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Deployment Guide

This document explains how to deploy the AI Stock Analysis System to a server.

Deployment Options Comparison

Option	Pros	Cons	Recommended For
Docker Compose ⭐	One-click deploy, isolated environment, easy migration, easy upgrade	Requires Docker installation	Recommended: Most scenarios
Direct Deployment	Simple, no extra dependencies	Environment dependencies, migration difficulties	Temporary testing
Systemd Service	System-level management, auto-start on boot	Complex configuration	Long-term stable operation
Supervisor	Process management, auto-restart	Requires additional installation	Multi-process management

Conclusion: Docker Compose is recommended for the fastest and most convenient migration!

Option 1: Docker Compose Deployment (Recommended)

1. Install Docker

# Ubuntu/Debian
curl -fsSL https://get.docker.com | sh
sudo usermod -aG docker $USER

# CentOS
sudo yum install -y docker docker-compose
sudo systemctl start docker
sudo systemctl enable docker

2. Prepare Configuration Files

# Clone code (or upload code to server)
git clone <your-repo-url> /opt/stock-analyzer
cd /opt/stock-analyzer

# Copy and edit configuration file
cp .env.example .env
vim .env  # Fill in real API Keys and configuration

3. One-Click Start

# Build and start
docker-compose -f ./docker/docker-compose.yml up -d

# View logs
docker-compose -f ./docker/docker-compose.yml logs -f

# View running status
docker-compose -f ./docker/docker-compose.yml ps

4. Common Management Commands

# Stop services
docker-compose -f ./docker/docker-compose.yml down

# Restart services
docker-compose -f ./docker/docker-compose.yml restart

# Redeploy after code update
git pull
docker-compose -f ./docker/docker-compose.yml build --no-cache
docker-compose -f ./docker/docker-compose.yml up -d

# Enter container for debugging
docker-compose -f ./docker/docker-compose.yml exec stock-analyzer bash

# Manually run analysis once
docker-compose -f ./docker/docker-compose.yml exec stock-analyzer python main.py --no-notify

5. Data Persistence

Data is automatically saved to host directories:

./data/ - Database files
./logs/ - Log files
./reports/ - Analysis reports

Option 2: Direct Deployment

1. Install Python Environment

# Install Python 3.10+
sudo apt update
sudo apt install -y python3.10 python3.10-venv python3-pip

# Create virtual environment
python3.10 -m venv /opt/stock-analyzer/venv
source /opt/stock-analyzer/venv/bin/activate

2. Install Dependencies

cd /opt/stock-analyzer
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

3. Configure Environment Variables

cp .env.example .env
vim .env  # Fill in configuration

4. Run

# Single run
python main.py

# Scheduled task mode (foreground)
python main.py --schedule

# Background run (using nohup)
nohup python main.py --schedule > /dev/null 2>&1 &

Option 3: Systemd Service

Create systemd service file for auto-start on boot and auto-restart:

1. Create Service File

sudo vim /etc/systemd/system/stock-analyzer.service

Contents:

[Unit]
Description=AI Stock Analysis System
After=network.target

[Service]
Type=simple
User=root
WorkingDirectory=/opt/stock-analyzer
Environment="PATH=/opt/stock-analyzer/venv/bin"
ExecStart=/opt/stock-analyzer/venv/bin/python main.py --schedule
Restart=always
RestartSec=30

[Install]
WantedBy=multi-user.target

2. Start Service

# Reload configuration
sudo systemctl daemon-reload

# Start service
sudo systemctl start stock-analyzer

# Enable auto-start on boot
sudo systemctl enable stock-analyzer

# View status
sudo systemctl status stock-analyzer

# View logs
journalctl -u stock-analyzer -f

Configuration Guide

Required Configuration

Config Item	Description	How to Get
`GEMINI_API_KEY`	Required for AI analysis	Google AI Studio
`STOCK_LIST`	Watchlist	Comma-separated stock codes
`WECHAT_WEBHOOK_URL`	WeChat push	WeChat Work group bot

Optional Configuration

Config Item	Default	Description
`SCHEDULE_ENABLED`	`false`	Enable scheduled tasks
`SCHEDULE_TIME`	`18:00`	Daily execution time
`MARKET_REVIEW_ENABLED`	`true`	Enable market review
`TAVILY_API_KEYS`	-	News search (optional)

Proxy Configuration

If server is in mainland China, accessing Gemini API requires proxy:

Docker Method

Edit docker-compose.yml:

environment:
  - http_proxy=http://your-proxy:port
  - https_proxy=http://your-proxy:port

Direct Deployment Method

Edit top of main.py:

os.environ["http_proxy"] = "http://your-proxy:port"
os.environ["https_proxy"] = "http://your-proxy:port"

Monitoring & Maintenance

View Logs

# Docker method
docker-compose -f ./docker/docker-compose.yml logs -f --tail=100

# Direct deployment
tail -f /opt/stock-analyzer/logs/stock_analysis_*.log

Health Check

# Check process
ps aux | grep main.py

# Check recent reports
ls -la /opt/stock-analyzer/reports/

Routine Maintenance

# Clean old logs (keep 7 days)
find /opt/stock-analyzer/logs -mtime +7 -delete

# Clean old reports (keep 30 days)
find /opt/stock-analyzer/reports -mtime +30 -delete

FAQ

1. Docker build failed

# Clear cache and rebuild
docker-compose -f ./docker/docker-compose.yml build --no-cache

2. API access timeout

Check proxy configuration, ensure server can access Gemini API.

