src/weekly_random_forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import time\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.tree import export_graphviz, DecisionTreeClassifier, DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, mean_squared_error, precision_score, recall_score\n",
    "from sklearn.datasets import load_breast_cancer, load_boston\n",
    "\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn import utils\n",
    "\n",
    "\n",
    "# Always make it pretty.\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# We get some interger -> float typecast warning from sklean below, this keeps them out of our hair.\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import itertools\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = pd.read_csv('data/cleaned_data_together.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def df_cleaner(df):\n",
    "    df['DateTime'] = pd.to_datetime(df['DateTime'])\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df_cleaner(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MOY'] =df['DateTime']\n",
    "df['WOY'] =df['DateTime']\n",
    "def moy(x):\n",
    "    #test = df_shift_final['DateTime'][x]\n",
    "    date = datetime.datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S\")\n",
    "    return date.month\n",
    "df['MOY'] = df['MOY'].apply(lambda x : moy(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def woy(x):\n",
    "    #test = df_shift_final['DateTime'][x]\n",
    "    date = datetime.datetime.strptime(str(x), \"%Y-%m-%d %H:%M:%S\")\n",
    "    return int(date.strftime(\"%V\"))\n",
    "df['WOY'] = df['WOY'].apply(lambda x : woy(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Henry Hub Price $/MMBtu</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Production MMCF</th>\n",
       "      <th>Storage BCFE</th>\n",
       "      <th>Reserves BCFE</th>\n",
       "      <th>Consumption MMCF</th>\n",
       "      <th>DOY</th>\n",
       "      <th>MOY</th>\n",
       "      <th>WOY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5.82</td>\n",
       "      <td>26.0317</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5.82</td>\n",
       "      <td>24.2249</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>5.82</td>\n",
       "      <td>23.5537</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>6.09</td>\n",
       "      <td>23.2831</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>6.19</td>\n",
       "      <td>24.5469</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DateTime  Henry Hub Price $/MMBtu     Temp  Production MMCF  Storage BCFE  \\\n",
       "0 2010-01-01                     5.82  26.0317        2197157.0        3117.0   \n",
       "1 2010-01-02                     5.82  24.2249        2197157.0        3117.0   \n",
       "2 2010-01-03                     5.82  23.5537        2197157.0        3117.0   \n",
       "3 2010-01-04                     6.09  23.2831        2197157.0        3117.0   \n",
       "4 2010-01-05                     6.19  24.5469        2197157.0        3117.0   \n",
       "\n",
       "   Reserves BCFE  Consumption MMCF  DOY  MOY  WOY  \n",
       "0       255035.0         1666520.0    1    1   53  \n",
       "1       255035.0         1666520.0    2    1   53  \n",
       "2       255035.0         1666520.0    3    1   53  \n",
       "3       255035.0         1666520.0    4    1    1  \n",
       "4       255035.0         1666520.0    5    1    1  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "doy = df.pop('DOY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DateTime</th>\n",
       "      <th>Henry Hub Price $/MMBtu</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Production MMCF</th>\n",
       "      <th>Storage BCFE</th>\n",
       "      <th>Reserves BCFE</th>\n",
       "      <th>Consumption MMCF</th>\n",
       "      <th>MOY</th>\n",
       "      <th>WOY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>5.82</td>\n",
       "      <td>26.0317</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5.82</td>\n",
       "      <td>24.2249</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-03</td>\n",
       "      <td>5.82</td>\n",
       "      <td>23.5537</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>6.09</td>\n",
       "      <td>23.2831</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>6.19</td>\n",
       "      <td>24.5469</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    DateTime  Henry Hub Price $/MMBtu     Temp  Production MMCF  Storage BCFE  \\\n",
       "0 2010-01-01                     5.82  26.0317        2197157.0        3117.0   \n",
       "1 2010-01-02                     5.82  24.2249        2197157.0        3117.0   \n",
       "2 2010-01-03                     5.82  23.5537        2197157.0        3117.0   \n",
       "3 2010-01-04                     6.09  23.2831        2197157.0        3117.0   \n",
       "4 2010-01-05                     6.19  24.5469        2197157.0        3117.0   \n",
       "\n",
       "   Reserves BCFE  Consumption MMCF  MOY  WOY  \n",
       "0       255035.0         1666520.0    1   53  \n",
       "1       255035.0         1666520.0    1   53  \n",
       "2       255035.0         1666520.0    1   53  \n",
       "3       255035.0         1666520.0    1    1  \n",
       "4       255035.0         1666520.0    1    1  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.set_index('DateTime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfw =df.resample('W').median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfw['one_back'] = dfw['Henry Hub Price $/MMBtu'].shift(1).fillna(method= 'backfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfw['one_week_back_temp'] = dfw['Henry Hub Price $/MMBtu'].shift(1).fillna(method= 'backfill')\n",
    "dfw['t_week_back_temp'] = dfw['Henry Hub Price $/MMBtu'].shift(1).fillna(method= 'backfill')\n",
    "dfw['one_week_back_temp'] = dfw['Henry Hub Price $/MMBtu'].shift(1).fillna(method= 'backfill')\n",
    "dfw['one_week_back_temp'] = dfw['Henry Hub Price $/MMBtu'].shift(1).fillna(method= 'backfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Henry Hub Price $/MMBtu</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Production MMCF</th>\n",
       "      <th>Storage BCFE</th>\n",
       "      <th>Reserves BCFE</th>\n",
       "      <th>Consumption MMCF</th>\n",
       "      <th>MOY</th>\n",
       "      <th>WOY</th>\n",
       "      <th>one_back</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DateTime</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>2010-01-03</th>\n",
