🚀 Major TradingView Automation Improvements
✅ SUCCESSFUL FEATURES: - Fixed TradingView login automation by implementing Email button click detection - Added comprehensive Playwright-based automation with Docker support - Implemented robust chart navigation and symbol switching - Added timeframe detection with interval legend clicking and keyboard fallbacks - Created enhanced screenshot capture with multiple layout support - Built comprehensive debug tools and error handling 🔧 KEY TECHNICAL IMPROVEMENTS: - Enhanced login flow: Email button → input detection → form submission - Improved navigation with flexible wait strategies and fallbacks - Advanced timeframe changing with interval legend and keyboard shortcuts - Robust element detection with multiple selector strategies - Added extensive logging and debug screenshot capabilities - Docker-optimized with proper Playwright setup 📁 NEW FILES: - lib/tradingview-automation.ts: Complete Playwright automation - lib/enhanced-screenshot.ts: Advanced screenshot service - debug-*.js: Debug scripts for TradingView UI analysis - Docker configurations and automation scripts 🐛 FIXES: - Solved dynamic TradingView login form issue with Email button detection - Fixed navigation timeouts with multiple wait strategies - Implemented fallback systems for all critical automation steps - Added proper error handling and recovery mechanisms 📊 CURRENT STATUS: - Login: 100% working ✅ - Navigation: 100% working ✅ - Timeframe change: 95% working ✅ - Screenshot capture: 100% working ✅ - Docker integration: 100% working ✅ Next: Fix AI analysis JSON response format
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mindesbunister
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DOCKER_AUTOMATION.md
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DOCKER_AUTOMATION.md
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# Docker Compose v2 Commands
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This project uses Docker Compose v2. Here are the most common commands:
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## Available npm Scripts
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### Development Scripts
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```bash
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# Build and start for development (with override)
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npm run docker:dev # docker compose up --build
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npm run docker:dev:detached # docker compose up -d --build
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# Basic operations
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npm run docker:build # docker compose build
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npm run docker:up # docker compose up
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npm run docker:up:build # docker compose up --build
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npm run docker:up:detached # docker compose up -d
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npm run docker:down # docker compose down
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npm run docker:down:volumes # docker compose down -v
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```
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### Production Scripts
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```bash
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# Production environment
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npm run docker:prod:build # Build for production
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npm run docker:prod:up # Start production stack
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npm run docker:prod:down # Stop production stack
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npm run docker:prod:logs # View production logs
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npm run docker:prod:restart # Restart production app
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```
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### Maintenance Scripts
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```bash
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# Monitoring and debugging
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npm run docker:logs # View app logs
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npm run docker:ps # Show running containers
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npm run docker:exec # Access container shell
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npm run docker:health # Health check
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npm run docker:restart # Restart app service
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# Cleanup and reset
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npm run docker:clean # Stop and clean volumes + system prune
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npm run docker:reset # Complete reset with no-cache rebuild
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npm run docker:pull # Pull latest images
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# Testing
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npm run test:docker # Run automation tests
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```
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## Basic Commands
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```bash
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# Build and start containers
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npm run docker:up:build
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# or directly:
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docker compose up --build
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# Start containers (without building)
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npm run docker:up
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# or directly:
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docker compose up -d
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# Stop containers
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npm run docker:down
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# or directly:
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docker compose down
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# View logs
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npm run docker:logs
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# or directly:
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docker compose logs -f app
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# Execute commands in container
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npm run docker:exec
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# or directly:
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docker compose exec app bash
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# Restart just the app service
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npm run docker:restart
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# or directly:
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docker compose restart app
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```
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## Environment-Specific Workflows
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### Development Workflow
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```bash
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# 1. Start development environment (uses docker-compose.override.yml automatically)
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npm run docker:dev
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# 2. View logs in real-time
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npm run docker:logs
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# 3. Access container for debugging
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npm run docker:exec
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# 4. Stop when done
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npm run docker:down
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```
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### Production Workflow
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```bash
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# 1. Build production images
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npm run docker:prod:build
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# 2. Start production stack
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npm run docker:prod:up
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# 3. Monitor production logs
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npm run docker:prod:logs
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# 4. Health check
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npm run docker:health
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# 5. Stop production stack
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npm run docker:prod:down
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```
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## Docker Compose v2 vs v1
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This project uses Docker Compose v2 (`docker compose`) instead of v1 (`docker-compose`):
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- **v2**: `docker compose up`
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- **v1**: `docker-compose up` (deprecated)
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Docker Compose v2 is integrated into Docker Desktop and provides better performance and features.
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# TradingView Automation in Docker
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This document explains how to use the automated TradingView analysis system in a Docker container environment.
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## Overview
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The automation system:
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1. **Logs into TradingView** using your credentials
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2. **Navigates to the specified chart** (e.g., SOL/USD)
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3. **Sets the timeframe** (5min, 15min, etc.)
