daily-stock-analysis▌
aradotso/trending-skills · updated May 14, 2026
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Skill by ara.so — Daily 2026 Skills collection.
Daily Stock Analysis (股票智能分析系统)
Skill by ara.so — Daily 2026 Skills collection.
LLM-powered stock analysis system for A-share, Hong Kong, and US markets. Automatically fetches quotes, news, and fundamentals, generates AI decision dashboards with buy/sell targets, and pushes results to WeChat/Feishu/Telegram/Discord/Email on a schedule via GitHub Actions — zero server cost.
What It Does
- AI Decision Dashboard: One-line conclusion + precise buy/sell/stop-loss prices + checklist per stock
- Multi-market: A-shares (CN), HK stocks, US stocks + indices (SPX, DJI, IXIC)
- Data sources: AkShare, Tushare, YFinance for quotes; Tavily/SerpAPI/Brave for news
- LLM backends: Gemini, OpenAI, Claude, DeepSeek, Qwen via LiteLLM (unified)
- Push channels: WeChat Work, Feishu, Telegram, Discord, DingTalk, Email, PushPlus
- Automation: GitHub Actions cron schedule, no server needed
- Web UI: Portfolio management, history, backtesting, Agent Q&A
- Agent: Multi-turn strategy Q&A with 11 built-in strategies (MA crossover, Elliott Wave, etc.)
Installation
Method 1: GitHub Actions (Recommended, Zero Cost)
Step 1: Fork the repository
https://github.com/ZhuLinsen/daily_stock_analysis
Step 2: Configure Secrets (Settings → Secrets and variables → Actions)
Required — at least one LLM key:
GEMINI_API_KEY # Google AI Studio (free tier available)
OPENAI_API_KEY # OpenAI or compatible (DeepSeek, Qwen, etc.)
OPENAI_BASE_URL # e.g. https://api.deepseek.com/v1
OPENAI_MODEL # e.g. deepseek-chat, gpt-4o
AIHUBMIX_KEY # AIHubMix (recommended, covers Gemini+GPT+Claude+DeepSeek)
ANTHROPIC_API_KEY # Claude
Required — stock list:
STOCKS # e.g. 600519,300750,AAPL,TSLA,00700.HK
Required — at least one notification channel:
TELEGRAM_BOT_TOKEN
TELEGRAM_CHAT_ID
FEISHU_WEBHOOK_URL
WECHAT_WEBHOOK_URL
EMAIL_SENDER / EMAIL_PASSWORD / EMAIL_RECEIVERS
DISCORD_WEBHOOK_URL
Step 3: Trigger manually or wait for cron
Go to Actions → stock_analysis → Run workflow
Method 2: Local / Docker
git clone https://github.com/ZhuLinsen/daily_stock_analysis
cd daily_stock_analysis
cp .env.example .env
# Edit .env with your keys
pip install -r requirements.txt
python main.py
Docker:
docker build -t stock-analysis .
docker run --env-file .env stock-analysis
Docker Compose:
docker-compose up -d
Configuration
.env File (Local)
# LLM - pick one or more
GEMINI_API_KEY=your_gemini_key
OPENAI_API_KEY=your_openai_key
OPENAI_BASE_URL=https://api.deepseek.com/v1
OPENAI_MODEL=deepseek-chat
AIHUBMIX_KEY=your_aihubmix_key
# Stock list (comma-separated)
STOCKS=600519,300750,AAPL,TSLA,00700.HK
# Notification
TELEGRAM_BOT_TOKEN=your_bot_token
TELEGRAM_CHAT_ID=your_chat_id
# Optional settings
REPORT_TYPE=full # simple | full | brief
ANALYSIS_DELAY=10 # seconds between stocks (avoid rate limiting)
MAX_WORKERS=3 # concurrent analysis threads
SINGLE_STOCK_NOTIFY=false # push each stock immediately when done
NEWS_MAX_AGE_DAYS=3 # ignore news older than N days
Multi-Channel LLM (Advanced)
LLM_CHANNELS=gemini,deepseek,claude
LLM_GEMINI_PROTOCOL=google
LLM_GEMINI_API_KEY=your_key
LLM_GEMINI_MODELS=gemini-2.0-flash,gemini-1.5-pro
LLM_GEMINI_ENABLED=true
LLM_DEEPSEEK_PROTOCOL=openai
LLM_DEEPSEEK_BASE_URL=https://api.deepseek.com/v1
LLM_DEEPSEEK_API_KEY=your_key
LLM_DEEPSEEK_MODELS=deepseek-chat
LLM_DEEPSEEK_ENABLED=true
Stock Grouping (Send Different Stocks to Different Emails)
STOCK_GROUP_1=600519,300750,000858
[email protected]
STOCK_GROUP_2=AAPL,TSLA,NVDA
[email protected]
Market Review Mode
MARKET_REVIEW=cn # cn | us | both
# cn = A-share three-phase review strategy
# us = US Regime Strategy (risk-on/neutral/risk-off)
# both = both markets
Key Commands (CLI)
# Run full analysis immediately
python main.py
# Analyze specific stocks only
STOCKS=600519,AAPL python main.py
# Run web dashboard
python web_app.py
# Access at http://localhost:5000
# Run with Docker (env file)
docker run --env-file .env stock-analysis python main.py
# Run schedule mode (waits for cron, then runs)
SCHEDULE_RUN_IMMEDIATELY=true python main.py
GitHub Actions Workflow
The workflow file .github/workflows/stock_analysis.yml runs on schedule:
# Default schedule - customize in the workflow file
on:
schedule:
- cron: '30 1 * * 1-5' # 9:30 AM CST (UTC+8) weekdays
workflow_dispatch: # manual trigger
To change schedule: Edit .github/workflows/stock_analysis.yml cron expression.
