{
 "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": 34,
   "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": 35,
   "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, format='json'):\n",
    "    \"\"\"\n",
    "    After receiving an Access Token, we can request information from the API.\n",
    "    \"\"\"\n",
    "    url = urljoin(API_BASE_URL, uri)\n",
    "    print(url)\n",
    "\n",
    "    if format == 'json':\n",
    "        headers = {\n",
    "            \"Authorization\": \"Bearer {}\".format(access_token),\n",
    "            \"Accept\": \"application/json\",\n",
    "        }\n",
    "    elif format == 'csv':\n",
    "        headers = {\n",
    "            \"Authorization\": \"Bearer {}\".format(access_token),\n",
    "            \"Accept\": \"text/csv\"\n",
    "        }\n",
    "\n",
    "    # HTTP POST 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",
    "    status = response.status\n",
    "    print(status)\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",
    "    # Storing response based on requested format\n",
    "    if format == 'json':\n",
    "        content = json.loads(resp_content)\n",
    "    elif format == 'csv':\n",
    "        content = 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": 37,
   "id": "35759cdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import StringIO\n",
    "\n",
    "## Defining the function\n",
    "\n",
    "def fetch_historical_price_releases(access_token, unit, latest=False, start=None, end=None, limit=None, offset=None, terminal_uuid=None, format='json'):\n",
    "    \n",
    "    # if latest=True, set query_params to call the \".../latest/\" endpoint\n",
    "    if latest == True:\n",
    "        query_params = 'latest/'\n",
    "        query_params += \"?unit={}\".format(unit)\n",
    "    \n",
    "    else:\n",
    "        query_params = \"?unit={}\".format(unit)\n",
    "\n",
    "    if start is not None:\n",
    "        query_params += \"&start={}\".format(start)\n",
    "    if end is not None:\n",
    "        query_params += \"&end={}\".format(end)\n",
    "    \n",
    "    if terminal_uuid is not None:\n",
    "        query_params += \"&terminal-uuid={}\".format(terminal_uuid)\n",
    "    \n",
    "\n",
    "    uri = \"/v1.0/lng/access/des-hub-netbacks/{}\".format(query_params)\n",
    "\n",
    "    content = do_api_get_query(\n",
    "        uri=uri, access_token=access_token, format=format\n",
    "    )\n",
    "\n",
    "    if format == 'json':\n",
    "        data = content\n",
    "    elif format == 'csv':\n",
    "        # if there's no data to show, returns raw response (empty string) and \"No Data to Show\" message\n",
    "        if len(content) == 0:\n",
    "            data = content\n",
    "            print('No Data to Show')\n",
    "        else:\n",
    "            data = content.decode('utf-8')\n",
    "            data = pd.read_csv(StringIO(data))  # automatically converting into a Pandas DataFrame when choosing CSV format\n",
    "\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aa81e2c",
   "metadata": {},
   "source": [
    "### Historical Data Function\n",
    "\n",
    "Currently, a maximum of 1 year's worth of historical data 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: the function calls the historical data 1 year at a time and combines the data into one Pandas DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9fae6c01",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "\n",
    "def loop_historical_data(access_token, hist_unit, hist_start, hist_end):\n",
    "    \n",
    "    hist_diff = (datetime.datetime.strptime(hist_end, '%Y-%m-%d') - datetime.datetime.strptime(hist_start, '%Y-%m-%d')).days\n",
    "    t = 0\n",
    "\n",
    "    starts = []\n",
    "    ends = []\n",
    "\n",
    "    w = 365\n",
    "\n",
    "    while t < hist_diff:\n",
    "        if t == 0:\n",
    "            diff_end = datetime.datetime.strftime(datetime.datetime.strptime(hist_start, '%Y-%m-%d') + pd.Timedelta(w, unit='days'), '%Y-%m-%d')\n",
    "            hist_df = fetch_historical_price_releases(access_token, unit=hist_unit, start=hist_start, end=diff_end, format='csv')\n",
    "\n",
    "            starts.append(hist_start)\n",
    "            ends.append(diff_end)\n",
    "\n",
    "        else:\n",
    "            if t < hist_diff-w:\n",
    "                diff_start = datetime.datetime.strftime(datetime.datetime.strptime(hist_start, '%Y-%m-%d') + pd.Timedelta(t+1, unit='days'), '%Y-%m-%d')\n",
