Day-Ahead Prices REST API

const API_TOKEN = 'YOUR_API_TOKEN';

async function getDayAheadPrices() {
  const response = await fetch(
    'https://api.cernion.de/api/entsoe/day-ahead-prices',
    {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${API_TOKEN}`,
        'Content-Type': 'application/json'
      },
      body: JSON.stringify({
        region: 'Germany',
        dateFrom: '2026-02-01',
        dateTo: '2026-02-07',
        includeStatistics: true
      })
    }
  );
  
  const data = await response.json();
  
  console.log(`Region: ${data.region} (${data.eicCode})`);
  console.log(`Data Points: ${data.statistics.totalDataPoints}`);
  console.log(`Price Range: ${data.statistics.minPrice} - ${data.statistics.maxPrice} €/MWh`);
  console.log(`Average: ${data.statistics.avgPrice.toFixed(2)} €/MWh\n`);
  
  // Find peak and valley hours
  const sortedPrices = [...data.prices].sort((a, b) => b.price - a.price);
  console.log('Top 5 Peak Hours:');
  sortedPrices.slice(0, 5).forEach(p => {
    console.log(`  ${p.timestamp}: ${p.price.toFixed(2)} €/MWh`);
  });
  
  return data;
}

getDayAheadPrices();

import requests
import pandas as pd
import matplotlib.pyplot as plt

API_TOKEN = 'YOUR_API_TOKEN'

def get_day_ahead_prices(region='Germany', date_from='2026-02-01', date_to='2026-02-07'):
    """Fetch day-ahead prices with analysis"""
    response = requests.post(
        'https://api.cernion.de/api/entsoe/day-ahead-prices',
        headers={
            'Authorization': f'Bearer {API_TOKEN}',
            'Content-Type': 'application/json'
        },
        json={
            'region': region,
            'dateFrom': date_from,
            'dateTo': date_to,
            'includeStatistics': True
        }
    )
    
    data = response.json()
    
    print(f'\n=== Day-Ahead Prices: {data["region"]} ===')
    print(f'Period: {date_from} to {date_to}')
    print(f'Data Points: {data["statistics"]["totalDataPoints"]}')
    print(f'\nPrice Statistics:')
    print(f'  Min: {data["statistics"]["minPrice"]:.2f} €/MWh')
    print(f'  Max: {data["statistics"]["maxPrice"]:.2f} €/MWh')
    print(f'  Avg: {data["statistics"]["avgPrice"]:.2f} €/MWh')
    print(f'  Median: {data["statistics"]["medianPrice"]:.2f} €/MWh')
    
    # Convert to DataFrame
    df = pd.DataFrame(data['prices'])
    df['timestamp'] = pd.to_datetime(df['timestamp'])
    df['hour'] = df['timestamp'].dt.hour
    
    # Peak/Valley Analysis
    print(f'\n=== Peak vs Valley ===')
    peak_hours = df[df['hour'].isin([17, 18, 19, 20])]  # 17-20h
    valley_hours = df[df['hour'].isin([2, 3, 4, 5])]   # 2-5h
    
    print(f'Peak Hours (17-20h): {peak_hours["price"].mean():.2f} €/MWh')
    print(f'Valley Hours (2-5h): {valley_hours["price"].mean():.2f} €/MWh')
    print(f'Peak/Valley Spread: {(peak_hours["price"].mean() - valley_hours["price"].mean()):.2f} €/MWh')
    
    # Negative Prices
    negative = df[df['price'] < 0]
    if len(negative) > 0:
        print(f'\n⚠️ Negative Prices: {len(negative)} intervals')
        print(f'Lowest: {negative["price"].min():.2f} €/MWh')
    
    return data, df

if __name__ == '__main__':
    data, df = get_day_ahead_prices('Germany', '2026-02-01', '2026-02-07')
    
    # Plot
    plt.figure(figsize=(12, 6))
    plt.plot(df['timestamp'], df['price'])
    plt.axhline(y=0, color='r', linestyle='--', alpha=0.5)
    plt.title('Day-Ahead Prices - Germany')
    plt.xlabel('Time')
    plt.ylabel('Price (€/MWh)')
    plt.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.savefig('day_ahead_prices.png')
    print('\nChart saved: day_ahead_prices.png')