WTI-Carry-Momentum-2023.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "88656400",
   "metadata": {},
   "source": [
    "## Trading WTI futures based on the WTI Carry and its momentum\n",
    "\n",
    "Here we are going to make use of an element that encompasses virtually all aspects of oil trading, i.e., the shape of the futures curve. Typically, this quantity is given a variety of names - depending on whether an economist, an investor, a fundamental or systematic trader is referring to it - but here we will simply use the term carry. The definition of carry is given below\n",
    "\n",
    "$$ C\\left( m_1, m_2\\right) = F_{m_1} - F_{m_2},\\ \\ \\ m_1 < m_2$$\n",
    "\n",
    "denoting the spread between two futures contracts with $ m_1 $ and $ m_2 $ months to expiration. Informally, carry represents the profit and loss (P&L) of a futures trading strategy in the ideal situation when the spot price remains flat and the term structure of futures does not change its shape.\n",
    "\n",
    "In general, if the oil market is in backwardation, carry is positive and going long oil futures tends to return profits historically. In contrast, when carry becomes negative, i.e., the futures curve moves into contango,\n",
    "going short oil futures seems to be a profitable strategy. Based on this observation and because going short is as easy as going long in the futures market, we can use a mixed strategy of buying and selling futures contracts depending on the direction of carry.\n",
    "\n",
    "The validity of the previous observation can be traced back to the fundamentals of supply and demand for the oil market. We quote from [1]:\n",
    "\n",
    "_The business model of the storage trader is to generate low-risk steady returns by\n",
    "providing the service of storing oil. In such a business model, the risk of volatile oil\n",
    "prices must be eliminated by hedging in the futures market. As soon as a physical\n",
    "barrel is purchased for storage, a financial barrel is sold in the futures market to\n",
    "reduce the risk. This downward pressure on futures price occurs when the contango\n",
    "is steep enough to cover the cost of storage. If the market flips to backwardation or\n",
    "the contango becomes too narrow to economically hold inventories, then the storage\n",
    "hedger is incentivized to pull oil out of storage and buy back short futures hedges.\n",
    "This creates an upward pressure on the futures price._\n",
    "\n",
    "_Carry, or the shape of the futures curve, is the ultimate mechanism that transmits\n",
    "fundamental information into futures prices. When we read media reports that the\n",
    "price of oil went up because of stronger Asian demand, or it went down because of\n",
    "increased OPEC production, these reports do not tell the full story. OPEC itself does\n",
    "not buy futures and very few oil consumers do. However, any change in supply and\n",
    "demand impacts the spot-futures spread via variations in convenience yields and\n",
    "storage costs. The actual futures are bought and sold by storage traders, who react to\n",
    "the economics determined by the carry. An inventory hedger is indeed the largest trader\n",
    "in the oil futures market. The systematic carry strategy  essentially trades ahead of\n",
    "anticipated behavior of the largest market participant, who is acting in response to\n",
    "the arrival of new fundamental information._\n",
    "\n",
    "According to [1], the carry strategy generated an annualized log-return of 20.3% over the thirty-year period from 1992 to 2022 with a Sharpe ratio of 0.53. In comparison, the S&P 500 index between February 1992 and May 2024 had a compound annual growth rate of 10.89% and a Sharpe ratio of 0.67.\n",
    "\n",
    "\n",
    "[1] I. Bouchouev, Virtual Barrels, Springer Texts in Business and Economics (2023).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21e10559",
   "metadata": {},
   "source": [
    "## Description of the strategy we follow\n",
    "\n",
    "In this notebook we will implement the previously described carry strategy along with a variation of it called carry momentum. To benchmark our implementation against real world data, we will use the results reported in [2] for the WTI futures contract during the entire 2023 for which we can readily download the data from the IBKR platform.\n",
    "\n",
    "Our analysis will be based on the 12 month spread $ C \\left( 1,13\\right) $ to avoid being affected by seasonal variabilities. We will be going long or short WTI contracts depending on the sign of $ C $ while the futures will be rolled on the last business day of each month, following [2]. To achieve this, we will compute synthetic contracts constructed using the cumulative roll differential on the last business day of every month.\n",
    "\n",
    "\n",
    "[2] I. Bouchouev, Energy Quantamentals: Myths and Realities about Algorithmic Oil Traders, Oxford Institute for Energy Studies (2024)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4001eff",
   "metadata": {},
   "source": [
    "## Read data for 2023\n",
    "\n",
    "Here we load the necessary Python libraries and download the WTI price data for 2023 as well as the last business and trading days for every front month future contract."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b684ec5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "#import time\n",
    "import pandas_market_calendars as mcal\n",
    "\n",
    "datos = pd.read_csv('data/datos_2023_all.csv')  \n",
    "last_Day = pd.read_csv('data/last_Day_2023_all.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b8d006a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(type(datos.loc[0,'date']))\n",
    "display(type(last_Day.lastBusinessDay.iloc[0]))\n",
    "display(type(last_Day.lastTradingDay.iloc[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "926634f9",
