{
"cells": [
{
"cell_type": "markdown",
"id": "03c87582",
"metadata": {},
"source": [
"# DES Hub Netbacks - Country Competitiveness Comparison\n",
"\n",
"This script is used to compare the regas terminal competitiveness, averaged by country, across Europe\n",
"\n",
"For a full explanation of how to import our DES Hub Netbacks data, please refer to our Python Jupyter Notebook Code Samples:\n",
"\n",
"https://www.sparkcommodities.com/api/code-examples/jupyter.html"
]
},
{
"cell_type": "markdown",
"id": "21bd6181",
"metadata": {},
"source": [
"### Have any questions?\n",
"\n",
"If you have any questions regarding our API, or need help accessing specific datasets, please contact us at:\n",
"\n",
"__data@sparkcommodities.com__\n"
]
},
{
"cell_type": "markdown",
"id": "0037c795",
"metadata": {},
"source": [
"## 1. Importing Data\n",
"\n",
"Here we define the functions that allow us to retrieve the valid credentials to access the Spark API.\n",
"\n",
"This section can remain unchanged for most Spark API users."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "5cbcba1b",
"metadata": {},
"outputs": [],
"source": [
"# import libraries for callin the API\n",
"import json\n",
"import os\n",
"import sys\n",
"import pandas as pd\n",
"from base64 import b64encode\n",
"from urllib.parse import urljoin\n",
"from pprint import pprint\n",
"import requests\n",
"from io import StringIO\n",
"import time\n",
"import numpy as np\n",
"import datetime\n",
"\n",
"try:\n",
" from urllib import request, parse\n",
" from urllib.error import HTTPError\n",
"except ImportError:\n",
" raise RuntimeError(\"Python 3 required\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b7442d2b",
"metadata": {},
"outputs": [],
"source": [
"# defining query functions \n",
"API_BASE_URL = \"https://api.sparkcommodities.com\"\n",
"\n",
"\n",
"def retrieve_credentials(file_path=None):\n",
" \"\"\"\n",
" Find credentials either by reading the client_credentials file or reading\n",
" environment variables\n",
" \"\"\"\n",
" if file_path is None:\n",
" client_id = os.getenv(\"SPARK_CLIENT_ID\")\n",
" client_secret = os.getenv(\"SPARK_CLIENT_SECRET\")\n",
" if not client_id or not client_secret:\n",
" raise RuntimeError(\n",
" \"SPARK_CLIENT_ID and SPARK_CLIENT_SECRET environment vars required\"\n",
" )\n",
" else:\n",
" # Parse the file\n",
" if not os.path.isfile(file_path):\n",
" raise RuntimeError(\"The file {} doesn't exist\".format(file_path))\n",
"\n",
" with open(file_path) as fp:\n",
" lines = [l.replace(\"\\n\", \"\") for l in fp.readlines()]\n",
"\n",
" if lines[0] in (\"clientId,clientSecret\", \"client_id,client_secret\"):\n",
" client_id, client_secret = lines[1].split(\",\")\n",
" else:\n",
" print(\"First line read: '{}'\".format(lines[0]))\n",
" raise RuntimeError(\n",
" \"The specified file {} doesn't look like to be a Spark API client \"\n",
" \"credentials file\".format(file_path)\n",
" )\n",
"\n",
" print(\">>>> Found credentials!\")\n",
" print(\n",
" \">>>> Client_id={}****, client_secret={}****\".format(\n",
" client_id[:5], client_secret[:5]\n",
" )\n",
" )\n",
"\n",
" return client_id, client_secret\n",
"\n",
"\n",
"def do_api_post_query(uri, body, headers):\n",
" \"\"\"\n",
" OAuth2 authentication requires a POST request with client credentials before accessing the API. \n",
" This POST request will return an Access Token which will be used for the API GET request.\n",
" \"\"\"\n",
" url = urljoin(API_BASE_URL, uri)\n",
"\n",
" data = json.dumps(body).encode(\"utf-8\")\n",
"\n",
" # HTTP POST request\n",
" req = request.Request(url, data=data, headers=headers)\n",
" try:\n",
" response = request.urlopen(req)\n",
" except HTTPError as e:\n",
" print(\"HTTP Error: \", e.code)\n",
" print(e.read())\n",
" sys.exit(1)\n",
"\n",
" resp_content = response.read()\n",
"\n",
" # The server must return HTTP 201. Raise an error if this is not the case\n",
" assert response.status == 201, resp_content\n",
"\n",
