AI Stock Analysis System

AI-powered stock analysis system for A-shares / Hong Kong / US stocks

Analyze your watchlist daily -> generate a decision dashboard -> push to Telegram / Discord / Slack / Email / WeChat Work / Feishu.

Key Features · Quick Start · Sample Output · Full Guide · FAQ · Changelog

This PR-doc update is documentation-only and does not introduce runtime implementation changes. Provider recommendations for Anspire / AIHubMix / SerpAPI reflect existing runtime capabilities and configuration semantics.

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💖 Sponsors

✨ Key Features

Detailed fields, fundamental P0 timeout semantics, trading rules, data-source priority, Web/API behavior, and troubleshooting live in the Full Guide.

Tech Stack & Data Sources

Full behavior is documented in Data Source Configuration.

🚀 Quick Start

Option 1: GitHub Actions (Recommended)

Deploy in about 5 minutes, with no server and no infrastructure cost.

1. Fork this repository

Click Fork in the upper-right corner. A star is very welcome if this project helps you.

2. Configure Secrets

Open your forked repository, then go to Settings -> Secrets and variables -> Actions -> New repository secret.

AI model configuration (configure at least one)

Start with one provider and one API key. For multi-model routing, image recognition, local models, or advanced routing, see the LLM Config Guide.

Ollama is better suited for local or Docker deployment. GitHub Actions is usually smoother with a cloud API.

Notification channels (configure at least one)

More channels, signatures, email groups, and Markdown-to-image settings are in Notification Configuration.

Watchlist (required)

News sources (recommended)

News search strongly improves sentiment, announcements, events, and catalyst quality. Configure at least one search provider if possible.

More search providers, social sentiment, and fallback behavior are in Search Configuration.

3. Enable Actions

Open the Actions tab and click I understand my workflows, go ahead and enable them.

4. Manual Test

Actions -> Daily Stock Analysis -> Run workflow -> Run workflow.

Done

By default, the workflow runs every weekday at 18:00 Beijing time and skips non-trading days. Forced runs, trading-day checks, and resume rules are covered in the Full Guide.

Option 2: Local / Docker Deployment

# Clone the project
git clone https://github.com/ZhuLinsen/daily_stock_analysis.git && cd daily_stock_analysis

# Install dependencies
pip install -r requirements.txt

# Configure environment variables
cp .env.example .env && vim .env

# Run analysis
python main.py

Common commands:

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

Docker deployment, scheduling, and cloud-server WebUI access are documented in the Full Guide.

📱 Sample Output

Decision Dashboard

🎯 2026-02-08 Decision Dashboard
Analyzed 3 stocks | 🟢 Buy:0 🟡 Watch:2 🔴 Sell:1

📊 Summary
🟡 000657: Watch | Score 65 | Bullish
🟡 600105: Watch | Score 48 | Range-bound
🔴 300260: Sell | Score 35 | Bearish

🚨 Risk Alerts:
Risk 1: Main-force funds showed notable outflow.
Risk 2: Chip concentration suggests short-term resistance.

✨ Positive Catalysts:
Catalyst 1: AI-server supply-chain exposure remains a market focus.
Catalyst 2: Recent earnings growth provides fundamental support.

Market Review

🎯 2026-01-10 Market Review

📊 Major Indices
- SSE Composite: 3250.12 (+0.85%)
- SZSE Component: 10521.36 (+1.02%)
- ChiNext: 2156.78 (+1.35%)

📈 Market Breadth
Up: 3920 | Down: 1349 | Limit up: 155 | Limit down: 3

⚙️ Configuration

Full environment variables, model routing, notification channels, data-source priority, trading rules, fundamental P0 semantics, and deployment details are in the Full Guide.

🖥️ Web UI

The Web workspace supports settings, task monitoring, manual analysis, history reports, backtest, portfolio management, smart import, and light/dark themes.

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

Visit http://127.0.0.1:8000. Authentication, smart import, autocomplete, report copying, and cloud-server access are documented in Local WebUI Management.

🤖 Agent Strategy Chat

After configuring any available AI API key, the Web /chat page can use strategy chat. Set AGENT_MODE=false only if you want to disable it explicitly.

• Built-in strategies include moving-average crossovers, Chan theory, Elliott wave, bull trend, and more
• Calls realtime quotes, K-line data, technical indicators, news, and risk context
• Supports follow-up questions, session export, notification sending, and background execution
• Supports custom strategy files and experimental multi-agent orchestration

Agent parameters, skill naming compatibility, multi-agent mode, and budget guards are covered in the Full Guide and LLM Config Guide.

Related Projects

DSA focuses on daily analysis reports. These sibling projects cover stock screening, strategy validation, and strategy evolution for users who want to extend the workflow. They are maintained independently today, with candidate import, backtest validation, and report handoff planned as future integration directions.

• AlphaSift: multi-factor stock screening and full-market scanning for building candidate watchlists.
• AlphaEvo: strategy backtesting and self-evolution experiments for validating rules and iteratively exploring strategy parameters and combinations.

🗺️ Roadmap

See supported features and release notes in the Changelog. Suggestions are welcome through GitHub Issues.

UI pages are still being polished. Please report style, interaction, or compatibility issues through Issues or Pull Requests.

☕ Support the Project

📄 License

MIT License © 2026 ZhuLinsen

If you use or build on this project, attribution with a link back to this repository is appreciated.

📞 Contact

• GitHub Issues: Report bugs or request features
• Discussions: Join discussions
• Email: [email protected] (sponsorship, custom development, private deployment, and integration work)

⭐ Star History

Star this repo if you find it useful.

⚠️ Disclaimer

This project is for informational and educational purposes only. AI-generated analysis is not investment advice. Stock market investing involves risk; do your own research and consult a licensed financial advisor when needed.