   "metadata": {},
   "source": [
    "### Convert dates from str to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "25b1824f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>FrontMonth</th>\n",
       "      <th>SecondMonth</th>\n",
       "      <th>ThirteenthMonth</th>\n",
       "      <th>FourteenthMonth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-11-28</td>\n",
       "      <td>77.24</td>\n",
       "      <td>77.33</td>\n",
       "      <td>74.39</td>\n",
       "      <td>74.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-11-29</td>\n",
       "      <td>78.20</td>\n",
       "      <td>78.28</td>\n",
       "      <td>75.22</td>\n",
       "      <td>74.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-11-30</td>\n",
       "      <td>80.55</td>\n",
       "      <td>80.66</td>\n",
       "      <td>77.46</td>\n",
       "      <td>77.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>81.22</td>\n",
       "      <td>81.14</td>\n",
       "      <td>77.42</td>\n",
       "      <td>76.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-12-02</td>\n",
       "      <td>79.98</td>\n",
       "      <td>80.05</td>\n",
       "      <td>77.46</td>\n",
       "      <td>77.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  FrontMonth  SecondMonth  ThirteenthMonth  FourteenthMonth\n",
       "0  2022-11-28       77.24        77.33            74.39            74.02\n",
       "1  2022-11-29       78.20        78.28            75.22            74.83\n",
       "2  2022-11-30       80.55        80.66            77.46            77.01\n",
       "3  2022-12-01       81.22        81.14            77.42            76.98\n",
       "4  2022-12-02       79.98        80.05            77.46            77.07"
      ]
     },
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     "output_type": "display_data"
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      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>lastBusinessDay</th>\n",
       "      <th>lastTradingDay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-11-30</td>\n",
       "      <td>2022-12-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>2023-01-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-31</td>\n",
       "      <td>2023-02-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-02-28</td>\n",
       "      <td>2023-03-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-03-31</td>\n",
       "      <td>2023-04-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  lastBusinessDay lastTradingDay\n",
       "0      2022-11-30     2022-12-20\n",
       "1      2022-12-30     2023-01-20\n",
       "2      2023-01-31     2023-02-21\n",
       "3      2023-02-28     2023-03-21\n",
       "4      2023-03-31     2023-04-20"
      ]
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   ],
   "source": [
    "for ind in datos.index:\n",
    "    datos.loc[ind,'date'] = dt.datetime.strptime(datos.loc[ind,'date'], '%Y-%m-%d').date()\n",
    "\n",
    "display(datos.head())\n",
    "\n",
    "for ind in last_Day.index:\n",
    "    last_Day.loc[ind,'lastBusinessDay'] = dt.datetime.strptime(last_Day.loc[ind,'lastBusinessDay'], '%Y-%m-%d').date()\n",
    "    last_Day.loc[ind,'lastTradingDay']  = dt.datetime.strptime(last_Day.loc[ind,'lastTradingDay'], '%Y-%m-%d').date()\n",
    "\n",
    "display(last_Day.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94aad203",
   "metadata": {},
   "source": [
    "### Insert a duplicate of the first row in the last_Day dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8f9d925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14 entries, 0 to 13\n",
      "Data columns (total 2 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   lastBusinessDay  14 non-null     object\n",
      " 1   lastTradingDay   14 non-null     object\n",
      "dtypes: object(2)\n",
      "memory usage: 352.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "last_Day.info()\n",
    "\n",
    "r1 = []\n",
    "r1.insert(0, {'lastBusinessDay': last_Day.loc[0,'lastBusinessDay'], 'lastTradingDay': last_Day.loc[0,'lastTradingDay']})\n",
    "\n",
    "last_Day = pd.concat([pd.DataFrame(r1), last_Day], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfbe4ae4",
   "metadata": {},
   "source": [
    "### Compute synthetic front month and thirteenth month contracts upon calculating the cumulative roll differential"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "247651a6",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>FrontMonth</th>\n",
       "      <th>SecondMonth</th>\n",
       "      <th>ThirteenthMonth</th>\n",
       "      <th>FourteenthMonth</th>\n",
       "      <th>FrontSynthetic</th>\n",
       "      <th>ThirteenthSynthetic</th>\n",
       "      <th>Carry</th>\n",
       "      <th>CarrySMA20</th>\n",
       "      <th>CarrySMA5</th>\n",
       "      <th>CarryMom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>2024-01-16</td>\n",
       "      <td>72.40</td>\n",
       "      <td>72.52</td>\n",
       "      <td>69.99</td>\n",
       "      <td>69.67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>2024-01-17</td>\n",
       "      <td>72.56</td>\n",
       "      <td>72.48</td>\n",
       "      <td>69.75</td>\n",
       "      <td>69.45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>2024-01-18</td>\n",
       "      <td>74.08</td>\n",
       "      <td>73.95</td>\n",
       "      <td>70.67</td>\n",
       "      <td>70.33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>2024-01-19</td>\n",
       "      <td>73.41</td>\n",
       "      <td>73.25</td>\n",
       "      <td>69.97</td>\n",
       "      <td>69.65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>75.19</td>\n",
       "      <td>74.76</td>\n",
       "      <td>71.44</td>\n",
