{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03c87582",
   "metadata": {},
   "source": [
    "# DES Hub Netbacks - Marginal Terminals\n",
    "\n",
    "Determining which terminals have been most marginal (i.e. WTP metric closest to 0) historically. \n",
    "\n",
    "WTP (Willingness to Pay) metric defined as: \n",
    "- WTP metric = Front Month hub price - variable regas costs - SparkNWE/SWE DES LNG price"
   ]
  },
  {
   "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.\n",
    "\n",
    "The code then prints the available prices that are callable from the API, and their corresponding Python ticker names are displayed as a list at the bottom of the Output."
   ]
  },
  {
   "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\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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d490f453",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sorting the JSON into a Pandas DataFrame\n",
    "\n",
    "def deshub_organise_dataframe(data):\n",
    "    \"\"\"\n",
    "    This function sorts the API content into a dataframe. The columns available are Release Date, Terminal, Month, Vessel Size, $/MMBtu and €/MWh. \n",
    "    Essentially, this function parses the Access database using the Month, Terminal and Vessel size columns as reference.\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",
    "    # defining \"Variable Regas Costs only\" - here, we treat slot costs as the only fixed regas cost component\n",
    "    df['DES Hub Netback - TTF Basis - Var Regas Costs Only'] = df['DES Hub Netback - TTF Basis'] \\\n",
    "                                                                + df['Basic Slot (B/U/S/R)'] \\\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": 6,
   "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": 7,
   "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, full_terms = loop_historical_data(access_token,loops)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66b16cd0",
   "metadata": {},
   "source": [
    "# 3. SparkNWE & SparkSWE - Data Import\n",
    "\n",
    "Calling the SparkNWE & SparkSWE front month prices (in TTF basis format) to compare against the terminal DES Hub netbacks and determine which terminals are \"in the money\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1c4262ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calling contracts endpoint to import cargo data\n",
    "def fetch_cargo_releases(access_token, ticker, limit=4, offset=None):\n",
    "\n",
    "    print(\">>>> Get price releases for {}\".format(ticker))\n",
    "\n",
    "    query_params = \"?limit={}\".format(limit)\n",
    "    if offset is not None:\n",
    "        query_params += \"&offset={}\".format(offset)\n",
    "\n",
    "    content = do_api_get_query(\n",
    "        uri=\"/v1.0/contracts/{}/price-releases/{}\".format(ticker, query_params),\n",
    "        access_token=access_token,\n",
    "    )\n",
    "\n",
    "    my_dict = content['data']\n",
    "    \n",
    "    return my_dict\n",
    "\n",
    "# Function to import data and then sort into a DataFrame\n",
    "def cargo_to_dataframe(access_token, ticker, limit, month):\n",
    "\n",
    "    # imports front month or forward curve prices, depending on the \"month\" user input\n",
    "    if month == 'M+1':\n",
    "        full_tick = ticker + '-b-f'\n",
    "        hist_data = fetch_cargo_releases(access_token, full_tick, limit)\n",
    "    else:\n",
    "        full_tick = ticker + '-b-fo'\n",
    "        hist_data = fetch_cargo_releases(access_token, full_tick, limit)\n",
    "         \n",
    "\n",
    "    release_dates = []\n",
    "    period_start = []\n",
    "    ticker = []\n",
    "    spark = []\n",
    "\n",
    "    spark_min = []\n",
    "    spark_max = []\n",
    "    cal_month = []\n",
    "\n",
    "    # iterating through historical data points to fetch relevant data\n",
    "    for release in hist_data:\n",
    "            release_date = release[\"releaseDate\"]\n",
    "            ticker.append(release['contractId'])\n",
    "            release_dates.append(release_date)\n",
    "\n",
