wti_vol_predict_rf.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Data\n",
    "\n",
    "**Fundamental Energy Data (Provided)**\n",
    "\n",
    "*Recent and Historic Energy Statistics*\n",
    "\n",
    "* crude_oil_production_deseasonalized.csv\n",
    "    \n",
    "    * Residual from detrended and de-seasonalized oil production data \n",
    "    \n",
    "\n",
    "* petroleum_consumption_deseasonalized.csv\n",
    "    \n",
    "    * Residual from detrended and de-seasonalized oil consumption data\n",
    "\n",
    "\n",
    "<br>\n",
    "\n",
    "**Market Data**\n",
    "\n",
    "*Futures Curves (From Refinitiv)*\n",
    "\n",
    "* wti_futures_curves_diffs.csv\n",
    "    * Contains spot prices, % difference of each consecutive futures contract from spot, and 1-month Implied Volatility (%) on the front-month futures contract (from volatility smile in wti_continuous_options_moneyness.csv)\n",
    "\n",
    "\n",
    "*Energy Sector Stocks and Commodities (HLOC) (Provided)*\n",
    "\n",
    "* wti_price.csv\n",
    "    * Price series of WTI Crude Oil\n",
    "\n",
    "* log_geom_diff_wti_price.csv \n",
    "    * Difference in logs of WTI price \n",
    "    \n",
    "* SPY.csv\n",
    "    * Price series of S&P 500 index\n",
    "\n",
    "* SPY_log_geom_diff.csv\n",
    "    * Difference in logs of SPY price\n",
    "\n",
    "* IYE.csv\n",
    "    * Price series of IYE (U.S. Energy Companies) Index\n",
    "* IYE_log_geom_diff.csv\n",
    "    * Difference in logs of IYE price\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Data\n",
    "\n",
    "# Consumption\n",
    "petroleum_consumption_deseasonalized = pd.read_csv(r\"data/cleaned_data/consumption_production/petroleum_consumption_deseasonalized.csv\")\n",
    "#Production\n",
    "crude_oil_production_deseasonalized = pd.read_csv(r\"data/cleaned_data/consumption_production/crude_oil_production_deseasonalized.csv\")\n",
    "\n",
    "# Futures\n",
    "wti_futures_curves_diffs = pd.read_csv(r\"data/cleaned_data/futures/wti_futures_diffs.csv\")\n",
    "\n",
    "# Stocks and Commodity Prices\n",
    "log_geom_diff_wti_price = pd.read_csv(r\"data/cleaned_data/prices/log_geom_diff_wti_price.csv\")\n",
    "log_geom_diff_spy = pd.read_csv(r\"data/cleaned_data/consumption_production/SPY_log_geom_diff.csv\")\n",
    "log_geom_diff_iye = pd.read_csv(r\"data/cleaned_data/consumption_production/IYE_log_geom_diff.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
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       "      <th>Petroleum Consumption</th>\n",
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       "      <td>1973-01-01</td>\n",
       "      <td>6326.696816</td>\n",
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       "      <td>-334.026214</td>\n",
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       "      <td>1973-02-01</td>\n",
       "      <td>6650.212493</td>\n",
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       "      <td>-190.028064</td>\n",
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       "      <th>2</th>\n",
       "      <td>1973-03-01</td>\n",
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       "      <td>1973-04-01</td>\n",
       "      <td>6267.279326</td>\n",
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       "      <td>-65.054813</td>\n",
       "      <td>NaN</td>\n",
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       "         Date  Petroleum Consumption  Trend    Seasonal  Residual\n",
       "0  1973-01-01            6326.696816    NaN -334.026214       NaN\n",
       "1  1973-02-01            6650.212493    NaN -190.028064       NaN\n",
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       "3  1973-04-01            6267.279326    NaN  -65.054813       NaN\n",
       "4  1973-05-01            6721.727989    NaN  -10.345677       NaN"
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       "      <th>0</th>\n",
       "      <td>1973-01-01</td>\n",
       "      <td>1.649833</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.029030</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1973-02-01</td>\n",
       "      <td>1.525783</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.088471</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1973-03-01</td>\n",
       "      <td>1.667094</td>\n",
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       "      <td>0.038220</td>\n",
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       "      <th>3</th>\n",
       "      <td>1973-04-01</td>\n",
       "      <td>1.616791</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.011250</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1973-05-01</td>\n",
       "      <td>1.665377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.022110</td>\n",
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       "         Date  Crude Oil Production  Trend  Seasonal  Residual\n",
       "0  1973-01-01              1.649833    NaN  0.029030       NaN\n",
       "1  1973-02-01              1.525783    NaN -0.088471       NaN\n",
       "2  1973-03-01              1.667094    NaN  0.038220       NaN\n",
       "3  1973-04-01              1.616791    NaN -0.011250       NaN\n",
       "4  1973-05-01              1.665377    NaN  0.022110       NaN"
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>avg_iv</th>\n",
       "      <th>spot</th>\n",
       "      <th>contract1 % diff from spot</th>\n",
       "      <th>contract2 % diff from spot</th>\n",
       "      <th>contract3 % diff from spot</th>\n",
       "      <th>contract4 % diff from spot</th>\n",
       "      <th>contract5 % diff from spot</th>\n",
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       "      <th>contract51 % diff from spot</th>\n",
       "      <th>contract52 % diff from spot</th>\n",
       "      <th>contract53 % diff from spot</th>\n",
       "      <th>contract54 % diff from spot</th>\n",
       "      <th>contract55 % diff from spot</th>\n",
       "      <th>contract56 % diff from spot</th>\n",
       "      <th>contract57 % diff from spot</th>\n",
       "      <th>contract58 % diff from spot</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-12-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>89.29</td>\n",
       "      <td>-0.000224</td>\n",
       "      <td>0.002808</td>\n",
       "      <td>0.006765</td>\n",
       "      <td>0.010525</td>\n",
       "      <td>0.013968</td>\n",
       "      <td>0.017434</td>\n",
       "      <td>0.020574</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052824</td>\n",
       "      <td>0.052576</td>\n",
       "      <td>0.052328</td>\n",
       "      <td>0.052080</td>\n",
       "      <td>0.051832</td>\n",
       "      <td>0.051460</td>\n",
       "      <td>0.051089</td>\n",
       "      <td>0.050718</td>\n",
       "      <td>0.050347</td>\n",
       "      <td>0.049976</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007-12-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.31</td>\n",
       "      <td>-0.000113</td>\n",
       "      <td>0.002270</td>\n",
       "      <td>0.005007</td>\n",
       "      <td>0.007760</td>\n",
       "      <td>0.010412</td>\n",
       "      <td>0.012846</td>\n",
