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

bash

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:

bash

docker build -t stock-analysis .
docker run --env-file .env stock-analysis

Docker Compose:

bash

docker-compose up -d

Configuration

.env

File (Local)

env

# 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)

env

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)

env

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

env

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)

bash

# 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:

yaml

# 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:

bash

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)

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

python

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

python

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

python

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)

python

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:

bash

python web_app.py

Route	Feature
`/`	Today's analysis dashboard
`/portfolio`	Holdings management, P&L tracking
`/history`	Past analysis reports (bulk delete supported)
`/backtest`	AI prediction accuracy backtest
`/agent`	Multi-turn strategy Q&A
`/settings`	LLM channels, notification config
`/import`	Import stocks from image/CSV/clipboard

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
IXIC      # NASDAQ

Built-in Trading Rules

Rule	Config
No chasing highs	`DEVIATION_THRESHOLD=5` (%, auto-relaxed for strong trend)
Trend trading	MA5 > MA10 > MA20 bullish alignment required
Precise targets	Buy price, stop-loss, take-profit per stock
News freshness	`NEWS_MAX_AGE_DAYS=3` (skip stale news)
Checklist	Each condition marked: ✅ Satisfied / ⚠️ Caution / ❌ Not Met

Troubleshooting

Analysis runs but no push received:

bash

# Check notification config
python -c "from notifier import test_all_channels; test_all_channels()"

# Verify secrets are set (GitHub Actions)
gh secret list

LLM API errors / rate limiting:

env

ANALYSIS_DELAY=15        # increase delay between stocks
MAX_WORKERS=1            # reduce concurrency
LITELLM_FALLBACK_MODELS=gemini-1.5-flash,deepseek-chat  # add fallbacks

AkShare data fetch fails (A-shares):

bash

pip install akshare --upgrade
# A-share data requires Chinese network or proxy

YFinance US stock data issues:

bash

pip install yfinance --upgrade
# US stocks use YFinance exclusively for consistency

GitHub Actions not triggering:

Check Actions are enabled:

Settings → Actions → General → Allow all actions

Verify cron syntax at crontab.guru
Check workflow file exists:
```
.github/workflows/stock_analysis.yml
```

Web auth issues (admin password):

env

# If auth was disabled and re-enabled, current password required
# Reset via environment variable
WEB_ADMIN_PASSWORD=new_password

Multi-worker deployment auth state:

bash

# Auth toggle only applies to current process
# Must restart all workers to sync state
docker-compose restart

Report Types

env

REPORT_TYPE=simple   # Concise: conclusion + key prices only
REPORT_TYPE=full     # Complete: all technical + fundamental + news analysis
REPORT_TYPE=brief    # 3-5 sentence summary

Full report includes:

一句话核心结论 (one-line core conclusion)
技术面分析 (technical: MA alignment, chip distribution)
基本面 (valuation, growth, earnings, institutional holdings)
舆情情报 (news sentiment, social media — US stocks)
精确买卖点位 (precise entry/exit levels)
操作检查清单 (action checklist)
板块涨跌榜 (sector performance boards)

LLM Priority Order

Gemini → Anthropic → OpenAI/AIHubMix/Compatible

AIHubMix is recommended for single-key access to all major models without VPN:

env

AIHUBMIX_KEY=$AIHUBMIX_KEY  # covers Gemini, GPT, Claude, DeepSeek
# No OPENAI_BASE_URL needed — auto-configured

daily-stock-analysis

NPX Install

Tags

SKILL.md Content

Daily Stock Analysis (股票智能分析系统)

What It Does

Installation

Method 1: GitHub Actions (Recommended, Zero Cost)

Method 2: Local / Docker

Configuration

`.env`
File (Local)

Multi-Channel LLM (Advanced)

Stock Grouping (Send Different Stocks to Different Emails)

Market Review Mode

Key Commands (CLI)

GitHub Actions Workflow

Code Examples

Programmatic Analysis (Python)

Custom Notification Integration

Using the Agent API

Backtest Analysis Accuracy

Import Stocks from Image (Vision LLM)

Web Dashboard Features

Supported Stock Formats

Built-in Trading Rules

Troubleshooting

Report Types

LLM Priority Order