{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "59d19f73",
   "metadata": {},
   "source": [
    "# Netbacks Arb Delta\n",
    "\n",
    "This script allows you to plot the Historical Evolution of Arbitrage Prices for a specific month.\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 Github or API website:\n",
    "\n",
    "- Github: https://github.com/spark-commodities/api-code-samples/blob/master/jupyter_notebooks/\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/request/netbacks.html\n",
    "\n",
    "__N.B. This script requires a Cargo subscription__"
   ]
  },
  {
   "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": 37,
   "id": "d9ea2c58",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import sys\n",
    "import numpy as np\n",
    "from base64 import b64encode\n",
    "from pprint import pprint\n",
    "from urllib.parse import urljoin\n",
    "import pandas as pd\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",
    "\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):\n",
    "    url = urljoin(API_BASE_URL, uri)\n",
    "\n",
    "    headers = {\n",
    "        \"Authorization\": \"Bearer {}\".format(access_token),\n",
    "        \"Accept\": \"application/json\",\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",
    "    # The server returned a JSON response\n",
    "    content = json.loads(resp_content)\n",
    "\n",
    "    return content\n",
    "\n",
    "\n",
    "def get_access_token(client_id, client_secret):\n",
    "    \"\"\"\n",
    "    Get a new access_token. Access tokens are the thing that applications use to make\n",
    "    API requests. Access tokens must be kept confidential in storage.\n",
    "\n",
    "    # Procedure:\n",
    "\n",
    "    Do a POST query with `grantType` and `scopes` in the body. A basic authorization\n",
    "    HTTP header is required. The \"Basic\" HTTP authentication scheme is defined in\n",
    "    RFC 7617, which transmits credentials as `clientId:clientSecret` pairs, encoded\n",
    "    using base64.\n",
    "    \"\"\"\n",
    "\n",
    "    # Note: for the sake of this example, we choose to use the Python urllib from the\n",
    "    # standard lib. One should consider using https://requests.readthedocs.io/\n",
    "\n",
    "    payload = \"{}:{}\".format(client_id, client_secret).encode()\n",
    "    headers = {\n",
    "        \"Authorization\": b64encode(payload).decode(),\n",
    "        \"Accept\": \"application/json\",\n",
    "        \"Content-Type\": \"application/json\",\n",
    "    }\n",
    "    body = {\n",
    "        \"grantType\": \"clientCredentials\",\n",
    "        \"scopes\": \"read:netbacks,read:access,read:prices,read:routes\"\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",
    "def list_netbacks(access_token):\n",
    "    \"\"\"\n",
    "    Fetch available routes. Return contract ticker symbols\n",
    "\n",
    "    # Procedure:\n",
    "\n",
    "    Do a GET query to /v1.0/routes/ with a Bearer token authorization HTTP header.\n",
    "    \"\"\"\n",
    "    content = do_api_get_query(uri=\"/v1.0/netbacks/reference-data/\", access_token=access_token)\n",
    "\n",
    "    print(\">>>> All the routes you can fetch\")\n",
    "    tickers = []\n",
    "    fobPort_names = []\n",
    "\n",
    "    availablevia = []\n",
    "    #reldates = []\n",
    "    \n",
    "    #availablevia = np.empty(shape=(len(content[\"data\"]['staticData']['fobPorts'])))\n",
    "    #reldates = np.empty(shape=(len(content[\"data\"]['staticData']['fobPorts'])))\n",
    "    \n",
    "    #c = 0\n",
    "    for contract in content[\"data\"]['staticData']['fobPorts']:\n",
    "        \n",
    "        #print(contract)\n",
    "        #print(contract[\"uuid\"])\n",
    "        tickers.append(contract[\"uuid\"])\n",
    "        fobPort_names.append(contract['name'])\n",
    "        \n",
    "        availablevia.append(contract['availableViaPoints'])\n",
    "    \n",
    "    reldates = content[\"data\"]['staticData']['sparkReleases']\n",
    "        \n",
    "        #availablevia[c] = contract['availableViaPoints']\n",
    "        #reldates[c] = contract[\"sparkReleases\"]\n",
    "        \n",
    "        #c += 1\n",
    "\n",
    "    #print(len(content))\n",
    "    #print(content[\"data\"]['routes'][0])\n",
    "    #print(content[\"data\"]['sparkReleaseDates'])\n",
    "    \n",
    "    #reldates = content[\"data\"]['sparkReleaseDates']\n",
    "    \n",