       "      <td>0.015174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.036989</td>\n",
       "      <td>0.036746</td>\n",
       "      <td>0.036502</td>\n",
       "      <td>0.036259</td>\n",
       "      <td>0.036016</td>\n",
       "      <td>0.035773</td>\n",
       "      <td>0.035530</td>\n",
       "      <td>0.035287</td>\n",
       "      <td>0.035045</td>\n",
       "      <td>0.034802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2007-12-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>87.45</td>\n",
       "      <td>-0.000457</td>\n",
       "      <td>0.001833</td>\n",
       "      <td>0.004595</td>\n",
       "      <td>0.007372</td>\n",
       "      <td>0.009932</td>\n",
       "      <td>0.012387</td>\n",
       "      <td>0.014737</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038722</td>\n",
       "      <td>0.038475</td>\n",
       "      <td>0.038229</td>\n",
       "      <td>0.037982</td>\n",
       "      <td>0.037736</td>\n",
       "      <td>0.037490</td>\n",
       "      <td>0.037244</td>\n",
       "      <td>0.036998</td>\n",
       "      <td>0.036752</td>\n",
       "      <td>0.036506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007-12-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>90.25</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>0.007254</td>\n",
       "      <td>0.011318</td>\n",
       "      <td>0.014957</td>\n",
       "      <td>0.018393</td>\n",
       "      <td>0.021621</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057287</td>\n",
       "      <td>0.057287</td>\n",
       "      <td>0.057287</td>\n",
       "      <td>0.057163</td>\n",
       "      <td>0.057039</td>\n",
       "      <td>0.056915</td>\n",
       "      <td>0.056792</td>\n",
       "      <td>0.056668</td>\n",
       "      <td>0.056544</td>\n",
       "      <td>0.056420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2007-12-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88.23</td>\n",
       "      <td>-0.000566</td>\n",
       "      <td>0.000908</td>\n",
       "      <td>0.003298</td>\n",
       "      <td>0.006273</td>\n",
       "      <td>0.009266</td>\n",
       "      <td>0.012160</td>\n",
       "      <td>0.015071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.044513</td>\n",
       "      <td>0.044513</td>\n",
       "      <td>0.044513</td>\n",
       "      <td>0.044389</td>\n",
       "      <td>0.044266</td>\n",
       "      <td>0.044142</td>\n",
       "      <td>0.044018</td>\n",
       "      <td>0.043895</td>\n",
       "      <td>0.043771</td>\n",
       "      <td>0.043648</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",
       "      <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",
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       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>2019-10-29</td>\n",
       "      <td>37.282222</td>\n",
       "      <td>55.34</td>\n",
       "      <td>-0.003601</td>\n",
       "      <td>-0.006106</td>\n",
       "      <td>-0.005392</td>\n",
       "      <td>-0.002344</td>\n",
       "      <td>0.002536</td>\n",
       "      <td>0.008198</td>\n",
       "      <td>0.014668</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072481</td>\n",
       "      <td>0.072689</td>\n",
       "      <td>0.072481</td>\n",
       "      <td>0.071650</td>\n",
       "      <td>0.070613</td>\n",
       "      <td>0.069785</td>\n",
       "      <td>0.069372</td>\n",
       "      <td>0.068546</td>\n",
       "      <td>0.067104</td>\n",
       "      <td>0.066076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>2019-10-30</td>\n",
       "      <td>37.282222</td>\n",
       "      <td>54.85</td>\n",
       "      <td>-0.003814</td>\n",
       "      <td>-0.004537</td>\n",
       "      <td>-0.001638</td>\n",
       "      <td>0.003476</td>\n",
       "      <td>0.009757</td>\n",
       "      <td>0.016682</td>\n",
       "      <td>0.024085</td>\n",
       "      <td>...</td>\n",
       "      <td>0.078451</td>\n",
       "      <td>0.078451</td>\n",
       "      <td>0.078239</td>\n",
       "      <td>0.077391</td>\n",
       "      <td>0.076123</td>\n",
       "      <td>0.075279</td>\n",
       "      <td>0.074858</td>\n",
       "      <td>0.073806</td>\n",
       "      <td>0.072336</td>\n",
       "      <td>0.071080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2997</th>\n",
       "      <td>2019-10-31</td>\n",
       "      <td>36.934444</td>\n",
       "      <td>54.02</td>\n",
       "      <td>-0.002953</td>\n",
       "      <td>-0.004240</td>\n",
       "      <td>-0.002585</td>\n",
       "      <td>0.001855</td>\n",
       "      <td>0.007272</td>\n",
       "      <td>0.013318</td>\n",
       "      <td>0.020015</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061297</td>\n",
       "      <td>0.061297</td>\n",
       "      <td>0.060880</td>\n",
       "      <td>0.060047</td>\n",
       "      <td>0.058800</td>\n",
       "      <td>0.057971</td>\n",
       "      <td>0.057557</td>\n",
       "      <td>0.056523</td>\n",
       "      <td>0.055078</td>\n",
       "      <td>0.053843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>36.116667</td>\n",
       "      <td>56.04</td>\n",
       "      <td>-0.002847</td>\n",
       "      <td>-0.004087</td>\n",
       "      <td>-0.001959</td>\n",
       "      <td>0.003222</td>\n",
       "      <td>0.009003</td>\n",
       "      <td>0.015217</td>\n",
       "      <td>0.021882</td>\n",
       "      <td>...</td>\n",
       "      <td>0.074181</td>\n",
       "      <td>0.073975</td>\n",
       "      <td>0.073358</td>\n",
       "      <td>0.072536</td>\n",
       "      <td>0.071306</td>\n",
       "      <td>0.070487</td>\n",
       "      <td>0.070078</td>\n",
       "      <td>0.069058</td>\n",
       "      <td>0.067429</td>\n",
       "      <td>0.066210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>2019-11-04</td>\n",
       "      <td>36.523333</td>\n",
       "      <td>56.33</td>\n",
       "      <td>-0.003714</td>\n",
       "      <td>-0.004770</td>\n",
       "      <td>-0.002479</td>\n",
       "      <td>0.002670</td>\n",
       "      <td>0.008956</td>\n",
       "      <td>0.015870</td>\n",
       "      <td>0.023252</td>\n",
       "      <td>...</td>\n",
       "      <td>0.089555</td>\n",
       "      <td>0.089344</td>\n",
       "      <td>0.088923</td>\n",
       "      <td>0.088082</td>\n",
       "      <td>0.087032</td>\n",
       "      <td>0.086194</td>\n",
       "      <td>0.085775</td>\n",
       "      <td>0.084729</td>\n",
       "      <td>0.083269</td>\n",
       "      <td>0.082021</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3000 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Unnamed: 0     avg_iv   spot  contract1 % diff from spot  \\\n",
       "0     2007-12-03        NaN  89.29                   -0.000224   \n",
       "1     2007-12-04        NaN  88.31                   -0.000113   \n",
       "2     2007-12-05        NaN  87.45                   -0.000457   \n",
       "3     2007-12-06        NaN  90.25                    0.000222   \n",
       "4     2007-12-07        NaN  88.23                   -0.000566   \n",
       "...          ...        ...    ...                         ...   \n",