    "            mi = int(month[-1])-2\n",
    "\n",
    "            data_point = release['data'][0]['dataPoints'][mi]\n",
    "\n",
    "            period_start_at = data_point[\"deliveryPeriod\"][\"startAt\"]\n",
    "            period_start.append(period_start_at)\n",
    "            \n",
    "            spark.append(data_point['derivedPrices']['usdPerMMBtu']['spark'])\n",
    "            spark_min.append(data_point['derivedPrices']['usdPerMMBtu']['sparkMin'])\n",
    "            spark_max.append(data_point['derivedPrices']['usdPerMMBtu']['sparkMax'])\n",
    "            \n",
    "            cal_month.append(datetime.datetime.strptime(period_start_at, '%Y-%m-%d').strftime('%b-%Y'))\n",
    "                \n",
    "\n",
    "    # Converting into DataFrame\n",
    "    hist_df = pd.DataFrame({\n",
    "        'Release Date': release_dates,\n",
    "        'ticker': ticker,\n",
    "        'Period Start': period_start,\n",
    "        'Price': spark,\n",
    "        })\n",
    "        \n",
    "    \n",
    "    hist_df['Price'] = pd.to_numeric(hist_df['Price'])\n",
    "    hist_df['Release Date'] = pd.to_datetime(hist_df['Release Date'])\n",
    "\n",
    "    hist_df['Release Date'] = hist_df['Release Date'].dt.tz_localize(None) \n",
    "\n",
    "    return hist_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7393260",
   "metadata": {},
   "source": [
    "# 4. Analysis\n",
    "\n",
    "- Input contract month required\n",
    "- Subtracting the SparkNWE/SWE prices from the DES Hub Netbacks to determine whether terminals are \"in\" or \"out of the money\".\n",
    "    - This will be labelled as \"WTP\", or the \"Willingness to Pay\" metric\n",
    "- Determining which terminal is considered the most \"marginal\" - i.e. closest to WTP=0\n",
    "- Plotting which terminals have been the most marginal historically\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86be1584",
   "metadata": {},
   "source": [
    "### Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1d15ea9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose which forward month you'd like to analyse - either front month (\"M+1\") or any other month up until M+11\n",
    "month = 'M+1'\n",
    "\n",
    "# Here we define which terminals we want to use in the analytics. The default is all terminals, but you can choose a subset if preferred (as demonstrated in comment below)\n",
    "terms = full_terms.copy()\n",
    "#terms = ['gate', 'dunkerque', 'zeebrugge']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec85e243",
   "metadata": {},
   "source": [
    "### Data Calling & Analytical Procedures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6fb701b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>> Get price releases for sparknwe-b-f\n",
      "Fetching https://api.sparkcommodities.com/v1.0/contracts/sparknwe-b-f/price-releases/?limit=450\n",
      ">>>> Get price releases for sparkswe-b-f\n",
      "Fetching https://api.sparkcommodities.com/v1.0/contracts/sparkswe-b-f/price-releases/?limit=450\n"
     ]
    }
   ],
   "source": [
    "# Import NWE/SWE LNG prices\n",
    "sparknwe = cargo_to_dataframe(access_token, 'sparknwe', loops*30, month=month)\n",
    "sparkswe = cargo_to_dataframe(access_token, 'sparkswe', loops*30, month=month)\n",
    "\n",
    "# retrieve the same amount of historical data for SparkSWE as SparkNWE \n",
    "sparkswe = sparkswe[sparkswe['Release Date'] >= sparknwe['Release Date'].iloc[-1]].copy()\n",
    "\n",
    "# Combine datasets and backfill SWE data as needed (due to reduced assessment frequency)\n",
    "cargo_df = pd.merge(sparknwe, sparkswe, how='left', on='Release Date')\n",
    "cargo_df['Price_y'] = cargo_df['Price_y'].bfill().copy()\n",
    "\n",
    "cargo_df = cargo_df[['Release Date', 'Price_x', 'Price_y']].copy()\n",
    "cargo_df = cargo_df.rename(columns={'Price_x': 'SparkNWE',\n",
    "                                    'Price_y': 'SparkSWE'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7f44cd95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining which terminals belong to NWE or SWE, so that the relevant DES LNG price can be subtracted to calculate the terminals' WTP\n",