    "    dicto1 = content[\"data\"]\n",
    "        \n",
    "    return tickers, fobPort_names, availablevia, reldates, dicto1\n",
    "    #return dicto1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e890e9e",
   "metadata": {},
   "source": [
    "### N.B. 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. Instructions for downloading your credentials can be found here:\n",
    "\n",
    "https://api.sparkcommodities.com/redoc#section/Authentication/Create-an-Oauth2-Client\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "51b8a89c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>>> Found credentials!\n",
      ">>>> Client_id=875f483b-19de-421a-8e9b-dceff6703e83, client_secret=6cdf8****\n",
      ">>>> Successfully fetched an access token eyJhb****, valid 604799 seconds.\n",
      ">>>> All the routes you can fetch\n"
     ]
    }
   ],
   "source": [
    "client_id, client_secret = retrieve_credentials(\n",
    "    file_path=\"/tmp/client_credentials.csv\"\n",
    ")\n",
    "\n",
    "# Authenticate:\n",
    "access_token = get_access_token(client_id, client_secret)\n",
    "\n",
    "# Fetch all contracts:\n",
    "tickers, fobPort_names, availablevia, reldates, dicto1 = list_netbacks(access_token)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d262ca9",
   "metadata": {},
   "source": [
    "### Data Import Base Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "912d9c4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Defining the function to fetch the data\n",
    "\n",
    "def fetch_netback(access_token, ticker, release, via=None, laden=None, ballast=None):\n",
    "    \"\"\"\n",
    "    For a route, fetch then display the route details\n",
    "\n",
    "    # Procedure:\n",
    "\n",
    "    Do GET queries to https://api.sparkcommodities.com/v1.0/routes/{route_uuid}/\n",
    "    with a Bearer token authorization HTTP header.\n",
    "    \"\"\"\n",
    "    \n",
    "    query_params = \"?fob-port={}\".format(ticker)\n",
    "    if release is not None:\n",
    "        query_params += \"&release-date={}\".format(release)\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,\n",
    "    )\n",
    "    \n",
    "    my_dict = content['data']\n",
    "\n",
    "    return my_dict\n",
    "\n",
    "\n",
    "# Define formatting data function\n",
    "\n",
    "def format_store(available_via, fob_names, tickrs):\n",
    "    dict_store = {\n",
    "        \"Index\": [],\n",
    "        \"Callable Ports\": [],\n",
    "        \"Corresponding Ticker\": [],\n",
    "        \"Available Via\": []\n",
    "    }\n",
    "    \n",
    "    c = 0\n",
    "    for a in available_via:\n",
    "        ## Check which routes have non-empty Netbacks data and save indices\n",
    "        if len(a) != 0:\n",
    "            dict_store['Index'].append(c)\n",
    "\n",
    "            # Use these indices to retrive the corresponding Netbacks info\n",
    "            dict_store['Callable Ports'].append(fob_names[c])\n",
    "            dict_store['Corresponding Ticker'].append(tickrs[c])\n",
    "            dict_store['Available Via'].append(available_via[c])\n",
    "        c += 1\n",
    "    # Show available Netbacks ports in a DataFrame (with corresponding indices)\n",
    "    dict_df = pd.DataFrame(dict_store)\n",
    "    return dict_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cabc5f86",
   "metadata": {},
   "source": [
    "### Netbacks History Data Call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "a1b156ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "# Defining function to get netbacks history data\n",
    "\n",
    "def netbacks_history(tick, reldates, my_via=None, laden =None, ballast=None):\n",
    "\n",
    "    months = []\n",
    "    nea_outrights = []\n",
    "    nea_ttfbasis = []\n",
    "    nwe_outrights = []\n",
    "    nwe_ttfbasis = []\n",
    "    delta_outrights = []\n",
    "    delta_ttfbasis = []\n",
    "    release_date = []\n",
    "    \n",
    "    port = []\n",
    "\n",
    "    for r in reldates:\n",
    "        try:\n",
    "            my_dict = fetch_netback(access_token, tickers[tick], release=r, via=my_via, laden=laden, ballast=ballast)\n",
    "            \n",
    "            for m in my_dict['netbacks']:\n",
    "\n",
    "                months.append(m['load']['month'])\n",
    "\n",
    "                nea_outrights.append(float(m['nea']['outright']['usdPerMMBtu']))\n",