    "                diff_end = datetime.datetime.strftime(datetime.datetime.strptime(diff_start, '%Y-%m-%d') + pd.Timedelta(w, unit='days'), '%Y-%m-%d')\n",
    "                historical_addition = fetch_historical_price_releases(access_token, unit=hist_unit, start=diff_start, end=diff_end, format='csv')\n",
    "                try:\n",
    "                    hist_df = pd.concat([hist_df,historical_addition])\n",
    "                #exception if hist_df is empty\n",
    "                except:\n",
    "                    hist_df = historical_addition.copy()\n",
    "                starts.append(hist_start)\n",
    "                ends.append(diff_end)\n",
    "            else:\n",
    "                diff_start = datetime.datetime.strftime(datetime.datetime.strptime(hist_start, '%Y-%m-%d') + pd.Timedelta(t+1, unit='days'), '%Y-%m-%d')\n",
    "                diff_end = datetime.datetime.strftime(datetime.datetime.strptime(diff_start, '%Y-%m-%d') + pd.Timedelta(hist_diff-t, unit='days'), '%Y-%m-%d')\n",
    "                historical_addition = fetch_historical_price_releases(access_token, unit=hist_unit, start=diff_start, end=diff_end, format='csv')\n",
    "                hist_df = pd.concat([hist_df, historical_addition])\n",
    "                starts.append(hist_start)\n",
    "                ends.append(diff_end)\n",
    "        \n",
    "        #looping by year\n",
    "        t += w\n",
    "\n",
    "    for c in list(hist_df.columns)[8:]:\n",
    "        hist_df[c] = pd.to_numeric(hist_df[c])\n",
    "    hist_df['ReleaseDate'] = pd.to_datetime(hist_df['ReleaseDate'])\n",
    "\n",
    "    return hist_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8fcf6066",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://api.sparkcommodities.com/v1.0/lng/access/des-hub-netbacks/?unit=usd-per-mmbtu&start=2024-01-01&end=2024-12-31\n",
      "200\n",
      "https://api.sparkcommodities.com/v1.0/lng/access/des-hub-netbacks/?unit=usd-per-mmbtu&start=2025-01-01&end=2026-01-01\n",
      "200\n",
      "https://api.sparkcommodities.com/v1.0/lng/access/des-hub-netbacks/?unit=usd-per-mmbtu&start=2026-01-01&end=2026-01-11\n",
      "200\n"
     ]
    },
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       "      <td>0.300</td>\n",
       "      <td>0.028</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  ReleaseDate DeliveryMonth DeliveryMonthName DeliveryMonthIndex  \\\n",
       "0  2024-12-31    2025-02-01             Feb25                M+1   \n",
       "1  2024-12-31    2025-03-01             Mar25                M+2   \n",
       "2  2024-12-31    2025-04-01             Apr25                M+3   \n",
       "3  2024-12-31    2025-05-01             May25                M+4   \n",
       "4  2024-12-31    2025-06-01             Jun25                M+5   \n",
       "\n",
       "                           TerminalUUID   TerminalName  \\\n",
       "0  00317185-978a-4df5-970c-2c28d3ab893c  Isle of Grain   \n",
       "1  00317185-978a-4df5-970c-2c28d3ab893c  Isle of Grain   \n",
       "2  00317185-978a-4df5-970c-2c28d3ab893c  Isle of Grain   \n",
       "3  00317185-978a-4df5-970c-2c28d3ab893c  Isle of Grain   \n",
       "4  00317185-978a-4df5-970c-2c28d3ab893c  Isle of Grain   \n",
       "\n",
       "  GasHubPriceSourceFrontMonth GasHubPriceSourceForwardCurve  NetbackTTFBasis  \\\n",
       "0                           m                           ukd           -0.871   \n",
       "1                           m                           ukd           -1.139   \n",
       "2                           m                           ukd           -1.196   \n",
       "3                           m                           ukd           -1.262   \n",
       "4                           m                           ukd           -1.436   \n",
       "\n",
       "   NetbackOutright  ...  SlotBerth  SlotBerthUnloadStorageRegas  \\\n",
       "0           13.966  ...        NaN                          NaN   \n",
       "1           13.765  ...        NaN                          NaN   \n",
       "2           13.648  ...        NaN                          NaN   \n",
       "3           13.536  ...        NaN                          NaN   \n",
       "4           13.353  ...        NaN                          NaN   \n",
       "\n",
       "   SlotLtCapacityEstimate  AdditionalStorage  AdditionalSendout  \\\n",
       "0                   0.305                NaN                NaN   \n",
       "1                   0.305                NaN                NaN   \n",
       "2                   0.305                NaN                NaN   \n",