    "terminal_region_dict = {\n",
    "    'gate': 'nwe',\n",
    "    'grain-lng': 'nwe',\n",
    "    'zeebrugge': 'nwe',\n",
    "    'south-hook': 'nwe',\n",
    "    'dunkerque': 'nwe',\n",
    "    'le-havre': 'nwe',\n",
    "    'montoir': 'nwe',\n",
    "    'eems-energy-terminal': 'nwe',\n",
    "    'brunsbuttel': 'nwe',\n",
    "    'deutsche-ostsee': 'nwe',\n",
    "    'wilhelmshaven': 'nwe',\n",
    "    'wilhelmshaven-2': 'nwe',\n",
    "    'stade': 'nwe',\n",
    "    'fos-cavaou': 'swe',\n",
    "    'adriatic': 'swe',\n",
    "    'olt-toscana': 'swe',\n",
    "    'piombino': 'swe',\n",
    "    'ravenna': 'swe',\n",
    "    'tvb': 'swe'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "62688b87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialising the \"month\" dataframe, which uses the \"month\" user input to create a DataFrame with all the relevant DES Hub netbacks data for that month for each terminal\n",
    "# the Gate DES Hub Netbacks data is used to set the \"Release Date\" and \"Delivery Month\" columns as Gate has the longest historical dataset\n",
    "# Here, we also use the \"Variable Regas Costs only\" Netbacks, which considers slot costs sunk (defined in the \"deshub_organise_dataframe\" function)\n",
    "month_df = hdf[(hdf['Terminal'] == 'gate') & (hdf['Month Index'] == month)][['Release Date', 'Delivery Month', 'DES Hub Netback - TTF Basis - Var Regas Costs Only']]\n",
    "month_df = month_df.rename(columns={'DES Hub Netback - TTF Basis - Var Regas Costs Only':'gate'})\n",
    "\n",
    "# defining a new list of terminals without \"gate\" in it\n",
    "terms2 = [x if x != 'gate' else None for x in terms]\n",
    "\n",
    "# iterating through list of terminals and adding data to the Terminal WTP dataframe\n",
    "for t in terms2:\n",
    "    if t is not None:\n",
    "        tdf = hdf[(hdf['Terminal'] == t) & (hdf['Month Index'] == month)][['Release Date', 'DES Hub Netback - TTF Basis - Var Regas Costs Only']]\n",
    "        month_df = month_df.merge(tdf, on='Release Date', how='left')\n",
    "        month_df = month_df.rename(columns={'DES Hub Netback - TTF Basis - Var Regas Costs Only':t})\n",
    "\n",
    "# Calculating the Average, Minimum and Maximum WTP values for all terminals (i.e. for Europe)\n",
    "month_df['Ave'] = month_df[terms].mean(axis=1)\n",
    "month_df['Min'] = month_df[terms].min(axis=1)\n",
    "month_df['Max'] = month_df[terms].max(axis=1)\n",
    "\n",
    "# Merging Cargo prices with the DataFrame, and backfilling data as needed so that the datasets are the same length (needed due to differing price release frequency)\n",
    "month_df = month_df.merge(cargo_df, how='left', on='Release Date')\n",
    "month_df['SparkNWE'] = month_df['SparkNWE'].bfill().copy()\n",
    "month_df['SparkSWE'] = month_df['SparkSWE'].bfill().copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "26c07bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a WTP dataframe, subtracting NWE/SWE prices from each terminals' DES Hub netbacks data.\n",
    "# The use of NWE or SWE prices for each terminal is set by the \"terminal_region_dict\" defined earlier in the script \n",
    "wtp_df = month_df[['Release Date', 'Delivery Month', 'SparkNWE', 'SparkSWE']].copy()\n",
    "\n",
    "for t in terms:\n",
    "    if terminal_region_dict[t] == 'nwe':\n",
    "        wtp_df[t] = month_df[t].copy() - month_df['SparkNWE'].copy()\n",
    "    elif terminal_region_dict[t] == 'swe':\n",
    "        wtp_df[t] = month_df[t].copy() - month_df['SparkSWE'].copy()\n",
    "    else:\n",
    "        wtp_df[t] = month_df[t].copy() - month_df['SparkNWE'].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0fc11fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the Average, Min and Max WTP metric over all terminals\n",
    "wtp_df['Ave'] = wtp_df[terms].mean(axis=1)\n",
    "wtp_df['Min'] = wtp_df[terms].min(axis=1)\n",
    "wtp_df['Max'] = wtp_df[terms].max(axis=1)\n",
    "    \n",
    "# Determining which is the marginal terminal for each historical date\n",
    "wtp_df['Marginal Terminal'] = wtp_df[terms].abs().idxmin(axis=\"columns\").copy()\n",