    "                nea_ttfbasis.append(float(m['nea']['ttfBasis']['usdPerMMBtu']))\n",
    "\n",
    "                nwe_outrights.append(float(m['nwe']['outright']['usdPerMMBtu']))\n",
    "                nwe_ttfbasis.append(float(m['nwe']['ttfBasis']['usdPerMMBtu']))\n",
    "\n",
    "                delta_outrights.append(float(m['neaMinusNwe']['outright']['usdPerMMBtu']))\n",
    "                delta_ttfbasis.append(float(m['neaMinusNwe']['ttfBasis']['usdPerMMBtu']))\n",
    "\n",
    "                release_date.append(my_dict['releaseDate'])\n",
    "                port.append(fobPort_names[tick])\n",
    "        except:\n",
    "            print('Bad Date: ' + r)\n",
    "        \n",
    "        # Including a sleep parameter to avoid rate limiting\n",
    "        time.sleep(0.2)\n",
    "        \n",
    "    historical_df = pd.DataFrame({\n",
    "                'Release Date': release_date,\n",
    "                'FoB Port': port,\n",
    "                'Month': months,\n",
    "                'NEA Outrights': nea_outrights,\n",
    "                'NEA TTF Basis': nea_ttfbasis,\n",
    "                'NWE Outrights': nwe_outrights,\n",
    "                'NWE TTF Basis': nwe_ttfbasis,\n",
    "                'Delta Outrights': delta_outrights,\n",
    "                'Delta TTF Basis': delta_ttfbasis,\n",
    "                })\n",
    "    \n",
    "\n",
    "    historical_df['Release Date'] = pd.to_datetime(historical_df['Release Date'])\n",
    "    historical_df['Month Start'] = pd.to_datetime(historical_df['Month'])\n",
    "    \n",
    "    return historical_df\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2e8ba5c",
   "metadata": {},
   "source": [
    "# 2. Calling data and sorting\n",
    "\n",
    "In this section, we call the data needed for the US Arb via COGH netback then we sort this data by choosing a load month across multiple years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cedc65a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Select Route\n",
    "\n",
    "via ='cogh'\n",
    "my_t = 'Sabine Pass'\n",
    "t = fobPort_names.index(my_t)\n",
    "\n",
    "my_rels = reldates[:]\n",
    "\n",
    "df_cogh = netbacks_history(t, my_rels, my_via='cogh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "078e2984",
   "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>FoB Port</th>\n",
       "      <th>Month</th>\n",
       "      <th>NEA Outrights</th>\n",
       "      <th>NEA TTF Basis</th>\n",
       "      <th>NWE Outrights</th>\n",
       "      <th>NWE TTF Basis</th>\n",
       "      <th>Delta Outrights</th>\n",
       "      <th>Delta TTF Basis</th>\n",
       "      <th>Month Start</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2025-01-15</td>\n",
       "      <td>Sabine Pass</td>\n",
       "      <td>2025-02</td>\n",
       "      <td>12.423</td>\n",
       "      <td>-1.774</td>\n",
       "      <td>13.268</td>\n",
       "      <td>-0.929</td>\n",
       "      <td>-0.845</td>\n",
       "      <td>-0.845</td>\n",
       "      <td>2025-02-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2025-01-15</td>\n",
       "      <td>Sabine Pass</td>\n",
       "      <td>2025-03</td>\n",
       "      <td>12.512</td>\n",
       "      <td>-1.696</td>\n",
       "      <td>13.281</td>\n",
       "      <td>-0.927</td>\n",
       "      <td>-0.769</td>\n",
       "      <td>-0.769</td>\n",
       "      <td>2025-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2025-01-15</td>\n",
       "      <td>Sabine Pass</td>\n",
       "      <td>2025-04</td>\n",
       "      <td>12.624</td>\n",
       "      <td>-1.614</td>\n",
       "      <td>13.317</td>\n",
       "      <td>-0.921</td>\n",
       "      <td>-0.693</td>\n",
       "      <td>-0.693</td>\n",
       "      <td>2025-04-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2025-01-15</td>\n",
       "      <td>Sabine Pass</td>\n",
       "      <td>2025-05</td>\n",
       "      <td>12.737</td>\n",
       "      <td>-1.519</td>\n",
       "      <td>13.376</td>\n",
       "      <td>-0.880</td>\n",
       "      <td>-0.639</td>\n",
       "      <td>-0.639</td>\n",
       "      <td>2025-05-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2025-01-15</td>\n",
       "      <td>Sabine Pass</td>\n",
       "      <td>2025-06</td>\n",
       "      <td>12.809</td>\n",
       "      <td>-1.509</td>\n",
       "      <td>13.413</td>\n",
       "      <td>-0.905</td>\n",
