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.

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.

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.

The Master Guide to AI for Java Developers! This course shatters the prejudice that AI is the exclusive domain of Python and empowers your Java applications with AI-driven eyes, ears, voice, and even coding capabilities. Grow into an AI Architect based on the OpenAI API. You will directly develop various AI experts such as an AI writer, translator, artist, and coding companion. You will also build a multi-functional AI assistant that integrates core features including text summarization, sentiment analysis, image description, Java code generation, speech-to-text/text-to-speech conversion, and image/subtitle generation. This is a golden opportunity to maximize the competitiveness of existing Java systems by learning how to assign AI roles through system messages and even implement harmful content moderation.

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.

Essential certification for the AI era, MS AI-900! 🚀 Did you know? ChatGPT operates exclusively on Microsoft Azure! If you want to properly utilize Microsoft Azure OpenAI, which exclusively provides the latest GPT models, now is the perfect opportunity. Grow into an Azure AI services expert through the AI-900 course and master enterprise-grade AI solutions that companies trust. Secure global official certification and enhance your competitiveness as a true AI professional!

We invite you to the fascinating world of coding where you analyze and visualize financial statements using only prompts. Without a single line of code, you create and deploy a service in consultation with AI.

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.

You can implement hands-on practice of infinitely generating cryptocurrency trading signals automatically using ChatGPT and Python, and backtesting and validating them. Additionally, you can learn how to combine all the strategies automatically generated by AI into a single strategy and apply it to actual trading.

This course is a practice-oriented program that goes beyond simple chatbots to build multi-agent systems where multiple agents collaborate. 👉 You will learn three frameworks—Swarm, LangGraph, and DeepAgents—step-by-step, completing six practical projects including a customer support chatbot, blog content creation, a PostgreSQL query agent, and an autonomous research agent. 👉 By implementing enterprise-grade features such as long-term memory using Redis and PostgreSQL, Human-in-the-loop security mechanisms, and cost monitoring, you will be able to build production-level systems that can be applied to real-world services immediately after the course.

Create a program using the GPT API, and even generate images and videos!

This course covers the entire process of designing and implementing Generative AI services through step-by-step hands-on practice, focusing on LangChain 1.0, LangGraph, and Deep Agents. Starting from the basics of Chat Models and Messages, you will master the core building blocks of LangChain—including Tool Calling-based Agents, memory, streaming, and structured output (Pydantic-based)—and then expand into LangGraph's StateGraph-based state machine architecture to directly implement production-ready AI system structures. You will systematically build agent design capabilities through real-world scenario-based exercises, such as RAG systems based on documents, PDFs, and web data (embeddings, ChromaDB, semantic search), SQL Agents (Chinook DB), multi-agent orchestration using the Supervisor pattern, and calculator agents utilizing the LangGraph Graph API. Furthermore, through Deep Agents (create_deep_agent), you will complete Generative AI applications equipped with stability, scalability, and controllability by utilizing sub-agent delegation, multi-turn conversations, and Deep Agents-specific middleware (SummarizationMiddleware, HumanInTheLoopMiddleware, ToolRetryMiddleware, PIIMiddleware, etc.). 👉 For those who want to accurately understand the internal structure and execution flow of LangChain/LangGraph/Deep Agents 👉 For those who want to implement RAG and Agents as production-level architectures rather than just "demos" 👉 This is the optimal course for those who need a practical roadmap covering state-based agents, SQL/document automation, and multi-agent orchestration.

My 14-day use, starting right after release: Claude Code! (AI Coding) - Immediately applicable to job seekers and pros! I'll tell you right away why I recommend it to everyone.

A course chosen by 3,000 students! Updated to GPT-5 version! How far can you go with ChatGPT? Create automated stock trading applications and web investment reports!

By simply entering a US company ticker, this system automatically collects financial accounting data, competitor analysis, recent news, and stock price information to generate professional investment reports through in-depth analysis using the OpenAI GPT API. The final results are automatically uploaded to Notion. By learning the basic concepts and terminology of financial accounting, even engineers can utilize this for competitor benchmarking, persuading supervisors, and planning new solutions. You will build a high-performance API server using Python and FastAPI, learning asynchronous programming, AI integration, and practical fintech skills. We support beginners so they can easily follow along, complete a system applicable to real-world investment and business, and grow into developers tailored for the AI era.

How did Assistant Manager Kim, who worked overtime every day, become a "leave-on-time pro" in just 1 hour? The secret to reclaiming your "clock-out time" Staying up all night because an Excel formula broke, or having an entire report ruined by a single typo—every office worker has experienced this at least once, right? This course was designed to put an end to that "recurring pain." You don't need to memorize complex coding syntax. We utilize Cursor AI, an AI-powered tool that writes code based on text commands, and Streamlit, which allows anyone to easily create websites. I have captured the entire process of solving the "pain points" I personally faced in the field—such as merging files by branch, automatically classifying items for repair, and generating infinite PDF certificates—all by "conversing" with AI without typing a single line of Python code yourself. By the end of this course, you will be reborn not as a mere office worker, but as a "Digital Transformation Expert" who revolutionizes your team's work efficiency.

Based on the experience of wondering how facial recognition works in offline environments on-site, this course will guide you through building the basic structure of a facial recognition-based access and attendance management system using React + Electron and Python's FastAPI-based DeepFace. Rather than focusing on perfection, we focus on understanding the operating principles and laying the foundation for you to expand into your own personal projects.

From core theories of deep learning and CNN to various CNN model implementation methods, and practical deep learning development know-how through real-world problems, If you want to become a deep learning CNN technology expert based on Pytorch, join us in this lecture :)

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.

* 100% Practical Curriculum: Focused on hands-on data handling rather than grammar-based learning * Essential Machine Learning Prep: A complete summary of data preprocessing techniques required before AI training * Data Analysis Tool Mastery: Conquer core functions of Numpy (numerical operations) and Pandas (data manipulation) * Deriving Visualization Insights: Transferring data visualization skills using Matplotlib and Seaborn * Building Statistical Foundations: Learning basic statistics and group analysis techniques to identify data characteristics

If you want to properly learn OCR technology that's truly used in practice, this one course is all you need! Aiming for over 98% accuracy even with unstructured documents and complex layouts, based on the latest SOTA models and real-world know-how, we build enterprise-level OCR projects together.