3. Database locked

# Stop service then delete lock file
rm /opt/stock-analyzer/data/*.lock

4. Insufficient memory

Adjust memory limits in docker-compose.yml:

deploy:
  resources:
    limits:
      memory: 1G

Quick Migration

Migrate from one server to another:

# Source server: Package
cd /opt/stock-analyzer
tar -czvf stock-analyzer-backup.tar.gz .env data/ logs/ reports/

# Target server: Deploy
mkdir -p /opt/stock-analyzer
cd /opt/stock-analyzer
git clone <your-repo-url> .
tar -xzvf stock-analyzer-backup.tar.gz
docker-compose -f ./docker/docker-compose.yml up -d

Option 4: GitHub Actions Deployment (Serverless)

The simplest option! No server needed, leverages GitHub's free compute resources.

Advantages

✅ Completely free (2000 minutes/month)
✅ No server needed
✅ Auto-scheduled execution
✅ Zero maintenance cost

Limitations

⚠️ Stateless (fresh environment each run)
⚠️ Scheduled timing may have few minutes delay
⚠️ Cannot provide HTTP API

Deployment Steps

1. Create GitHub Repository

# Initialize git (if not already)
cd /path/to/daily_stock_analysis
git init
git add .
git commit -m "Initial commit"

# Create GitHub repo and push
# After creating new repo on GitHub web:
git remote add origin https://github.com/your-username/daily_stock_analysis.git
git branch -M main
git push -u origin main

2. Configure Secrets (Important!)

Go to repo page → Settings → Secrets and variables → Actions → New repository secret

Add these Secrets:

Secret Name	Description	Required
`GEMINI_API_KEY`	Gemini AI API Key	✅
`WECHAT_WEBHOOK_URL`	WeChat Work Bot Webhook	Optional*
`FEISHU_WEBHOOK_URL`	Feishu Bot Webhook	Optional*
`TELEGRAM_BOT_TOKEN`	Telegram Bot Token	Optional*
`TELEGRAM_CHAT_ID`	Telegram Chat ID	Optional*
`TELEGRAM_MESSAGE_THREAD_ID`	Telegram Topic ID	Optional*
`EMAIL_SENDER`	Sender email	Optional*
`EMAIL_PASSWORD`	Email authorization code	Optional*
`SERVERCHAN3_SENDKEY`	ServerChan v3 Sendkey	Optional*
`CUSTOM_WEBHOOK_URLS`	Custom Webhook (comma-separated for multiple)	Optional*
`STOCK_LIST`	Watchlist, e.g., `600519,300750`	✅
`TAVILY_API_KEYS`	Tavily Search API Key	Recommended
`SERPAPI_API_KEYS`	SerpAPI Key	Optional
`TUSHARE_TOKEN`	Tushare Token	Optional
`GEMINI_MODEL`	Model name (default gemini-2.0-flash)	Optional

*Note: Configure at least one notification channel, multiple channels supported for simultaneous push

3. Verify Workflow File

Ensure .github/workflows/daily_analysis.yml file exists and is committed:

git add .github/workflows/daily_analysis.yml
git commit -m "Add GitHub Actions workflow"
git push

4. Manual Test Run

Go to repo page → Actions tab
Select "Daily Stock Analysis" workflow
Click "Run workflow" button
Select run mode:
- full - Full analysis (stocks + market)
- market-only - Market review only
- stocks-only - Stock analysis only
Click green "Run workflow" button

5. View Execution Logs

Actions page shows run history
Click specific run record to view detailed logs
Analysis reports are saved as Artifacts for 30 days

Schedule Details

Default configuration: Monday to Friday, 18:00 Beijing Time auto-execution

Modify time: Edit cron expression in .github/workflows/daily_analysis.yml:

schedule:
  - cron: '0 10 * * 1-5'  # UTC time, +8 = Beijing time

Common cron examples:

Expression	Description
`'0 10 * * 1-5'`	Mon-Fri 18:00 (Beijing)
`'30 7 * * 1-5'`	Mon-Fri 15:30 (Beijing)
`'0 10 * * *'`	Daily 18:00 (Beijing)
`'0 2 * * 1-5'`	Mon-Fri 10:00 (Beijing)

Modify Watchlist

Method 1: Modify repo Secret STOCK_LIST

Method 2: Modify code directly then push:

# Modify .env.example or set default value in code
git commit -am "Update stock list"
git push

FAQ

Q: Why isn't the scheduled task running? A: GitHub Actions scheduled tasks may have 5-15 minute delays, and only trigger when repo has activity. Long periods without commits may cause workflow to be disabled.

Q: How to view historical reports? A: Actions → Select run record → Artifacts → Download analysis-reports-xxx

Q: Is the free quota enough? A: Each run takes about 2-5 minutes, 22 workdays per month = 44-110 minutes, well below the 2000 minute limit.

Wishing you a smooth deployment!

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