The Secret of Algorithmic Trading: How AI Predicts Stock Prices

Revolutionize your stock investing with AI! In this course, you’ll build a fully functional AI automated trading system that targets the top 25 Nasdaq stocks. Gain experience and know-how through paper trading with the Korea Investment & Securities API, and take advantage of this special opportunity to conduct AI-powered automated stock trading with a real account. Even if you’re a complete beginner, there’s no problem. We’ll explain the basic concepts of stocks and why you should invest in U.S. stocks in an easy-to-understand way, and provide all source code along with remote support. From now on, investing is essential for the next 60 years of our lives. Build your own AI investment partner to accompany you!

You can systematically learn stock trading automation using Python and the Interactive Brokers API, which holds the top spot in global market share.

If you receive stock recommendations from AI and leave the timing of buying and selling to it, will you actually make a profit? AI is not a prophetic tool that predicts future stock prices. Instead, it can be a powerful investment assistant that checks the emotions swaying your investment decisions, analyzes complex information, and helps you stick to long-term investment principles. In this course, you will understand the typical behavioral errors of individual investors—such as loss aversion, panic selling, and FOMO—and learn how to create unwavering investment criteria using AI prompts and investment analysis tools. From back-calculating target assets to portfolio design, utilizing tax-saving accounts, and setting up automated recurring purchases, complete your own AI investment system that even busy office workers can consistently execute. Point 1. Understand the limits of AI investment and use it properly Understand through data why AI is not suitable for stock recommendations and predicting trade timing Utilize AI in areas it excels at, such as earnings call analysis, ETF overlap diagnosis, and portfolio reviews Learn how to review counter-scenarios and risk factors so you don't just believe optimistic answers Point 2. Correct the malfunctions that sway investment psychology Diagnose recurring investment behavior patterns such as loss aversion, the disposition effect, panic selling, and FOMO Practice the 72-hour rule to reduce impulsive trading and routines for formalizing selling conditions Design AI prompts that help maintain long-term investment principles despite market volatility Point 3. Create a long-term investment system that sustains itself automatically Design a recurring investment plan and portfolio based on target assets and investment periods Review investment strategies through Monte Carlo analysis and tax-saving account simulations Complete an investment structure that executes without constant deliberation by setting up automated purchases and cash buffers

This course covers everything from core recommendation system algorithms to practical implementation. - Content-based filtering - Collaborative filtering and deep learning-based recommendation model implementation - Two-step recommender systems implementation - Hands-on practice using PyTorch/RecBole - Industry know-how and recommendation result visualization

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

This introductory course for AI engineers is designed to provide a brief hands-on experience of the entire process, from data processing and model development to cloud, MLOps, and ethical considerations. It focuses on building practical skills by helping students understand the process of connecting models to actual services, rather than just creating them. The course includes hands-on exercises and examples so that even those learning AI for the first time can easily follow along.

This is a lecture where you can learn Deep Learning using TensorFlow. Learn linear regression, logistic regression, and softmax regression models, and finally create an MLP model that can classify fashion images!

From the basics of machine learning & deep learning! Creating and utilizing classification/regression/clustering/artificial neural networks with Python

Have you been getting stuck on linear algebra formulas every time you try to study AI? Instead of memorizing formulas, this lecture shows you visually how vectors and matrices actually move in space. By the end of the course, you will be able to interpret the mathematics behind neural networks, PCA, and SVD on your own.

In Part 3 of Creating a Python Trading Room for Quant Investment, you will learn about portfolio theory and concepts necessary for real investment, as well as how to implement them with code. In Parts 1 & 2, you mainly learned about code implementation methods for analyzing financial data, and in Part 3, you will learn about 'portfolios', which are an important part of quantitative investment. With the question of why you should create a portfolio, you will learn portfolio theory, implement it in Python, and practice the code necessary for real portfolio investment. I hope this lecture will be helpful for your own portfolio investment.

👉 Recommended for those who want to view AI results not as “values that simply came out,” but as understandable judgments. What exactly does AI use as the basis for making judgments? This course structurally explains how data affects AI’s judgments and why data is said to be important. You will learn how AI interprets numerical information and develops criteria for making judgments.

This course is a comprehensive program that covers everything you need to know to utilize generative AI in practical work settings, from the basic principles of LLM (Large Language Models) to RAG (Retrieval-Augmented Generation), and even the latest technologies like AI Agent and MCP (Modular Command Protocol). It is structured to naturally teach you how generative AI understands, searches, judges information, and extends into AI Agents that take action, following the flow of technological development.

Starting with the question, "If I were on the Titanic, would I have survived?", this course completes a full-stack project where you develop an AI model to predict survival probabilities based on real data and deploy it as a web service. You will gain hands-on experience in the entire process of AI and web development, from deep learning modeling using PyTorch to building a backend server with FastAPI and implementing a user interface with Next.js.

This course is a hands-on masterclass that completely dissects the core technological evolution of generative AI, from LDM (Latent Diffusion Model) to DiT (Diffusion Transformer). We directly analyze the latent space-based learning principles of LDM, the structure of Stable Diffusion, and the implementation methods of the latest Diffusion Transformer through papers and code. Students will systematically learn the latest trends and structural evolution of generative models by directly implementing LDM, CFG (Classifier-Free Guidance), and DiT models using PyTorch.

On your way to work tomorrow morning, your phone will have “Today’s stocks selected by the system and the reasons why” waiting for you. An AI-powered Korean stock auto-trading system that reads foreign and institutional buying trends and handles everything from purchasing to taking profits and cutting losses automatically—you build it yourself, making it truly yours, rather than relying on a program made by someone else.

You can learn the skills and know-how needed to improve work efficiency by utilizing Open API and Python.

You will learn Apache Airflow, the most widely used Orchestrator for creating software data pipelines.

With the advent of the AI era, building data pipelines has become a core competency that determines a company's competitiveness. Learn how to build efficient data pipelines using Airflow, the most widely used tool, directly from a Silicon Valley expert (formerly Head of the Data Team at Udemy, currently a professor in the Data Masters program at San Jose State University) with practical experience and extensive lecturing experience.

Now display Python results as web pages! You can create your own web page using only Python. This is an opportunity to create an Interactive Dashboard that allows you to check results created with Python, such as data analysis and machine learning, on a web page!

"Why does my automated trading system collapse in a falling market?" I, too, spent a great deal of time grappling with the same question. Most automated trading strategies on the market may appear to generate profits during bull markets, but the moment market conditions change, they lose their ability to adapt. To solve this problem, I combined the expertise I developed through two years of hands-on research with the latest AI technology. In this course, you will learn how to design a 'Transformer-based deep learning model' that simultaneously reads macroeconomic trends and the movements of individual assets, and how to automatically train and deploy it in a free GPU environment. In particular, by introducing sophisticated data normalization techniques on par with those used by hedge funds, you will build an 'intelligent comprehensive scoring system' that goes beyond simple indicators to incorporate news sentiment. On top of that, an ‘AI safety gate’ infused with the insight of a trader with 20 years of Wall Street experience adds a final layer of conviction from the perspective of a human trader just before a purchase. Going beyond simple buying and selling, you will implement the entire process of an institutional-grade algorithmic system yourself—including dynamic take-profit and stop-loss logic, eight self-recovery mechanisms, and real-time Slack reports.