{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "92475c69",
   "metadata": {},
   "source": [
    "# Python API Example - Intraday Arb Breakevens\n",
    "\n",
    "Here we import Live, Historical and Revised prices from the Spark Intraday Arb Breakevens Python API. \n",
    "\n",
    "This guide is designed to provide an example of how to access the Spark API:\n",
    "- The path to your client credentials is the only input needed to run this script (just before Section 2)\n",
    "- This script has been designed to display the raw outputs of requests from the API, and then shows you how to format those outputs to enable easy reading and analysis\n",
    "- This script can be copied and pasted by customers for quick use of the API\n",
    "\n",
    "__N.B. This guide is just for Intraday Arb Breakevens data. If you're looking for other API data products (such as Freight routes or Netbacks), please refer to their according code example files.__ "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5716130",
   "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": 6,
   "id": "33fb0640",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from base64 import b64encode\n",
    "from pprint import pprint\n",
    "from urllib.parse import urljoin\n",
    "import datetime\n",
    "\n",
    "\n",
    "try:\n",
    "    from urllib import request, parse\n",
    "    from urllib.error import HTTPError\n",
    "except ImportError:\n",
    "    raise RuntimeError(\"Python 3 required\")\n",
    "\n",
    "\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(client_id, client_secret[:5])\n",
    "    )\n",
    "\n",
    "    return client_id, client_secret\n",
    "\n",
    "\n",
    "def do_api_post_query(uri, body, headers):\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",
    "\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",
    "\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:intraday\",\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\"]\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd3171a8",
   "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": 7,
   "id": "fd7e89bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>> Found credentials!\n",
      ">>>> Client_id=01c23590-ef6c-4a36-8237-c89c3f1a3b2a, client_secret=80763****\n",
      ">>>> Successfully fetched an access token eyJhb****, valid 604799 seconds.\n",
      "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiIwMWMyMzU5MC1lZjZjLTRhMzYtODIzNy1jODljM2YxYTNiMmEiLCJzdWJfdHlwZSI6Im9hdXRoLWNsaWVudCIsImV4cCI6MTc2MzIzMTU2MCwidHlwZSI6ImFjY2Vzc1Rva2VuIiwiaGFzaGVkX3NlY3JldCI6InBia2RmMl9zaGEyNTYkNjAwMDAwJGhNdEw0OWsxRlRpVXNMTjY2OWpzak8kdUJJc3FyaXlvU0dXMlNLUC9odUs0eHd5Nnh3dUM3TTVpR0ZGbjc3aXhLVT0iLCJvcmdfdXVpZCI6IjQ5MzhiMGJiLTVmMjctNDE2NC04OTM4LTUyNTdmYmQzNTNmZiIsImNsaWVudF90eXBlIjoib2F1dGgtY2xpZW50In0.6yT3K8YIABMi_o-Nf6Ye3a8yH3RpcLk6jI2y6op_FfI\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)\n",
    "print(access_token)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10bafcf1",
   "metadata": {},
   "source": [
    "# 2. Reference Data\n",
    "\n",
    "The Reference Data endpoint returns the UUID codes associated with each FoB Port available on the Arb Breakevens endpoint. \n",
    "\n",
    "If there are two arb breakevens available for a single FoB Port, e.g. \"Sabine Pass via COGH\" or \"Sabine Pass via Panama\", these are included as separate entries in the Reference Data response. The FoB Port UUID remains the same between these two entries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4d211cdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/beta/intraday/breakevens/reference-data/\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fobPortUuid</th>\n",
       "      <th>fobPortName</th>\n",
       "      <th>viaPoint</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>003f92ce-86d5-4d03-9761-311036c47812</td>\n",
       "      <td>Hammerfest</td>\n",
       "      <td>cogh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>003afd77-2b63-41e4-8b7a-6d6294236d78</td>\n",
       "      <td>Bethioua</td>\n",
       "      <td>cogh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>003dec0a-ce8f-41db-8c24-4d7ef6addf70</td>\n",
       "      <td>Sabine Pass</td>\n",
       "      <td>cogh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>003dec0a-ce8f-41db-8c24-4d7ef6addf70</td>\n",
       "      <td>Sabine Pass</td>\n",
       "      <td>panama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0034fc9c-bb57-42b6-a91b-6f08c3795a25</td>\n",
       "      <td>Bonny LNG</td>\n",
       "      <td>cogh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>003f9d1b-b4ad-4de9-8c8d-bd7fbcacd3df</td>\n",
       "      <td>Ras Laffan</td>\n",
       "      <td>cogh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>003342b7-ba5b-4f0e-b6df-4d95837a5691</td>\n",