    "\n",
    "# saving list of marginal terminals\n",
    "marg_list = list(wtp_df['Marginal Terminal'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "adab54c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Release Date</th>\n",
       "      <th>Delivery Month</th>\n",
       "      <th>SparkNWE</th>\n",
       "      <th>SparkSWE</th>\n",
       "      <th>adriatic</th>\n",
       "      <th>brunsbuttel</th>\n",
       "      <th>deutsche-ostsee</th>\n",
       "      <th>dunkerque</th>\n",
       "      <th>eems-energy-terminal</th>\n",
       "      <th>fos-cavaou</th>\n",
       "      <th>...</th>\n",
       "      <th>south-hook</th>\n",
       "      <th>stade</th>\n",
       "      <th>tvb</th>\n",
       "      <th>wilhelmshaven</th>\n",
       "      <th>wilhelmshaven-2</th>\n",
       "      <th>zeebrugge</th>\n",
       "      <th>Ave</th>\n",
       "      <th>Min</th>\n",
       "      <th>Max</th>\n",
       "      <th>Marginal Terminal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-06-13</td>\n",
       "      <td>2025-07-01</td>\n",
       "      <td>-0.460</td>\n",
       "      <td>-0.440</td>\n",
       "      <td>0.135</td>\n",
       "      <td>1.350</td>\n",
       "      <td>0.676</td>\n",
       "      <td>0.684</td>\n",
       "      <td>0.799</td>\n",
       "      <td>-0.263</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.722</td>\n",
       "      <td>1.376</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>1.350</td>\n",
       "      <td>1.275</td>\n",
       "      <td>0.258</td>\n",
       "      <td>0.666526</td>\n",
       "      <td>-1.038</td>\n",
       "      <td>2.332</td>\n",
       "      <td>tvb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-06-12</td>\n",
       "      <td>2025-07-01</td>\n",
       "      <td>-0.465</td>\n",
       "      <td>-0.440</td>\n",
       "      <td>0.139</td>\n",
       "      <td>1.381</td>\n",
       "      <td>0.714</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.809</td>\n",
       "      <td>-0.263</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.816</td>\n",
       "      <td>1.405</td>\n",
       "      <td>-0.013</td>\n",
       "      <td>1.381</td>\n",
       "      <td>1.309</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.670789</td>\n",
       "      <td>-1.121</td>\n",
       "      <td>2.343</td>\n",
       "      <td>tvb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-06-11</td>\n",
       "      <td>2025-07-01</td>\n",
       "      <td>-0.440</td>\n",
       "      <td>-0.435</td>\n",
       "      <td>0.137</td>\n",
       "      <td>1.342</td>\n",
       "      <td>0.687</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.779</td>\n",
       "      <td>-0.299</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.784</td>\n",
       "      <td>1.365</td>\n",
       "      <td>-0.022</td>\n",
       "      <td>1.342</td>\n",
       "      <td>1.271</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.645737</td>\n",
       "      <td>-1.083</td>\n",
       "      <td>2.322</td>\n",
       "      <td>tvb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-06-10</td>\n",
       "      <td>2025-07-01</td>\n",
       "      <td>-0.455</td>\n",
       "      <td>-0.435</td>\n",
       "      <td>0.125</td>\n",
       "      <td>1.339</td>\n",
       "      <td>0.698</td>\n",
       "      <td>0.631</td>\n",
       "      <td>0.796</td>\n",
       "      <td>-0.305</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.785</td>\n",
       "      <td>1.361</td>\n",
       "      <td>-0.033</td>\n",
       "      <td>1.339</td>\n",
       "      <td>1.271</td>\n",
       "      <td>0.306</td>\n",
       "      <td>0.649316</td>\n",
       "      <td>-1.075</td>\n",
       "      <td>2.300</td>\n",
       "      <td>tvb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-06-09</td>\n",
       "      <td>2025-07-01</td>\n",
       "      <td>-0.450</td>\n",
       "      <td>-0.460</td>\n",
       "      <td>0.065</td>\n",
       "      <td>1.322</td>\n",
       "      <td>0.677</td>\n",
       "      <td>0.634</td>\n",
       "      <td>0.788</td>\n",