" # The server returned a JSON response\n",
" content = json.loads(resp_content)\n",
"\n",
" return content\n",
"\n",
"\n",
"def do_api_get_query(uri, access_token):\n",
" \"\"\"\n",
" After receiving an Access Token, we can request information from the API.\n",
" \"\"\"\n",
" url = urljoin(API_BASE_URL, uri)\n",
"\n",
" headers = {\n",
" \"Authorization\": \"Bearer {}\".format(access_token),\n",
" \"accept\": \"application/json\",\n",
" }\n",
"\n",
" print(f\"Fetching {url}\")\n",
"\n",
" # HTTP GET request\n",
" req = request.Request(url, headers=headers)\n",
" try:\n",
" response = request.urlopen(req)\n",
" except HTTPError as e:\n",
" print(\"HTTP Error: \", e.code)\n",
" print(e.read())\n",
" sys.exit(1)\n",
"\n",
" resp_content = response.read()\n",
"\n",
" # The server must return HTTP 201. Raise an error if this is not the case\n",
" assert response.status == 200, resp_content\n",
"\n",
" # The server returned a JSON response\n",
" content = json.loads(resp_content)\n",
"\n",
" return content\n",
"\n",
"\n",
"def get_access_token(client_id, client_secret):\n",
" \"\"\"\n",
" Get a new access_token. Access tokens are the thing that applications use to make\n",
" API requests. Access tokens must be kept confidential in storage.\n",
"\n",
" # Procedure:\n",
"\n",
" Do a POST query with `grantType` and `scopes` in the body. A basic authorization\n",
" HTTP header is required. The \"Basic\" HTTP authentication scheme is defined in\n",
" RFC 7617, which transmits credentials as `clientId:clientSecret` pairs, encoded\n",
" using base64.\n",
" \"\"\"\n",
"\n",
" # Note: for the sake of this example, we choose to use the Python urllib from the\n",
" # standard lib. One should consider using https://requests.readthedocs.io/\n",
"\n",
" payload = \"{}:{}\".format(client_id, client_secret).encode()\n",
" headers = {\n",
" \"Authorization\": b64encode(payload).decode(),\n",
" \"Accept\": \"application/json\",\n",
" \"Content-Type\": \"application/json\",\n",
" }\n",
" body = {\n",
" \"grantType\": \"clientCredentials\",\n",
" \"scopes\": \"read:access,read:prices\"\n",
" }\n",
"\n",
" content = do_api_post_query(uri=\"/oauth/token/\", body=body, headers=headers)\n",
"\n",
" print(\n",
" \">>>> Successfully fetched an access token {}****, valid {} seconds.\".format(\n",
" content[\"accessToken\"][:5], content[\"expiresIn\"]\n",
" )\n",
" )\n",
"\n",
" return content[\"accessToken\"]"
]
},
{
"cell_type": "markdown",
"id": "9b4b250b",
"metadata": {},
"source": [
"## N.B. Credentials\n",
"\n",
"Here we call the above functions, and input the file path to our credentials.\n",
"\n",
"N.B. You must have downloaded your client credentials CSV file before proceeding. Please refer to the API documentation if you have not dowloaded them already."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3ec2647c",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
">>>> Found credentials!\n",
">>>> Client_id=01c23****, client_secret=80763****\n",
">>>> Successfully fetched an access token eyJhb****, valid 604799 seconds.\n"
]
}
],
"source": [
"# Insert file path to your client credentials here\n",
"client_id, client_secret = retrieve_credentials(file_path=\"/tmp/client_credentials.csv\")\n",
"\n",
"# Authenticate:\n",
"access_token = get_access_token(client_id, client_secret)"
]
},
{
"cell_type": "markdown",
"id": "e0328738",
"metadata": {},
"source": [
"## 2. DES Hub Netbacks - Data Import\n",
"\n",
"Calling the DES Hub Netbacks data, and sorting into a Historical DataFrame"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "35759cdd",
"metadata": {},
"outputs": [],
"source": [
"## Defining the function to import the data\n",
"\n",
"def fetch_deshub_releases(access_token, unit, limit=None, offset=None, terminal=None):\n",
" \n",
" query_params = \"?unit={}\".format(unit)\n",
" if limit is not None:\n",
" query_params += \"&limit={}\".format(limit)\n",
" if offset is not None:\n",
" query_params += \"&offset={}\".format(offset)\n",