       "      <td>Bintulu</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>00381c87-4180-4430-80f1-bf828099124f</td>\n",
       "      <td>NWS</td>\n",
       "      <td>cogh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            fobPortUuid  fobPortName viaPoint\n",
       "0  003f92ce-86d5-4d03-9761-311036c47812   Hammerfest     cogh\n",
       "1  003afd77-2b63-41e4-8b7a-6d6294236d78     Bethioua     cogh\n",
       "2  003dec0a-ce8f-41db-8c24-4d7ef6addf70  Sabine Pass     cogh\n",
       "3  003dec0a-ce8f-41db-8c24-4d7ef6addf70  Sabine Pass   panama\n",
       "4  0034fc9c-bb57-42b6-a91b-6f08c3795a25    Bonny LNG     cogh\n",
       "5  003f9d1b-b4ad-4de9-8c8d-bd7fbcacd3df   Ras Laffan     cogh\n",
       "6  003342b7-ba5b-4f0e-b6df-4d95837a5691      Bintulu     None\n",
       "7  00381c87-4180-4430-80f1-bf828099124f          NWS     cogh"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# define reference data function\n",
    "\n",
    "def fetch_reference_data(access_token):\n",
    "\n",
    "    uri=\"/beta/intraday/breakevens/reference-data/\"\n",
    "    print(uri)\n",
    "    \n",
    "    content = do_api_get_query(\n",
    "        uri,  access_token=access_token\n",
    "    )\n",
    "\n",
    "    return content\n",
    "\n",
    "# call data\n",
    "reference_raw = fetch_reference_data(access_token)\n",
    "\n",
    "# convert into Pandas DataFrame\n",
    "ref_df = pd.json_normalize(reference_raw['data'])\n",
    "ref_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1389e400",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "003dec0a-ce8f-41db-8c24-4d7ef6addf70\n"
     ]
    }
   ],
   "source": [
    "# Save your chosen FoB Port UUID as a variable\n",
    "my_fobport = ref_df[ref_df['fobPortName'] == 'Sabine Pass']['fobPortUuid'].iloc[0]\n",
    "\n",
    "print(my_fobport)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7138d5ee",
   "metadata": {},
   "source": [
    "# 3. Live Feed\n",
    "\n",
    "Here we define the function used to call the 'intraday/breakevens/live' endpoint. This endpoint retrieves the latest published curves from the Intraday platform, and takes 2 required parameters:\n",
    "\n",
    "- \"fob-port\": which FoB port you'd like to see Arb data for. UUIDs can be found in the reference data called above\n",
    "- \"via-point\": the corresponding via point for the chosen FoB port, used in either the NWE or NEA netback calculation (only if applicable)\n",
    "- \"breakeven-type\": which breakeven you'd like to retrieve ('jkm-ttf', 'jkm', 'ttf')\n",
    "- \"unit\": which unit you'd like the prices to be in ('usd-per-mmbtu')\n",
    "- \"percent-hire\": whether you'd like to include freight hire costs in the arb breakeven calculations (\"100\") or consider them as sunk costs (\"0\"). \"0\" and \"100\" are the only accepted values - any custom % value needs to be locally interpolated\n",
    "\n",
    "__N.B__ This function will only run once and retrieve the latest forward curve. For a continuous feed of prices, you need to repeatedly call this function every 10 minutes for each new price release"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b4186220",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/beta/intraday/breakevens/live/?fob-port=003dec0a-ce8f-41db-8c24-4d7ef6addf70&via-point=cogh&breakeven-type=jkm-ttf&unit=usd-per-mmbtu&percent-hire=100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'errors': [],\n",
       " 'data': [{'asOf': '2025-11-07T17:00:00Z',\n",
       "   'periodType': 'month',\n",
       "   'periodFrom': '2026-03-01',\n",
       "   'periodName': 'Mar26',\n",
       "   'percentHire': 100,\n",
       "   'value': '0.331',\n",
       "   'revision': 0},\n",
       "  {'asOf': '2025-11-07T17:00:00Z',\n",
       "   'periodType': 'month',\n",
       "   'periodFrom': '2026-04-01',\n",
       "   'periodName': 'Apr26',\n",
       "   'percentHire': 100,\n",
       "   'value': '0.201',\n",
       "   'revision': 0},\n",
       "  {'asOf': '2025-11-07T17:00:00Z',\n",
       "   'periodType': 'month',\n",
       "   'periodFrom': '2026-05-01',\n",
       "   'periodName': 'May26',\n",
       "   'percentHire': 100,\n",
       "   'value': '0.123',\n",
       "   'revision': 0},\n",
       "  {'asOf': '2025-11-07T17:00:00Z',\n",
       "   'periodType': 'month',\n",
       "   'periodFrom': '2026-06-01',\n",
       "   'periodName': 'Jun26',\n",
       "   'percentHire': 100,\n",
       "   'value': '0.085',\n",
       "   'revision': 0},\n",
       "  {'asOf': '2025-11-07T17:00:00Z',\n",
       "   'periodType': 'month',\n",
       "   'periodFrom': '2026-07-01',\n",
       "   'periodName': 'Jul26',\n",
       "   'percentHire': 100,\n",
       "   'value': '0.123',\n",
       "   'revision': 0},\n",