       "      <td>71.09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  FrontMonth  SecondMonth  ThirteenthMonth  FourteenthMonth  \\\n",
       "291  2024-01-16       72.40        72.52            69.99            69.67   \n",
       "292  2024-01-17       72.56        72.48            69.75            69.45   \n",
       "293  2024-01-18       74.08        73.95            70.67            70.33   \n",
       "294  2024-01-19       73.41        73.25            69.97            69.65   \n",
       "295  2024-01-22       75.19        74.76            71.44            71.09   \n",
       "\n",
       "     FrontSynthetic  ThirteenthSynthetic  Carry  CarrySMA20  CarrySMA5  \\\n",
       "291               0                    0      0           0          0   \n",
       "292               0                    0      0           0          0   \n",
       "293               0                    0      0           0          0   \n",
       "294               0                    0      0           0          0   \n",
       "295               0                    0      0           0          0   \n",
       "\n",
       "     CarryMom  \n",
       "291         0  \n",
       "292         0  \n",
       "293         0  \n",
       "294         0  \n",
       "295         0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datos['FrontSynthetic'] = 0\n",
    "datos['ThirteenthSynthetic'] = 0\n",
    "datos['Carry'] = 0\n",
    "datos['CarrySMA20'] = 0\n",
    "datos['CarrySMA5'] = 0\n",
    "datos['CarryMom'] = 0\n",
    "\n",
    "display(datos.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "15ad3ed0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-01-22\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>FrontMonth</th>\n",
       "      <th>SecondMonth</th>\n",
       "      <th>ThirteenthMonth</th>\n",
       "      <th>FourteenthMonth</th>\n",
       "      <th>FrontSynthetic</th>\n",
       "      <th>ThirteenthSynthetic</th>\n",
       "      <th>Carry</th>\n",
       "      <th>CarrySMA20</th>\n",
       "      <th>CarrySMA5</th>\n",
       "      <th>CarryMom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-11-28</td>\n",
       "      <td>77.24</td>\n",
       "      <td>77.33</td>\n",
       "      <td>74.39</td>\n",
       "      <td>74.02</td>\n",
       "      <td>74.85</td>\n",
       "      <td>69.13</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-11-29</td>\n",
       "      <td>78.20</td>\n",
       "      <td>78.28</td>\n",
       "      <td>75.22</td>\n",
       "      <td>74.83</td>\n",
       "      <td>75.81</td>\n",
       "      <td>69.96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-11-30</td>\n",
       "      <td>80.55</td>\n",
       "      <td>80.66</td>\n",
       "      <td>77.46</td>\n",
       "      <td>77.01</td>\n",
       "      <td>78.16</td>\n",
       "      <td>72.20</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-12-01</td>\n",
       "      <td>81.22</td>\n",
       "      <td>81.14</td>\n",
       "      <td>77.42</td>\n",
       "      <td>76.98</td>\n",
       "      <td>78.64</td>\n",
       "      <td>72.17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-12-02</td>\n",
       "      <td>79.98</td>\n",
       "      <td>80.05</td>\n",
       "      <td>77.46</td>\n",
       "      <td>77.07</td>\n",
       "      <td>77.55</td>\n",
       "      <td>72.26</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  FrontMonth  SecondMonth  ThirteenthMonth  FourteenthMonth  \\\n",
       "0  2022-11-28       77.24        77.33            74.39            74.02   \n",
       "1  2022-11-29       78.20        78.28            75.22            74.83   \n",
       "2  2022-11-30       80.55        80.66            77.46            77.01   \n",
       "3  2022-12-01       81.22        81.14            77.42            76.98   \n",
       "4  2022-12-02       79.98        80.05            77.46            77.07   \n",
       "\n",
       "   FrontSynthetic  ThirteenthSynthetic  Carry  CarrySMA20  CarrySMA5  CarryMom  \n",
       "0           74.85                69.13      0           0          0         0  \n",
       "1           75.81                69.96      0           0          0         0  \n",
       "2           78.16                72.20      0           0          0         0  \n",
       "3           78.64                72.17      0           0          0         0  \n",
       "4           77.55                72.26      0           0          0         0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create synthetic contract\n",
    "\n",
    "roll_diff_F = 0\n",
    "roll_diff_T = 0\n",
    "\n",
    "# index of date used to compute synthetic contracts (could also be today or last date of the series)\n",
    "indE = 295    # 22-01-2024\n",
    "print(datos.loc[indE,'date'])\n",
    "\n",
    "# find index of previous closer lastTradingDay\n",
    "for indT in range(last_Day.index[-1],-1,-1):\n",
    "    if datos.loc[indE,'date'] > last_Day.loc[indT,'lastTradingDay']:\n",
    "        break\n",
    "\n",
    "# find closer lastBusinessDay\n",
    "indB = indT + 1\n",
    "\n",
    "if datos.loc[indE,'date'] == last_Day.loc[indB,'lastBusinessDay']:\n",
    "    roll_diff_F = roll_diff_F + datos.loc[indE,'SecondMonth']     - datos.loc[indE,'FrontMonth']\n",
    "    roll_diff_T = roll_diff_T + datos.loc[indE,'FourteenthMonth'] - datos.loc[indE,'ThirteenthMonth']\n",
    "    datos.loc[indE,'FrontSynthetic'] = datos.loc[indE,'FrontMonth'] + roll_diff_F\n",
    "    datos.loc[indE,'ThirteenthSynthetic'] = datos.loc[indE,'ThirteenthMonth'] + roll_diff_T\n",
    "elif datos.loc[indE,'date'] < last_Day.loc[indB,'lastBusinessDay']:\n",
    "    datos.loc[indE,'FrontSynthetic'] = datos.loc[indE,'FrontMonth'] + roll_diff_F\n",
    "    datos.loc[indE,'ThirteenthSynthetic'] = datos.loc[indE,'ThirteenthMonth'] + roll_diff_T\n",
    "else:\n",
    "    datos.loc[indE,'FrontSynthetic'] = datos.loc[indE,'SecondMonth'] + roll_diff_F\n",