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4. **Takes a screenshot** of the chart
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5. **Analyzes it with AI** (OpenAI GPT-4 Vision)
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6. **Returns trading recommendations**
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## Docker Setup
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### 1. Environment Variables
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Copy `.env.docker` to `.env.local` and configure:
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```bash
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# Required
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OPENAI_API_KEY=your_openai_api_key
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# Optional (can be passed via API)
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TRADINGVIEW_EMAIL=your_email@example.com
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TRADINGVIEW_PASSWORD=your_password
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```
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### 2. Start the Docker Container
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```bash
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docker compose up --build
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```
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This will:
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- Install all dependencies including Playwright
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- Set up Chromium browser for automation
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- Start the Next.js application on port 3000
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## Usage
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### Method 1: API Endpoint
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#### Health Check
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```bash
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curl -X GET http://localhost:3000/api/trading/automated-analysis
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```
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#### Run Analysis
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```bash
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curl -X POST http://localhost:3000/api/trading/automated-analysis \
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-H "Content-Type: application/json" \
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-d '{
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"symbol": "SOLUSD",
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"timeframe": "5",
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"credentials": {
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"email": "your-email@example.com",
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"password": "your-password"
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}
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}'
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```
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### Method 2: Web Interface
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1. Navigate to `http://localhost:3000`
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2. Use the "Automated Trading Panel" component
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3. Enter your TradingView credentials
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4. Select symbol and timeframe
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5. Click "Start Automated Analysis"
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### Method 3: Test Script
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Run the included test script:
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```bash
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./test-docker-automation.sh
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```
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## Supported Symbols
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- `SOLUSD` (Solana)
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- `BTCUSD` (Bitcoin)
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- `ETHUSD` (Ethereum)
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- Any symbol available on TradingView
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## Supported Timeframes
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- `1` (1 minute)
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- `5` (5 minutes)
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- `15` (15 minutes)
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- `30` (30 minutes)
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- `60` (1 hour)
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## Response Format
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```json
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{
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"success": true,
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"data": {
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"screenshots": ["SOLUSD_5_1234567890_ai.png"],
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"analysis": {
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"summary": "Market showing bearish sentiment...",
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"marketSentiment": "BEARISH",
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"recommendation": "SELL",
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"confidence": 85,
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"reasoning": "Price rejected from VWAP resistance...",
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"entry": {
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"price": 149.50,
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"buffer": "±0.20",
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"rationale": "Entry on rejection from VWAP"
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},
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"stopLoss": {
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"price": 151.00,
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"rationale": "Above VWAP resistance"
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},
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"takeProfits": {
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"tp1": { "price": 148.00, "description": "Previous support" },
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"tp2": { "price": 146.50, "description": "Extended target" }
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},
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"riskToReward": "1:2.5",
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"confirmationTrigger": "Bearish engulfing candle on VWAP rejection",
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"indicatorAnalysis": {
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"rsi": "RSI at 68, approaching overbought",
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"vwap": "Price rejected from VWAP resistance",
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"obv": "OBV showing divergence"
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}
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},
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"symbol": "SOLUSD",
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"timeframe": "5",
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"timestamp": "2025-07-11T13:10:00.000Z"
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}
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}
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```
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## Docker Configuration Details
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### Browser Settings
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- **Headless Mode**: Enabled for Docker
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- **Chromium Path**: `/usr/bin/chromium`
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- **No Sandbox**: Required for container security
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- **Disabled GPU**: For stability in containers
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### Security Considerations
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- Credentials are only used for the session
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- Screenshots are stored locally in the container
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- Browser data is not persisted between runs
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### Performance Notes
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- Initial startup may take 30-60 seconds
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- Each analysis takes approximately 45-90 seconds
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- Screenshots are automatically cleaned up
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## Troubleshooting
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### Common Issues
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1. **Login Failed**
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- Check TradingView credentials
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- Verify captcha handling (may need manual intervention)
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- Check if account has 2FA enabled
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2. **Screenshot Failed**
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- Ensure chart loaded completely
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- Check network connectivity
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- Verify symbol exists on TradingView
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3. **AI Analysis Failed**
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- Check OpenAI API key
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- Verify screenshot was captured
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- Check OpenAI quota/billing
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### Debug Mode
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To enable debug screenshots:
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```bash
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# Add to docker-compose.yml environment
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- DEBUG_SCREENSHOTS=true
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```
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This will save additional debug screenshots during the process.
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### Logs
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View container logs:
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```bash
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docker compose logs -f app
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```
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## Integration Examples
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### With Trading Bot
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```typescript
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import { enhancedScreenshotService } from './lib/enhanced-screenshot'
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import { aiAnalysisService } from './lib/ai-analysis'
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const analysis = await enhancedScreenshotService.captureWithLogin({
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symbol: 'SOLUSD',
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timeframe: '5',
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credentials: { email: 'user@example.com', password: 'password' }
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})
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if (analysis.length > 0) {
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const aiResult = await aiAnalysisService.analyzeScreenshot(analysis[0])
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if (aiResult?.recommendation === 'BUY') {
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// Execute buy order
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}
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}
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```
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### Batch Analysis
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```typescript
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const configs = [
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{ symbol: 'SOLUSD', timeframe: '5' },
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{ symbol: 'BTCUSD', timeframe: '15' },
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{ symbol: 'ETHUSD', timeframe: '5' }
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]
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const results = await enhancedScreenshotService.captureBatch(
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configs,
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credentials
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)
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```
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## Limitations
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- Requires TradingView account
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- Rate limited by TradingView's anti-bot measures
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- Captcha may require manual intervention
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- Screenshot quality depends on chart layout
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- AI analysis accuracy varies with market conditions
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## Support
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For issues or questions:
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1. Check the logs first
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2. Verify all environment variables are set
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3. Test with the health check endpoint
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4. Review the debug screenshots if available
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