       "  {'asOf': '2025-11-07T17:00:00Z',\n",
       "   'periodType': 'month',\n",
       "   'periodFrom': '2026-08-01',\n",
       "   'periodName': 'Aug26',\n",
       "   'percentHire': 100,\n",
       "   'value': '0.238',\n",
       "   'revision': 0},\n",
       "  {'asOf': '2025-11-07T17:00:00Z',\n",
       "   'periodType': 'month',\n",
       "   'periodFrom': '2026-09-01',\n",
       "   'periodName': 'Sep26',\n",
       "   'percentHire': 100,\n",
       "   'value': '0.262',\n",
       "   'revision': 0},\n",
       "  {'asOf': '2025-11-07T17:00:00Z',\n",
       "   'periodType': 'month',\n",
       "   'periodFrom': '2026-10-01',\n",
       "   'periodName': 'Oct26',\n",
       "   'percentHire': 100,\n",
       "   'value': '0.227',\n",
       "   'revision': 0}],\n",
       " 'metaData': {'Unit': 'usd-per-mmbtu',\n",
       "  'BreakevenType': 'jkm-ttf',\n",
       "  'FobPortUUID': '003dec0a-ce8f-41db-8c24-4d7ef6addf70',\n",
       "  'ViaPoint': 'cogh',\n",
       "  'PercentHire': '100'},\n",
       " 'revisions': []}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Defining the function\n",
    "\n",
    "\n",
    "def fetch_live_releases(access_token, ticker, via, breakeven_type, unit='usd-per-mmbtu', percent_hire=100, format='json'):\n",
    "    query_params = \"?fob-port={}\".format(ticker)\n",
    "    query_params += \"&via-point={}\".format(via)\n",
    "    query_params += \"&breakeven-type={}\".format(breakeven_type)\n",
    "    query_params += \"&unit={}\".format(unit)\n",
    "    query_params += \"&percent-hire={}\".format(percent_hire)\n",
    "\n",
    "    my_uri=\"/beta/intraday/breakevens/live/{}\".format(query_params)\n",
    "    print(my_uri)\n",
    "    \n",
    "    content = do_api_get_query(\n",
    "        uri=my_uri, access_token=access_token, format=format\n",
    "    )\n",
    "    \n",
    "\n",
    "    return content\n",
    "\n",
    "# calling the function to fetch the latest curves\n",
    "live = fetch_live_releases(access_token, ticker='003dec0a-ce8f-41db-8c24-4d7ef6addf70', via='cogh', breakeven_type='jkm-ttf', unit='usd-per-mmbtu', percent_hire=100)\n",
    "live"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c08606c8",
   "metadata": {},
   "outputs": [
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       "    .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>asOf</th>\n",
       "      <th>periodType</th>\n",
       "      <th>periodFrom</th>\n",
       "      <th>periodName</th>\n",
       "      <th>percentHire</th>\n",
       "      <th>value</th>\n",
       "      <th>revision</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-11-07 17:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-03-01</td>\n",
       "      <td>Mar26</td>\n",
       "      <td>100</td>\n",
       "      <td>0.331</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-11-07 17:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>100</td>\n",
       "      <td>0.201</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-11-07 17:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-05-01</td>\n",
       "      <td>May26</td>\n",
       "      <td>100</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-11-07 17:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>Jun26</td>\n",
       "      <td>100</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-11-07 17:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>Jul26</td>\n",
       "      <td>100</td>\n",
       "      <td>0.123</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2025-11-07 17:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-08-01</td>\n",
       "      <td>Aug26</td>\n",
       "      <td>100</td>\n",
       "      <td>0.238</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2025-11-07 17:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-09-01</td>\n",
       "      <td>Sep26</td>\n",
       "      <td>100</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2025-11-07 17:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-10-01</td>\n",
       "      <td>Oct26</td>\n",
       "      <td>100</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 asOf periodType  periodFrom periodName  percentHire  value  \\\n",
       "0 2025-11-07 17:00:00      month  2026-03-01      Mar26          100  0.331   \n",
       "1 2025-11-07 17:00:00      month  2026-04-01      Apr26          100  0.201   \n",
       "2 2025-11-07 17:00:00      month  2026-05-01      May26          100  0.123   \n",
       "3 2025-11-07 17:00:00      month  2026-06-01      Jun26          100  0.085   \n",
       "4 2025-11-07 17:00:00      month  2026-07-01      Jul26          100  0.123   \n",
       "5 2025-11-07 17:00:00      month  2026-08-01      Aug26          100  0.238   \n",
