{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "25c8380d",
   "metadata": {},
   "source": [
    "# FFA Seasonality Charts\n",
    "\n",
    "This script allows you to plot seasonality charts for a specific contract month from our Spark30FFA and Spark25FFA freight rates.\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/contracts.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe1a7ca7",
   "metadata": {},
   "source": [
    "### Have any questions?\n",
    "\n",
    "If you have any questions regarding our API, or need help accessing specific datasets, please contact us at:\n",
    "\n",
    "__data@sparkcommodities.com__"
   ]
  },
  {
   "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": 18,
   "id": "fcb94776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing packages for calling the API\n",
    "import json\n",
    "import os\n",
    "import sys\n",
    "from base64 import b64encode\n",
    "from pprint import pprint\n",
    "from urllib.parse import urljoin\n",
    "from datetime import datetime\n",
    "import pandas as pd\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",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9a0e42dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining functions for API request\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",
    "    \"\"\"\n",
    "    OAuth2 authentication requires a POST request with client credentials before accessing the API. \n",
    "    This POST request will return an Access Token which will be used for the API GET request.\n",
    "    \"\"\"\n",
    "    url = urljoin(API_BASE_URL, uri)\n",
    "\n",
    "    data = json.dumps(body).encode(\"utf-8\")\n",
    "\n",
    "    # HTTP POST request\n",
    "    req = request.Request(url, data=data, headers=headers)\n",
    "    try:\n",
    "        response = request.urlopen(req)\n",
    "    except HTTPError as e:\n",
    "        print(\"HTTP Error: \", e.code)\n",
    "        print(e.read())\n",
    "        sys.exit(1)\n",
    "\n",
    "    resp_content = response.read()\n",
    "\n",
    "    # The server must return HTTP 201. Raise an error if this is not the case\n",
    "    assert response.status == 201, resp_content\n",
    "\n",
    "    # The server returned a JSON response\n",
    "    content = json.loads(resp_content)\n",
    "\n",
    "    return content\n",
    "\n",
    "\n",
    "def do_api_get_query(uri, access_token):\n",
    "    \"\"\"\n",
    "    After receiving an Access Token, we can request information from the API.\n",
    "    \"\"\"\n",
    "    url = urljoin(API_BASE_URL, uri)\n",
    "\n",
    "    headers = {\n",
    "        \"Authorization\": \"Bearer {}\".format(access_token),\n",
    "        \"Accept\": \"application/json\",\n",
    "    }\n",
    "\n",
    "    # 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:lng-freight-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\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "691c889f",
   "metadata": {},
   "source": [
    "## Defining Fetch Request\n",
    "\n",
    "Here is where we define what type of data we want to fetch from the API.\n",
    "\n",
    "In my fetch request, I use the URL:\n",
    "\n",
    "__uri=\"/v1.0/contracts/\"__\n",
    "\n",
    "This is to query contract price data specifically. Other data products (such as shipping route costs) require different URL's in the fetch request (refer to other Python API examples)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7d5fad75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining function for collecting the list of contracts\n",
    "def list_contracts(access_token):\n",
    "    \"\"\"\n",
    "    Fetch available contracts. Return contract ticker symbols\n",
    "\n",
    "    # Procedure:\n",
    "\n",
    "    Do a GET query to /v1.0/contracts/ with a Bearer token authorization HTTP header.\n",
    "    \"\"\"\n",
    "    content = do_api_get_query(uri=\"/v1.0/contracts/\", access_token=access_token)\n",
    "\n",
    "    print(\">>>> All the contracts you can fetch\")\n",
    "    tickers = []\n",
    "    for contract in content[\"data\"]:\n",
    "        print(contract[\"fullName\"])\n",
    "        tickers.append(contract[\"id\"])\n",
    "\n",
    "    return tickers"
   ]
  },
  {
   "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. Instructions for downloading your credentials can be found here:\n",
    "\n",