" if terminal is not None:\n",
" query_params += \"&terminal={}\".format(terminal)\n",
"\n",
"\n",
" content = do_api_get_query(\n",
" uri=\"/beta/access/des-hub-netbacks/{}\".format(query_params), access_token=access_token\n",
" )\n",
"\n",
" return content\n",
"\n",
"\n",
"# Sorting the JSON into a Pandas DataFrame\n",
"\n",
"def deshub_organise_dataframe(data):\n",
"\n",
" # create columns\n",
" data_dict = {\n",
" 'Release Date':[],\n",
" 'Terminal':[],\n",
" 'Month Index':[],\n",
" 'Delivery Month':[],\n",
" 'DES Hub Netback - TTF Basis':[],\n",
" 'DES Hub Netback - Outright':[],\n",
" 'Total Regas':[],\n",
" 'Basic Slot (Berth)':[],\n",
" 'Basic Slot (Unload/Stor/Regas)':[],\n",
" 'Basic Slot (B/U/S/R)':[],\n",
" 'Additional Storage':[],\n",
" 'Additional Sendout':[],\n",
" 'Gas in Kind': [],\n",
" 'Entry Capacity':[],\n",
" 'Commodity Charge':[]\n",
" }\n",
"\n",
" # loop for each Terminal\n",
" for l in data['data']:\n",
" \n",
" # assigning values to each column\n",
" data_dict['Release Date'].append(l[\"releaseDate\"])\n",
" data_dict['Terminal'].append(data['metaData']['terminals'][l['terminalUuid']])\n",
" data_dict['Month Index'].append(l['monthIndex'])\n",
" data_dict['Delivery Month'].append(l['deliveryMonth'])\n",
"\n",
" data_dict['DES Hub Netback - TTF Basis'].append(float(l['netbackTtfBasis']))\n",
" data_dict['DES Hub Netback - Outright'].append(float(l['netbackOutright']))\n",
" data_dict['Total Regas'].append(float(l['totalRegasificationCost']))\n",
" data_dict['Basic Slot (Berth)'].append(float(l['slotBerth']))\n",
" data_dict['Basic Slot (Unload/Stor/Regas)'].append(float(l['slotUnloadStorageRegas']))\n",
" data_dict['Basic Slot (B/U/S/R)'].append(float(l['slotBerthUnloadStorageRegas']))\n",
" data_dict['Additional Storage'].append(float(l['additionalStorage']))\n",
" data_dict['Additional Sendout'].append(float(l['additionalSendout']))\n",
" data_dict['Gas in Kind'].append(float(l['gasInKind']))\n",
" data_dict['Entry Capacity'].append(float(l['entryCapacity']))\n",
" data_dict['Commodity Charge'].append(float(l['commodityCharge']))\n",
" \n",
" \n",
" # convert into dataframe\n",
" df = pd.DataFrame(data_dict)\n",
" \n",
" df['Delivery Month'] = pd.to_datetime(df['Delivery Month'])\n",
" df['Release Date'] = pd.to_datetime(df['Release Date'])\n",
"\n",
" # Create column that treats slot costs as sunk\n",
" df['DES Hub Netback - TTF Basis - Var Regas Costs Only'] = df['DES Hub Netback - TTF Basis'] \\\n",
" + df['Basic Slot (Unload/Stor/Regas)'] \\\n",
" + df['Basic Slot (Berth)'] \\\n",
" + df['Basic Slot (B/U/S/R)']\n",
" \n",
" return df\n"
]
},
{
"cell_type": "markdown",
"id": "4aa81e2c",
"metadata": {},
"source": [
"### Historical Data Function\n",
"\n",
"Currently, a maximum of 30 historical datasets can be called at one time due to the size of the data file. \n",
"\n",
"If more data points are required, the below code can be used. It calls 30 historical datasets at a time, but utilises the 'offset' parameter to call datasets further back in the historical database. To call more history, increase the 'n_offset' parameter in the first line of the code. The 'n_offset' parameter describes the number of historical data requests to be executed."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "9fae6c01",
"metadata": {},
"outputs": [],
"source": [
"def loop_historical_data(token,n_offset):\n",
" # initalise first set of historical data and initialising dataframe\n",
" historical = fetch_deshub_releases(access_token, unit='usd-per-mmbtu', limit=30)\n",
" hist_df = deshub_organise_dataframe(historical)\n",
" terminal_list = list(historical['metaData']['terminals'].values())\n",
"\n",
" # Looping through earlier historical data and adding to the historical dataframe\n",
" for i in range(1,n_offset+1):\n",
" historical = fetch_deshub_releases(access_token, unit='usd-per-mmbtu', limit=30, offset=i*30)\n",
" hist_df = pd.concat([hist_df,deshub_organise_dataframe(historical)])\n",