    "    datos.loc[indE,'ThirteenthSynthetic'] = datos.loc[indE,'FourteenthMonth'] + roll_diff_T\n",
    "\n",
    "for ind in range(indE-1,-1,-1):\n",
    "    if datos.loc[ind,'date'] == last_Day.loc[indT,'lastTradingDay']:\n",
    "        indT = indT - 1\n",
    "        indB = indB - 1\n",
    "        datos.loc[ind,'FrontSynthetic'] = datos.loc[ind,'SecondMonth'] + roll_diff_F\n",
    "        datos.loc[ind,'ThirteenthSynthetic'] = datos.loc[ind,'FourteenthMonth'] + roll_diff_T\n",
    "    else:\n",
    "        if datos.loc[ind,'date'] == last_Day.loc[indB,'lastBusinessDay']:\n",
    "            roll_diff_F = roll_diff_F + datos.loc[ind,'SecondMonth']     - datos.loc[ind,'FrontMonth']\n",
    "            roll_diff_T = roll_diff_T + datos.loc[ind,'FourteenthMonth'] - datos.loc[ind,'ThirteenthMonth']\n",
    "            datos.loc[ind,'FrontSynthetic'] = datos.loc[ind,'FrontMonth'] + roll_diff_F\n",
    "            datos.loc[ind,'ThirteenthSynthetic'] = datos.loc[ind,'ThirteenthMonth'] + roll_diff_T\n",
    "        elif datos.loc[ind,'date'] < last_Day.loc[indB,'lastBusinessDay']:\n",
    "            datos.loc[ind,'FrontSynthetic'] = datos.loc[ind,'FrontMonth'] + roll_diff_F\n",
    "            datos.loc[ind,'ThirteenthSynthetic'] = datos.loc[ind,'ThirteenthMonth'] + roll_diff_T\n",
    "        else:\n",
    "            datos.loc[ind,'FrontSynthetic'] = datos.loc[ind,'SecondMonth'] + roll_diff_F\n",
    "            datos.loc[ind,'ThirteenthSynthetic'] = datos.loc[ind,'FourteenthMonth'] + roll_diff_T\n",
    "        \n",
    "display(datos.head())\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b9666945",
   "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>date</th>\n",
       "      <th>FrontMonth</th>\n",
       "      <th>SecondMonth</th>\n",
       "      <th>ThirteenthMonth</th>\n",
       "      <th>FourteenthMonth</th>\n",
       "      <th>FrontSynthetic</th>\n",
       "      <th>ThirteenthSynthetic</th>\n",
       "      <th>Carry</th>\n",
       "      <th>CarrySMA20</th>\n",
       "      <th>CarrySMA5</th>\n",
       "      <th>CarryMom</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>2024-01-16</td>\n",
       "      <td>72.40</td>\n",
       "      <td>72.52</td>\n",
       "      <td>69.99</td>\n",
       "      <td>69.67</td>\n",
       "      <td>72.52</td>\n",
       "      <td>69.67</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.0825</td>\n",
       "      <td>2.306</td>\n",
       "      <td>0.3275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>2024-01-17</td>\n",
       "      <td>72.56</td>\n",
       "      <td>72.48</td>\n",
       "      <td>69.75</td>\n",
       "      <td>69.45</td>\n",
       "      <td>72.48</td>\n",
       "      <td>69.45</td>\n",
       "      <td>2.81</td>\n",
       "      <td>2.2040</td>\n",
       "      <td>2.426</td>\n",
       "      <td>0.6060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>2024-01-18</td>\n",
       "      <td>74.08</td>\n",
       "      <td>73.95</td>\n",
       "      <td>70.67</td>\n",
       "      <td>70.33</td>\n",
       "      <td>73.95</td>\n",
       "      <td>70.33</td>\n",
       "      <td>3.41</td>\n",
       "      <td>2.3505</td>\n",
       "      <td>2.642</td>\n",
       "      <td>1.0595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>2024-01-19</td>\n",
       "      <td>73.41</td>\n",
       "      <td>73.25</td>\n",
       "      <td>69.97</td>\n",
       "      <td>69.65</td>\n",
       "      <td>73.25</td>\n",
       "      <td>69.65</td>\n",
       "      <td>3.44</td>\n",
       "      <td>2.4340</td>\n",
       "      <td>2.872</td>\n",
       "      <td>1.0060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>75.19</td>\n",
       "      <td>74.76</td>\n",
       "      <td>71.44</td>\n",
       "      <td>71.09</td>\n",
       "      <td>74.76</td>\n",
       "      <td>71.09</td>\n",
       "      <td>3.75</td>\n",
       "      <td>2.5195</td>\n",
       "      <td>3.164</td>\n",
       "      <td>1.2305</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  FrontMonth  SecondMonth  ThirteenthMonth  FourteenthMonth  \\\n",
       "291  2024-01-16       72.40        72.52            69.99            69.67   \n",
       "292  2024-01-17       72.56        72.48            69.75            69.45   \n",
       "293  2024-01-18       74.08        73.95            70.67            70.33   \n",
       "294  2024-01-19       73.41        73.25            69.97            69.65   \n",
       "295  2024-01-22       75.19        74.76            71.44            71.09   \n",
       "\n",
       "     FrontSynthetic  ThirteenthSynthetic  Carry  CarrySMA20  CarrySMA5  \\\n",
       "291           72.52                69.67   2.41      2.0825      2.306   \n",
       "292           72.48                69.45   2.81      2.2040      2.426   \n",
       "293           73.95                70.33   3.41      2.3505      2.642   \n",
       "294           73.25                69.65   3.44      2.4340      2.872   \n",
       "295           74.76                71.09   3.75      2.5195      3.164   \n",
       "\n",
       "     CarryMom  \n",
       "291    0.3275  \n",
       "292    0.6060  \n",
       "293    1.0595  \n",
       "294    1.0060  \n",
       "295    1.2305  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#datos['Carry'] = datos['FrontSynthetic'] - datos['ThirteenthSynthetic']\n",
    "\n",
    "datos['Carry'] = datos['FrontMonth'] - datos['ThirteenthMonth']\n",
    "\n",
    "datos['CarrySMA20'] = datos['Carry'].rolling(20).mean()\n",
    "\n",
    "datos['CarrySMA5'] = datos['Carry'].rolling(5).mean()\n",
    "\n",
    "datos['CarryMom'] = datos['Carry'] - datos['CarrySMA20']\n",
    "\n",
    "display(datos.tail())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec583ab5",
   "metadata": {},
   "source": [