       "6 2025-11-07 17:00:00      month  2026-09-01      Sep26          100  0.262   \n",
       "7 2025-11-07 17:00:00      month  2026-10-01      Oct26          100  0.227   \n",
       "\n",
       "   revision  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "5         0  \n",
       "6         0  \n",
       "7         0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert JSON to DataFrame\n",
    "live_df = pd.json_normalize(live['data'])\n",
    "live_df['asOf'] = pd.to_datetime(live_df['asOf'], utc=True).dt.tz_localize(None)\n",
    "live_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0798174",
   "metadata": {},
   "source": [
    "### Data included\n",
    "\n",
    "The JSON includes 4 fields:\n",
    "- 'errors': will be empty if the API call is successful, otherwise returns relevant error code\n",
    "- 'data': contains the full forward curve of the requested contract\n",
    "- 'revisions': contains any revisions made since the last price release - this can be for a revision for any historical price\n",
    "- 'metaData': provides details on the fetched data and time of API call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "690b953b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['errors', 'data', 'metaData', 'revisions'])\n",
      "{'Unit': 'usd-per-mmbtu', 'BreakevenType': 'jkm-ttf', 'FobPortUUID': '003dec0a-ce8f-41db-8c24-4d7ef6addf70', 'ViaPoint': 'cogh', 'PercentHire': '100'}\n"
     ]
    }
   ],
   "source": [
    "print(live.keys())\n",
    "print(live['metaData'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e27e05fb",
   "metadata": {},
   "source": [
    "# 4. Historical Data\n",
    "\n",
    "Here we define the function used to call the 'intraday/breakevens/historical' endpoint. This endpoint retrieves the historical published curves from the Intraday platform, and takes 4 required parameters:\n",
    "\n",
    "- \"fob-port\": which FoB port Arb Breakevens you'd like to pull\n",
    "- \"via-point\": the corresponding via point for the chosen FoB port, used in either the NWE or NEA netback calculation (only if applicable)\n",
    "- \"unit\": which unit you'd like the prices to be in ('usd-per-mmbtu')\n",
    "- \"breakeven-type\": which breakeven you'd like to retrieve ('jkm-ttf', 'jkm', 'ttf')\n",
    "- \"start\": which date you'd like the data to start from\n",
    "- \"end\": which date you'd like the data to run up until\n",
    "- \"percent-hire\": whether you'd like to include freight hire costs in the arb breakevens calculations (\"100\") or consider them as sunk costs (\"0\"). \"0\" and \"100\" are the only accepted values - any custom % value needs to be locally interpolated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "24adff55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/beta/intraday/breakevens/historical/?fob-port=003dec0a-ce8f-41db-8c24-4d7ef6addf70&via-point=cogh&breakeven-type=jkm-ttf&unit=usd-per-mmbtu&percent-hire=100&start=2025-10-01&end=2025-10-31\n"
     ]
    }
   ],
   "source": [
    "## Defining the function\n",
    "\n",
    "\n",
    "def fetch_historical_releases(access_token, ticker, via, breakeven_type, start, end, unit='usd-per-mmbtu', percent_hire=100, format='json'):\n",
    "    query_params = \"?fob-port={}\".format(ticker)\n",
    "    query_params += \"&via-point={}\".format(via)\n",
    "    query_params += \"&breakeven-type={}\".format(breakeven_type)\n",
    "    query_params += \"&unit={}\".format(unit)\n",
    "    query_params += \"&percent-hire={}\".format(percent_hire)\n",
    "\n",
    "    query_params += \"&start={}\".format(start)\n",
    "    query_params += \"&end={}\".format(end)\n",
    "\n",
    "    uri=\"/beta/intraday/breakevens/historical/{}\".format(query_params)\n",
    "    print(uri)\n",
    "    \n",
    "    content = do_api_get_query(\n",
    "        uri=uri, access_token=access_token, format=format\n",
    "    )\n",
    "\n",
    "    return content\n",
    "\n",
    "# call function to fetch historical data\n",
    "\n",
    "historical = fetch_historical_releases(access_token, ticker='003dec0a-ce8f-41db-8c24-4d7ef6addf70', via='cogh', breakeven_type='jkm-ttf', start='2025-10-01', end='2025-10-31', unit='usd-per-mmbtu', percent_hire=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2af8dff",
   "metadata": {},
   "source": [
    "### Data included\n",
    "\n",
    "The JSON includes 4 fields:\n",
    "- 'errors': will be empty if the API call is successful, otherwise returns relevant error code\n",
    "- 'data': contains the historical dataset of the requested contract\n",
    "- 'metaData': provides details on the fetched data and time of API call\n",
    "\n",
    "__N.B.__ for historical revisions, please refer to the \"Intraday Revisions\" code sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "24eb1a58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['errors', 'data', 'metaData'])\n",