       "3                   0.305                NaN                NaN   \n",
       "4                   0.305                NaN                NaN   \n",
       "\n",
       "   FuelGasLossesGasInKind  EntryCapacity  Emissions  Power  EntryVariable  \n",
       "0                   0.231           0.48      0.036  0.228          0.028  \n",
       "1                   0.228           0.48      0.036  0.224          0.028  \n",
       "2                   0.226           0.48      0.036  0.195          0.028  \n",
       "3                   0.224           0.48      0.036  0.226          0.028  \n",
       "4                   0.223           0.48      0.036  0.300          0.028  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deshub_df = loop_historical_data(access_token, hist_unit='usd-per-mmbtu', hist_start='2024-01-01', hist_end='2026-01-10')\n",
    "deshub_df.head(5)"
   ]
  },
  {
   "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": 40,
   "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 referred to as the Moneyness metric\n",
    "- Calculating the percentage of terminals in/out of the money historically\n",
    "- Plotting this fractional Moneyness historical evolution over time"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86be1584",
   "metadata": {},
   "source": [
    "### Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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",
    "deshub_terms = list(deshub_df['TerminalName'].unique())\n",
    "terms = deshub_terms.copy()\n",
    "#terms = ['gate', 'dunkerque', 'zeebrugge']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec85e243",
   "metadata": {},
   "source": [
    "### Data Calling & Analytical Procedures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6fb701b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>> Get price releases for sparknwe-b-f\n",
      "https://api.sparkcommodities.com/v1.0/contracts/sparknwe-b-f/price-releases/?limit=2000\n",
      "200\n",
      ">>>> Get price releases for sparkswe-b-f\n",
      "https://api.sparkcommodities.com/v1.0/contracts/sparkswe-b-f/price-releases/?limit=2000\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "# Import NWE/SWE LNG prices\n",
    "sparknwe = cargo_to_dataframe(access_token, 'sparknwe', 2000, month=month)\n",
    "sparkswe = cargo_to_dataframe(access_token, 'sparkswe', 2000, 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": null,
   "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' Moneyness\n",
    "terminal_region_dict = {\n",
    "    'Gate': 'nwe',\n",
    "    'Isle of Grain': 'nwe',\n",
    "    'Zeebrugge': 'nwe',\n",
    "    'South Hook': 'nwe',\n",
    "    'Dunkerque': 'nwe',\n",
    "    'Le Havre': 'nwe',\n",
    "    'Montoir': 'nwe',\n",
    "    'EemsEnergyTerminal': 'nwe',\n",
    "    'Brunsbuttel': 'nwe',\n",
    "    'Deutsche Ostsee': 'nwe',\n",
    "    'Deutsche Ostsee Phase 1': 'nwe',\n",
    "    'Wilhelmshaven 1': 'nwe',\n",
    "    'Wilhelmshaven 2': 'nwe',\n",
    "    'Fos Cavaou': 'swe',\n",
    "    'Adriatic': 'swe',\n",
    "    'OLT Toscana': 'swe',\n",
    "    'Piombino': 'swe',\n",
    "    'Ravenna': 'swe',\n",
    "    'Spain TVB': 'swe'\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "83282879",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting which costs to consider sunk and using a boolean variable to control whether these costs are removed from the DES Hub Netback values\n",
    "\n",
    "sunk_bool = True\n",
    "\n",
    "sunk_costs_list = ['SlotUnloadStorageRegas','SlotBerth', 'SlotBerthUnloadStorageRegas', 'SlotLtCapacityEstimate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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 \"ReleaseDate\" and \"DeliveryMonthIndex\" columns as Gate has the longest historical dataset\n",
    "month_df = deshub_df[(deshub_df['TerminalName'] == 'Gate') & (deshub_df['DeliveryMonthIndex'] == month)][['ReleaseDate', 'DeliveryMonthIndex', 'NetbackTTFBasis']]\n",
    "\n",
    "# Removing sunk costs for Gate only\n",
    "if sunk_bool == True:\n",
    "    month_df['NetbackTTFBasis'] = month_df['NetbackTTFBasis'] +  \\\n",
    "        deshub_df[(deshub_df['TerminalName'] == 'Gate') & (deshub_df['DeliveryMonthIndex'] == month)][sunk_costs_list].sum(axis=1)\n",
    "\n",
    "month_df = month_df.rename(columns={'NetbackTTFBasis':'Gate'})\n",
    "\n",
    "# defining a new list of terminals without \"Gate\" in it\n",
    "terms2 = [x if x != 'Gate' else None for x in deshub_terms]\n",
    "\n",
    "# iterating through list of terminals and adding data to the Terminal Moneyness dataframe\n",
    "for t in terms2:\n",