       "2995  2019-10-29  37.282222  55.34                   -0.003601   \n",
       "2996  2019-10-30  37.282222  54.85                   -0.003814   \n",
       "2997  2019-10-31  36.934444  54.02                   -0.002953   \n",
       "2998  2019-11-01  36.116667  56.04                   -0.002847   \n",
       "2999  2019-11-04  36.523333  56.33                   -0.003714   \n",
       "\n",
       "      contract2 % diff from spot  contract3 % diff from spot  \\\n",
       "0                       0.002808                    0.006765   \n",
       "1                       0.002270                    0.005007   \n",
       "2                       0.001833                    0.004595   \n",
       "3                       0.003447                    0.007254   \n",
       "4                       0.000908                    0.003298   \n",
       "...                          ...                         ...   \n",
       "2995                   -0.006106                   -0.005392   \n",
       "2996                   -0.004537                   -0.001638   \n",
       "2997                   -0.004240                   -0.002585   \n",
       "2998                   -0.004087                   -0.001959   \n",
       "2999                   -0.004770                   -0.002479   \n",
       "\n",
       "      contract4 % diff from spot  contract5 % diff from spot  \\\n",
       "0                       0.010525                    0.013968   \n",
       "1                       0.007760                    0.010412   \n",
       "2                       0.007372                    0.009932   \n",
       "3                       0.011318                    0.014957   \n",
       "4                       0.006273                    0.009266   \n",
       "...                          ...                         ...   \n",
       "2995                   -0.002344                    0.002536   \n",
       "2996                    0.003476                    0.009757   \n",
       "2997                    0.001855                    0.007272   \n",
       "2998                    0.003222                    0.009003   \n",
       "2999                    0.002670                    0.008956   \n",
       "\n",
       "      contract6 % diff from spot  contract7 % diff from spot  ...  \\\n",
       "0                       0.017434                    0.020574  ...   \n",
       "1                       0.012846                    0.015174  ...   \n",
       "2                       0.012387                    0.014737  ...   \n",
       "3                       0.018393                    0.021621  ...   \n",
       "4                       0.012160                    0.015071  ...   \n",
       "...                          ...                         ...  ...   \n",
       "2995                    0.008198                    0.014668  ...   \n",
       "2996                    0.016682                    0.024085  ...   \n",
       "2997                    0.013318                    0.020015  ...   \n",
       "2998                    0.015217                    0.021882  ...   \n",
       "2999                    0.015870                    0.023252  ...   \n",
       "\n",
       "      contract50 % diff from spot  contract51 % diff from spot  \\\n",
       "0                        0.052824                     0.052576   \n",
       "1                        0.036989                     0.036746   \n",
       "2                        0.038722                     0.038475   \n",
       "3                        0.057287                     0.057287   \n",
       "4                        0.044513                     0.044513   \n",
       "...                           ...                          ...   \n",
       "2995                     0.072481                     0.072689   \n",
       "2996                     0.078451                     0.078451   \n",
       "2997                     0.061297                     0.061297   \n",
       "2998                     0.074181                     0.073975   \n",
       "2999                     0.089555                     0.089344   \n",
       "\n",
       "      contract52 % diff from spot  contract53 % diff from spot  \\\n",
       "0                        0.052328                     0.052080   \n",
       "1                        0.036502                     0.036259   \n",
       "2                        0.038229                     0.037982   \n",
       "3                        0.057287                     0.057163   \n",
       "4                        0.044513                     0.044389   \n",
       "...                           ...                          ...   \n",
       "2995                     0.072481                     0.071650   \n",
       "2996                     0.078239                     0.077391   \n",
       "2997                     0.060880                     0.060047   \n",
       "2998                     0.073358                     0.072536   \n",
       "2999                     0.088923                     0.088082   \n",
       "\n",
       "      contract54 % diff from spot  contract55 % diff from spot  \\\n",
       "0                        0.051832                     0.051460   \n",
       "1                        0.036016                     0.035773   \n",
       "2                        0.037736                     0.037490   \n",
       "3                        0.057039                     0.056915   \n",
       "4                        0.044266                     0.044142   \n",
       "...                           ...                          ...   \n",
       "2995                     0.070613                     0.069785   \n",
       "2996                     0.076123                     0.075279   \n",
       "2997                     0.058800                     0.057971   \n",
       "2998                     0.071306                     0.070487   \n",
       "2999                     0.087032                     0.086194   \n",
       "\n",
       "      contract56 % diff from spot  contract57 % diff from spot  \\\n",
       "0                        0.051089                     0.050718   \n",
       "1                        0.035530                     0.035287   \n",
       "2                        0.037244                     0.036998   \n",
       "3                        0.056792                     0.056668   \n",
       "4                        0.044018                     0.043895   \n",
       "...                           ...                          ...   \n",
       "2995                     0.069372                     0.068546   \n",
       "2996                     0.074858                     0.073806   \n",
       "2997                     0.057557                     0.056523   \n",
       "2998                     0.070078                     0.069058   \n",
       "2999                     0.085775                     0.084729   \n",
       "\n",
       "      contract58 % diff from spot  contract59 % diff from spot  \n",
       "0                        0.050347                     0.049976  \n",
       "1                        0.035045                     0.034802  \n",
       "2                        0.036752                     0.036506  \n",
