks every day? Searching through Naver Finance to find surging stocks after the market closes, Reading news to judge whether it's good or bad news, Checking foreign/institutional supply and demand one by one, Opening charts to analyze patterns... Aren't you repeating this every single day? I used to do that too. I spent 2 to 3 hours after work analyzing stocks, and yet I still missed more stocks than I found. With over 2,500 stocks across KOSPI and KOSDAQ combined, it's impossible for a human to see them all every day. --- So, I built a system. I created a system that automatically analyzes 2,500 stocks every day after the market closes. - Automatically collects market prices, supply/demand, and news - AI (Gemini) reads the news and determines if it's positive or negative - Scores stocks out of 15 points based on 6 factors - Selects only the stocks that pass the criteria and calculates entry, stop-loss, and take-profit prices - Sends notifications via Telegram Now, I just check my phone after work. Additionally, every Saturday, the system analyzes its own performance from the past week and automatically adjusts stop-loss lines, take-profit lines, and holding periods. It is a structure where the system learns and improves on its own. --- But I am not a developer. I didn't write the code for this system myself. I gave all the instructions to the AI verbally. "Filter out stocks that rose more than 5% today with a trading volume exceeding 50 billion won." "Send these 3 news articles to Gemini and have it judge if they are positive." "Create a scheduler so this runs automatically every day at 4 PM." When I speak like this, the AI (Claude) generates the code. This is "Vibe Coding." --- In this course, we will build this exact system. Over 58 lessons, we will build the system I actually use every day from start to finish. Starting from data collection, AI news analysis, scoring engine, signal generation, Flask API server, Next.js web dashboard, Telegram automatic notifications, and even the self-learning system. The final product is not a Jupyter Notebook. The final product is a web dashboard and Telegram notifications that actually run every day. You don't need to know how to code. In every lesson, I will show you how to talk to Claude, and if you follow along, you will get the same results. --- Recommended for: - Office worker investors who lack the time to analyze stocks every day - Those who want an automated system but don't know how to code - Those curious about quant/system trading but don't know where to start - Those curious about how to utilize AI in practice --- ⚠️ This course does not guarantee investment returns. This is a programming course on building your own stock analysis tools. Actual investment decisions are the responsibility of the student.

AI agents, the latest trend in generative AI! How to stay ahead with Cursor, which handles all your development tasks, and MCP, which expands its capabilities!

Explore → Plan → Implement → Commit. Master a proven AI development workflow with Claude Code. Learn everything from MCP and sub-agents to prompt engineering through 23 missions, and complete 3 real-world projects.

It's okay if you don't know a single line of code. We are now in the era of 'Vibe Coding,' where you develop by 'conversing' with AI.

Building an MVP with AI is no longer difficult. However, most projects stop at the next stage. 👉 You’ve built the features 👉 But development doesn't continue Why does this happen? The problem isn't the code; 👉 It's because there is no structure that allows AI to work continuously. --- In this course, based on an existing project, 👉 We will cover the process of building a structure 👉 That allows AI to continue development on its own. --- Instead of simply using AI tools, we will: * Create a docs structure * Define the SSOT (Single Source of Truth) * Execute development on a per-ticket basis * Connect QA and iteration cycles 👉 To complete a single "AI Development System." --- Through this process, you will: 👉 Create a structure where development continues 👉 Even without a human manually coding. In other words, you can: 👉 Build and understand a development system 👉 That operates AI like a team. --- This course is designed for: 👉 Those who want to build a structure applicable to their projects immediately 👉 Those who started development with AI but found it difficult to sustain 👉 Those who want to move to the next level after "Vibe Coding" --- Beyond just learning, we provide an experience where you: 👉 Take action 👉 Build a structure that actually works 👉 And walk away with a framework you can apply to your own projects.

This is a Silicon Valley internship and 30 Big Tech system design & open-source contribution mastery course that ensures job security even in the AI era. In this course, you will design and scale 30 real-world system designs using cases from Big Tech companies such as Netflix, Tesla, Meta, and Tinder. Finally, you will design your own service from architecture to DB and scaling.

Are you feeling anxious because it seems like everyone is trying Vibe Coding except you? Unsure of where to even begin? Don't worry. SSG Coach is here to help. Game development isn't difficult when you're Vibe Coding with AI. I will guide you so that anyone can create a game using only the free Gemini CLI. I will also provide essential game development knowledge, so that after completing this course, you'll be ready to take on other game projects as well.

"Mastering Model Context Protocol (MCP)" is a practice-oriented engineering course designed to help developers build secure, production-ready AI backends. Based on my experience helping thousands of students resolve confusion in LLM integration, Tool Calling, and backend architecture, I aimed to address the most common challenges through this course. "How can I build a reliable backend that an LLM can call securely?" "Should I choose SSE, stdio, or streamable-http?" "How can I scale MCP to a real-world application level using FastAPI, Auth0, and LangGraph?" "How should I structure MCP Tools, Resources, Prompts, and Context?" This course provides detailed, step-by-step guidance, from running a minimal MCP server to deploying a fully secured, Docker-based system. All lessons are practice-focused, centering on reducing complexity and providing clear, repeatable workflows for building modern AI systems. If you are tired of vague tutorials and want a clear, concrete, engineering-level understanding of MCP, this course was made for you.