    "### Graph of WTI price against the annual Carry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0d68c67e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:red'\n",
    "ax1.set_ylabel('WTI Price ($/b)', rotation=90)\n",
    "ax1.plot(datos['date'].iloc[:], datos['FrontMonth'].iloc[:], color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Carry ($/b)', rotation=90)\n",
    "ax2.plot(datos['date'].iloc[:], datos['Carry'].iloc[:], color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "plt.grid(color = 'gray', linestyle = '--', linewidth = 0.5)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e35493b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-12-30\n",
      "2024-01-02\n"
     ]
    }
   ],
   "source": [
    "print(datos.loc[23,'date'])\n",
    "print(datos.loc[281,'date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc1acb36",
   "metadata": {},
   "source": [
    "### Backtesting the Carry strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aef303db",
   "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>date</th>\n",
       "      <th>PnL</th>\n",
       "      <th>logPnL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-03</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-0.043768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-04</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>-0.055524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.011579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-06</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.001683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   PnL    logPnL\n",
       "0  2022-12-30  0.00  0.000000\n",
       "1  2023-01-03 -3.33 -0.043768\n",
       "2  2023-01-04 -4.02 -0.055524\n",
       "3  2023-01-05  0.82  0.011579\n",
       "4  2023-01-06  0.12  0.001683"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total PnL in $/b for the given time period:\n",
      "-13.26\n",
      "Total log-return (%) for the given time period:\n",
      "-18.30\n"
     ]
    }
   ],
   "source": [
    "# WTI Carry strategy with rollover on the last business day of each month\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# these two indices determine initial and final dates for the analysis\n",
    "ind = 23\n",
    "indE = 281   # End date\n",
    "\n",
    "pos = 0\n",
    "open_p = -1\n",
    "close_p = -1\n",
    "PnL = [0]\n",
    "logPnL = [0]\n",
    "cross_index = []\n",
    "\n",
    "open_p = datos.iloc[ind].FrontSynthetic\n",
    "if datos.iloc[ind].Carry > 0:\n",
    "    pos = 1\n",
    "else:\n",
    "    pos = -1\n",
    "cross_index.append(ind)\n",
    "\n",
    "# change position when Carry changes sign\n",
    "for index in datos.iloc[ind+1:indE+1].iterrows():\n",
    "    indy = index[0]\n",
    "    \n",
    "    close_p = datos['FrontSynthetic'].iloc[indy]\n",
    "    PnL.append(pos*(close_p-open_p))\n",
    "    logPnL.append(pos*np.log(close_p/open_p))\n",
    "    open_p = close_p\n",
    "    \n",
    "    if datos.iloc[indy].Carry*datos.iloc[indy-1].Carry < 0:\n",
    "        \n",
    "        if datos.iloc[indy].Carry > 0:\n",
    "            pos = 1\n",
    "        else:\n",
    "            pos = -1\n",
    "        cross_index.append(indy)\n",
    "\n",
    "results = pd.DataFrame(\n",
    "                {'date': datos['date'].iloc[ind:indE+1],\n",
    "                 'PnL': PnL,\n",
    "                 'logPnL': logPnL\n",
    "                })\n",
    "results.reset_index(drop=True, inplace=True)\n",
    "display(results.head())\n",
    "\n",
    "print('Total PnL in $/b for the given time period:')\n",
    "print('%.2f' % results.PnL.sum())\n",
    "\n",
    "logPnL_sum = results.logPnL.sum() * 100\n",
    "\n",
    "print('Total log-return (%) for the given time period:')\n",
    "print('%.2f' % logPnL_sum)\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f69b7b99",
   "metadata": {},
   "source": [
    "The total log-return here is in close agreement with the results presented in [2] which show a log-return of approximately -20% for 2023. This is one of the few negative yearly performances over the last 30-year period. Furthermore, as one can see in the file _WTI-Carry-Momentum-06-23-06-24.ipynb_, where we obtain a log-return of 14.61% from june 2023 to June 2024, this loss is quickly offset in 2024.\n",
    "\n",
    "[2] I. Bouchouev, Energy Quantamentals: Myths and Realities about Algorithmic Oil Traders, Oxford Institute for Energy Studies (2024)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2403a732",
   "metadata": {},
   "source": [
    "### Obtain results for every month in the time interval of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "70b57534",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>PnL</th>\n",
       "      <th>logPnL</th>\n",
       "      <th>Month</th>\n",
       "      <th>logPnL_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023</td>\n",
       "      <td>January</td>\n",
       "      <td>-1.58</td>\n",
       "      <td>-0.020528</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>-2.05282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023</td>\n",
       "      <td>February</td>\n",
       "      <td>-2.12</td>\n",
       "      <td>-0.028223</td>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>-4.875158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023</td>\n",
       "      <td>March</td>\n",
       "      <td>-1.52</td>\n",
       "      <td>-0.020737</td>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>-6.948903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>April</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.013419</td>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>-5.606968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>May</td>\n",
       "      <td>-8.52</td>\n",
