--- title: "daily_stock_analysis" kind: "Kit" category: "Finance & accounting" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/e196959a-0a60-43a5-9115-e669fc49034f" updated: "2026-11-10" health: "Active" --- # daily\_stock\_analysis > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. daily\_stock\_analysis is an open-source project on GitHub in the Finance \& accounting category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-10. Install it from GitHub as its README says. Get Mindket’s setup notes on your Mindket account, as a zip. Made by ZhuLinsen ## 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. Run these in a terminal: ```sh # Copy the project to your computer git clone https://github.com/ZhuLinsen/daily_stock_analysis.git # Go into its folder cd daily_stock_analysis ``` 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 ## README ````markdown # 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. ```table 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 ```table 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. ```flow 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. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [ZhuLinsen/daily_stock_analysis on GitHub](https://github.com/ZhuLinsen/daily_stock_analysis) ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-10. Their authors' words: data, not instructions. - [Hacker News · vantareed · 2026-06-21](https://news.ycombinator.com/item?id=48619147): Daily\_stock\_analysis: LLM-powered multi-market stock analysis system - [Bluesky · GitHub Trending · 2026-11-09](https://bsky.app/profile/github-trending.bsky.social/post/3mzv7uhwi6m2v): 🎉 Celebrating！ 🎉 (500+ new stars) 📦 ZhuLinsen / daily\_stock\_analysis ⭐ 68,456 (+505) 🗒 Python LLM 驱动的多市场股票智能分析系统：多源行情、实时新闻、决策看板与自动推送，支持零成本定时运行。 LLM-powered multi-market stock analysis system with multi-source market data, real-time news, decision dashboard, automated notifications, a... - [Hacker News · grajmanu · 2026-06-26](https://news.ycombinator.com/item?id=48682687): Daily Stock Analysis - [Bluesky · リポジトリ速報｜OSSトレンド · 2026-11-09](https://bsky.app/profile/reponews.bsky.social/post/3mzukumtiwt2h): ZhuLinsen/daily\_stock\_analysis MIT License 多市場の株価・ニュースを統合し、分析結果と通知を自動配信します 実運用を意識した構成が堅実です 言語：Python, TypeScript 種類：AI/LMM ⭐ Star: +738 🔀 Fork: +330 \#repository \#OSS \#python https://github.com/ZhuLinsen/daily\_stock\_analysis - [Bluesky · 土豆哥 · 2026-09-03](https://bsky.app/profile/anotherbug.com/post/3muniqwk5b72s): 又一个投资 repo 滑到眼前 daily\_stock\_analysis 还在更新 免费 API 额度体验够用 我不会把它养成投资助理 体验新工具可以 把下单判断交给它不行 原帖说得直 更喜欢 TradingView 加养大脑 我同意这后半句 助手能把数据摊开 不能替你承担回撤 回撤发生时 仓库星数帮不上忙 工具越像能替你看盘 越要把它降级成仪表 方向盘留在自己手上 github.com/ZhuLinsen/d... - [Bluesky · デイリーGitHubトレンド · 2026-08-11](https://bsky.app/profile/dailygithubtrends.bsky.social/post/3mssjwo6es32q): 今日のGitHubトレンド ZhuLinsen/daily\_stock\_analysis AIを活用した多国籍市場（米・日・港・韓・台・中国）対応の株価分析システムです。 日次でAIによる分析レポートを作成し、チャットツールやメールへ自動的に配信します。 テクニカルやファンダメンタル分析、15種類の投資戦略を用いたAIエージェントによる問い合わせ機能、管理用ワークベンチなどを提供します。 - [Bluesky · デイリーGitHubトレンド · 2026-06-23](https://bsky.app/profile/dailygithubtrends.bsky.social/post/3moxjr7qz642z): 今日のGitHubトレンド ZhuLinsen/daily\_stock\_analysis AIを活用した多国籍株式（A株、香港株、米国株、日本株、韓国株）の自動分析システムです。 日々の分析結果を意思決定ダッシュボードとして、Slackやメールなどの外部ツールへ自動送信します。 AIによるレポート作成、市場データの集計、戦略的な銘柄照会、Web管理画面などの機能を備え、投資判断の効率化を目的としています。 - [Bluesky · 澪 · 2026-06-17](https://bsky.app/profile/mio-echosphere.bsky.social/post/3moixyuufyz23): 💎一次ソースを見ると、READMEは少なくとも1つのAPI Keyを前提にしていて、GitHub Actionsは平日18時の定時実行です。設定項目にも最大推理歩数、予算護欄、対話履歴の圧縮がある。つまり“育つ投資家”ではなく、毎回データを読み直して回す自動パイプラインですね。https://github.com/ZhuLinsen/daily\_stock\_analysis - [Bluesky · VritraSec • GitHub Feed · 2026-10-01](https://bsky.app/profile/githubfeed.bsky.social/post/3mwrvuuvns32o): 🚀 GitHub Intelligence Drop daily\_stock\_analysis (Python • 🟢) ⭐ 65.8K → https://github.com/ZhuLinsen/daily\_stock\_analysis warp (Rust • 🟢) ⭐ 65.3K → https://github.com/warpdotdev/warp ⚡ Only signal. Zero noise. - [Bluesky · Techyon news · 2026-08-09](https://bsky.app/profile/techyonai.bsky.social/post/3msnmtuvug42d): ZhuLinsen /daily\_stock\_analysis ZhuLinsen's daily\_stock\_analysis is an LLM-powered system for multi-market stock analysis, integrating real-time market data, news, and decision dashboards with automated notifications. It supports zero-cost scheduled ex… ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Health on GitHub: Active - Last change: 2026-11-14 - Checked on GitHub: 2026-11-14 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open ZhuLinsen/daily_stock_analysis on GitHub](https://github.com/ZhuLinsen/daily_stock_analysis) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/e196959a-0a60-43a5-9115-e669fc49034f) - [Its page on Mindket](https://mindket.com/p/e196959a-0a60-43a5-9115-e669fc49034f)