{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "59d19f73",
   "metadata": {},
   "source": [
    "# US Front Month Historical Arb\n",
    "\n",
    "This script uses the Spark API to plot the US Front Month Historical Arb across different via-points, as well as the most current forward curve.\n",
    "\n",
    "This script uses elements from our API code samples. If you'd like a more basic and informative example of how to pull data via the Spark API, please visit our API website:\n",
    "\n",
    "- API Website: https://www.sparkcommodities.com/api/code-examples/jupyter.html\n",
    "\n",
    "\n",
    "### 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",
    "\n",
    "or refer to our API website for more information about this endpoint:\n",
    "https://www.sparkcommodities.com/api/lng-cargo/netbacks.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e00ae34",
   "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": null,
   "id": "d9ea2c58",
   "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",
    "from io import StringIO\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",
    "    else:\n",
    "        raise ValueError('The format parameter only takes `csv` or `json` as inputs')\n",
    "\n",
    "    # HTTP GET request\n",
    "    req = request.Request(url, headers=headers)\n",
    "    try:\n",
    "        response = request.urlopen(req)\n",
    "    except HTTPError as e:\n",
    "        print(\"HTTP Error: \", e.code)\n",
    "        print(e.read())\n",
    "        sys.exit(1)\n",
    "\n",
    "    resp_content = response.read()\n",
    "    #status = response.status\n",
    "\n",
    "    # The server must return HTTP 200. 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` 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\": \"Basic {}\".format(b64encode(payload).decode()),\n",
    "        \"Accept\": \"application/json\",\n",
    "        \"Content-Type\": \"application/json\",\n",
    "    }\n",
    "    body = {\n",
    "        \"grantType\": \"clientCredentials\",\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",
    "\n",
    "# Define the function for listing all netbacks\n",
    "def list_netbacks(access_token):\n",
    "\n",
    "    content = do_api_get_query(\n",
    "        uri=\"/v1.0/netbacks/reference-data/\", access_token=access_token\n",
    "    )\n",
    "\n",
    "    # retrieve release dates separately\n",
    "    reldates = content[\"data\"][\"staticData\"][\"sparkReleases\"]\n",
    "\n",
    "    # return reference data\n",
    "    refdata_dict = content[\"data\"]\n",
    "\n",
    "    return reldates, refdata_dict\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e890e9e",
   "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": "51b8a89c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input the 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": "code",
   "execution_count": null,
   "id": "94023c5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch reference data:\n",
    "reldates, refdata_dict = list_netbacks(access_token)\n",
    "\n",
    "# formatting as a Dataframe\n",
    "available_df = pd.json_normalize(refdata_dict['staticData']['fobPorts'])\n",
    "available_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d262ca9",
   "metadata": {},
   "source": [
    "## Data Import Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "912d9c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import StringIO\n",
    "\n",
    "def fetch_netback(access_token, ticker, release=None, via=None, laden=None, ballast=None, start=None, end=None, format='json'):\n",
    "    \n",
    "    query_params = \"?fob-port={}\".format(ticker)\n",
    "    if release is not None:\n",
    "        query_params += \"&release-date={}\".format(release)\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",
    "    if via is not None:\n",
    "        query_params += \"&via-point={}\".format(via)\n",
    "    if laden is not None:\n",
    "        query_params += \"&laden-congestion-days={}\".format(laden)\n",
    "    if ballast is not None:\n",
    "        query_params += \"&ballast-congestion-days={}\".format(ballast)\n",
    "    \n",
    "    \n",
    "    content = do_api_get_query(\n",
    "        uri=\"/v1.0/netbacks/{}\".format(query_params),\n",
    "        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",
    "            data['ReleaseDate'] = pd.to_datetime(data['ReleaseDate'])\n",
    "            data['LoadDate'] = pd.to_datetime(data['LoadDate'])\n",
    "            data['LoadMonthDate'] = pd.to_datetime(data['LoadMonth'])\n",
    "\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e8ba5c",
   "metadata": {},
   "source": [
    "# Calling data and Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cedc65a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetching port & via point inputs\n",
    "port = \"Sabine Pass\"\n",
    "my_ticker = available_df[available_df[\"name\"] == port][\"uuid\"].iloc[0]\n",
    "\n",
    "df_cogh = fetch_netback(access_token, start='2025-01-01', end='2026-01-28', ticker=my_ticker, via='cogh', format='csv')\n",
    "df_panama = fetch_netback(access_token, start='2025-01-01', end='2026-01-28', ticker=my_ticker, via='panama', format='csv')\n",
    "\n",
    "df_cogh.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b93aaee4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# filtering for Front Month Arbs\n",
    "df_cogh_front = df_cogh[df_cogh['LoadMonthIndex'] == 1]\n",
    "df_panama_front = df_panama[df_panama['LoadMonthIndex'] == 1]\n",
    "\n",
    "\n",
    "# Filtering for latest forward curve\n",
    "latest = df_cogh['ReleaseDate'].unique()[0]\n",
    "df_cogh_latest = df_cogh[df_cogh['ReleaseDate'] == latest]\n",
    "df_panama_latest = df_panama[df_panama['ReleaseDate'] == latest]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16165250",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15,6))\n",
    "\n",
    "plt.axhline(0, color='grey')\n",
    "\n",
    "ax.plot(df_cogh_front['ReleaseDate'], df_cogh_front['DeltaNeaNwe'], color='#48C38D', linewidth=2.0, label='Sabine Pass Arb - NEA via COGH')\n",
    "ax.plot(df_panama_front['ReleaseDate'], df_panama_front['DeltaNeaNwe'], color='#4F41F4', linewidth=2.0, label='Sabine Pass Arb - NEA via Panama')\n",
    "\n",
    "ax.legend(loc=4)\n",
    "\n",
    "ax.plot(df_cogh_latest['LoadMonthDate'], df_cogh_latest['DeltaNeaNwe'], color='#48C38D', linewidth=2.0, linestyle='--', marker='s', markersize=7)\n",
    "ax.plot(df_panama_latest['LoadMonthDate'], df_panama_latest['DeltaNeaNwe'], color='#4F41F4', linewidth=2.0, linestyle='--', marker='s', markersize=5)\n",
    "\n",
    "plt.ylabel('$/MMBtu')\n",
    "plt.xlabel('Release Date')\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
}