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waylon/ashare-quant-strategies

Analysis updated 2026-05-18

337PythonAudience · researcherComplexity · 1/5

TLDR

A collection of 119 written articles explaining different quantitative stock trading strategies for the Chinese A-share market, meant for reading and study rather than running as software.

Mindmap

mindmap
  root((ashare quant strategies))
    What it does
      Collects strategy articles
      Covers A-share market
      Reading library not software
    Tech stack
      Markdown files
      Python code snippets
    Use cases
      Learn stock picking ideas
      Study momentum strategies
      Research reference material
    Audience
      Researchers
      Quant learners
    Notes
      Study only
      Not investment advice
      Some links may be stale

Code map

Detail Auto

An interactive map of this repo's files and how they connect — its source is parsed live in your browser. Click Visualize to build it.

filefunction / class

What do people build with it?

USE CASE 1

Read individual strategy writeups to learn how quant traders approach A-share stock picking

USE CASE 2

Study examples of multi-factor, momentum, and dividend-based stock selection methods

USE CASE 3

Use the articles as reference material when researching quantitative trading ideas

What is it built with?

PythonMarkdown

How does it compare?

waylon/ashare-quant-strategiestencent-hunyuan/hy3d-benchkadevin/ilab-gpt-conjure
Stars337336339
LanguagePythonPythonPython
Setup difficultyhardmoderate
Complexity1/54/52/5
Audienceresearcherresearchervibe coder

Figures from each repo's GitHub metadata at analysis time.

In plain English

This repository is a research library of quantitative trading strategies for the Chinese A-share stock market. It collects 119 written articles, each one describing a different strategy for picking or timing stock trades, such as combining several financial factors to rank stocks, following price momentum, rotating between sectors, or reacting to dividend yield and growth signals. Each article lives in its own folder and follows the same layout: a content.md file holds the main write up, formatted to match the original source as closely as possible, including headings, lists, and code blocks. A code folder holds any code snippets tied to that article. An assets and images folder holds pictures when the original article had them. A downloads.md file lists any extra attachments, and a meta.json file stores basic information about the article. To use the project, a reader simply opens the content.md file inside the folder for the strategy that interests them. There is no software to install and no strategy engine bundled in the repository. This is a reading and reference collection, not a runnable trading system. The project was shared and discussed within a community called LINUX DO, and the author credits members there for feedback. The README is direct about the limits of the material: it is meant only for study and research, not as investment advice. Some of the strategies described may be outdated, and any external storage links mentioned inside individual articles may no longer work since their availability depends on whoever originally shared them. For a non-technical reader, this repository is best understood as a library of stock market strategy writeups rather than working software. Someone curious about how quantitative traders think about the A-share market could browse the article titles and read the ones that match their interest, while keeping in mind that none of this is financial advice and some content may be stale.

Copy-paste prompts

Prompt 1
Summarize the multi-factor stock picking strategy described in this article in plain English
Prompt 2
Explain what momentum rotation means in the context of this strategy writeup
Prompt 3
List the key financial factors this strategy article uses to rank stocks
Prompt 4
What are the risks or limitations mentioned in this quant strategy article

Frequently asked questions

What is ashare-quant-strategies?

A collection of 119 written articles explaining different quantitative stock trading strategies for the Chinese A-share market, meant for reading and study rather than running as software.

What language is ashare-quant-strategies written in?

Mainly Python. The stack also includes Python, Markdown.

Who is ashare-quant-strategies for?

Mainly researcher.

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