Daily Stock Analysis

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_GROUP_1=investor1@example.com

STOCK_GROUP_2=AAPL,TSLA,NVDA
EMAIL_GROUP_2=investor2@example.com

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

Supported Stock Formats

# A-shares (6-digit code)
600519    # 贵州茅台
300750    # 宁德时代
000858    # 五粮液

# HK stocks (5-digit + .HK)
00700.HK  # 腾讯控股
09988.HK  # 阿里巴巴

# US stocks (ticker)
AAPL
TSLA
NVDA

# US indices
SPX       # S&P 500
DJI       # Dow Jones