    "https://api.sparkcommodities.com/redoc#section/Authentication/Create-an-Oauth2-Client\n",
    "\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": 21,
   "id": "602d2492",
   "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.eyJ0eXBlIjoiYWNjZXNzVG9rZW4iLCJzdWIiOiIwMWMyMzU5MC1lZjZjLTRhMzYtODIzNy1jODljM2YxYTNiMmEiLCJzdWJUeXBlIjoib2F1dGgtY2xpZW50IiwiZXhwIjoxNzMwNzI2NTYzLCJoYXNoZWRTZWNyZXQiOiJwYmtkZjJfc2hhMjU2JDYwMDAwMCRoTXRMNDlrMUZUaVVzTE42Njlqc2pPJHVCSXNxcml5b1NHVzJTS1AvaHVLNHh3eTZ4d3VDN001aUdGRm43N2l4S1U9Iiwib3JnVXVpZCI6IjQ5MzhiMGJiLTVmMjctNDE2NC04OTM4LTUyNTdmYmQzNTNmZiIsInNjb3BlcyI6WyJyZWFkOnJvdXRlcyIsInJlYWQ6cHJpY2VzIl0sImNsaWVudFR5cGUiOiJvYXV0aC1jbGllbnQifQ.itQucx6vvWjS8YAo3pQTPjO3FMtaT819kGkMW6OLWxw\n",
      ">>>> All the contracts you can fetch\n",
      "Spark25F Pacific 160 TFDE\n",
      "Spark30F Atlantic 160 TFDE\n",
      "Spark25S Pacific\n",
      "Spark25Fo Pacific\n",
      "Spark25FFA Pacific\n",
      "Spark25FFAYearly Pacific\n",
      "Spark30S Atlantic\n",
      "Spark30Fo Atlantic\n",
      "Spark30FFA Atlantic\n",
      "Spark30FFAYearly Atlantic\n",
      "SparkNWE DES 1H\n",
      "SparkNWE-B 1H\n",
      "SparkNWE DES 2H\n",
      "SparkNWE-B 2H\n",
      "SparkNWE-B F\n",
      "SparkNWE DES F\n",
      "SparkNWE-B Fo\n",
      "SparkNWE DES Fo\n",
      "SparkNWE-DES-Fin Monthly\n",
      "SparkNWE-Fin Monthly\n",
      "SparkSWE-B F\n",
      "SparkSWE DES F\n",
      "SparkSWE-B Fo\n",
      "SparkSWE DES Fo\n",
      "SparkSWE-DES-Fin Monthly\n",
      "SparkSWE-Fin Monthly\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)\n",
    "\n",
    "# Fetch all contracts:\n",
    "tickers = list_contracts(access_token)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0e0e030",
   "metadata": {},
   "source": [
    "# Data Import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3302a85f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_historical_price_releases(access_token, ticker, limit, offset=None):\n",
    "    \"\"\"\n",
    "    For a selected contract, this endpoint returns all the Price Releases you can\n",
    "    access according to your current subscription, ordered by release date descending.\n",
    "\n",
    "    **Note**: Unlimited access to historical data and full forward curves is only\n",
    "    available to those with Premium access. Get in touch to find out more.\n",
    "\n",
    "    **Params**\n",
    "\n",
    "    limit: optional integer value to set an upper limit on the number of price\n",
    "           releases returned by the endpoint.\n",
    "\n",
    "    offset: optional integer value to set from where to start returning data.\n",
    "            Default is 0.\n",
    "\n",
    "    # Procedure:\n",
    "\n",
    "    Do GET queries to /v1.0/contracts/{contract_ticker_symbol}/price-releases/\n",
    "    with a Bearer token authorization HTTP header.\n",
    "    \"\"\"\n",
    "    print(\">>>> Get price releases for {}\".format(ticker))\n",
    "\n",
    "    query_params = \"?limit={}\".format(limit)\n",
    "    if offset is not None:\n",
    "        query_params += \"&offset={}\".format(offset)\n",
    "\n",
    "    content = do_api_get_query(\n",
    "        uri=\"/v1.0/contracts/{}/price-releases/{}\".format(ticker, query_params),\n",
    "        access_token=access_token,\n",
    "    )\n",
    "\n",
    "    my_dict = content[\"data\"]\n",
    "\n",
    "    return my_dict\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99be9416",
   "metadata": {},
   "source": [
    "### Function to call data and store as a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "32d6eb83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the function\n",
    "def fetch_ffa_prices(my_tick, my_lim):\n",
    "    print(my_tick)\n",
    "\n",
    "    my_dict_hist = fetch_historical_price_releases(access_token, my_tick, limit=my_lim)\n",
    "\n",
    "    release_dates = []\n",
    "\n",
    "    period_start = []\n",
    "    period_end = []\n",
    "    period_name = []\n",
    "    cal_month = []\n",
    "    ticker = []\n",
    "    usd_day = []\n",
    "    day_min = []\n",
    "    day_max = []\n",
    "    \n",
    "    calmonth = []\n",
    "\n",
    "    for release in my_dict_hist:\n",
    "        release_date = release[\"releaseDate\"]\n",
    "        data = release[\"data\"]\n",
    "\n",
    "        for d in data:\n",
    "            data_points = d[\"dataPoints\"]\n",
    "            for data_point in data_points:\n",
