trading_bot_v4/docs/setup/N8N_DATABASE_SETUP.md

n8n Database Integration Setup Guide

Overview

This guide shows you how to connect your n8n instance to the Trading Bot v4 PostgreSQL database for automated analysis and insights.

Database Connection Details

⚠️ IMPORTANT: n8n is on a different Docker network than the trading bot postgres. You MUST use the host machine IP or localhost.

✅ CORRECT Connection (n8n is on different network)

Type: PostgreSQL
Host: host.docker.internal  (or your machine's IP like 172.18.0.1)
Port: 5432
Database: trading_bot_v4
User: postgres
Password: postgres
SSL Mode: disable

Alternative: Use localhost with host networking

If host.docker.internal doesn't work, find your docker network gateway:

docker inspect n8n --format '{{range .NetworkSettings.Networks}}{{.Gateway}}{{end}}'
# Result: 172.18.0.1 (use this as Host)

Network Details (for reference)

• n8n network: compose_files_default (172.18.0.0/16)
• Trading bot network: traderv4_trading-net (172.28.0.0/16)
• PostgreSQL container: trading-bot-postgres on traderv4_trading-net (172.28.0.2)
• PostgreSQL exposed port: 5432 → localhost:5432

Since they're on different networks, use the host machine as bridge.

Setup Steps

1. Access n8n

Open your browser and navigate to:

http://localhost:8098

2. Create PostgreSQL Credential

1. Click on Credentials in the left sidebar
2. Click Add Credential
3. Search for Postgres and select it
4. Fill in the connection details (see above)
5. Name it: Trading Bot Database
6. Click Test Connection to verify
7. Click Save

3. Import Workflows

Four pre-built workflow templates are ready in your workspace:

A. Database Analytics (n8n-database-analytics.json)

Purpose: Query and analyze closed trades with statistical calculations

Features:

• Fetches last 100 closed trades
• Calculates win rate, P&L, profit factor
• Breaks down by symbol, direction, and exit reason
• Identifies best performing setups

To import:

1. Go to Workflows → Add Workflow
2. Click ... menu → Import from File
3. Select n8n-database-analytics.json
4. Update PostgreSQL node to use "Trading Bot Database" credential
5. Click Save
6. Click Execute Workflow to test

B. Daily Trading Report (n8n-daily-report.json)

Purpose: Automated daily summary at midnight (stores in DailyStats table)

Features:

• Runs automatically at 00:05 every day
• Calculates yesterday's performance
• Breaks down by symbol
• Stores in DailyStats table for historical tracking
• Calculates win rate, profit factor, avg hold time

To import:

1. Import workflow from file
2. Update both PostgreSQL nodes with "Trading Bot Database" credential
3. Activate the workflow (toggle in top right)
4. Will run automatically at midnight

C. Pattern Analysis (n8n-pattern-analysis.json)

Purpose: Discover which times/conditions produce best results

Features:

• Hourly Analysis: Which hours have best win rate
• Daily Analysis: Which days perform best
• Hold Time Analysis: Optimal position duration
• Generates actionable recommendations

Example insights:

• "Focus trading around 14:00-16:00 (75% win rate)"
• "Trade more on Tuesday, avoid Friday"
• "Target exits in 15-30 min range"

To import:

1. Import workflow
2. Update all 3 PostgreSQL nodes with credential
3. Run manually to see insights

D. Stop Loss Analysis (n8n-stop-loss-analysis.json)

Purpose: Optimize stop loss distances and understand exit patterns

Features:

• Exit reason breakdown (stopped out vs targets hit)
• Stop distance effectiveness (tight vs wide stops)
• Symbol-specific stop performance
• Calculates profit factor (avg win / avg loss)
• Recommendations for stop optimization

Example insights:

• "⚠️ High stop hit rate - consider wider stops"
• "💡 Normal (1-1.5%) stops perform 45% better than tight stops"
• "✅ Risk/reward ratio is positive"

To import:

1. Import workflow
2. Update all 3 PostgreSQL nodes with credential
3. Run manually to analyze

Database Schema Reference

Trade Table (Main table)

Key fields for analysis:

id, symbol, direction, entryPrice, exitPrice, quantity,
notionalSize, realizedPnL, realizedPnLPercent,
entryTime, exitTime, holdTimeSeconds,
stopLossPrice, takeProfitPrice1, takeProfitPrice2,
exitReason, status, isTestTrade

Common Queries

Get all closed trades (last 30 days)

SELECT * FROM "Trade"
WHERE status = 'closed'
  AND "isTestTrade" = false
  AND "entryTime" >= NOW() - INTERVAL '30 days'
ORDER BY "entryTime" DESC;