       "3                        0.056544                     0.056420  \n",
       "4                        0.043771                     0.043648  \n",
       "...                           ...                          ...  \n",
       "2995                     0.067104                     0.066076  \n",
       "2996                     0.072336                     0.071080  \n",
       "2997                     0.055078                     0.053843  \n",
       "2998                     0.067429                     0.066210  \n",
       "2999                     0.083269                     0.082021  \n",
       "\n",
       "[3000 rows x 62 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date_Time</th>\n",
       "      <th>Log Geom Diff WTI Crude Oil Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-19</td>\n",
       "      <td>-0.021082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-18</td>\n",
       "      <td>0.008513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-17</td>\n",
       "      <td>-0.020802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-01-16</td>\n",
       "      <td>-0.002201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-01-11</td>\n",
       "      <td>-0.010890</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    Date_Time  Log Geom Diff WTI Crude Oil Price\n",
       "0  2024-01-19                          -0.021082\n",
       "1  2024-01-18                           0.008513\n",
       "2  2024-01-17                          -0.020802\n",
       "3  2024-01-16                          -0.002201\n",
       "4  2024-01-11                          -0.010890"
      ]
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       "  <thead>\n",
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       "      <th>Date-Time</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-01-31</td>\n",
       "      <td>-0.013003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-01-30</td>\n",
       "      <td>0.016452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-01-29</td>\n",
       "      <td>0.000774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-01-26</td>\n",
       "      <td>-0.007888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024-01-25</td>\n",
       "      <td>0.001271</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Date-Time     Close\n",
       "0  2024-01-31 -0.013003\n",
       "1  2024-01-30  0.016452\n",
       "2  2024-01-29  0.000774\n",
       "3  2024-01-26 -0.007888\n",
       "4  2024-01-25  0.001271"
      ]
     },
     "metadata": {},
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    },
    {
     "data": {
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       "      <th>Date</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2000-06-19 00:00:00-04:00</td>\n",
       "      <td>-0.022281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-06-20 00:00:00-04:00</td>\n",
       "      <td>-0.016807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-06-21 00:00:00-04:00</td>\n",
       "      <td>0.037275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-06-22 00:00:00-04:00</td>\n",
       "      <td>-0.022321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-06-23 00:00:00-04:00</td>\n",
       "      <td>0.002162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Date     Close\n",
       "0  2000-06-19 00:00:00-04:00 -0.022281\n",
       "1  2000-06-20 00:00:00-04:00 -0.016807\n",
       "2  2000-06-21 00:00:00-04:00  0.037275\n",
       "3  2000-06-22 00:00:00-04:00 -0.022321\n",
       "4  2000-06-23 00:00:00-04:00  0.002162"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# wti_futures_curves_diffs.dropna(inplace=True)\n",
    "\n",
    "display(petroleum_consumption_deseasonalized.head())\n",
    "display(crude_oil_production_deseasonalized.head())\n",
    "display(wti_futures_curves_diffs.head(3000))\n",
    "display(log_geom_diff_wti_price.head())\n",
    "display(log_geom_diff_spy.head())\n",
    "display(log_geom_diff_iye.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get monthly volatility in WTI Oil Price\n",
    "log_geom_diff_wti_price.set_index('Date_Time', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9264\n"
     ]
    }
   ],
   "source": [
    "print(len(log_geom_diff_wti_price))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "log_geom_diff_wti_price.index = pd.to_datetime(log_geom_diff_wti_price.index)\n",
    "WTI_monthly_vol = log_geom_diff_wti_price.groupby(pd.Grouper(freq='ME')).std()\n",
    "WTI_monthly_vol.rename(columns={'WTI_Price': 'WTI_Monthly_Vol'}, inplace=True)\n",
    "WTI_monthly_vol.plot()\n",
    "plt.title('WTI Monthly Volatility')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename \"Unnamed: 0\" to \"Date\" and then convert it to datetime\n",
    "wti_futures_curves_diffs.rename(columns={'Unnamed: 0': 'Date'}, inplace=True)\n",
    "wti_futures_curves_diffs['Date'] = pd.to_datetime(wti_futures_curves_diffs['Date'])\n",
    "\n",
    "# Set the renamed column as the index\n",
    "wti_futures_curves_diffs.set_index('Date', inplace=True)\n",
    "\n",
    "# Assuming your DataFrame is named df and it has a datetime index named 'Date'\n",
    "\n",
    "# Resample the data by month-end frequency and calculate the mean of each month\n",
    "monthly_averages = wti_futures_curves_diffs.resample('ME').mean()\n",
    "\n",
    "# 'M' specifies the month-end frequency, which groups the data by the end of each month\n",
    "# and then calculates the mean for each group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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>avg_iv</th>\n",
       "      <th>spot</th>\n",
       "      <th>contract1 % diff from spot</th>\n",
       "      <th>contract2 % diff from spot</th>\n",
       "      <th>contract3 % diff from spot</th>\n",
       "      <th>contract4 % diff from spot</th>\n",
       "      <th>contract5 % diff from spot</th>\n",
       "      <th>contract6 % diff from spot</th>\n",
       "      <th>contract7 % diff from spot</th>\n",
       "      <th>contract8 % diff from spot</th>\n",
       "      <th>...</th>\n",
       "      <th>contract50 % diff from spot</th>\n",
       "      <th>contract51 % diff from spot</th>\n",
       "      <th>contract52 % diff from spot</th>\n",
       "      <th>contract53 % diff from spot</th>\n",
       "      <th>contract54 % diff from spot</th>\n",
       "      <th>contract55 % diff from spot</th>\n",
       "      <th>contract56 % diff from spot</th>\n",
       "      <th>contract57 % diff from spot</th>\n",
       "      <th>contract58 % diff from spot</th>\n",
       "      <th>contract59 % diff from spot</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>91.687000</td>\n",
       "      <td>-0.000384</td>\n",
       "      <td>0.001049</td>\n",
       "      <td>0.004426</td>\n",
       "      <td>0.008483</td>\n",
       "      <td>0.012594</td>\n",
       "      <td>0.016709</td>\n",
       "      <td>0.020639</td>\n",
       "      <td>0.024382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.072113</td>\n",
       "      <td>0.072001</td>\n",
       "      <td>0.071889</td>\n",
       "      <td>0.071765</td>\n",
       "      <td>0.071622</td>\n",
       "      <td>0.071466</td>\n",
       "      <td>0.071310</td>\n",