      "{'unit': 'usd-per-mmbtu', 'BreakevenType': 'jkm-ttf', 'fobPortUuid': '003dec0a-ce8f-41db-8c24-4d7ef6addf70', 'viaPoint': 'cogh', 'percentHire': '100', 'start': '2025-10-01T00:00:00+01:00', 'end': '2025-10-31T00:00:00+00:00', 'visibility': 'complete'}\n"
     ]
    }
   ],
   "source": [
    "print(historical.keys())\n",
    "print(historical['metaData'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09efaf31",
   "metadata": {},
   "source": [
    "### Historical Data: DataFrame Conversion & Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2df46e36",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-10-01 06:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-02-01</td>\n",
       "      <td>Feb26</td>\n",
       "      <td>4</td>\n",
       "      <td>0.339</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-10-01 06:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-03-01</td>\n",
       "      <td>Mar26</td>\n",
       "      <td>5</td>\n",
       "      <td>0.321</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-10-01 06:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-10-01 06:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-05-01</td>\n",
       "      <td>May26</td>\n",
       "      <td>7</td>\n",
       "      <td>0.191</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-10-01 06:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-06-01</td>\n",
       "      <td>Jun26</td>\n",
       "      <td>8</td>\n",
       "      <td>0.122</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2025-10-01 06:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-07-01</td>\n",
       "      <td>Jul26</td>\n",
       "      <td>9</td>\n",
       "      <td>0.198</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2025-10-01 06:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-08-01</td>\n",
       "      <td>Aug26</td>\n",
       "      <td>10</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2025-10-01 06:10:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-02-01</td>\n",
       "      <td>Feb26</td>\n",
       "      <td>4</td>\n",
       "      <td>0.338</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2025-10-01 06:10:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-03-01</td>\n",
       "      <td>Mar26</td>\n",
       "      <td>5</td>\n",
       "      <td>0.318</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2025-10-01 06:10:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 asOf periodType  periodFrom periodName  monthIndex  value  \\\n",
       "0 2025-10-01 06:00:00      month  2026-02-01      Feb26           4  0.339   \n",
       "1 2025-10-01 06:00:00      month  2026-03-01      Mar26           5  0.321   \n",
       "2 2025-10-01 06:00:00      month  2026-04-01      Apr26           6  0.275   \n",
       "3 2025-10-01 06:00:00      month  2026-05-01      May26           7  0.191   \n",
       "4 2025-10-01 06:00:00      month  2026-06-01      Jun26           8  0.122   \n",
       "5 2025-10-01 06:00:00      month  2026-07-01      Jul26           9  0.198   \n",
       "6 2025-10-01 06:00:00      month  2026-08-01      Aug26          10  0.233   \n",
       "7 2025-10-01 06:10:00      month  2026-02-01      Feb26           4  0.338   \n",
       "8 2025-10-01 06:10:00      month  2026-03-01      Mar26           5  0.318   \n",
       "9 2025-10-01 06:10:00      month  2026-04-01      Apr26           6  0.273   \n",
       "\n",
       "   revision  \n",
       "0         0  \n",
       "1         0  \n",
       "2         0  \n",
       "3         0  \n",
       "4         0  \n",
       "5         0  \n",
       "6         0  \n",
       "7         0  \n",
       "8         0  \n",
       "9         0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting to Pandas DataFrame\n",
    "hist_df = pd.json_normalize(historical['data'])\n",
    "hist_df['asOf'] = pd.to_datetime(hist_df['asOf'], utc=True).dt.tz_localize(None)\n",
    "hist_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "22f3ae40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Apr26', 'Aug26', 'Feb26', 'Jul26', 'Jun26', 'Mar26', 'May26', 'Sep26']\n"
     ]
    },
    {
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       "      <th>2</th>\n",
       "      <td>2025-10-01 06:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2025-10-01 06:10:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.273</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2025-10-01 06:20:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2025-10-01 06:30:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2025-10-01 06:40:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2025-10-01 06:50:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2025-10-01 07:00:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2025-10-01 07:10:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.276</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>2025-10-01 07:20:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>2025-10-01 07:30:00</td>\n",