    "    if t is not None:\n",
    "        tdf = deshub_df[(deshub_df['TerminalName'] == t) & (deshub_df['DeliveryMonthIndex'] == month)][['ReleaseDate', 'NetbackTTFBasis']]\n",
    "        if sunk_bool == True:\n",
    "            tdf['NetbackTTFBasis'] = tdf['NetbackTTFBasis'] +  \\\n",
    "            deshub_df[(deshub_df['TerminalName'] == t) & (deshub_df['DeliveryMonthIndex'] == month)][sunk_costs_list].sum(axis=1)\n",
    "        month_df = month_df.merge(tdf, on='ReleaseDate', how='left')\n",
    "        month_df = month_df.rename(columns={'NetbackTTFBasis':t})\n",
    "\n",
    "# Calculating the Average, Minimum and Maximum Moneyness values for all terminals (i.e. for Europe)\n",
    "month_df['Ave'] = month_df[deshub_terms].mean(axis=1)\n",
    "month_df['Min'] = month_df[deshub_terms].min(axis=1)\n",
    "month_df['Max'] = month_df[deshub_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 = pd.merge(month_df,cargo_df, how='left', left_on='ReleaseDate', right_on='Release Date')\n",
    "month_df['SparkNWE'] = month_df['SparkNWE'].bfill().copy()\n",
    "month_df['SparkSWE'] = month_df['SparkSWE'].bfill().copy()\n",
    "\n",
    "month_df = month_df.sort_values('ReleaseDate', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "26c07bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a Moneyness 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",
    "money_df = month_df[['Release Date', 'DeliveryMonthIndex', 'SparkNWE', 'SparkSWE']].copy()\n",
    "\n",
    "for t in terms:\n",
    "    if terminal_region_dict[t] == 'nwe':\n",
    "        money_df[t] = month_df[t].copy() - month_df['SparkNWE'].copy()\n",
    "    elif terminal_region_dict[t] == 'swe':\n",
    "        money_df[t] = month_df[t].copy() - month_df['SparkSWE'].copy()\n",
    "    else:\n",
    "        money_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 Moneyness metric over all terminals\n",
    "money_df['Ave'] = money_df[terms].mean(axis=1)\n",
    "money_df['Min'] = money_df[terms].min(axis=1)\n",
    "money_df['Max'] = money_df[terms].max(axis=1)\n",
    "    \n",
    "# Calculate fraction of terminals in the money\n",
    "money_df['Fraction'] = money_df[terms].gt(0).sum(axis=1)/(len(terms)-money_df.isna().sum(axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>DeliveryMonthIndex</th>\n",
       "      <th>SparkNWE</th>\n",
       "      <th>SparkSWE</th>\n",
       "      <th>Isle of Grain</th>\n",
       "      <th>Zeebrugge</th>\n",
       "      <th>Spain TVB</th>\n",
       "      <th>Dunkerque</th>\n",
       "      <th>Brunsbuttel</th>\n",
       "      <th>OLT Toscana</th>\n",
       "      <th>...</th>\n",
       "      <th>South Hook</th>\n",
       "      <th>Deutsche Ostsee</th>\n",
       "      <th>Gate</th>\n",
       "      <th>Deutsche Ostsee Phase 1</th>\n",
       "      <th>Ravenna</th>\n",
       "      <th>Wilhelmshaven 2</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>507</th>\n",
       "      <td>2026-01-09</td>\n",
       "      <td>M+1</td>\n",
       "      <td>-0.530</td>\n",
       "      <td>-0.535</td>\n",
       "      <td>-0.261</td>\n",
       "      <td>0.347</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.185</td>\n",
       "      <td>0.776</td>\n",
       "      <td>1.077</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.183</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.996</td>\n",
       "      <td>0.547</td>\n",
       "      <td>0.391944</td>\n",
       "      <td>-0.261</td>\n",
       "      <td>1.133</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>2026-01-08</td>\n",
       "      <td>M+1</td>\n",
       "      <td>-0.545</td>\n",
       "      <td>-0.535</td>\n",
       "      <td>-0.242</td>\n",
       "      <td>0.364</td>\n",
       "      <td>0.180</td>\n",
       "      <td>0.199</td>\n",
       "      <td>0.767</td>\n",
       "      <td>1.080</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.166</td>\n",
       "      <td>0.415</td>\n",
       "      <td>0.133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.001</td>\n",
       "      <td>0.541</td>\n",
       "      <td>0.399111</td>\n",
       "      <td>-0.242</td>\n",
       "      <td>1.137</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>2026-01-07</td>\n",
       "      <td>M+1</td>\n",
       "      <td>-0.540</td>\n",
       "      <td>-0.535</td>\n",
       "      <td>-0.214</td>\n",
       "      <td>0.362</td>\n",
       "      <td>0.178</td>\n",
       "      <td>0.194</td>\n",
       "      <td>0.762</td>\n",
