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wtf is eastmoney-monthly?

yuu-ramsey/eastmoney-monthly — explained in plain English

Analysis updated 2026-05-18

16PythonAudience · researcherComplexity · 4/5LicenseSetup · hard

TL;DR

A personal research toolkit for monthly analysis of Chinese A-share stocks that combines transformer models, momentum signals, and a language model to identify which stocks to avoid, with a Chrome extension for Eastmoney pages.

Mindmap

mindmap
  root((eastmoney-monthly))
    Analysis methods
      Kronos transformer model
      LightGBM model
      Momentum signals
      Language model explanations
    Market regime
      Trending market
      Volatile market
      Sideways market
    Access modes
      Chrome extension
      Node.js CLI tool
      Python training scripts
    Research integrity
      Delisted stock fix
      Scoring bug fix

Code map

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Why would anyone build with this?

REASON 1

Run monthly A-share stock analysis using ensemble models to identify which stocks to avoid holding.

REASON 2

Add AI analysis buttons directly to Eastmoney stock pages using the Chrome extension for quick signal checks.

REASON 3

Retrain the LightGBM or Kronos model on your own A-share dataset using the provided Python scripts.

REASON 4

Get plain-language summaries of what the quantitative signals say about a specific stock via the language model component.

What's in the stack?

PythonNode.jsLightGBMChrome Extension

How it stacks up

yuu-ramsey/eastmoney-monthly920linjerry-stack/capital-studioadya84/ha-world-cup-2026
Stars161616
LanguagePythonPythonPython
Setup difficultyhardeasyeasy
Complexity4/53/52/5
Audienceresearcherresearchergeneral

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

How do you spin it up?

Difficulty · hard Time to first run · 1day+

Requires setting up the Chrome extension, Node.js native host bridge, and Python ML environment with model weights before analysis works end to end.

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

Wtf does this do

This is a personal research project for analyzing Chinese A-share stocks on a monthly basis. The stated goal is avoiding losses rather than chasing profits. It combines several independent analysis methods, including statistical factors, machine learning models, and an AI language model, and tries to blend them intelligently based on current market conditions. The core idea is that no single model works well in all market environments. The system first classifies what kind of market it is currently in, such as a trending market, a volatile one, or a sideways one, and then routes to the signals that have been validated specifically for that condition. The regime detection uses three independent methods that vote, so no single failure brings down the whole system. The AI language model is kept completely separate from the decision logic: it only generates plain-language explanations of what the other signals are saying, it does not influence the actual scoring. The project went through a methodological research cycle that uncovered some significant problems in earlier versions. Excluding delisted stocks from the evaluation pool inflated apparent returns by 8.4 percentage points, so the team rebuilt the dataset to include them. The original scoring system also had a bug that made predicting neutral outcomes every single time the optimal strategy. After fixing both issues, the validated signals were: a transformer model called Kronos (reliable across most conditions), momentum and a LightGBM model (work well in bear markets, flip negative in bull markets), and the language model (predicts neutral 70% of the time, but shows a notable edge when it does commit to a strong call). The project can be used as a Chrome extension that adds analysis buttons directly to Eastmoney stock pages, as a Node.js command-line tool for batch processing, or through Python scripts for model training and research. A native host bridges the extension to local compute for database access and ML inference. This is described explicitly as a personal research project with no guarantees of continued updates or backward compatibility. It does not execute trades or connect to brokerages. The license is MIT.

Yoink these prompts

Prompt 1
Install the Eastmoney Monthly Chrome extension and connect it to the local native host bridge so I can run AI-powered stock analysis directly on Eastmoney pages, walk me through the full setup.
Prompt 2
Use the Python training scripts in eastmoney-monthly to retrain the LightGBM model on a different A-share dataset, show me how to structure the input data and run the training pipeline.
Prompt 3
Explain how the three-method market regime detection in this project works and show me how to check which regime the system has classified the current market as.

Frequently asked questions

wtf is eastmoney-monthly?

A personal research toolkit for monthly analysis of Chinese A-share stocks that combines transformer models, momentum signals, and a language model to identify which stocks to avoid, with a Chrome extension for Eastmoney pages.

What language is eastmoney-monthly written in?

Mainly Python. The stack also includes Python, Node.js, LightGBM.

What license does eastmoney-monthly use?

Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

How hard is eastmoney-monthly to set up?

Setup difficulty is rated hard, with roughly 1day+ to a first successful run.

Who is eastmoney-monthly for?

Mainly researcher.

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