       "      <td>month</td>\n",
       "      <td>2026-04-01</td>\n",
       "      <td>Apr26</td>\n",
       "      <td>6</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  asOf periodType  periodFrom periodName  monthIndex  value  \\\n",
       "2  2025-10-01 06:00:00      month  2026-04-01      Apr26           6  0.275   \n",
       "9  2025-10-01 06:10:00      month  2026-04-01      Apr26           6  0.273   \n",
       "16 2025-10-01 06:20:00      month  2026-04-01      Apr26           6  0.276   \n",
       "23 2025-10-01 06:30:00      month  2026-04-01      Apr26           6  0.274   \n",
       "30 2025-10-01 06:40:00      month  2026-04-01      Apr26           6   0.27   \n",
       "37 2025-10-01 06:50:00      month  2026-04-01      Apr26           6  0.274   \n",
       "44 2025-10-01 07:00:00      month  2026-04-01      Apr26           6  0.276   \n",
       "51 2025-10-01 07:10:00      month  2026-04-01      Apr26           6  0.276   \n",
       "58 2025-10-01 07:20:00      month  2026-04-01      Apr26           6  0.274   \n",
       "65 2025-10-01 07:30:00      month  2026-04-01      Apr26           6  0.271   \n",
       "\n",
       "    revision  \n",
       "2          0  \n",
       "9          0  \n",
       "16         0  \n",
       "23         0  \n",
       "30         0  \n",
       "37         0  \n",
       "44         0  \n",
       "51         0  \n",
       "58         0  \n",
       "65         0  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group dataframe by \"Contract\" field\n",
    "contract_groups = hist_df.groupby('periodName')\n",
    "\n",
    "# list available contracts (not listed chronologically)\n",
    "contract_keys = list(contract_groups.groups.keys())\n",
    "print(contract_keys)\n",
    "\n",
    "# Fetch historical prices for a specific contract\n",
    "cdf = contract_groups.get_group(contract_keys[0])\n",
    "cdf.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3fa015e",
   "metadata": {},
   "source": [
    "## N.B. Historical Data Limits\n",
    "\n",
    "Currently, a maximum of 30 historical days 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 days at a time. To call more history, increase the 'loops' parameter in the first line of the code. The 'loops' parameter describes the number of historical data requests to be executed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "129599ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loop_historical_data(token, hist_fob, hist_via, hist_unit, hist_start, hist_end, hist_percent_hire=100):\n",
    "    \n",
    "    # calculating the amount of days to retrieve data for\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",
    "    # iterating through the historical data, 20 days at a time\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(20, unit='days'), '%Y-%m-%d')\n",
    "            historical = fetch_historical_releases(access_token, ticker=hist_fob, via=hist_via, unit=hist_unit, start=hist_start, end=diff_end, percent_hire=hist_percent_hire)\n",
    "            hist_df = pd.json_normalize(historical['data'])\n",
    "\n",
    "            starts.append(hist_start)\n",
    "            ends.append(diff_end)\n",
    "\n",
    "        else:\n",
    "            if t < hist_diff-20:\n",
    "                diff_start = datetime.datetime.strftime(datetime.datetime.strptime(hist_start, '%Y-%m-%d') + pd.Timedelta(t, unit='days'), '%Y-%m-%d')\n",
    "                diff_end = datetime.datetime.strftime(datetime.datetime.strptime(diff_start, '%Y-%m-%d') + pd.Timedelta(20, unit='days'), '%Y-%m-%d')\n",
    "                historical = fetch_historical_releases(access_token, ticker=hist_fob, via=hist_via, unit=hist_unit, start=diff_start, end=diff_end, percent_hire=hist_percent_hire)\n",
    "                hist_df = pd.concat([hist_df,pd.json_normalize(historical['data'])])\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, 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 = fetch_historical_releases(access_token, ticker=hist_fob, via=hist_via, unit=hist_unit, start=diff_start, end=diff_end, percent_hire=hist_percent_hire)\n",
    "                hist_df = pd.concat([hist_df,pd.json_normalize(historical['data'])])\n",
    "                starts.append(hist_start)\n",
    "                ends.append(diff_end)\n",
    "        \n",
    "        t += 20\n",
    "\n",
    "    hist_df['value'] = pd.to_numeric(hist_df['value'])\n",
    "    hist_df['asOf'] = pd.to_datetime(hist_df['asOf'], utc=True).dt.tz_localize(None)\n",