    "                period_start_at = data_point[\"deliveryPeriod\"][\"startAt\"]\n",
    "                period_start.append(period_start_at)\n",
    "                period_end_at = data_point[\"deliveryPeriod\"][\"endAt\"]\n",
    "                period_end.append(period_end_at)\n",
    "                period_name.append(data_point[\"deliveryPeriod\"][\"name\"])\n",
    "\n",
    "                release_dates.append(release_date)               \n",
    "                ticker.append(release[\"contractId\"])\n",
    "                cal_month.append(\n",
    "                    datetime.strptime(period_start_at, \"%Y-%m-%d\").strftime(\"%b-%Y\")\n",
    "                )\n",
    "\n",
    "                usd_day.append(int(data_point[\"derivedPrices\"][\"usdPerDay\"][\"spark\"]))\n",
    "                day_min.append(\n",
    "                    int(data_point[\"derivedPrices\"][\"usdPerDay\"][\"sparkMin\"])\n",
    "                )\n",
    "                day_max.append(\n",
    "                    int(data_point[\"derivedPrices\"][\"usdPerDay\"][\"sparkMax\"])\n",
    "                )\n",
    "                \n",
    "\n",
    "\n",
    "    historical_df = pd.DataFrame(\n",
    "        {\n",
    "            \"Release Date\": release_dates,\n",
    "            \"ticker\": ticker,\n",
    "            \"Period Name\": period_name,\n",
    "            \"Period Start\": period_start,\n",
    "            \"Period End\": period_end,\n",
    "            \"Calendar Month\": cal_month,\n",
    "            \"Spark\": usd_day,\n",
    "            \"SparkMin\": day_min,\n",
    "            \"SparkMax\": day_max,\n",
    "        }\n",
    "    )\n",
    "\n",
    "    historical_df['Release Date'] = pd.to_datetime(historical_df['Release Date'],format='%Y-%m-%d')\n",
    "    \n",
    "    historical_df['Period Start'] = pd.to_datetime(historical_df['Period Start'])\n",
    "    historical_df['Period End'] = pd.to_datetime(historical_df['Period End'])\n",
    "    historical_df['Time Diff'] = (historical_df['Release Date'] - historical_df['Period Start']).dt.days\n",
    "\n",
    "    return historical_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7135318a",
   "metadata": {},
   "source": [
    "### Call those functions for Spark30FFA and Spark25FFA\n",
    "\n",
    "We call the function defined above and create two dataframes:\n",
    "\n",
    "- spark25ffa - storing all historical Spark25FFA data\n",
    "- spark30ffa - storing all historical Spark30FFA data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b367acfe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spark25ffa-monthly\n",
      ">>>> Get price releases for spark25ffa-monthly\n",
      "spark30ffa-monthly\n",
      ">>>> Get price releases for spark30ffa-monthly\n"
     ]
    }
   ],
   "source": [
    "spark25ffa = fetch_ffa_prices(tickers[4], 900)\n",
    "\n",
    "spark30ffa = fetch_ffa_prices(tickers[8], 900)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9126aa2b",
   "metadata": {},
   "source": [
    "# FFA Contract Evolution + Seasonality\n",
    "\n",
    "Compare Spark30FFA December contracts from the past 3 years, and track how these contracts have priced in the year leading up to contract settlement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3676a9e1",
   "metadata": {},
   "source": [
    "## Sorting Data\n",
    "\n",
    "We then create new columns to categorise the data we have. \n",
    "- By creating the 'Years', 'Quarters' and 'Seasons' columns, we can filter the data accordingly.\n",
    "- By creating the 'Day of Year' column, we can plot the yearly data on the same x-axis range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e698cbfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sort_years(df):\n",
    "    \n",
    "    reldates = df['Release Date'].to_list()\n",
    "    startdates = df['Period 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": 26,
   "id": "8ba60d6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark30ffa['Release Date'].iloc[0].month"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bbd5a39b",
   "metadata": {},
   "source": [
    "## Group Dataframe by CalMonth, Quarter or Season and choose contracts to compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "73853999",
   "metadata": {},
   "outputs": [],
   "source": [
    "groups = spark30ffa.groupby('Calendar Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "27f1f2b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dec24 = groups.get_group('Dec-2024')\n",
