daily_stock_analysis

daily_stock_analysis

Analyzes your watchlist of stocks across six markets with a language model every day and pushes a decision dashboard to WeCom, Feishu, Telegram, Discord, Slack or email.

Made by ZhuLinsen on GitHub. These notes are Mindket's, written from its README; the project's own files and license are in its repository.

About

What it is: daily_stock_analysis is a Python system that uses a language model to analyze the stocks on your watchlist. It pulls market data from several sources and real-time news, builds a decision dashboard and sends it to you automatically. The project says its scheduled runs cost nothing.

It covers six markets: A-shares, Hong Kong, the US, Japan, Korea and Taiwan.

Who it suits: It suits people who follow a set of stocks and want a short daily analysis delivered to the chat app or inbox they already use.

Industry | Who uses it | How they use it | Example
Personal investing | Investors with a watchlist | Get a daily analysis of the stocks they follow | Receive the dashboard in Telegram each day
Quantitative finance | Developers interested in quant work | Build on a Python system that joins market data, news and a language model | Read the code to see how data sources feed the analysis
Teams | Small groups that share market views | Share the daily dashboard in team chat | Post it to a Slack channel

If you do not follow stock markets, you can skip it.

Where to use it:

• WeCom, Feishu, Telegram, Discord and Slack: chat apps that receive the dashboard
• Email: the same dashboard in your inbox
• Python: the system itself is written in it

Use case | Who | What they get back
Daily watchlist analysis | Investors | A decision dashboard pushed to you every day
News and data in one view | Anyone following several markets | Analysis built from several data sources and real-time news
Scheduled runs | Anyone who wants it hands-off | Runs on a schedule that the project says costs nothing

How to set it up: You need a few things first.

• Python, since the project is written in it
• A language model the system can call, as the README describes
• At least one place to receive the dashboard, such as Telegram, Slack or email
• Git, to copy the project to your computer

1. Copy the repository to your computer with git clone, using the address on its GitHub page.
2. Open the README and follow its quick start.
3. Add the stocks you want to watch and connect your language model.
4. Connect the channel that should receive the dashboard.

Nothing in this kit runs by itself, so nothing is analyzed until that setup is done.

How to use it: Once it is set up, it analyzes your watchlist each day and pushes the decision dashboard to the channel you connected. You read the result there. The README holds the exact way to start and schedule it.

Example: You put a handful of US and Hong Kong stocks on your watchlist and connect Telegram. Each day the system gathers data and news, has the model analyze them, and sends you the dashboard.

For newcomers and experts: Think of it as a daily briefing on the stocks you care about. A first small thing to try is a watchlist of two or three stocks, sent to one channel.

Under the hood, it combines market data from several sources and real-time news, passes them to a language model and formats the result as a dashboard. Delivery goes out through the chat or email channel you configure. The trade-off is that the analysis comes from a language model, so check anything you plan to act on.

Market data and news come in
A language model analyzes your watchlist
The decision dashboard is built
It is pushed to your chat or email

Good to know:

• License: MIT
• It is written in Python
• It covers A-shares, Hong Kong, US, Japan, Korea and Taiwan stocks
• The dashboard can go to WeCom, Feishu, Telegram, Discord, Slack or email
• The analysis comes from a language model, so it can be wrong
• The kit holds notes and a pointer to the code, and nothing in it runs by itself

On GitHub, 10 November 2026

• Stars: 68,913 (506 that day)
• Forks: 56,397
• Watching: 274
• Open issues and pull requests: 38
• Language: Python
• License: MIT
• Topics: a-stock, ai-agent, aigc, llm, quant, quantitative-finance, quantitative-trading
• Trending: #9 repository of the day on GitHub trending (10 November 2026)

Links

• Code: https://github.com/ZhuLinsen/daily_stock_analysis
• Website: https://dsa.zhulinsen.tech/
• Releases: https://github.com/ZhuLinsen/daily_stock_analysis/releases
• Issues: https://github.com/ZhuLinsen/daily_stock_analysis/issues
• Star history: https://www.star-history.com/#ZhuLinsen/daily_stock_analysis&Date

Get the code

git clone https://github.com/ZhuLinsen/daily_stock_analysis.git

Then follow the setup in its README. Nothing in this kit runs by itself.