       "      <td>-0.123170</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>-17.923989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023</td>\n",
       "      <td>June</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0.036258</td>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>-14.298214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023</td>\n",
       "      <td>July</td>\n",
       "      <td>11.02</td>\n",
       "      <td>0.151434</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>0.845184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023</td>\n",
       "      <td>August</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.029031</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>3.748309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023</td>\n",
       "      <td>September</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.092570</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>13.005313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023</td>\n",
       "      <td>October</td>\n",
       "      <td>-7.78</td>\n",
       "      <td>-0.091951</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>3.810225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023</td>\n",
       "      <td>November</td>\n",
       "      <td>-4.54</td>\n",
       "      <td>-0.057843</td>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>-1.97409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2023</td>\n",
       "      <td>December</td>\n",
       "      <td>-10.52</td>\n",
       "      <td>-0.146181</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>-16.592173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2024</td>\n",
       "      <td>January</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-0.017128</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>-18.304976</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year      month    PnL    logPnL      Month   logPnL_c\n",
       "0   2023    January  -1.58 -0.020528 2023-01-01   -2.05282\n",
       "1   2023   February  -2.12 -0.028223 2023-02-01  -4.875158\n",
       "2   2023      March  -1.52 -0.020737 2023-03-01  -6.948903\n",
       "3   2023      April   0.98  0.013419 2023-04-01  -5.606968\n",
       "4   2023        May  -8.52 -0.123170 2023-05-01 -17.923989\n",
       "5   2023       June   2.40  0.036258 2023-06-01 -14.298214\n",
       "6   2023       July  11.02  0.151434 2023-07-01   0.845184\n",
       "7   2023     August   2.31  0.029031 2023-08-01   3.748309\n",
       "8   2023  September   7.83  0.092570 2023-09-01  13.005313\n",
       "9   2023    October  -7.78 -0.091951 2023-10-01   3.810225\n",
       "10  2023   November  -4.54 -0.057843 2023-11-01   -1.97409\n",
       "11  2023   December -10.52 -0.146181 2023-12-01 -16.592173\n",
       "12  2024    January  -1.22 -0.017128 2024-01-01 -18.304976"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.date = pd.to_datetime(results.date)\n",
    "PnL_monthly = results.groupby([results['date'].dt.year.rename('year'), results['date'].dt.month_name().rename('month')], sort=False)[['PnL','logPnL']].sum().reset_index()\n",
    "PnL_monthly['Month'] = pd.to_datetime(PnL_monthly['year'].astype(str)  + PnL_monthly['month'], format='%Y%B')\n",
    "\n",
    "PnL_monthly.drop([0], inplace=True)\n",
    "PnL_monthly.reset_index(inplace=True, drop=True)\n",
    "PnL_monthly['logPnL_c'] = ''\n",
    "PnL_monthly.loc[0,'logPnL_c'] = PnL_monthly.loc[0,'logPnL'] * 100\n",
    "for ind in PnL_monthly.index[1:]:\n",
    "    PnL_monthly.loc[ind,'logPnL_c'] = PnL_monthly.loc[ind-1,'logPnL_c'] + PnL_monthly.loc[ind,'logPnL'] * 100\n",
    "    \n",
    "display(PnL_monthly)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "177a9199",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax1.set_ylabel('Monthly PnL ($/b)', rotation=90)\n",
    "ax1.bar(PnL_monthly.Month, PnL_monthly.PnL, width=10.8)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "plt.grid(color = 'gray', linestyle = '--', linewidth = 0.5)\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "color = 'tab:red'\n",
    "ax2.set_ylabel('Cumulative Return (%)', rotation=90)\n",
    "ax2.plot(PnL_monthly.Month, PnL_monthly.logPnL_c, color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "#ax2.set_ylim([-20, 20])\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfee140d",
   "metadata": {},
   "source": [
    "### Total real PnL after commissions in the IBKR platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af67f324",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total PnL (in $) after commissions on IBKR platform for the given time period:\n",
      "-3994.860\n"
     ]
    }
   ],
   "source": [
    "## number of MCL contracts traded\n",
    "n_mcl = 3\n",
    "\n",
    "# price of 1 MCL contract\n",
    "pr_mcl = .77\n",
    "\n",
    "# total PnL (in $) after commissions on IBKR before taxes\n",
    "print('Total PnL (in $) after commissions on IBKR platform for the given time period:')\n",
    "# Here we don't take into account the rolling of futures.\n",
    "total_PnL = results.PnL.sum()*100*n_mcl - pr_mcl*n_mcl*2*len(cross_index) - 1*len(cross_index)\n",
    "print('%.3f' % total_PnL)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4b60b25",
   "metadata": {},
   "source": [
    "## WTI Carry-Momentum Strategy\n",
    "\n",
    "The main problem with following the sign of the carry signal is that the oil market does not move regularly between  backwardation and contango and, thus, this strategy does not respond efficiently to swiftly altering market conditions. This is why it makes sense to focus on the change of carry instead of the carry itself. To do this, we can apply the widely used momentum metric to the carry obtaining the so-called carry momentum strategy. According to it, we go long or short the oil futures depending on whether the carry is larger or smaller than the moving average of carry for a given time period. Using the 20-day moving average which corresponds to approximately a one-month lookback period, this strategy generated 24.7% annualized log-returns with Sharpe ratio 0.64 for the 30-year period from 1992 to 2022 (see [1]).\n",
