What problem it solves

Investors need to combine market data, news, technical information, and analysis across multiple markets without manually assembling each report. They also need a repeatable way to run daily analysis and deliver the resulting decision dashboard to their preferred communication channel.

Key features

• AI Decision Reports — Generate stock analysis reports with core conclusions, scores, trends, trading levels, risk alerts, catalysts, and operational checklists.

  Source: README

• Multi-Market Data Aggregation — Aggregate quotes, candlesticks, technical indicators, news, announcements, fundamentals, and report-assistance data for A-share, Hong Kong, US, Japanese, Korean, Taiwanese, and ETF markets.

  Source: README

• Web and Desktop Workbench — Use a workspace for manual analysis, task progress, historical reports, full Markdown reports, backtesting, portfolios, configuration, and light or dark themes.

  Source: README, webui.py

• Agent Strategy Questions — Ask multi-turn stock questions using built-in strategies and access live quotes, candlesticks, technical indicators, news, and risk information through Web, bot, or API interfaces.

  Source: README

• Automation and Notifications — Run scheduled analysis through GitHub Actions, Docker, local scheduling, or a FastAPI service, then push results to supported enterprise and messaging channels.

  Source: README

Quick start

Copied verbatim from the project's README.

# 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

Who it may suit

This may suit individual investors or researchers who want repeatable analysis of watchlists across several markets. It may also suit teams that need scheduled reports delivered through enterprise messaging, chat platforms, or email.

Topics to discuss

• Which additional markets or asset classes should be included in daily analysis?

• Which data-source and fallback rules should be used for reliable scheduled runs?

• Which Agent strategies and dashboard fields would be most useful for decision support?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/ZhuLinsen/daily_stock_analysis#readme

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