{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "03c87582",
   "metadata": {},
   "source": [
    "# DES Hub Netbacks - Percentage of Terminals \"In the Money\"\n",
    "Determining how many terminals are considered \"in the money\", as a percentage of the all the major LNG European terminals on Spark Access. Useful as a signal for European LNG flows."
   ]
  },
  {
   "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": 31,
   "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": 32,
   "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": 34,
   "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": 36,
   "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": 37,
   "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": 38,
   "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",
    "- Calculating the percentage of terminals in/out of the money historically\n",
    "- Plotting this fractional WTP historical evolution over time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86be1584",
   "metadata": {},
   "source": [
    "### Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 40,
   "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": 41,
   "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": 42,
   "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": 43,
   "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": 44,
   "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",
    "# Calculate fraction of terminals in the money\n",
    "wtp_df['Fraction'] = wtp_df[terms].gt(0).sum(axis=1)/(len(terms)-wtp_df.isna().sum(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>Fraction</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>0.736842</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>0.736842</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>0.736842</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>0.736842</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>0.736842</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  Fraction  \n",
       "0  0.666526 -1.038  2.332  0.736842  \n",
       "1  0.670789 -1.121  2.343  0.736842  \n",
       "2  0.645737 -1.083  2.322  0.736842  \n",
       "3  0.649316 -1.075  2.300  0.736842  \n",
       "4  0.628316 -1.099  2.235  0.736842  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 45,
     "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, alongside the percentage of terminals in Europe that are considered in/out of the money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "13caa839",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x700 with 2 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",
    "fig2, ax2 = plt.subplots(figsize=(15, 7))\n",
    "\n",
    "ax2.plot(wtp_df['Release Date'], wtp_df['Ave'], color='steelblue', linewidth=2.0, zorder=0, label='European Average WTP')\n",
    "ax2.plot(wtp_df['Release Date'], wtp_df['Min'], color='steelblue', linewidth=1.0, alpha=0.06)\n",
    "ax2.plot(wtp_df['Release Date'], wtp_df['Max'], color='steelblue', linewidth=1.0, alpha=0.06)\n",
    "ax2.fill_between(wtp_df['Release Date'], wtp_df['Min'], wtp_df['Max'], color='steelblue', alpha=0.2)\n",
    "\n",
    "ax22 = ax2.twinx()\n",
    "\n",
    "ax22.plot(wtp_df['Release Date'], wtp_df['Fraction']*100, color='firebrick', linewidth=2.0, alpha=0.8, zorder=1, label='Perc. of Terminals `in the money`')\n",
    "ax22.yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "ax22.grid(False)\n",
    "\n",
    "plt.title('European Average WTP & Range vs Percentage of terminals `in the money`')\n",
    "ax2.legend(loc=2)\n",
    "ax22.legend(loc=0)\n",
    "\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
}