       "      <td>1.077</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.134</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.126</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.995</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.396667</td>\n",
       "      <td>-0.214</td>\n",
       "      <td>1.133</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>2026-01-06</td>\n",
       "      <td>M+1</td>\n",
       "      <td>-0.535</td>\n",
       "      <td>-0.535</td>\n",
       "      <td>-0.196</td>\n",
       "      <td>0.353</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.187</td>\n",
       "      <td>0.744</td>\n",
       "      <td>1.081</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000</td>\n",
       "      <td>0.518</td>\n",
       "      <td>0.394167</td>\n",
       "      <td>-0.196</td>\n",
       "      <td>1.137</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>2026-01-05</td>\n",
       "      <td>M+1</td>\n",
       "      <td>-0.535</td>\n",
       "      <td>-0.490</td>\n",
       "      <td>-0.222</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0.207</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.741</td>\n",
       "      <td>0.977</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.146</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0.122</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.898</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0.368611</td>\n",
       "      <td>-0.222</td>\n",
       "      <td>1.034</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Release Date DeliveryMonthIndex  SparkNWE  SparkSWE  Isle of Grain  \\\n",
       "507   2026-01-09                M+1    -0.530    -0.535         -0.261   \n",
       "508   2026-01-08                M+1    -0.545    -0.535         -0.242   \n",
       "509   2026-01-07                M+1    -0.540    -0.535         -0.214   \n",
       "510   2026-01-06                M+1    -0.535    -0.535         -0.196   \n",
       "511   2026-01-05                M+1    -0.535    -0.490         -0.222   \n",
       "\n",
       "     Zeebrugge  Spain TVB  Dunkerque  Brunsbuttel  OLT Toscana  ...  \\\n",
       "507      0.347      0.173      0.185        0.776        1.077  ...   \n",
       "508      0.364      0.180      0.199        0.767        1.080  ...   \n",
       "509      0.362      0.178      0.194        0.762        1.077  ...   \n",
       "510      0.353      0.167      0.187        0.744        1.081  ...   \n",
       "511      0.321      0.207      0.197        0.741        0.977  ...   \n",
       "\n",
       "     South Hook  Deutsche Ostsee   Gate  Deutsche Ostsee Phase 1  Ravenna  \\\n",
       "507      -0.183            0.420  0.118                      NaN    0.996   \n",
       "508      -0.166            0.415  0.133                      NaN    1.001   \n",
       "509      -0.134            0.404  0.126                      NaN    0.995   \n",
       "510      -0.117            0.391  0.121                      NaN    1.000   \n",
       "511      -0.146            0.391  0.122                      NaN    0.898   \n",
       "\n",
       "     Wilhelmshaven 2       Ave    Min    Max  Fraction  \n",
       "507            0.547  0.391944 -0.261  1.133  0.833333  \n",
       "508            0.541  0.399111 -0.242  1.137  0.833333  \n",
       "509            0.530  0.396667 -0.214  1.133  0.833333  \n",
       "510            0.518  0.394167 -0.196  1.137  0.833333  \n",
       "511            0.517  0.368611 -0.222  1.034  0.833333  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "money_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e86c4f",
   "metadata": {},
   "source": [
    "### Plotting\n",
    "\n",
    "Plotting the average European Moneyness and min/max range, alongside the percentage of terminals in Europe that are considered in/out of the money"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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(money_df['Release Date'], money_df['Ave'], color='steelblue', linewidth=2.0, zorder=0, label='European Average Moneyness')\n",
    "ax2.plot(money_df['Release Date'], money_df['Min'], color='steelblue', linewidth=1.0, alpha=0.06)\n",
    "ax2.plot(money_df['Release Date'], money_df['Max'], color='steelblue', linewidth=1.0, alpha=0.06)\n",
    "ax2.fill_between(money_df['Release Date'], money_df['Min'], money_df['Max'], color='steelblue', alpha=0.2)\n",
    "\n",
    "ax22 = ax2.twinx()\n",
    "\n",
    "ax22.plot(money_df['Release Date'], money_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 Moneyness 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
}