    "dec23 = groups.get_group('Dec-2023')\n",
    "dec22 = groups.get_group('Dec-2022')\n",
    "\n",
    "dec24 = sort_years(dec24)\n",
    "dec23 = sort_years(dec23)\n",
    "dec22 = sort_years(dec22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "eff578db",
   "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>ticker</th>\n",
       "      <th>Period Name</th>\n",
       "      <th>Period Start</th>\n",
       "      <th>Period End</th>\n",
       "      <th>Calendar Month</th>\n",
       "      <th>Spark</th>\n",
       "      <th>SparkMin</th>\n",
       "      <th>SparkMax</th>\n",
       "      <th>Time Diff</th>\n",
       "      <th>Day of Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4099</th>\n",
       "      <td>2023-12-28</td>\n",
       "      <td>spark30ffa-monthly</td>\n",
       "      <td>M+0</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>Dec-2023</td>\n",
       "      <td>140500</td>\n",
       "      <td>127250</td>\n",
       "      <td>150000</td>\n",
       "      <td>27</td>\n",
       "      <td>362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4124</th>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>spark30ffa-monthly</td>\n",
       "      <td>M+0</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>Dec-2023</td>\n",
       "      <td>141000</td>\n",
       "      <td>127500</td>\n",
       "      <td>150000</td>\n",
       "      <td>26</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4149</th>\n",
       "      <td>2023-12-22</td>\n",
       "      <td>spark30ffa-monthly</td>\n",
       "      <td>M+0</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>Dec-2023</td>\n",
       "      <td>142000</td>\n",
       "      <td>128000</td>\n",
       "      <td>150000</td>\n",
       "      <td>21</td>\n",
       "      <td>356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4174</th>\n",
       "      <td>2023-12-21</td>\n",
       "      <td>spark30ffa-monthly</td>\n",
       "      <td>M+0</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>Dec-2023</td>\n",
       "      <td>142500</td>\n",
       "      <td>130000</td>\n",
       "      <td>150000</td>\n",
       "      <td>20</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4199</th>\n",
       "      <td>2023-12-20</td>\n",
       "      <td>spark30ffa-monthly</td>\n",
       "      <td>M+0</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>Dec-2023</td>\n",
       "      <td>143500</td>\n",
       "      <td>133000</td>\n",
       "      <td>150000</td>\n",
       "      <td>19</td>\n",
       "      <td>354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15017</th>\n",
       "      <td>2021-10-08</td>\n",
       "      <td>spark30ffa-monthly</td>\n",
       "      <td>M+26</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>Dec-2023</td>\n",
       "      <td>112000</td>\n",
       "      <td>90000</td>\n",
       "      <td>140000</td>\n",
       "      <td>-784</td>\n",
       "      <td>-449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15044</th>\n",
       "      <td>2021-10-07</td>\n",
       "      <td>spark30ffa-monthly</td>\n",
       "      <td>M+26</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>Dec-2023</td>\n",
       "      <td>110500</td>\n",
       "      <td>90000</td>\n",
       "      <td>140000</td>\n",
       "      <td>-785</td>\n",
       "      <td>-450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15071</th>\n",
       "      <td>2021-10-06</td>\n",
       "      <td>spark30ffa-monthly</td>\n",
       "      <td>M+26</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>Dec-2023</td>\n",
       "      <td>110500</td>\n",
       "      <td>90000</td>\n",
       "      <td>140000</td>\n",
       "      <td>-786</td>\n",
       "      <td>-451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15098</th>\n",
       "      <td>2021-10-05</td>\n",
       "      <td>spark30ffa-monthly</td>\n",
       "      <td>M+26</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>Dec-2023</td>\n",
       "      <td>110000</td>\n",
       "      <td>90000</td>\n",
       "      <td>140000</td>\n",
       "      <td>-787</td>\n",
       "      <td>-452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15125</th>\n",
       "      <td>2021-10-04</td>\n",
       "      <td>spark30ffa-monthly</td>\n",