    "\n",
    "\n",
    "[1] I. Bouchouev, Virtual Barrels, Springer Texts in Business and Economics (2023)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46b89efa",
   "metadata": {},
   "source": [
    "### Graph of WTI price against the 20-day momentum of Carry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b4a4ba54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:red'\n",
    "ax1.set_ylabel('WTI Price ($/b)', rotation=90)\n",
    "ax1.plot(datos['date'].iloc[:], datos['FrontMonth'].iloc[:], color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax2.set_ylabel('Carry Momentum($/b)', rotation=90)\n",
    "ax2.plot(datos['date'].iloc[:], datos['CarryMom'].iloc[:], color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "plt.grid(color = 'gray', linestyle = '--', linewidth = 0.5)\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27249092",
   "metadata": {},
   "source": [
    "### Backtesting the Carry-Momentum strategy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8b733c03",
   "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>date</th>\n",
       "      <th>PnL</th>\n",
       "      <th>logPnL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023-01-03</td>\n",
       "      <td>-3.33</td>\n",
       "      <td>-0.043768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023-01-04</td>\n",
       "      <td>-4.02</td>\n",
       "      <td>-0.055524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>-0.82</td>\n",
       "      <td>-0.011579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-01-06</td>\n",
       "      <td>-0.12</td>\n",
       "      <td>-0.001683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   PnL    logPnL\n",
       "0  2022-12-30  0.00  0.000000\n",
       "1  2023-01-03 -3.33 -0.043768\n",
       "2  2023-01-04 -4.02 -0.055524\n",
       "3  2023-01-05 -0.82 -0.011579\n",
       "4  2023-01-06 -0.12 -0.001683"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total PnL in $/b for the given time period:\n",
      "-9.00\n",
      "Total log-return (%) for the given time period:\n",
      "-13.93\n"
     ]
    }
   ],
   "source": [
    "# WTI Carry strategy with rollover on the last business day of each month\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# these two indices determine initial and final dates for the analysis\n",
    "ind = 23\n",
    "indE = 281   # End date\n",
    "\n",
    "pos = 0\n",
    "open_p = -1\n",
    "close_p = -1\n",
    "PnL = [0]\n",
    "logPnL = [0]\n",
    "cross_index = []\n",
    "\n",
    "open_p = datos.iloc[ind].FrontSynthetic\n",
    "if datos.iloc[ind].CarryMom > 0:\n",
    "    pos = 1\n",
    "else:\n",
    "    pos = -1\n",
    "cross_index.append(ind)\n",
    "\n",
    "# change position when Carry changes sign\n",
    "for index in datos.iloc[ind+1:indE+1].iterrows():\n",
    "    indy = index[0]\n",
    "    \n",
    "    close_p = datos['FrontSynthetic'].iloc[indy]\n",
    "    PnL.append(pos*(close_p-open_p))\n",
    "    logPnL.append(pos*np.log(close_p/open_p))\n",
    "    open_p = close_p\n",
    "    \n",
    "    if datos.iloc[indy].CarryMom*datos.iloc[indy-1].CarryMom < 0:\n",
    "        \n",
    "        if datos.iloc[indy].CarryMom > 0:\n",
    "            pos = 1\n",
    "        else:\n",
    "            pos = -1\n",
    "        cross_index.append(indy)\n",
    "\n",
    "results = pd.DataFrame(\n",
    "                {'date': datos['date'].iloc[ind:indE+1],\n",
    "                 'PnL': PnL,\n",
    "                 'logPnL': logPnL\n",
    "                })\n",
    "results.reset_index(drop=True, inplace=True)\n",
    "display(results.head())\n",
    "\n",
    "print('Total PnL in $/b for the given time period:')\n",
    "print('%.2f' % results.PnL.sum())\n",
    "\n",
    "logPnL_sum = results.logPnL.sum() * 100\n",
    "\n",
    "print('Total log-return (%) for the given time period:')\n",
    "print('%.2f' % logPnL_sum)\n",
    "        \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f3f5270",
   "metadata": {},
   "source": [
    "The total log-return here is in close agreement with the results presented in [2] which show a log-return of approximately -12% for 2023.\n",
    "\n",
    "Again, as in the case of the carry strategy, despite the bad returns in 2023, the picture drastically changes in 2024. One can see in the file _WTI-Carry-Momentum-06-23-06-24.ipynb_ that the carry-momentum strategy produced a log-return of 21.93% from june 2023 to June 2024.\n",
    "\n",
    "[2] I. Bouchouev, Energy Quantamentals: Myths and Realities about Algorithmic Oil Traders, Oxford Institute for Energy Studies (2024)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dccc121f",
   "metadata": {},
   "source": [
    "### Obtain results for every month in the time interval of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3748a127",
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>PnL</th>\n",
       "      <th>logPnL</th>\n",
       "      <th>Month</th>\n",
       "      <th>logPnL_c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2023</td>\n",
       "      <td>January</td>\n",
       "      <td>-15.60</td>\n",
       "      <td>-0.210348</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>-21.034789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2023</td>\n",
       "      <td>February</td>\n",
       "      <td>-3.60</td>\n",
       "      <td>-0.047653</td>\n",
       "      <td>2023-02-01</td>\n",
       "      <td>-25.800063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2023</td>\n",
       "      <td>March</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.027485</td>\n",
