Pythonを使用した株価バックテストシステムの構築

Financial data such as stocks, exchange rates, coins, real estate, and bonds are easily accessible and provide us with opportunities to make money, but most people miss valuable opportunities because they do not know their characteristics and how to handle them. Through this lecture, you can learn how to understand the meaning of various financial data and how to analyze them using Python.

This lecture guides you through data analysis using Python and how to apply it to the IT security field.

Train SQL queries with 50 diverse problems, preparing for real analysis environments and coding tests. * If you're new to SQL, taking the SQL [Concepts Course] first will help you follow along easily.

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.

I want to create an algorithm trading bot that makes unlimited money and run it automatically. Where should I start?

Revolutionize your stock investments with AI! In this lecture, you will directly build a real, working AI automated trading system targeting the top 25 NASDAQ stocks. We provide a special opportunity to gain experience and know-how through simulated investments with the 한국투자증권 API, and to perform AI stock automated trading with a real account. Even stock beginners will have no problem. We will easily explain basic stock concepts and why you should invest in US stocks, and provide all source code and remote support. From now on, investment is essential for the next 60 years of our lives. Create your own AI investment partner that will be with you!

Finally, the Probability and Statistics course for everyone is here! Learn the principles of probability and statistics step by step through theory and coding practice. Reference book "Probability and Statistics for Everyone" https://wikidocs.net/book/18165

Learn the essential SQL and database fundamentals needed for practical work. Not only developers, but also planners and data analysts can learn SQL in depth.

This lecture is a follow-up lecture to 'Python Data-Based Stock Quant Investment Part 1'. If Part 1 was more of an introduction, Part 2 is an in-depth lecture that focuses on the entire flow of practical strategy implementation and quantitative investment development. This class focuses on advanced Pandas techniques for handling time series data, and how to implement signal-based strategies and static/dynamic asset allocation strategies that require adjusting asset weights at various intervals based on this. Furthermore, it goes beyond strategy implementation and learns about 'code framework' that directly verifies and backtests various investment strategies with minimal code modifications, how to extend this to improve it so that it can lead to actual investment, and what to watch out for in this process. In addition to the programming component, you can experience the best Python quant investment flow that you cannot find in investment books, blogs, YouTube, etc. by deeply covering theoretical contents such as the two types of return concepts (simple return, log return) and evaluation indicators related to backtesting.

We explored the essential Python syntax necessary for big data analysis and, through various examples, presented methods such as big data file processing and visualization.

Easy and fun SQL stories learned from professional instructors! Lectures are conducted systematically and step-by-step for beginners. We offer opportunities to learn with sufficient repetition through a 3-step learning process.

Learn database design systematically through three stages: conceptual, logical, and physical, and strengthen your capabilities with a practical shopping mall project. Acquire real-world expertise through modern design techniques and the latest trends.

This is a basic course on Python Excel programming that you can learn with the xlsxwriter tutorial. You can analyze data and automate tasks using Python and Excel.

A course to understand and practice machine learning that efficiently performs complex analysis and prediction! Machine learning, which seemed difficult and complicated, can be easily and enjoyably learned in its core with Maso Campus' <Python Machine Learning>.

Time Series: Become a Data Analysis Expert! Let's be analysts strong in theory & practice!

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

If you're just learning Python and don't know what you can do or what you should do? Let's learn how to use web technologies with Python, such as web crawling, automation, and APIs.

In this class, you will learn techniques for retrieving (crawling) and processing data from various operating sites using Python, and apply them directly to various examples that you may encounter in the field. Through this, you will learn techniques that focus on solving practical problems based on an understanding of the basic principles of how the web works, rather than one-dimensional task automation by copying and pasting given codes, and accordingly, you will be able to collect and process data from any site in the future. You will also learn how to resolve various exceptions that occur in this process with code, and ultimately, you will be able to solve and automate various problems that occur in daily life with programming thinking.

Unlike existing lectures, this course has been newly designed based on the instructor's initial failures in learning machine learning, enabling easy understanding and application to real-world problems.

You can create a deep learning analysis environment in the cloud using Docker. When Docker images managed by Google, MS, etc. are combined with the cloud, you can work with the latest deep learning analysis methods on your computer.