To add secrets via GitHub CLI:
gh secret set GEMINI_API_KEY --body "$GEMINI_API_KEY"
gh secret set STOCKS --body "600519,300750,AAPL,TSLA"
gh secret set TELEGRAM_BOT_TOKEN --body "$TG_TOKEN"
gh secret set TELEGRAM_CHAT_ID --body "$TG_CHAT_ID"
Code Examples
Programmatic Analysis (Python)
# Run analysis for specific stocks programmatically
import asyncio
from analyzer import StockAnalyzer
async def analyze():
analyzer = StockAnalyzer()
# Analyze a single A-share stock
result = await analyzer.analyze_stock("600519") # Moutai
print(result['conclusion'])
print(result['buy_price'])
print(result['stop_loss'])
print(result['target_price'])
asyncio.run(analyze())
Custom Notification Integration
from notifier import NotificationManager
notifier = NotificationManager()
# Send to Telegram
await notifier.send_telegram(
token=os.environ['TELEGRAM_BOT_TOKEN'],
chat_id=os.environ['TELEGRAM_CHAT_ID'],
message="📈 Analysis complete\n600519: BUY at 1680, SL: 1620, TP: 1800"
)
# Send to Feishu webhook
await notifier.send_feishu(
webhook_url=os.environ['FEISHU_WEBHOOK_URL'],
content=analysis_report
)
Using the Agent API
import requests
# Ask the stock agent a strategy question
response = requests.post('http://localhost:5000/api/agent/chat', json={
"message": "600519现在适合买入吗？用均线金叉策略分析",
"stock_code": "600519",
"strategy": "ma_crossover" # ma_crossover, elliott_wave, chan_theory, etc.
})
print(response.json()['reply'])
Backtest Analysis Accuracy
import requests
# Trigger backtest for a stock
response = requests.post('http://localhost:5000/api/backtest', json={
"stock_code": "600519",
"days": 30 # evaluate last 30 days of AI predictions
})
result = response.json()
print(f"Direction accuracy: {result['direction_accuracy']}%")
print(f"Take-profit hit rate: {result['tp_hit_rate']}%")
print(f"Stop-loss hit rate: {result['sl_hit_rate']}%")
Import Stocks from Image (Vision LLM)
import requests
# Upload screenshot of stock list for AI extraction
with open('watchlist_screenshot.png', 'rb') as f:
response = requests.post(
'http://localhost:5000/api/stocks/import/image',
files={'image': f}
)
stocks = response.json()['extracted_stocks']
# Returns: [{"code": "600519", "name": "贵州茅台", "confidence": 0.98}, ...]
Web Dashboard Features
Start the web app:
python web_app.py
| Route | Feature |
|---|---|
/ |
Today's analysis dashboard |
/portfolio |
Holdings management, P& how to use daily-stock-analysis How to use daily-stock-analysis on CursorAI-first code editor with Composer 1 PrerequisitesBefore installing skills in Cursor, ensure your development environment meets these requirements:
2 Execute installation commandExecute the skills CLI command in your project's root directory to begin installation: $npx skills add https://github.com/aradotso/trending-skills --skill daily-stock-analysis The skills CLI fetches 3 Select Cursor when promptedThe CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor: ◆ Which agents do you want to install to? │ │ ── Universal (.agents/skills) ── always included ──── │ • Amp │ • Antigravity │ • Cline │ • Codex │ ●Cursor(selected) │ • Cursor │ • Windsurf 4 Verify installationConfirm successful installation by checking the skill directory location: .cursor/skills/daily-stock-analysis Reload or restart Cursor to activate daily-stock-analysis. Access the skill through slash commands (e.g., ⚠ Security & Verification NoticeWe perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use. Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment. List & Monetize Your SkillSubmit your Claude Code skill and start earning Use Cases▌Task Automation & EfficiencyAutomate repetitive workflows and reduce manual effort Example Generate reports, summarize documents, draft communications ✓ Save 3-5 hours per week on routine tasks Knowledge EnhancementLearn new skills, understand complex topics, get expert guidance Example Explain concepts, provide examples, suggest learning resources ✓ Accelerate learning and skill development by 2x Quality ImprovementEnhance output quality through reviews, suggestions, and refinements Example Review drafts, suggest improvements, catch errors ✓ Improve work quality by 30-40% with less effort Implementation Guide▌Prerequisites
Time Estimate15-45 minutes depending on use case complexity Installation Steps
Common Pitfalls
Best Practices▌✓ Do
✗ Don't
💡 Pro Tips
When to Use This▌✓ Use WhenUse when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement. ✗ Avoid WhenAvoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion. Learning Path▌
DiscussionProduct Hunt–style comments (not star reviews)
general reviews Ratings4.5★★★★★46 reviews
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