       "      <td>0.071154</td>\n",
       "      <td>0.070960</td>\n",
       "      <td>0.070767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-01-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>92.970476</td>\n",
       "      <td>0.000809</td>\n",
       "      <td>0.004434</td>\n",
       "      <td>0.008668</td>\n",
       "      <td>0.012744</td>\n",
       "      <td>0.016753</td>\n",
       "      <td>0.020616</td>\n",
       "      <td>0.024306</td>\n",
       "      <td>0.027872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.075376</td>\n",
       "      <td>0.075072</td>\n",
       "      <td>0.074749</td>\n",
       "      <td>0.074427</td>\n",
       "      <td>0.074087</td>\n",
       "      <td>0.073735</td>\n",
       "      <td>0.073355</td>\n",
       "      <td>0.072963</td>\n",
       "      <td>0.072565</td>\n",
       "      <td>0.072185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-02-29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>95.386000</td>\n",
       "      <td>-0.000245</td>\n",
       "      <td>0.002209</td>\n",
       "      <td>0.004823</td>\n",
       "      <td>0.007690</td>\n",
       "      <td>0.010342</td>\n",
       "      <td>0.012809</td>\n",
       "      <td>0.015161</td>\n",
       "      <td>0.017426</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051934</td>\n",
       "      <td>0.051736</td>\n",
       "      <td>0.051520</td>\n",
       "      <td>0.051287</td>\n",
       "      <td>0.051048</td>\n",
       "      <td>0.050798</td>\n",
       "      <td>0.050548</td>\n",
       "      <td>0.050292</td>\n",
       "      <td>0.049997</td>\n",
       "      <td>0.049695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>105.454000</td>\n",
       "      <td>0.001303</td>\n",
       "      <td>0.009811</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>0.025840</td>\n",
       "      <td>0.031987</td>\n",
       "      <td>0.036953</td>\n",
       "      <td>0.041080</td>\n",
       "      <td>0.044942</td>\n",
       "      <td>...</td>\n",
       "      <td>0.082096</td>\n",
       "      <td>0.081769</td>\n",
       "      <td>0.081405</td>\n",
       "      <td>0.081007</td>\n",
       "      <td>0.080598</td>\n",
       "      <td>0.080179</td>\n",
       "      <td>0.079749</td>\n",
       "      <td>0.079296</td>\n",
       "      <td>0.078856</td>\n",
       "      <td>0.078416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008-04-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>112.580455</td>\n",
       "      <td>0.001492</td>\n",
       "      <td>0.007230</td>\n",
       "      <td>0.013614</td>\n",
       "      <td>0.019817</td>\n",
       "      <td>0.025429</td>\n",
       "      <td>0.030617</td>\n",
       "      <td>0.035555</td>\n",
       "      <td>0.040531</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107247</td>\n",
       "      <td>0.107328</td>\n",
       "      <td>0.107400</td>\n",
       "      <td>0.107436</td>\n",
       "      <td>0.107458</td>\n",
       "      <td>0.107480</td>\n",
       "      <td>0.107482</td>\n",
       "      <td>0.107419</td>\n",
       "      <td>0.107351</td>\n",
       "      <td>0.107268</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",
       "      <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>2023-09-30</th>\n",
       "      <td>31.556667</td>\n",
       "      <td>89.425000</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.012949</td>\n",
       "      <td>0.026751</td>\n",
       "      <td>0.040713</td>\n",
       "      <td>0.053779</td>\n",
       "      <td>0.065470</td>\n",
       "      <td>0.075980</td>\n",
       "      <td>0.085618</td>\n",
       "      <td>...</td>\n",
       "      <td>0.341490</td>\n",
       "      <td>0.346959</td>\n",
       "      <td>0.352429</td>\n",
       "      <td>0.357137</td>\n",
       "      <td>0.362099</td>\n",
       "      <td>0.366501</td>\n",
       "      <td>0.370964</td>\n",
       "      <td>0.375984</td>\n",
       "      <td>0.381713</td>\n",
       "      <td>0.385944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-31</th>\n",
       "      <td>42.428942</td>\n",
       "      <td>85.639524</td>\n",
       "      <td>0.002942</td>\n",
       "      <td>0.017067</td>\n",
       "      <td>0.031215</td>\n",
       "      <td>0.044201</td>\n",
       "      <td>0.055053</td>\n",
       "      <td>0.064342</td>\n",
       "      <td>0.072573</td>\n",
       "      <td>0.080192</td>\n",
       "      <td>...</td>\n",
       "      <td>0.290071</td>\n",
       "      <td>0.294715</td>\n",
       "      <td>0.298381</td>\n",
       "      <td>0.302550</td>\n",
       "      <td>0.306183</td>\n",
       "      <td>0.309598</td>\n",
       "      <td>0.313519</td>\n",
       "      <td>0.318152</td>\n",
       "      <td>0.321857</td>\n",
       "      <td>0.324379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-11-30</th>\n",
       "      <td>41.192222</td>\n",
       "      <td>77.685000</td>\n",
       "      <td>0.003642</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>0.004934</td>\n",
       "      <td>0.007333</td>\n",
       "      <td>0.010240</td>\n",
       "      <td>0.013748</td>\n",
       "      <td>0.017812</td>\n",
       "      <td>0.022448</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200746</td>\n",
       "      <td>0.203716</td>\n",
       "      <td>0.206916</td>\n",
       "      <td>0.209748</td>\n",
       "      <td>0.212299</td>\n",
       "      <td>0.215572</td>\n",
       "      <td>0.219182</td>\n",
       "      <td>0.221995</td>\n",
       "      <td>0.224213</td>\n",
       "      <td>0.226399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-12-31</th>\n",
       "      <td>36.675444</td>\n",
       "      <td>71.900000</td>\n",
       "      <td>-0.003470</td>\n",
       "      <td>-0.006754</td>\n",
       "      <td>-0.009388</td>\n",
       "      <td>-0.011344</td>\n",
       "      <td>-0.012346</td>\n",
       "      <td>-0.011946</td>\n",
       "      <td>-0.010199</td>\n",
       "      <td>-0.007304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.130222</td>\n",
       "      <td>0.132031</td>\n",
       "      <td>0.133478</td>\n",
       "      <td>0.134604</td>\n",
       "      <td>0.136541</td>\n",
       "      <td>0.138673</td>\n",
       "      <td>0.140052</td>\n",
       "      <td>0.140965</td>\n",
       "      <td>0.141376</td>\n",
       "      <td>0.142331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-01-31</th>\n",
       "      <td>37.582540</td>\n",
       "      <td>72.772143</td>\n",
       "      <td>0.003381</td>\n",
       "      <td>0.002633</td>\n",
       "      <td>0.002130</td>\n",
       "      <td>0.002240</td>\n",
       "      <td>0.003607</td>\n",
       "      <td>0.006301</td>\n",
       "      <td>0.010351</td>\n",
       "      <td>0.015102</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153133</td>\n",
       "      <td>0.154400</td>\n",
       "      <td>0.155069</td>\n",
       "      <td>0.156158</td>\n",
       "      <td>0.158550</td>\n",
       "      <td>0.159500</td>\n",
       "      <td>0.160346</td>\n",
       "      <td>0.160150</td>\n",
       "      <td>0.160588</td>\n",