"\n",
" return hist_df, terminal_list"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "8fcf6066",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=30\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=60\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=90\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=120\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=150\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=180\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=210\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=240\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=270\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=300\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=330\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=360\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=390\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=420\n",
"Fetching https://api.sparkcommodities.com/beta/access/des-hub-netbacks/?unit=usd-per-mmbtu&limit=30&offset=450\n"
]
}
],
"source": [
"loops = 15\n",
"hdf, deshub_terms = loop_historical_data(access_token,loops)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "41f80066",
"metadata": {},
"outputs": [
{
"data": {
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" ... \n",
" 0.047 \n",
" -0.647 \n",
" 0.023 \n",
" -0.111 \n",
" -0.341 \n",
" -0.240211 \n",
" -1.331 \n",
" 0.660 \n",
" -0.590 \n",
" -0.615 \n",
" \n",
" \n",
" 4 \n",
" 2025-05-16 \n",
" 2025-06-01 \n",
" -0.124 \n",
" 0.654 \n",
" 0.032 \n",
" -0.350 \n",
" -0.562 \n",
" -0.218 \n",
" -0.592 \n",
" -1.344 \n",
" ... \n",
" 0.056 \n",
" -0.654 \n",
" 0.032 \n",
" -0.101 \n",
" -0.358 \n",
" -0.242526 \n",
" -1.344 \n",
" 0.654 \n",
" -0.605 \n",
" -0.615 \n",
" \n",
" \n",
"
\n",
"
5 rows × 26 columns
\n",
"
"
]
},
"metadata": {},
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],
"source": [
"# Plotting the Data for a subset of countries\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"\n",
"fig, ax = plt.subplots(figsize=(15, 7))\n",
"\n",
"ax.hlines(0, countries_df['Release Date'].iloc[-1], countries_df['Release Date'].iloc[0], color='grey')\n",
"\n",
"# User input for which countries to plot, and associated plot colours\n",
"my_countries = ['Germany', 'UK', 'France']\n",
"colours = ['orange', 'firebrick', 'steelblue']\n",
"\n",
"print(\"Latest Assessment: \" + str(countries_df['Release Date'].iloc[0]))\n",
"\n",
"cz = 0\n",
"for c in my_countries:\n",
" ax.scatter(countries_df['Release Date'].iloc[0], countries_df[c + ' Ave'].iloc[0], color=colours[cz], marker='o', s=80)\n",
" print(c + ' = ' + str(countries_df[c + ' Ave'].iloc[0]))\n",
"\n",
" ax.plot(countries_df['Release Date'], countries_df[c + ' Ave'], linewidth=2.0, label=c, zorder=cz, color=colours[cz])\n",
" ax.plot(countries_df['Release Date'], countries_df[c + ' Min'], linewidth=1.0, alpha=0.06, color=colours[cz])\n",
" ax.plot(countries_df['Release Date'], countries_df[c + ' Max'], linewidth=1.0, alpha=0.06, color=colours[cz])\n",
" ax.fill_between(countries_df['Release Date'], countries_df[c + ' Min'], countries_df[c + ' Max'], alpha=0.2, color=colours[cz])\n",
" cz+=1\n",
"\n",
"# Shading <0 area of the chart\n",
"negrange = [countries_df['Release Date'].iloc[-1] -pd.Timedelta(20, unit='day'), countries_df['Release Date'].iloc[0] +pd.Timedelta(20, unit='day')]\n",
"\n",
"ax.plot(negrange,[-2.0,-2.0], color='red', alpha=0.05)\n",
"ax.plot(negrange,[0,0], color='red', alpha=0.05)\n",
"ax.fill_between(negrange, 0, -2.0, color='red', alpha=0.05)\n",
"\n",
"plt.xlim([countries_df['Release Date'].iloc[-1]-pd.Timedelta(7, unit='day'), countries_df['Release Date'].iloc[0]+pd.Timedelta(20, unit='day')])\n",
"plt.ylim(-1.7, 1)\n",
"\n",
"plt.title('Country Average WTP & Range')\n",
"plt.legend()\n",
"plt.grid()\n",
"\n",
"sns.despine(left=True, bottom=True)"
]
}
],
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