       "      <td>-0.604</td>\n",
       "      <td>-0.604</td>\n",
       "      <td>2025-06-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Release Date     FoB Port    Month  NEA Outrights  NEA TTF Basis  \\\n",
       "0   2025-01-15  Sabine Pass  2025-02         12.423         -1.774   \n",
       "1   2025-01-15  Sabine Pass  2025-03         12.512         -1.696   \n",
       "2   2025-01-15  Sabine Pass  2025-04         12.624         -1.614   \n",
       "3   2025-01-15  Sabine Pass  2025-05         12.737         -1.519   \n",
       "4   2025-01-15  Sabine Pass  2025-06         12.809         -1.509   \n",
       "\n",
       "   NWE Outrights  NWE TTF Basis  Delta Outrights  Delta TTF Basis Month Start  \n",
       "0         13.268         -0.929           -0.845           -0.845  2025-02-01  \n",
       "1         13.281         -0.927           -0.769           -0.769  2025-03-01  \n",
       "2         13.317         -0.921           -0.693           -0.693  2025-04-01  \n",
       "3         13.376         -0.880           -0.639           -0.639  2025-05-01  \n",
       "4         13.413         -0.905           -0.604           -0.604  2025-06-01  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View output\n",
    "\n",
    "df_cogh.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "b035b86c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define functon to calculate the \"Day of Year\" number for each datapoint. \n",
    "# Y-1 datapoints are offset by 365 days, and Y-2 by 730 days, so that timeseries can be plotted in a single, trackable graph.\n",
    "def sort_years(df):\n",
    "\n",
    "    if 'Month Start' not in df.columns:\n",
    "        df['Month Start']  = pd.to_datetime(df['Month'])\n",
    "    \n",
    "    reldates = df['Release Date'].to_list()\n",
    "    startdates = df['Month Start'].to_list()\n",
    "\n",
    "    dayofyear = []\n",
    "    \n",
    "    for r in reldates:\n",
    "        ir = reldates.index(r)\n",
    "        if r.year - startdates[ir].year == -1:\n",
    "            dayofyear.append(r.timetuple().tm_yday - 365)\n",
    "        elif r.year - startdates[ir].year == -2:\n",
    "            dayofyear.append(r.timetuple().tm_yday - 730)\n",
    "        else:\n",
    "            dayofyear.append(r.timetuple().tm_yday)\n",
    "            \n",
    "    df['Day of Year'] = dayofyear\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "3ddbdf75",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v3/5pn2lssn077ch9xm2rttdmym0000gn/T/ipykernel_16512/1852031194.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Day of Year'] = dayofyear\n",
      "/var/folders/v3/5pn2lssn077ch9xm2rttdmym0000gn/T/ipykernel_16512/1852031194.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Day of Year'] = dayofyear\n",
      "/var/folders/v3/5pn2lssn077ch9xm2rttdmym0000gn/T/ipykernel_16512/1852031194.py:22: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['Day of Year'] = dayofyear\n"
     ]
    },
    {
     "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>FoB Port</th>\n",
       "      <th>Month</th>\n",
       "      <th>NEA Outrights</th>\n",
       "      <th>NEA TTF Basis</th>\n",
       "      <th>NWE Outrights</th>\n",
       "      <th>NWE TTF Basis</th>\n",
       "      <th>Delta Outrights</th>\n",
       "      <th>Delta TTF Basis</th>\n",
       "      <th>Month Start</th>\n",
       "      <th>Day of Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2025-01-15</td>\n",
       "      <td>Sabine Pass</td>\n",
       "      <td>2025-07</td>\n",
       "      <td>12.793</td>\n",
       "      <td>-1.537</td>\n",
       "      <td>13.409</td>\n",
       "      <td>-0.921</td>\n",
       "      <td>-0.616</td>\n",
       "      <td>-0.616</td>\n",
       "      <td>2025-07-01</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2025-01-14</td>\n",
       "      <td>Sabine Pass</td>\n",
       "      <td>2025-07</td>\n",
       "      <td>12.824</td>\n",
       "      <td>-1.518</td>\n",
       "      <td>13.419</td>\n",
       "      <td>-0.923</td>\n",
       "      <td>-0.595</td>\n",
       "      <td>-0.595</td>\n",
       "      <td>2025-07-01</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2025-01-13</td>\n",
       "      <td>Sabine Pass</td>\n",
       "      <td>2025-07</td>\n",
       "      <td>13.070</td>\n",
       "      <td>-1.548</td>\n",
       "      <td>13.688</td>\n",
       "      <td>-0.930</td>\n",