       "      <td>0.161502</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>194 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               avg_iv        spot  contract1 % diff from spot  \\\n",
       "Date                                                            \n",
       "2007-12-31        NaN   91.687000                   -0.000384   \n",
       "2008-01-31        NaN   92.970476                    0.000809   \n",
       "2008-02-29        NaN   95.386000                   -0.000245   \n",
       "2008-03-31        NaN  105.454000                    0.001303   \n",
       "2008-04-30        NaN  112.580455                    0.001492   \n",
       "...               ...         ...                         ...   \n",
       "2023-09-30  31.556667   89.425000                    0.000282   \n",
       "2023-10-31  42.428942   85.639524                    0.002942   \n",
       "2023-11-30  41.192222   77.685000                    0.003642   \n",
       "2023-12-31  36.675444   71.900000                   -0.003470   \n",
       "2024-01-31  37.582540   72.772143                    0.003381   \n",
       "\n",
       "            contract2 % diff from spot  contract3 % diff from spot  \\\n",
       "Date                                                                 \n",
       "2007-12-31                    0.001049                    0.004426   \n",
       "2008-01-31                    0.004434                    0.008668   \n",
       "2008-02-29                    0.002209                    0.004823   \n",
       "2008-03-31                    0.009811                    0.018443   \n",
       "2008-04-30                    0.007230                    0.013614   \n",
       "...                                ...                         ...   \n",
       "2023-09-30                    0.012949                    0.026751   \n",
       "2023-10-31                    0.017067                    0.031215   \n",
       "2023-11-30                    0.003451                    0.004934   \n",
       "2023-12-31                   -0.006754                   -0.009388   \n",
       "2024-01-31                    0.002633                    0.002130   \n",
       "\n",
       "            contract4 % diff from spot  contract5 % diff from spot  \\\n",
       "Date                                                                 \n",
       "2007-12-31                    0.008483                    0.012594   \n",
       "2008-01-31                    0.012744                    0.016753   \n",
       "2008-02-29                    0.007690                    0.010342   \n",
       "2008-03-31                    0.025840                    0.031987   \n",
       "2008-04-30                    0.019817                    0.025429   \n",
       "...                                ...                         ...   \n",
       "2023-09-30                    0.040713                    0.053779   \n",
       "2023-10-31                    0.044201                    0.055053   \n",
       "2023-11-30                    0.007333                    0.010240   \n",
       "2023-12-31                   -0.011344                   -0.012346   \n",
       "2024-01-31                    0.002240                    0.003607   \n",
       "\n",
       "            contract6 % diff from spot  contract7 % diff from spot  \\\n",
       "Date                                                                 \n",
       "2007-12-31                    0.016709                    0.020639   \n",
       "2008-01-31                    0.020616                    0.024306   \n",
       "2008-02-29                    0.012809                    0.015161   \n",
       "2008-03-31                    0.036953                    0.041080   \n",
       "2008-04-30                    0.030617                    0.035555   \n",
       "...                                ...                         ...   \n",
       "2023-09-30                    0.065470                    0.075980   \n",
       "2023-10-31                    0.064342                    0.072573   \n",
       "2023-11-30                    0.013748                    0.017812   \n",
       "2023-12-31                   -0.011946                   -0.010199   \n",
       "2024-01-31                    0.006301                    0.010351   \n",
       "\n",
       "            contract8 % diff from spot  ...  contract50 % diff from spot  \\\n",
       "Date                                    ...                                \n",
       "2007-12-31                    0.024382  ...                     0.072113   \n",
       "2008-01-31                    0.027872  ...                     0.075376   \n",
       "2008-02-29                    0.017426  ...                     0.051934   \n",
       "2008-03-31                    0.044942  ...                     0.082096   \n",
       "2008-04-30                    0.040531  ...                     0.107247   \n",
       "...                                ...  ...                          ...   \n",
       "2023-09-30                    0.085618  ...                     0.341490   \n",
       "2023-10-31                    0.080192  ...                     0.290071   \n",
       "2023-11-30                    0.022448  ...                     0.200746   \n",
       "2023-12-31                   -0.007304  ...                     0.130222   \n",
       "2024-01-31                    0.015102  ...                     0.153133   \n",
       "\n",
       "            contract51 % diff from spot  contract52 % diff from spot  \\\n",
       "Date                                                                   \n",
       "2007-12-31                     0.072001                     0.071889   \n",
       "2008-01-31                     0.075072                     0.074749   \n",
       "2008-02-29                     0.051736                     0.051520   \n",
       "2008-03-31                     0.081769                     0.081405   \n",
       "2008-04-30                     0.107328                     0.107400   \n",
       "...                                 ...                          ...   \n",
       "2023-09-30                     0.346959                     0.352429   \n",
       "2023-10-31                     0.294715                     0.298381   \n",
       "2023-11-30                     0.203716                     0.206916   \n",
       "2023-12-31                     0.132031                     0.133478   \n",
       "2024-01-31                     0.154400                     0.155069   \n",
       "\n",
       "            contract53 % diff from spot  contract54 % diff from spot  \\\n",
       "Date                                                                   \n",
       "2007-12-31                     0.071765                     0.071622   \n",
       "2008-01-31                     0.074427                     0.074087   \n",
       "2008-02-29                     0.051287                     0.051048   \n",
       "2008-03-31                     0.081007                     0.080598   \n",
       "2008-04-30                     0.107436                     0.107458   \n",
       "...                                 ...                          ...   \n",
       "2023-09-30                     0.357137                     0.362099   \n",
