trading_bot_v3/DOCKER_AUTOMATION.md

Docker Compose v2 Commands

This project uses Docker Compose v2. Here are the most common commands:

Available npm Scripts

Development Scripts

# Build and start for development (with override)
npm run docker:dev                    # docker compose up --build
npm run docker:dev:detached          # docker compose up -d --build

# Basic operations
npm run docker:build                  # docker compose build
npm run docker:up                     # docker compose up
npm run docker:up:build              # docker compose up --build
npm run docker:up:detached           # docker compose up -d
npm run docker:down                  # docker compose down
npm run docker:down:volumes          # docker compose down -v

Production Scripts

# Production environment
npm run docker:prod:build            # Build for production
npm run docker:prod:up               # Start production stack
npm run docker:prod:down             # Stop production stack
npm run docker:prod:logs             # View production logs
npm run docker:prod:restart          # Restart production app

Maintenance Scripts

# Monitoring and debugging
npm run docker:logs                  # View app logs
npm run docker:ps                    # Show running containers
npm run docker:exec                  # Access container shell
npm run docker:health                # Health check
npm run docker:restart               # Restart app service

# Cleanup and reset
npm run docker:clean                 # Stop and clean volumes + system prune
npm run docker:reset                 # Complete reset with no-cache rebuild
npm run docker:pull                  # Pull latest images

# Testing
npm run test:docker                  # Run automation tests

Basic Commands

# Build and start containers
npm run docker:up:build
# or directly:
docker compose up --build

# Start containers (without building)
npm run docker:up
# or directly:
docker compose up -d

# Stop containers
npm run docker:down
# or directly:
docker compose down

# View logs
npm run docker:logs
# or directly:
docker compose logs -f app

# Execute commands in container
npm run docker:exec
# or directly:
docker compose exec app bash

# Restart just the app service
npm run docker:restart
# or directly:
docker compose restart app

Environment-Specific Workflows

Development Workflow

# 1. Start development environment (uses docker-compose.override.yml automatically)
npm run docker:dev

# 2. View logs in real-time
npm run docker:logs

# 3. Access container for debugging
npm run docker:exec

# 4. Stop when done
npm run docker:down

Production Workflow

# 1. Build production images
npm run docker:prod:build

# 2. Start production stack
npm run docker:prod:up

# 3. Monitor production logs
npm run docker:prod:logs

# 4. Health check
npm run docker:health

# 5. Stop production stack
npm run docker:prod:down

Docker Compose v2 vs v1

This project uses Docker Compose v2 (docker compose) instead of v1 (docker-compose):

• v2: docker compose up
• v1: docker-compose up (deprecated)

Docker Compose v2 is integrated into Docker Desktop and provides better performance and features.

TradingView Automation in Docker

This document explains how to use the automated TradingView analysis system in a Docker container environment.

Overview

The automation system:

1. Logs into TradingView using your credentials
2. Navigates to the specified chart (e.g., SOL/USD)
3. Sets the timeframe (5min, 15min, etc.)
4. Takes a screenshot of the chart
5. Analyzes it with AI (OpenAI GPT-4 Vision)
6. Returns trading recommendations

Docker Setup

1. Environment Variables

Copy .env.docker to .env.local and configure:

# Required
OPENAI_API_KEY=your_openai_api_key

# Optional (can be passed via API)
TRADINGVIEW_EMAIL=your_email@example.com
TRADINGVIEW_PASSWORD=your_password

2. Start the Docker Container

docker compose up --build

This will:

• Install all dependencies including Playwright
• Set up Chromium browser for automation
• Start the Next.js application on port 3000

Usage

Method 1: API Endpoint

Health Check

curl -X GET http://localhost:3000/api/trading/automated-analysis

Run Analysis

curl -X POST http://localhost:3000/api/trading/automated-analysis \
  -H "Content-Type: application/json" \
  -d '{
    "symbol": "SOLUSD",
    "timeframe": "5",
    "credentials": {
      "email": "your-email@example.com",
      "password": "your-password"
    }
  }'