       "      <td>-0.618</td>\n",
       "      <td>-0.618</td>\n",
       "      <td>2025-07-01</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2025-01-10</td>\n",
       "      <td>Sabine Pass</td>\n",
       "      <td>2025-07</td>\n",
       "      <td>12.251</td>\n",
       "      <td>-1.469</td>\n",
       "      <td>12.824</td>\n",
       "      <td>-0.896</td>\n",
       "      <td>-0.573</td>\n",
       "      <td>-0.573</td>\n",
       "      <td>2025-07-01</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2025-01-09</td>\n",
       "      <td>Sabine Pass</td>\n",
       "      <td>2025-07</td>\n",
       "      <td>12.291</td>\n",
       "      <td>-1.460</td>\n",
       "      <td>12.848</td>\n",
       "      <td>-0.903</td>\n",
       "      <td>-0.557</td>\n",
       "      <td>-0.557</td>\n",
       "      <td>2025-07-01</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Release Date     FoB Port    Month  NEA Outrights  NEA TTF Basis  \\\n",
       "5    2025-01-15  Sabine Pass  2025-07         12.793         -1.537   \n",
       "16   2025-01-14  Sabine Pass  2025-07         12.824         -1.518   \n",
       "27   2025-01-13  Sabine Pass  2025-07         13.070         -1.548   \n",
       "38   2025-01-10  Sabine Pass  2025-07         12.251         -1.469   \n",
       "49   2025-01-09  Sabine Pass  2025-07         12.291         -1.460   \n",
       "\n",
       "    NWE Outrights  NWE TTF Basis  Delta Outrights  Delta TTF Basis  \\\n",
       "5          13.409         -0.921           -0.616           -0.616   \n",
       "16         13.419         -0.923           -0.595           -0.595   \n",
       "27         13.688         -0.930           -0.618           -0.618   \n",
       "38         12.824         -0.896           -0.573           -0.573   \n",
       "49         12.848         -0.903           -0.557           -0.557   \n",
       "\n",
       "   Month Start  Day of Year  \n",
       "5   2025-07-01           15  \n",
       "16  2025-07-01           14  \n",
       "27  2025-07-01           13  \n",
       "38  2025-07-01           10  \n",
       "49  2025-07-01            9  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sorting dataframes my load month and adding day of year column\n",
    "\n",
    "m25 = df_cogh[df_cogh['Month']=='2025-07']\n",
    "m24 = df_cogh[df_cogh['Month']=='2024-07']\n",
    "m23 = df_cogh[df_cogh['Month']=='2023-07']\n",
    "\n",
    "m25 = sort_years(m25)\n",
    "m24 = sort_years(m24)\n",
    "m23 = sort_years(m23)\n",
    "\n",
    "# View one example of output\n",
    "m25.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b98bdb",
   "metadata": {},
   "source": [
    "# 3. Plotting\n",
    "\n",
    "In this script, we have chosen the load month of July for August delivery across 2023, 2024 and 2025."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "48c06193",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/v3/5pn2lssn077ch9xm2rttdmym0000gn/T/ipykernel_16512/2855135621.py:25: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  plt.gca().set_yticklabels(['$ {:,.0f}'.format(x) for x in current_values])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Plotting\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(m23['Day of Year'], m23['Delta Outrights'], color='darkorange', label='2023', linewidth=1)\n",
    "ax.plot(m24['Day of Year'], m24['Delta Outrights'], color='darkblue', label='2024', linewidth=1.5)\n",
    "ax.plot(m25['Day of Year'], m25['Delta Outrights'], color='firebrick', label='2025', linewidth=2)\n",
    "\n",
    "plt.ylim(-4,1)\n",
    "\n",
    "# Setting custom x-axis ticks location and labels.\n",
    "xlabels = ['Y-1', 'September', 'October', 'November', 'December', 'Y+0', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December', 'Year End']\n",
    "\n",
    "# xpos gives the first day of every other month in terms of 'day of year'\n",
    "xpos = [-152,-121,-91,-60,-32,0,30,60,90,121,152,182,213,244,274,305,335,365]\n",
    "\n",
    "plt.xticks(xpos, xlabels)\n",
    "\n",
    "plt.title('US Arb - ' + df_cogh['FoB Port'].iloc[0] + ' - Monthly Arb Evolution')\n",
    "plt.ylabel('$/MMBtu')\n",
    "plt.xlabel('Release Date')\n",
    "\n",
    "plt.xlim(-152,max([m25[\"Day of Year\"].max(), m24[\"Day of Year\"].max(), m23[\"Day of Year\"].max()])+10)\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "sns.despine(left=True, bottom=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}