       "      <td>-0.271</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.806</td>\n",
       "      <td>1.345</td>\n",
       "      <td>-0.021</td>\n",
       "      <td>1.322</td>\n",
       "      <td>1.252</td>\n",
       "      <td>0.261</td>\n",
       "      <td>0.628316</td>\n",
       "      <td>-1.099</td>\n",
       "      <td>2.235</td>\n",
       "      <td>tvb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Release Date Delivery Month  SparkNWE  SparkSWE  adriatic  brunsbuttel  \\\n",
       "0   2025-06-13     2025-07-01    -0.460    -0.440     0.135        1.350   \n",
       "1   2025-06-12     2025-07-01    -0.465    -0.440     0.139        1.381   \n",
       "2   2025-06-11     2025-07-01    -0.440    -0.435     0.137        1.342   \n",
       "3   2025-06-10     2025-07-01    -0.455    -0.435     0.125        1.339   \n",
       "4   2025-06-09     2025-07-01    -0.450    -0.460     0.065        1.322   \n",
       "\n",
       "   deutsche-ostsee  dunkerque  eems-energy-terminal  fos-cavaou  ...  \\\n",
       "0            0.676      0.684                 0.799      -0.263  ...   \n",
       "1            0.714      0.691                 0.809      -0.263  ...   \n",
       "2            0.687      0.627                 0.779      -0.299  ...   \n",
       "3            0.698      0.631                 0.796      -0.305  ...   \n",
       "4            0.677      0.634                 0.788      -0.271  ...   \n",
       "\n",
       "   south-hook  stade    tvb  wilhelmshaven  wilhelmshaven-2  zeebrugge  \\\n",
       "0      -0.722  1.376 -0.011          1.350            1.275      0.258   \n",
       "1      -0.816  1.405 -0.013          1.381            1.309      0.262   \n",
       "2      -0.784  1.365 -0.022          1.342            1.271      0.227   \n",
       "3      -0.785  1.361 -0.033          1.339            1.271      0.306   \n",
       "4      -0.806  1.345 -0.021          1.322            1.252      0.261   \n",
       "\n",
       "        Ave    Min    Max  Marginal Terminal  \n",
       "0  0.666526 -1.038  2.332                tvb  \n",
       "1  0.670789 -1.121  2.343                tvb  \n",
       "2  0.645737 -1.083  2.322                tvb  \n",
       "3  0.649316 -1.075  2.300                tvb  \n",
       "4  0.628316 -1.099  2.235                tvb  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wtp_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e86c4f",
   "metadata": {},
   "source": [
    "### Plotting\n",
    "\n",
    "Plotting the average European WTP and min/max range, as well as the marginal terminal going back historically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "13caa839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.ticker as mtick\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 7))\n",
    "\n",
    "ax.plot(wtp_df['Release Date'], wtp_df['Ave'], color='mediumseagreen', linewidth=2.0, label='European Average WTP')\n",
    "\n",
    "# Option to plot min/max range of European Average WTP\n",
    "#ax.plot(wtp_df['Release Date'], wtp_df['Min'], color='steelblue', linewidth=1.0, alpha=0.06)\n",
    "#ax.plot(wtp_df['Release Date'], wtp_df['Max'], color='steelblue', linewidth=1.0, alpha=0.06)\n",
    "#ax.fill_between(wtp_df['Release Date'], wtp_df['Min'], wtp_df['Max'], color='steelblue', alpha=0.2)\n",
    "\n",
    "for m in marg_list:\n",
    "    tdf = wtp_df[wtp_df['Marginal Terminal'] == m][['Release Date', m]]\n",
    "    ax.scatter(tdf['Release Date'], tdf[m], label=m)\n",
    "\n",
    "# Shading <0 area of the chart\n",
    "negrange = [wtp_df['Release Date'].iloc[-1] -pd.Timedelta(20, unit='day'), wtp_df['Release Date'].iloc[0] +pd.Timedelta(20, unit='day')]\n",
    "ax.plot(negrange,[-1.0,-1.0], color='red', alpha=0.05)\n",
    "ax.plot(negrange,[0,0], color='red', alpha=0.05)\n",
    "ax.fill_between(negrange, 0, -1.0, color='red', alpha=0.05)\n",
    "\n",
    "# Chart Aesthetics\n",
    "ax.set_ylim(-0.5, 1.4)\n",
    "plt.title('European Average WTP & Range vs Marginal Cargo')\n",
    "plt.legend()\n",
    "\n",
    "sns.despine(left=True, bottom=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}