daily_stock_analysis is an MIT-licensed Python system that uses large language models to analyse self-selected stocks across the A-share, Hong Kong, US, Japanese, Korean and Taiwanese markets and push a daily decision dashboard to chat and email channels, aimed at individual investors and quant-minded developers who want scheduled AI analysis without operating a server.

What it is

daily_stock_analysis is an LLM-driven multi-market stock analysis system written in Python. It aggregates quotes, K-line data, technical indicators, news, announcements and fundamentals for user-maintained watchlists covering A股, 港股, 美股, 日股, 韩股, 台股 and ETFs, then produces a daily AI decision report and delivers it as a decision dashboard to 企业微信, 飞书, Telegram, Discord, Slack or email. It runs as a GitHub Actions workflow, as a Docker container, as a local scheduled task, or as a FastAPI service, and it also ships a web and desktop workbench alongside an agent that answers follow-up questions about strategy.

The concrete problem it replaces is the manual morning routine of checking several quote terminals, news sites and announcement feeds, then writing up conclusions by hand. Instead of a proprietary terminal subscription or a hand-built script chain, the project packages data acquisition, LLM analysis, report rendering and notification delivery into one repository. The GitHub Actions path is explicitly positioned as the zero-cost, no-server option: a user forks the repository, fills in secrets, and receives a pushed report on a schedule without provisioning infrastructure.

Key capabilities

• Generates an AI decision report containing core conclusion, score, trend, buy and sell points, risk alerts, catalyst factors and an operation checklist.
• Aggregates multi-market data for A股, 港股, 美股, 日股, 韩股, 台股 and ETFs, including quotes, K-line, technical indicators, news, announcements and fundamentals.
• Provides an Agent 策略问股 mode with multi-turn follow-up and 15 built-in strategies covering 均线, 缠论, 波浪, 趋势, 热点, 事件, 成长 and 预期, reachable through Web, Bot and API.
• Imports watchlists from images, CSV/Excel files and the clipboard, with completion by stock code, name, pinyin or alias.
• Pushes results to 企业微信, 飞书, Telegram, Discord, Slack and email, with configuration for signature verification, grouped mail and Markdown-to-image conversion.
• Runs through GitHub Actions, Docker, local scheduled tasks or a FastAPI service, and supports the published image zhulinsen/daily_stock_analysis.
• Supports LLM backends including Anspire, AIHubMix, Gemini, OpenAI-compatible endpoints, DeepSeek, 通义千问, Claude and local Ollama, with market data from AkShare, Baostock, YFinance, TickFlow, Tushare, Pytdx and Longbridge, and news search via Anspire, SerpAPI, Tavily, Bocha, Brave, MiniMax and SearXNG.

Who uses it and how

• Individual investors holding watchlists spanning A股, 港股 and 美股 who want one scheduled morning report rather than several manual lookups.
• Users with no server and no budget: fork the repository, add secrets, and let GitHub Actions run the analysis at zero cost.
• Self-hosters running Docker or local scheduled tasks, including those pointing the system at Ollama for local model inference.
• Developers and traders who need programmatic access, using the FastAPI service or Bot interface to drive the 15 built-in agent strategies in multi-turn sessions.
• Workbench users who review manual analyses, task progress, historical reports, full Markdown output, backtests and position records with light or dark themes.

Getting started

The README recommends forking the repository and configuring repository secrets under Settings, then Secrets and variables, then Actions, using identifiers such as ANSPIRE_API_KEYS or AIHUBMIX_KEY for the model, one of WECHAT_WEBHOOK_URL, FEISHU_WEBHOOK_URL, TELEGRAM_BOT_TOKEN with TELEGRAM_CHAT_ID, DISCORD_WEBHOOK_URL, SLACK_BOT_TOKEN with SLACK_CHANNEL_ID, or EMAIL_SENDER with EMAIL_PASSWORD for delivery, and a watchlist secret that is required. The README describes this path as roughly five minutes with no server; local and Docker deployment targets Python 3.10 and above, and Ollama is suggested for those local or Docker setups.

How it compares

No list of paid products that this project replaces is provided in the available facts, and no comparable tools are named either, so the honest statement is that it stands alone in this registry entry. Readers should treat it as an open-source, MIT-licensed, self-operated alternative assessed on its own terms rather than against a mapped set of commercial competitors.

When to use it — and when not to

Use it if the appeal of an MIT-licensed analysis pipeline that runs on GitHub Actions, Docker or a local scheduler outweighs the setup work of supplying at least one LLM API key and one notification channel. Be aware that the default free market data sources, AkShare, Baostock and YFinance, can run zero-configuration but are affected by upstream rate limiting, interface changes and network instability, so the README itself recommends token-backed sources such as TickFlow, Tushare or Longbridge for long-running schedules and batch analysis. Do not choose it if guaranteed, stable market data is a hard requirement and no paid data token will be configured, or if nobody is willing to manage API keys and webhook secrets; the project also carries 44 open issues, and the primary documentation is Chinese-first, with English and Traditional Chinese translations available under docs/.

# 克隆项目
git clone https://github.com/ZhuLinsen/daily_stock_analysis.git && cd daily_stock_analysis

# 安装依赖
pip install -r requirements.txt

# 配置环境变量
cp .env.example .env && vim .env

# 运行分析
python main.py

python main.py --debug
python main.py --dry-run
python main.py --stocks 600519,hk00700,AAPL,2330.TW
python main.py --market-review
python main.py --schedule
python main.py --serve-only

🎯 2026-02-08 决策仪表盘
共分析3只股票 | 🟢买入:0 🟡观望:2 🔴卖出:1

📊 分析结果摘要
⚪ 中钨高新(000657): 观望 | 评分 65 | 看多
⚪ 永鼎股份(600105): 观望 | 评分 48 | 震荡
🟡 新莱应材(300260): 卖出 | 评分 35 | 看空

⚪ 中钨高新 (000657)
📰 重要信息速览
💭 舆情情绪: 市场关注其AI属性与业绩高增长，情绪偏积极，但需消化短期获利盘和主力流出压力。
📊 业绩预期: 基于舆情信息，公司2025年前三季度业绩同比大幅增长，基本面强劲，为股价提供支撑。

🚨 风险警报:

风险点1：2月5日主力资金大幅净卖出3.63亿元，需警惕短期抛压。
风险点2：筹码集中度高达35.15%，表明筹码分散，拉升阻力可能较大。
风险点3：舆情中提及公司历史违规记录及重组相关风险提示，需保持关注。
✨ 利好催化:

利好1：公司被市场定位为AI服务器HDI核心供应商，受益于AI产业发展。
利好2：2025年前三季度扣非净利润同比暴涨407.52%，业绩表现强劲。
📢 最新动态: 【最新消息】舆情显示公司是AI PCB微钻领域龙头，深度绑定全球头部PCB/载板厂。2月5日主力资金净卖出3.63亿元，需关注后续资金流向。

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生成时间: 18:00

🎯 2026-01-10 大盘复盘

📊 主要指数
- 上证指数: 3250.12 (🟢+0.85%)
- 深证成指: 10521.36 (🟢+1.02%)
- 创业板指: 2156.78 (🟢+1.35%)

📈 市场概况
上涨: 3920 | 下跌: 1349 | 涨停: 155 | 跌停: 3

🔥 板块表现
领涨: 互联网服务、文化传媒、小金属
领跌: 保险、航空机场、光伏设备

python main.py --webui
python main.py --webui-only