       "      <td>M+26</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>Dec-2023</td>\n",
       "      <td>110000</td>\n",
       "      <td>90000</td>\n",
       "      <td>140000</td>\n",
       "      <td>-788</td>\n",
       "      <td>-453</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>561 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Release Date              ticker Period Name Period Start Period End  \\\n",
       "4099    2023-12-28  spark30ffa-monthly         M+0   2023-12-01 2023-12-31   \n",
       "4124    2023-12-27  spark30ffa-monthly         M+0   2023-12-01 2023-12-31   \n",
       "4149    2023-12-22  spark30ffa-monthly         M+0   2023-12-01 2023-12-31   \n",
       "4174    2023-12-21  spark30ffa-monthly         M+0   2023-12-01 2023-12-31   \n",
       "4199    2023-12-20  spark30ffa-monthly         M+0   2023-12-01 2023-12-31   \n",
       "...            ...                 ...         ...          ...        ...   \n",
       "15017   2021-10-08  spark30ffa-monthly        M+26   2023-12-01 2023-12-31   \n",
       "15044   2021-10-07  spark30ffa-monthly        M+26   2023-12-01 2023-12-31   \n",
       "15071   2021-10-06  spark30ffa-monthly        M+26   2023-12-01 2023-12-31   \n",
       "15098   2021-10-05  spark30ffa-monthly        M+26   2023-12-01 2023-12-31   \n",
       "15125   2021-10-04  spark30ffa-monthly        M+26   2023-12-01 2023-12-31   \n",
       "\n",
       "      Calendar Month   Spark  SparkMin  SparkMax  Time Diff  Day of Year  \n",
       "4099        Dec-2023  140500    127250    150000         27          362  \n",
       "4124        Dec-2023  141000    127500    150000         26          361  \n",
       "4149        Dec-2023  142000    128000    150000         21          356  \n",
       "4174        Dec-2023  142500    130000    150000         20          355  \n",
       "4199        Dec-2023  143500    133000    150000         19          354  \n",
       "...              ...     ...       ...       ...        ...          ...  \n",
       "15017       Dec-2023  112000     90000    140000       -784         -449  \n",
       "15044       Dec-2023  110500     90000    140000       -785         -450  \n",
       "15071       Dec-2023  110500     90000    140000       -786         -451  \n",
       "15098       Dec-2023  110000     90000    140000       -787         -452  \n",
       "15125       Dec-2023  110000     90000    140000       -788         -453  \n",
       "\n",
       "[561 rows x 11 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec23"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c354e2df",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "713215a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2489a685010>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style()\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 9))\n",
    "\n",
    "ax.scatter(dec24[\"Day of Year\"], dec24[\"Spark\"])\n",
    "ax.scatter(dec23[\"Day of Year\"], dec23[\"Spark\"])\n",
    "ax.scatter(dec22[\"Day of Year\"], dec22[\"Spark\"])\n",
    "\n",
    "ax.plot(dec24[\"Day of Year\"], dec24[\"Spark\"], label='2024')\n",
    "ax.plot(dec23[\"Day of Year\"], dec23[\"Spark\"], label='2023')\n",
    "ax.plot(dec22[\"Day of Year\"], dec22[\"Spark\"], label='2022')\n",
    "\n",
    "plt.xlabel(\"Release Date\")\n",
    "plt.ylabel(\"Cost in USD/day\")\n",
    "\n",
    "# Setting custom x-axis ticks location and labels.\n",
    "#xlabels = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December', 'Year End']\n",
    "xlabels = ['Y-1', 'March', 'May', 'July', 'September', 'November', 'Y+0', 'March', 'May', 'July', 'September', 'November', 'Year End']\n",
    "\n",
    "# xpos gives the first day of every other month in terms of 'day of year'\n",
    "#xpos = [0,32,60,91,121,152,182,213,244,274,305,335, 365]\n",
    "xpos = [-365,-305,-244,-183,-121,-60,0,60,121,182,244,305,365]\n",
    "\n",
    "current_values = plt.gca().get_yticks()\n",
    "plt.gca().set_yticklabels(['$ {:,.0f}'.format(x) for x in current_values])\n",
    "\n",
    "plt.xticks(xpos, xlabels)\n",
    "\n",
    "plt.xlim(-365,max([dec22[\"Day of Year\"].max(), dec23[\"Day of Year\"].max(), dec24[\"Day of Year\"].max()]))\n",
    "\n",
    "# Setting the graph legend\n",
    "plt.legend()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ba3309",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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
}