This repository appears to be a personal page or portal for an individual focused on quantitative finance, machine learning, and software development. It offers educational resources, courses, and articles aimed at individuals looking to master quantitative trading, providing practical insights and tutorials on complex topics like data handling and strategy development. The primary benefit is access to structured learning materials and expert-written content in the quantitative finance domain.

How It Works

The project centers around providing educational content and resources for quantitative trading. This includes a series of courses covering topics from foundational concepts to advanced machine learning strategies, supplemented by articles that delve into specific challenges such as data standardization, avoiding forward-looking bias using Point-in-Time methods, and analyzing data discrepancies from sources like Tushare and Dongcai. The approach emphasizes practical application and addressing common pitfalls in quantitative strategy development.

Quick Start & Requirements

This repository does not appear to host a specific software project with installation instructions. Instead, it directs users to external resources for courses and articles. Interested users can visit ke.quantide.cn and the public account "Quantide" for course information and the latest articles. Specific prerequisites for the courses or content are not detailed but likely involve a foundational understanding of programming and finance.

Highlighted Details

• Offers courses like "Quantide. Quantitative 24 Lessons," "Quantide. Factor Analysis and Machine Learning Strategies," and "Quantide. Numpy and Pandas for Quant People."
• Features articles discussing critical issues such as "forward-looking bias" in data standardization and the importance of "rolling window" or "Point-in-Time" data processing.
• Addresses practical data source challenges, including discrepancies in daily return data from Tushare and Dongcai, and the complexities of historical data复权 (restoration).

Maintenance & Community

Resources and further information are available via the website ke.quantide.cn and the public WeChat account "Quantide." The author is a software developer, quantitative trader, and entrepreneur, and the author of 'Best Practices for Python'. Further community or direct project maintenance details are not provided.

Licensing & Compatibility

No specific open-source license is mentioned for the content or any associated code within the provided README. Users should assume all rights are reserved or inquire directly regarding usage permissions, especially for commercial applications.

Limitations & Caveats

The provided text focuses heavily on promoting educational courses and articles rather than detailing a specific, installable software project. Consequently, information regarding project scope, specific technical implementations, benchmarks, or potential limitations of a software artifact is absent. Users seeking a ready-to-use tool may need to seek further clarification.