       "2023-10-31                     0.302550                     0.306183   \n",
       "2023-11-30                     0.209748                     0.212299   \n",
       "2023-12-31                     0.134604                     0.136541   \n",
       "2024-01-31                     0.156158                     0.158550   \n",
       "\n",
       "            contract55 % diff from spot  contract56 % diff from spot  \\\n",
       "Date                                                                   \n",
       "2007-12-31                     0.071466                     0.071310   \n",
       "2008-01-31                     0.073735                     0.073355   \n",
       "2008-02-29                     0.050798                     0.050548   \n",
       "2008-03-31                     0.080179                     0.079749   \n",
       "2008-04-30                     0.107480                     0.107482   \n",
       "...                                 ...                          ...   \n",
       "2023-09-30                     0.366501                     0.370964   \n",
       "2023-10-31                     0.309598                     0.313519   \n",
       "2023-11-30                     0.215572                     0.219182   \n",
       "2023-12-31                     0.138673                     0.140052   \n",
       "2024-01-31                     0.159500                     0.160346   \n",
       "\n",
       "            contract57 % diff from spot  contract58 % diff from spot  \\\n",
       "Date                                                                   \n",
       "2007-12-31                     0.071154                     0.070960   \n",
       "2008-01-31                     0.072963                     0.072565   \n",
       "2008-02-29                     0.050292                     0.049997   \n",
       "2008-03-31                     0.079296                     0.078856   \n",
       "2008-04-30                     0.107419                     0.107351   \n",
       "...                                 ...                          ...   \n",
       "2023-09-30                     0.375984                     0.381713   \n",
       "2023-10-31                     0.318152                     0.321857   \n",
       "2023-11-30                     0.221995                     0.224213   \n",
       "2023-12-31                     0.140965                     0.141376   \n",
       "2024-01-31                     0.160150                     0.160588   \n",
       "\n",
       "            contract59 % diff from spot  \n",
       "Date                                     \n",
       "2007-12-31                     0.070767  \n",
       "2008-01-31                     0.072185  \n",
       "2008-02-29                     0.049695  \n",
       "2008-03-31                     0.078416  \n",
       "2008-04-30                     0.107268  \n",
       "...                                 ...  \n",
       "2023-09-30                     0.385944  \n",
       "2023-10-31                     0.324379  \n",
       "2023-11-30                     0.226399  \n",
       "2023-12-31                     0.142331  \n",
       "2024-01-31                     0.161502  \n",
       "\n",
       "[194 rows x 61 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_averages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2007-12-31', '2008-01-31', '2008-02-29', '2008-03-31',\n",
      "               '2008-04-30', '2008-05-31', '2008-06-30', '2008-07-31',\n",
      "               '2008-08-31', '2008-09-30',\n",
      "               ...\n",
      "               '2023-04-30', '2023-05-31', '2023-06-30', '2023-07-31',\n",
      "               '2023-08-31', '2023-09-30', '2023-10-31', '2023-11-30',\n",
      "               '2023-12-31', '2024-01-31'],\n",
      "              dtype='datetime64[ns]', name='Date', length=194, freq='ME')\n",
      "DatetimeIndex(['1986-01-31', '1986-02-28', '1986-03-31', '1986-04-30',\n",
      "               '1986-05-31', '1986-06-30', '1986-07-31', '1986-08-31',\n",
      "               '1986-09-30', '1986-10-31',\n",
      "               ...\n",
      "               '2023-04-30', '2023-05-31', '2023-06-30', '2023-07-31',\n",
      "               '2023-08-31', '2023-09-30', '2023-10-31', '2023-11-30',\n",
      "               '2023-12-31', '2024-01-31'],\n",
      "              dtype='datetime64[ns]', name='Date_Time', length=457, freq='ME')\n"
     ]
    }
   ],
   "source": [
    "# display(wti_futures_curves_diffs)\n",
    "print(monthly_averages.index)\n",
    "print(WTI_monthly_vol.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "263\n",
      "457\n"
     ]
    }
   ],
   "source": [
    "i = 0\n",
    "j = 0\n",
    "for ind in WTI_monthly_vol.index:\n",
    "    j += 1\n",
    "    if ind not in monthly_averages.index:\n",
    "        i += 1\n",
    "\n",
    "print(i)\n",
    "print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['avg_iv', 'spot', 'contract1 % diff from spot',\n",
      "       'contract2 % diff from spot', 'contract3 % diff from spot',\n",
      "       'contract4 % diff from spot', 'contract5 % diff from spot',\n",
      "       'contract6 % diff from spot', 'contract7 % diff from spot',\n",
      "       'contract8 % diff from spot', 'contract9 % diff from spot',\n",
      "       'contract10 % diff from spot', 'contract11 % diff from spot',\n",
      "       'contract12 % diff from spot', 'contract13 % diff from spot',\n",
      "       'contract14 % diff from spot', 'contract15 % diff from spot',\n",
      "       'contract16 % diff from spot', 'contract17 % diff from spot',\n",
      "       'contract18 % diff from spot', 'contract19 % diff from spot',\n",
      "       'contract20 % diff from spot', 'contract21 % diff from spot',\n",
      "       'contract22 % diff from spot', 'contract23 % diff from spot',\n",
      "       'contract24 % diff from spot', 'contract25 % diff from spot',\n",
      "       'contract26 % diff from spot', 'contract27 % diff from spot',\n",
      "       'contract28 % diff from spot', 'contract29 % diff from spot',\n",
      "       'contract30 % diff from spot', 'contract31 % diff from spot',\n",
      "       'contract32 % diff from spot', 'contract33 % diff from spot',\n",
      "       'contract34 % diff from spot', 'contract35 % diff from spot',\n",
      "       'contract36 % diff from spot', 'contract37 % diff from spot',\n",
      "       'contract38 % diff from spot', 'contract39 % diff from spot',\n",
      "       'contract40 % diff from spot', 'contract41 % diff from spot',\n",
      "       'contract42 % diff from spot', 'contract43 % diff from spot',\n",
      "       'contract44 % diff from spot', 'contract45 % diff from spot',\n",
      "       'contract46 % diff from spot', 'contract47 % diff from spot',\n",
      "       'contract48 % diff from spot', 'contract49 % diff from spot',\n",
      "       'contract50 % diff from spot', 'contract51 % diff from spot',\n",
      "       'contract52 % diff from spot', 'contract53 % diff from spot',\n",
      "       'contract54 % diff from spot', 'contract55 % diff from spot',\n",
      "       'contract56 % diff from spot', 'contract57 % diff from spot',\n",