    "\n",
    "    return hist_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a3ac2ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calling the extended historical data function\n",
    "\n",
    "#hdf = loop_historical_data(access_token, hist_fob='003dec0a-ce8f-41db-8c24-4d7ef6addf70', hist_via='cogh', \n",
    "#                            hist_unit='usd-per-mmbtu', hist_start='2025-08-01', hist_end='2025-10-28', hist_percent_hire=100)\n",
    "#hdf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "922a757e",
   "metadata": {},
   "source": [
    "# 5. Revisions\n",
    "\n",
    "Here we define the function used to call the 'intraday/breakevens/revisions' endpoint. This endpoint retrieves the historical price revisions from the Intraday platform, and takes 2 required parameters:\n",
    "\n",
    "- \"fob-port\": which FoB port you'd like to see Arb Breakevens data for. UUIDs can be found in the reference data called above\n",
    "- \"via-point\": the corresponding via point for the chosen FoB port, used in either the NWE or NEA netback calculation (only if applicable)\n",
    "- \"unit\": which unit you'd like the prices to be in ('usd-per-mmbtu')\n",
    "- \"percent-hire\": whether you'd like to include freight hire costs in the arb breakeven calculations (\"100\") or consider them as sunk costs (\"0\"). \"0\" and \"100\" are the only accepted values - any custom % value needs to be locally interpolated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "054ccebe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/beta/intraday/breakevens/revisions/?fob-port=003dec0a-ce8f-41db-8c24-4d7ef6addf70&via-point=cogh&breakeven-type=jkm-ttf&unit=usd-per-mmbtu&percent-hire=100\n"
     ]
    }
   ],
   "source": [
    "def fetch_historical_revisions(access_token, ticker, via, breakeven_type, unit='usd-per-mmbtu', percent_hire=100, format='json'):\n",
    "    query_params = \"?fob-port={}\".format(ticker)\n",
    "    query_params += \"&via-point={}\".format(via)\n",
    "    query_params += \"&breakeven-type={}\".format(breakeven_type)\n",
    "    query_params += \"&unit={}\".format(unit)\n",
    "    query_params += \"&percent-hire={}\".format(percent_hire)\n",
    "\n",
    "    uri=\"/beta/intraday/breakevens/revisions/{}\".format(query_params)\n",
    "    print(uri)\n",
    "    \n",
    "    content = do_api_get_query(\n",
    "        uri,  access_token=access_token, format=format\n",
    "    )\n",
    "\n",
    "    return content\n",
    "\n",
    "revs = fetch_historical_revisions(access_token, ticker=my_fobport, via='cogh', breakeven_type='jkm-ttf', unit='usd-per-mmbtu', percent_hire=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45988eac",
   "metadata": {},
   "source": [
    "__N.B.__\n",
    "\n",
    "Revisions are only stored in this endpoint for all revisions published since the start of the previous business day. If you'd like to check for any price revisions published earlier than this, we recommend downloading our historical datasets as these will have the most up-to-date prices (including revised prices)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c19aff5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'errors': [],\n",
       " 'data': [{'as_of': '2025-11-05T07:00:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2026-10-01',\n",
       "   'period_name': 'Oct26',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.243',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:00:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2026-11-01',\n",
       "   'period_name': 'Nov26',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.199',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:00:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2026-12-01',\n",
       "   'period_name': 'Dec26',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.317',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:00:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-01-01',\n",
       "   'period_name': 'Jan27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.516',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:00:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-02-01',\n",
       "   'period_name': 'Feb27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.581',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:00:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-03-01',\n",
       "   'period_name': 'Mar27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.87',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:00:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-04-01',\n",
       "   'period_name': 'Apr27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.69',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:00:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-05-01',\n",