Method 2: Web Interface

1. Navigate to http://localhost:3000
2. Use the "Automated Trading Panel" component
3. Enter your TradingView credentials
4. Select symbol and timeframe
5. Click "Start Automated Analysis"

Method 3: Test Script

Run the included test script:

./test-docker-automation.sh

Supported Symbols

• SOLUSD (Solana)
• BTCUSD (Bitcoin)
• ETHUSD (Ethereum)
• Any symbol available on TradingView

Supported Timeframes

• 1 (1 minute)
• 5 (5 minutes)
• 15 (15 minutes)
• 30 (30 minutes)
• 60 (1 hour)

Response Format

{
  "success": true,
  "data": {
    "screenshots": ["SOLUSD_5_1234567890_ai.png"],
    "analysis": {
      "summary": "Market showing bearish sentiment...",
      "marketSentiment": "BEARISH",
      "recommendation": "SELL",
      "confidence": 85,
      "reasoning": "Price rejected from VWAP resistance...",
      "entry": {
        "price": 149.50,
        "buffer": "±0.20",
        "rationale": "Entry on rejection from VWAP"
      },
      "stopLoss": {
        "price": 151.00,
        "rationale": "Above VWAP resistance"
      },
      "takeProfits": {
        "tp1": { "price": 148.00, "description": "Previous support" },
        "tp2": { "price": 146.50, "description": "Extended target" }
      },
      "riskToReward": "1:2.5",
      "confirmationTrigger": "Bearish engulfing candle on VWAP rejection",
      "indicatorAnalysis": {
        "rsi": "RSI at 68, approaching overbought",
        "vwap": "Price rejected from VWAP resistance",
        "obv": "OBV showing divergence"
      }
    },
    "symbol": "SOLUSD",
    "timeframe": "5",
    "timestamp": "2025-07-11T13:10:00.000Z"
  }
}

Docker Configuration Details

Browser Settings

• Headless Mode: Enabled for Docker
• Chromium Path: /usr/bin/chromium
• No Sandbox: Required for container security
• Disabled GPU: For stability in containers

Security Considerations

• Credentials are only used for the session
• Screenshots are stored locally in the container
• Browser data is not persisted between runs

Performance Notes

• Initial startup may take 30-60 seconds
• Each analysis takes approximately 45-90 seconds
• Screenshots are automatically cleaned up

Troubleshooting

Common Issues

1. Login Failed

   • Check TradingView credentials
   • Verify captcha handling (may need manual intervention)
   • Check if account has 2FA enabled

2. Screenshot Failed

   • Ensure chart loaded completely
   • Check network connectivity
   • Verify symbol exists on TradingView

3. AI Analysis Failed

   • Check OpenAI API key
   • Verify screenshot was captured
   • Check OpenAI quota/billing

Debug Mode

To enable debug screenshots:

# Add to docker-compose.yml environment
- DEBUG_SCREENSHOTS=true

This will save additional debug screenshots during the process.

Logs

View container logs:

docker compose logs -f app

Integration Examples

With Trading Bot

import { enhancedScreenshotService } from './lib/enhanced-screenshot'
import { aiAnalysisService } from './lib/ai-analysis'

const analysis = await enhancedScreenshotService.captureWithLogin({
  symbol: 'SOLUSD',
  timeframe: '5',
  credentials: { email: 'user@example.com', password: 'password' }
})

if (analysis.length > 0) {
  const aiResult = await aiAnalysisService.analyzeScreenshot(analysis[0])
  
  if (aiResult?.recommendation === 'BUY') {
    // Execute buy order
  }
}

Batch Analysis

const configs = [
  { symbol: 'SOLUSD', timeframe: '5' },
  { symbol: 'BTCUSD', timeframe: '15' },
  { symbol: 'ETHUSD', timeframe: '5' }
]

const results = await enhancedScreenshotService.captureBatch(
  configs, 
  credentials
)

Limitations

• Requires TradingView account
• Rate limited by TradingView's anti-bot measures
• Captcha may require manual intervention
• Screenshot quality depends on chart layout
• AI analysis accuracy varies with market conditions

Support

For issues or questions:

1. Check the logs first
2. Verify all environment variables are set
3. Test with the health check endpoint
4. Review the debug screenshots if available