       "      <td>2023-03-01</td>\n",
       "      <td>-23.051579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>April</td>\n",
       "      <td>2.16</td>\n",
       "      <td>0.029405</td>\n",
       "      <td>2023-04-01</td>\n",
       "      <td>-20.111045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>May</td>\n",
       "      <td>-0.06</td>\n",
       "      <td>-0.002472</td>\n",
       "      <td>2023-05-01</td>\n",
       "      <td>-20.358274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2023</td>\n",
       "      <td>June</td>\n",
       "      <td>-12.30</td>\n",
       "      <td>-0.182107</td>\n",
       "      <td>2023-06-01</td>\n",
       "      <td>-38.56897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2023</td>\n",
       "      <td>July</td>\n",
       "      <td>12.76</td>\n",
       "      <td>0.177418</td>\n",
       "      <td>2023-07-01</td>\n",
       "      <td>-20.827164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2023</td>\n",
       "      <td>August</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.012093</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>-19.617876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2023</td>\n",
       "      <td>September</td>\n",
       "      <td>7.83</td>\n",
       "      <td>0.092570</td>\n",
       "      <td>2023-09-01</td>\n",
       "      <td>-10.360872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2023</td>\n",
       "      <td>October</td>\n",
       "      <td>-5.66</td>\n",
       "      <td>-0.065598</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>-16.920709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2023</td>\n",
       "      <td>November</td>\n",
       "      <td>4.54</td>\n",
       "      <td>0.057843</td>\n",
       "      <td>2023-11-01</td>\n",
       "      <td>-11.136394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2023</td>\n",
       "      <td>December</td>\n",
       "      <td>-0.74</td>\n",
       "      <td>-0.010853</td>\n",
       "      <td>2023-12-01</td>\n",
       "      <td>-12.221694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2024</td>\n",
       "      <td>January</td>\n",
       "      <td>-1.22</td>\n",
       "      <td>-0.017128</td>\n",
       "      <td>2024-01-01</td>\n",
       "      <td>-13.934497</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    year      month    PnL    logPnL      Month   logPnL_c\n",
       "0   2023    January -15.60 -0.210348 2023-01-01 -21.034789\n",
       "1   2023   February  -3.60 -0.047653 2023-02-01 -25.800063\n",
       "2   2023      March   1.90  0.027485 2023-03-01 -23.051579\n",
       "3   2023      April   2.16  0.029405 2023-04-01 -20.111045\n",
       "4   2023        May  -0.06 -0.002472 2023-05-01 -20.358274\n",
       "5   2023       June -12.30 -0.182107 2023-06-01  -38.56897\n",
       "6   2023       July  12.76  0.177418 2023-07-01 -20.827164\n",
       "7   2023     August   0.99  0.012093 2023-08-01 -19.617876\n",
       "8   2023  September   7.83  0.092570 2023-09-01 -10.360872\n",
       "9   2023    October  -5.66 -0.065598 2023-10-01 -16.920709\n",
       "10  2023   November   4.54  0.057843 2023-11-01 -11.136394\n",
       "11  2023   December  -0.74 -0.010853 2023-12-01 -12.221694\n",
       "12  2024    January  -1.22 -0.017128 2024-01-01 -13.934497"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.date = pd.to_datetime(results.date)\n",
    "PnL_monthly = results.groupby([results['date'].dt.year.rename('year'), results['date'].dt.month_name().rename('month')], sort=False)[['PnL','logPnL']].sum().reset_index()\n",
    "PnL_monthly['Month'] = pd.to_datetime(PnL_monthly['year'].astype(str)  + PnL_monthly['month'], format='%Y%B')\n",
    "\n",
    "PnL_monthly.drop([0], inplace=True)\n",
    "PnL_monthly.reset_index(inplace=True, drop=True)\n",
    "PnL_monthly['logPnL_c'] = ''\n",
    "PnL_monthly.loc[0,'logPnL_c'] = PnL_monthly.loc[0,'logPnL'] * 100\n",
    "for ind in PnL_monthly.index[1:]:\n",
    "    PnL_monthly.loc[ind,'logPnL_c'] = PnL_monthly.loc[ind-1,'logPnL_c'] + PnL_monthly.loc[ind,'logPnL'] * 100\n",
    "    \n",
    "display(PnL_monthly)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4e78f4df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax1.set_ylabel('Monthly PnL ($/b)', rotation=90)\n",
    "ax1.bar(PnL_monthly.Month, PnL_monthly.PnL, width=10.8)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "plt.grid(color = 'gray', linestyle = '--', linewidth = 0.5)\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "\n",
    "ax2 = ax1.twinx()\n",
    "\n",
    "color = 'tab:red'\n",
    "ax2.set_ylabel('Cumulative Return (%)', rotation=90)\n",
    "ax2.plot(PnL_monthly.Month, PnL_monthly.logPnL_c, color=color)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "#ax2.set_ylim([-20, 20])\n",
    "\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "708ba628",
   "metadata": {},
   "source": [
    "### Total real PnL after commissions in the IBKR platform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1df87bff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total PnL (in $) after commissions on IBKR platform for the given time period:\n",
      "-2851.740\n"
     ]
    }
   ],
   "source": [
    "## number of MCL contracts traded\n",
    "n_mcl = 3\n",
    "\n",
    "# price of 1 MCL contract\n",
    "pr_mcl = .77\n",
    "\n",
    "# total PnL (in $) after commissions on IBKR before taxes\n",
    "print('Total PnL (in $) after commissions on IBKR platform for the given time period:')\n",
    "# Here we don't take into account the rolling of futures.\n",
    "total_PnL = results.PnL.sum()*100*n_mcl - pr_mcl*n_mcl*2*len(cross_index) - 1*len(cross_index)\n",
    "print('%.3f' % total_PnL)"
   ]
  }
 ],
 "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": "3.9.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}