       "   'period_name': 'May27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.429',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:00:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-06-01',\n",
       "   'period_name': 'Jun27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.219',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:00:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-07-01',\n",
       "   'period_name': 'Jul27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.317',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:00:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-08-01',\n",
       "   'period_name': 'Aug27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.408',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:00:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-09-01',\n",
       "   'period_name': 'Sep27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.498',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:10:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2026-10-01',\n",
       "   'period_name': 'Oct26',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.261',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:10:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2026-11-01',\n",
       "   'period_name': 'Nov26',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.115',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:10:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2026-12-01',\n",
       "   'period_name': 'Dec26',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.264',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:10:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-01-01',\n",
       "   'period_name': 'Jan27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.583',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:10:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-02-01',\n",
       "   'period_name': 'Feb27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.592',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:10:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-03-01',\n",
       "   'period_name': 'Mar27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.826',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:10:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-04-01',\n",
       "   'period_name': 'Apr27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.655',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:10:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-05-01',\n",
       "   'period_name': 'May27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.488',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:10:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-06-01',\n",
       "   'period_name': 'Jun27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.313',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:10:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-07-01',\n",
       "   'period_name': 'Jul27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.278',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:10:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-08-01',\n",
       "   'period_name': 'Aug27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.294',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:10:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-09-01',\n",
       "   'period_name': 'Sep27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.441',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:20:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2026-10-01',\n",
       "   'period_name': 'Oct26',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.245',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:20:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2026-11-01',\n",
       "   'period_name': 'Nov26',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.172',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:20:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2026-12-01',\n",
       "   'period_name': 'Dec26',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.31',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:20:00Z',\n",
       "   'period_type': 'month',\n",
       "   'period_from': '2027-01-01',\n",
       "   'period_name': 'Jan27',\n",
       "   'percent_hire': 100,\n",
       "   'value': '0.537',\n",
       "   'revision': 1,\n",
       "   'revision_dt': '2025-11-06T18:25:31.388687Z'},\n",
       "  {'as_of': '2025-11-05T07:20:00Z',\n",
       "   'period_type': 'month',\n",
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     "metadata": {},
     "output_type": "execute_result"
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