The goal is to master the complex GPT API and Python in the easiest way possible through hands-on practice. You will develop a YouTube video summarization AI using the latest ChatGPT API and implement it as a web application using Streamlit.

This course conducts hands-on practice related to Diffusion models among generative artificial intelligence models. By reading and implementing the prompt-to-prompt paper, which is a representative Diffusion model application paper, we expect to cultivate the ability to understand the latest artificial intelligence papers.

Recently, all the remarkable achievements in the field of artificial intelligence are being announced in the area of reinforcement learning. This covers reinforcement learning technology—which is bringing about true innovation in AI such as robotics, autonomous driving, and humanoid machines—from basic to advanced levels in an easy-to-understand way for beginners.

This course not only expands your ideas on blog management by creating a Python-based automated posting program for CPA affiliate marketing on WordPress but also allows you to perfectly master practical Python through real-world application rather than just theory.

Understand the operating principles of generative AI models using deep learning and acquire practical application skills through hands-on exercises.

This course proceeds with Python theory and practical sessions that even non-majors can easily understand. This is the first step in the overall roadmap built for application development - the Python programming introduction course. [Complete-Roadmap] Python Basics->Advanced Python->Automated Trading-Backtest Development->Automated Trading-Real-time Development->Automated Trading-LLM Application

This is a practice-oriented course for experienced developers with a computer science background transitioning to the latest AI technology stack. From the design to the implementation of LLM-based applications, you will establish AI solution adoption strategies by leveraging existing software engineering knowledge. Rebuild your career by integrating traditional development experience with modern AI technology.

A course to truly make you a hands-on game developer. Push your limits with 4 hands-on projects and 3 AI-specialized games!

Time is constantly flowing, and data is constantly accumulating. In this dynamic world, time series data plays a vital role in every aspect of our lives. From the volatility of financial markets to subtle signals of climate change, time series data captures it all. Now learn how to interpret and leverage this powerful data using the power of Python and deep learning!

In an era where AI writes code for you, will you remain a 'Coder' or leap forward to become an 'Architect'? This course is designed for junior developers who can handle basic implementation but feel stuck about the next steps or are anxious about falling into the trap of 'fake experience'. We aim to go beyond simply learning clean architecture theory and turn Robert C. Martin's (Uncle Bob) high-level design principles into weapons for the AI era.

How long are you going to manually look through 2,500 stocks every day? Searching through Naver Finance to find surging stocks after the market closes, Reading news to judge whether it's good or bad news, Checking foreign/institutional supply and demand one by one, Opening charts to analyze patterns... Aren't you repeating this every single day? I used to do that too. I spent 2 to 3 hours after work analyzing stocks, and yet I still missed more stocks than I found. With over 2,500 stocks across KOSPI and KOSDAQ combined, it's impossible for a human to see them all every day. --- So, I built a system. I created a system that automatically analyzes 2,500 stocks every day after the market closes. - Automatically collects market prices, supply/demand, and news - AI (Gemini) reads the news and determines if it's positive or negative - Scores stocks out of 15 points based on 6 factors - Selects only the stocks that pass the criteria and calculates entry, stop-loss, and take-profit prices - Sends notifications via Telegram Now, I just check my phone after work. Additionally, every Saturday, the system analyzes its own performance from the past week and automatically adjusts stop-loss lines, take-profit lines, and holding periods. It is a structure where the system learns and improves on its own. --- But I am not a developer. I didn't write the code for this system myself. I gave all the instructions to the AI verbally. "Filter out stocks that rose more than 5% today with a trading volume exceeding 50 billion won." "Send these 3 news articles to Gemini and have it judge if they are positive." "Create a scheduler so this runs automatically every day at 4 PM." When I speak like this, the AI (Claude) generates the code. This is "Vibe Coding." --- In this course, we will build this exact system. Over 58 lessons, we will build the system I actually use every day from start to finish. Starting from data collection, AI news analysis, scoring engine, signal generation, Flask API server, Next.js web dashboard, Telegram automatic notifications, and even the self-learning system. The final product is not a Jupyter Notebook. The final product is a web dashboard and Telegram notifications that actually run every day. You don't need to know how to code. In every lesson, I will show you how to talk to Claude, and if you follow along, you will get the same results. --- Recommended for: - Office worker investors who lack the time to analyze stocks every day - Those who want an automated system but don't know how to code - Those curious about quant/system trading but don't know where to start - Those curious about how to utilize AI in practice --- ⚠️ This course does not guarantee investment returns. This is a programming course on building your own stock analysis tools. Actual investment decisions are the responsibility of the student.

You can learn everything from basic usage of generative AI to automation and creating customized AI chatbots for practical work applications all in one comprehensive course.

Deterministically control AI agents through a 5-layer harness architecture and 4 workflow layers, and immediately apply a scalable project structure to real-world practice. (Based on Claude Code, but the same principles apply to other AI tools.)