Calculate win rate

SELECT 
    COUNT(*) as total_trades,
    COUNT(CASE WHEN "realizedPnL" > 0 THEN 1 END) as wins,
    ROUND(COUNT(CASE WHEN "realizedPnL" > 0 THEN 1 END)::numeric / COUNT(*) * 100, 2) as win_rate
FROM "Trade"
WHERE status = 'closed' AND "isTestTrade" = false;

Best performing symbols

SELECT 
    symbol,
    COUNT(*) as trades,
    SUM("realizedPnL") as total_pnl,
    AVG("realizedPnL") as avg_pnl
FROM "Trade"
WHERE status = 'closed' AND "isTestTrade" = false
GROUP BY symbol
ORDER BY total_pnl DESC;

Workflow Automation Ideas

1. Performance Alerts

Trigger: Schedule (every 6 hours) Query: Check if win rate drops below 50% in last 24h Action: Send Telegram notification to pause trading

2. Best Setup Detector

Trigger: Manual or daily Query: Find symbol + direction + time combinations with >70% win rate Action: Save insights to config for bot to prioritize

3. Drawdown Monitor

Trigger: After each trade (webhook) Query: Calculate rolling 10-trade P&L Action: Auto-reduce position size if in drawdown

4. Exit Optimization

Trigger: Weekly Query: Compare TP1 vs TP2 hit rates and P&L Action: Recommend adjustment to TP levels

Connecting Workflows to Trading Bot

Webhook from Trading Bot to n8n

In your n8n workflow:

1. Add Webhook trigger node
2. Set HTTP Method: POST
3. Note the webhook URL: http://localhost:8098/webhook/your-unique-id

In trading bot code (e.g., after trade closes):

// Send trade data to n8n for analysis
await fetch('http://localhost:8098/webhook/your-unique-id', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    tradeId: trade.id,
    symbol: trade.symbol,
    pnl: trade.realizedPnL,
    exitReason: trade.exitReason
  })
});

Update Bot Config from n8n

In your n8n workflow (after analysis):

1. Add HTTP Request node
2. URL: http://trading-bot-v4:3000/api/settings
3. Method: POST
4. Body: Updated config based on analysis

Example - adjust stop loss based on analysis:

{
  "STOP_LOSS_PERCENT": 1.5,
  "USE_DUAL_STOPS": true
}

Advanced Use Cases

Machine Learning Integration

Use n8n to:

1. Export trade data to CSV
2. Send to Python ML service via HTTP
3. Receive predictions
4. Update bot configuration

Multi-Timeframe Analysis

Create workflow that:

1. Queries trades by hour/day/week
2. Identifies patterns at each timeframe
3. Generates trading schedule recommendations

Risk Management Automation

Build workflow that:

1. Monitors account balance
2. Calculates daily/weekly profit target
3. Auto-pauses bot after hitting target
4. Resumes trading next day/week

Troubleshooting

Connection Refused

• Verify PostgreSQL container is running: docker ps | grep postgres
• Check port mapping: docker port trading-bot-postgres
• Ensure n8n and postgres are on same Docker network

Query Timeouts

• Add indexes to frequently queried columns
• Limit result sets with LIMIT clause
• Use date range filters to reduce dataset

Empty Results

• Check isTestTrade filter (you may want to include test trades for testing)
• Verify date range in queries
• Ensure trades have been closed (status = 'closed')

Testing Your Setup

Quick Test Query

In n8n, create a workflow with:

1. Manual Trigger
2. PostgreSQL node with query:

SELECT COUNT(*) as total_trades, 
       MAX("entryTime") as last_trade
FROM "Trade";

3. Execute and verify results

Verify Test Trade Flag

SELECT 
    "isTestTrade",
    COUNT(*) as count
FROM "Trade"
GROUP BY "isTestTrade";

Expected output:

• false: Production trades
• true: Test trades from /api/trading/test-db

Next Steps

1. Import all 4 workflows and test each one
2. Activate the Daily Report workflow for automated tracking
3. Run Pattern Analysis to discover your best trading times
4. Run Stop Loss Analysis to optimize risk management
5. Create custom workflows based on your specific needs

Support

If you encounter issues:

1. Check n8n logs: docker logs n8n
2. Check postgres logs: docker logs trading-bot-postgres
3. Test database connection from host: psql -h localhost -p 5432 -U postgres -d trading_bot_v4
4. Verify bot is writing to database: Check /analytics page in web UI

---

Pro Tip: Start with the Pattern Analysis workflow to understand your trading patterns, then use those insights to create automated optimization workflows!