      "       'contract58 % diff from spot', 'contract59 % diff from spot'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(wti_futures_curves_diffs.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Unnamed: 0'] not found in axis\"",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[194], line 8\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m mean_squared_error\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Assuming wti_futures_curves_diffs is your DataFrame\u001b[39;00m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;66;03m# First, remove the 'Unnamed: 0' column if it's just an index\u001b[39;00m\n\u001b[0;32m----> 8\u001b[0m wti_futures_curves_diffs \u001b[38;5;241m=\u001b[39m \u001b[43mwti_futures_curves_diffs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mUnnamed: 0\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;66;03m# Define the features (X) and target (y)\u001b[39;00m\n\u001b[1;32m     11\u001b[0m X \u001b[38;5;241m=\u001b[39m wti_futures_curves_diffs\u001b[38;5;241m.\u001b[39mdrop(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mavg_iv\u001b[39m\u001b[38;5;124m'\u001b[39m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m~/Documents/GitHub/datathon-s24-team4/.conda/lib/python3.11/site-packages/pandas/core/frame.py:5568\u001b[0m, in \u001b[0;36mDataFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5420\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdrop\u001b[39m(\n\u001b[1;32m   5421\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   5422\u001b[0m     labels: IndexLabel \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5429\u001b[0m     errors: IgnoreRaise \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mraise\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   5430\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   5431\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   5432\u001b[0m \u001b[38;5;124;03m    Drop specified labels from rows or columns.\u001b[39;00m\n\u001b[1;32m   5433\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   5566\u001b[0m \u001b[38;5;124;03m            weight  1.0     0.8\u001b[39;00m\n\u001b[1;32m   5567\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 5568\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5569\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlabels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5570\u001b[0m \u001b[43m        \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5571\u001b[0m \u001b[43m        \u001b[49m\u001b[43mindex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5572\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5573\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5574\u001b[0m \u001b[43m        \u001b[49m\u001b[43minplace\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minplace\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5575\u001b[0m \u001b[43m        \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5576\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/GitHub/datathon-s24-team4/.conda/lib/python3.11/site-packages/pandas/core/generic.py:4785\u001b[0m, in \u001b[0;36mNDFrame.drop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4783\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m axis, labels \u001b[38;5;129;01min\u001b[39;00m axes\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m   4784\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m labels \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 4785\u001b[0m         obj \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_drop_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlevel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlevel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4787\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[1;32m   4788\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_inplace(obj)\n",
      "File \u001b[0;32m~/Documents/GitHub/datathon-s24-team4/.conda/lib/python3.11/site-packages/pandas/core/generic.py:4827\u001b[0m, in \u001b[0;36mNDFrame._drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4825\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mdrop(labels, level\u001b[38;5;241m=\u001b[39mlevel, errors\u001b[38;5;241m=\u001b[39merrors)\n\u001b[1;32m   4826\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 4827\u001b[0m         new_axis \u001b[38;5;241m=\u001b[39m \u001b[43maxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merrors\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merrors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4828\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m axis\u001b[38;5;241m.\u001b[39mget_indexer(new_axis)\n\u001b[1;32m   4830\u001b[0m \u001b[38;5;66;03m# Case for non-unique axis\u001b[39;00m\n\u001b[1;32m   4831\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/Documents/GitHub/datathon-s24-team4/.conda/lib/python3.11/site-packages/pandas/core/indexes/base.py:7070\u001b[0m, in \u001b[0;36mIndex.drop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   7068\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m mask\u001b[38;5;241m.\u001b[39many():\n\u001b[1;32m   7069\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m errors \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m-> 7070\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlabels[mask]\u001b[38;5;241m.\u001b[39mtolist()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m not found in axis\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   7071\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m indexer[\u001b[38;5;241m~\u001b[39mmask]\n\u001b[1;32m   7072\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdelete(indexer)\n",
      "\u001b[0;31mKeyError\u001b[0m: \"['Unnamed: 0'] not found in axis\""
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Assuming wti_futures_curves_diffs is your DataFrame\n",
    "# First, remove the 'Unnamed: 0' column if it's just an index\n",
    "wti_futures_curves_diffs = wti_futures_curves_diffs.drop(columns=['Unnamed: 0'])\n",
    "\n",
    "# Define the features (X) and target (y)\n",
    "X = wti_futures_curves_diffs.drop('avg_iv', axis=1)\n",
    "y = wti_futures_curves_diffs['avg_iv']\n",
    "\n",
    "# Split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Initialize the Random Forest Regressor\n",
    "rf_regressor = RandomForestRegressor(n_estimators=100)\n",
    "\n",
    "# Fit the model to the training data\n",
    "rf_regressor.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "predictions = rf_regressor.predict(X_test)\n",
    "\n",
    "# Calculate the Mean Squared Error (MSE) to evaluate the model\n",
    "mse = mean_squared_error(y_test, predictions)\n",
    "\n",
    "print(f\"Mean Squared Error: {mse}\")\n",
    "\n",
    "\n",
    "# Assuming `y_test` and `predictions` are available from the previous steps\n",
    "# y_test contains the actual values, and predictions contains the model's predictions\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, predictions, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)  # Diagonal line\n",
    "plt.xlabel('Actual Average IV')\n",
    "plt.ylabel('Predicted Average IV')\n",
    "plt.title('Actual vs. Predicted Average Implied Volatility')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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