Data-driven stock quant investment with Python Part 2

In this class, you will learn about the principles and methods of analyzing and processing various types of financial data using Python's Pandas library, apply them to situations you may encounter in the real world, and ultimately learn how to implement backtesting based on financial statement data (based on Kang Hwan-guk's book, "You Can Do Quant Investment"). As a result, you can break away from being a "passive investor" who simply follows what others say without verifying or basing the investment logic, and become a "self-directed and active investor" who can freely extract various elements necessary for strategy implementation from data and quantitatively analyze them using Python and Pandas.

Do you want to create a tool that can help you with your real-world investments using Python? In this course, you will learn how to create an interactive web dashboard for financial data analysis for intermediate Python users and learn about pair trading strategies for in-depth quantitative work. We hope that you will be able to create your own useful analysis tools to help you analyze financial data more powerfully and make wise investment decisions.

Build data-driven investment strategies with Python quant investing and put them into practice. You can implement various asset allocation strategies and ultimately create your own unique investment strategy.

The fact that you can analyze data more easily and effectively with Python Pandas instead of Excel! Learn the basic functions of the Pandas library and how to use it in practice through financial data analysis.

"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.

This is a course for beginners in stock data analysis. Get started with stock data analysis using Python!

Starting from a blank starter app, you’ll build a fully functional investment analysis workflow by manually adding 65 nodes for stock screening, VCP pattern analysis, supply-and-demand tracking, backtesting, AI commentary, and Telegram alerts. This hands-on course teaches you how to connect AI-generated code to real app features and complete your own analysis pipeline covering both the Korean and U.S. stock markets.

Have you ever found it difficult to know which numbers to look at first when reviewing financial statements and business reports, or to interpret why revenue and profit have changed? For more than 15 years, I have analyzed companies across a wide range of industries in the corporate finance field, and I have learned that even when looking at the same financial statements, the numbers that matter most differ depending on the industry and business model. Corporate analysis is not simply about calculating financial ratios; it is the process of understanding what a company sells to make money, what costs it bears, and how industry risks appear in the numbers. This course begins with finding the information needed for analysis in the “Business Overview” section of a business report and in the notes to the financial statements. We examine the causes of growth by breaking revenue down into selling price (P) × sales volume (Q), and assess pricing power and bargaining power by comparing raw material prices with product prices. We then classify key costs into fixed and variable costs and connect CAPEX, depreciation, capacity utilization, and cost ratios to analyze how a company’s profitability and risks differ. We also examine what needs to be reviewed differently in business reports across industries such as manufacturing, food, semiconductors, secondary batteries, shipbuilding, construction, pharmaceuticals and biotechnology, and rentals. Through real-world company examples, you will learn how to connect numbers with business structures. The goal of this course is not to memorize the many numbers in a business report. It is to learn the flow of analysis—from industry and business structure → revenue structure → cost structure → profitability and risk—and establish a framework for analyzing companies independently.

Course Title: Big Data Analysis Engineer Practical Exam Pass Codebook This is a practical, certification-preparation course for candidates preparing for the Big Data Analysis Engineer practical exam. To reduce the burden of having to study extensive theory and content that is not directly necessary for the exam, this course focuses only on the key concepts and code needed to solve actual exam problems. The course consists of 23 lessons and covers everything step by step, from Python basics to Task Type 1 (data processing), Task Type 2 (machine learning), and Task Type 3 (statistical analysis). Rather than simply presenting completed code, the course explains the role of each section of code and why it is written, enabling students to follow along directly and develop their problem-solving skills. Main Course Content - Essential Python and pandas fundamentals required for the exam - Task Type 1 data processing and practical problem solving - Task Type 2 data preprocessing, machine learning modeling, and submission of prediction results - Task Type 3 statistical tests and regression analysis - Practical problem solving and code review for each session - How to use the exam practice environment and final review immediately before the exam Learning Materials Provided - An e-book summarizing key concepts and solution code - Python practice files for hands-on learning - Problems and explanations for each session - Final review materials for before the exam The primary goal of this course is to help students avoid wasting time studying unnecessary material and enable them to write code independently and solve problems at the exam site. The course is structured so that even candidates with limited Python fundamentals can learn in order, allowing them to prepare for everything from learning essential code to solving practical problems in one course.

Based on the instructor’s experience of struggling when first learning machine learning, this course has been newly designed—unlike conventional courses—to help you understand machine learning easily and apply it to real-world problems.

Automated trading program, source code ready-to-use! Deploy to production with Oracle Free Tier server!

If you ask an AI to "build a model with this data," the first few lines might look plausible, but as soon as things get slightly complex, it starts using the wrong columns, introduces data leakage, and results in a model that performs worse despite a higher score. This course does not teach you better prompts. Instead, it covers how to directly design a loop (harness) where an agent understands data, plans, writes code, validates its own results, and backtracks when wrong. After manually walking through the five-piece loop of DS-STAR, you will assemble components one by one, from data profile cards, hooks, and skill hierarchies to independent validators and ablation.

This course covers the process of automating algorithmic trading in local and cloud environments, and focuses on hands-on practice.

This is an introductory real estate auction course where auction expert Bugatti Kim teaches in an easy and practical way without complex legal jargon. You will learn the entire process—from searching for properties to winning bids, eviction, and monetization—through core tips based on real-world experience, helping you overcome the fear of real estate investment through auctions and gain confidence.

A course to understand and practice machine learning for efficiently conducting complex analysis and predictions! Even machine learning, which may seem difficult and complex, can be easy and fun to master with Masocampus's <Python Machine Learning>.

We will examine the changes that AI technology has brought to the entire financial industry through various cases such as fintech, investment, credit risk, and fraud detection. Through hands-on practice using Python libraries, you will understand the application methods and strategic insights of financial AI and learn how to safely integrate generative AI into financial services.

* 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

We’ll guide non-majors and beginners to quickly earn the Big Data Analysis Engineer practical certification! Keep the theory light and the practical training solid. Even without complex background knowledge, you’ll focus on past exam questions and study only the key points that are sure to appear on the test.

Let's learn how to collect data from the web and create a supervised learning model for machine learning!

This lecture is designed for job seekers preparing for Evaluation and Analysis roles in Samsung Electronics' DS Division (Memory, S.LSI, Foundry, TSP, AVP) and PE, AE, and Solution PE roles at SK hynix, who are struggling with the differences between business units and job functions. You will learn how to categorize job titles by company based on the actual workflow of Product Evaluation roles and how to translate your major-related experiences into professional terminology suitable for resumes and interviews. We will help you establish an application strategy by connecting experiences in Analog circuit design, Verilog/FPGA, process experiments, and data analysis with key industry terms such as Yield, Wafer Map, Shmoo Plot, and Failure Analysis.