       "      <td>5.82</td>\n",
       "      <td>24.2249</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>5.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-10</th>\n",
       "      <td>6.56</td>\n",
       "      <td>23.2831</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>3117.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-17</th>\n",
       "      <td>5.66</td>\n",
       "      <td>35.7195</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>2850.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>6.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-24</th>\n",
       "      <td>5.66</td>\n",
       "      <td>37.1215</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>2607.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>5.32</td>\n",
       "      <td>28.3624</td>\n",
       "      <td>2197157.0</td>\n",
       "      <td>2521.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1666520.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>5.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-07</th>\n",
       "      <td>5.51</td>\n",
       "      <td>31.3111</td>\n",
       "      <td>2144157.0</td>\n",
       "      <td>2406.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1776171.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-14</th>\n",
       "      <td>5.48</td>\n",
       "      <td>28.8482</td>\n",
       "      <td>2144157.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1776171.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-21</th>\n",
       "      <td>5.40</td>\n",
       "      <td>33.4098</td>\n",
       "      <td>2144157.0</td>\n",
       "      <td>2026.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1776171.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>4.84</td>\n",
       "      <td>31.8510</td>\n",
       "      <td>2144157.0</td>\n",
       "      <td>1853.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1776171.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-07</th>\n",
       "      <td>4.76</td>\n",
       "      <td>37.3725</td>\n",
       "      <td>2196325.0</td>\n",
       "      <td>1738.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2491864.0</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-14</th>\n",
       "      <td>4.44</td>\n",
       "      <td>41.5551</td>\n",
       "      <td>2196325.0</td>\n",
       "      <td>1627.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2491864.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-21</th>\n",
       "      <td>4.19</td>\n",
       "      <td>42.8659</td>\n",
       "      <td>2196325.0</td>\n",
       "      <td>1614.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2491864.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>4.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-28</th>\n",
       "      <td>4.01</td>\n",
       "      <td>43.6955</td>\n",
       "      <td>2196325.0</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2491864.0</td>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "      <td>4.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-04</th>\n",
       "      <td>3.72</td>\n",
       "      <td>50.1082</td>\n",
       "      <td>2210163.0</td>\n",
       "      <td>1638.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2809788.0</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-11</th>\n",
       "      <td>3.92</td>\n",
       "      <td>49.7275</td>\n",
       "      <td>2210163.0</td>\n",
       "      <td>1670.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2809788.0</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>3.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-18</th>\n",
       "      <td>3.97</td>\n",
       "      <td>52.7891</td>\n",
       "      <td>2210163.0</td>\n",
       "      <td>1754.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2809788.0</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>3.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-25</th>\n",
       "      <td>4.02</td>\n",
       "      <td>53.5033</td>\n",
       "      <td>2210163.0</td>\n",
       "      <td>1828.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2809788.0</td>\n",
       "      <td>4</td>\n",
       "      <td>16</td>\n",
       "      <td>3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-02</th>\n",
       "      <td>4.18</td>\n",
       "      <td>52.4570</td>\n",
       "      <td>2210163.0</td>\n",
       "      <td>1912.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2809788.0</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "      <td>4.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-09</th>\n",
       "      <td>3.91</td>\n",
       "      <td>55.0015</td>\n",
       "      <td>2047815.0</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2480950.0</td>\n",
       "      <td>5</td>\n",
       "      <td>18</td>\n",
       "      <td>4.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-16</th>\n",
       "      <td>4.26</td>\n",
       "      <td>56.2390</td>\n",
       "      <td>2047815.0</td>\n",
       "      <td>2091.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2480950.0</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>3.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-23</th>\n",
       "      <td>4.12</td>\n",
       "      <td>61.2489</td>\n",
       "      <td>2047815.0</td>\n",
       "      <td>2165.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2480950.0</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-30</th>\n",
       "      <td>4.22</td>\n",
       "      <td>64.7964</td>\n",
       "      <td>2047815.0</td>\n",
       "      <td>2268.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2480950.0</td>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "      <td>4.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-06</th>\n",
       "      <td>4.46</td>\n",
       "      <td>67.4317</td>\n",
       "      <td>2276546.0</td>\n",
       "      <td>2357.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2142925.0</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>4.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-13</th>\n",
       "      <td>4.68</td>\n",
       "      <td>67.8670</td>\n",
       "      <td>2276546.0</td>\n",
       "      <td>2456.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2142925.0</td>\n",
       "      <td>6</td>\n",
       "      <td>23</td>\n",
       "      <td>4.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-20</th>\n",
       "      <td>5.14</td>\n",
       "      <td>68.9569</td>\n",
       "      <td>2276546.0</td>\n",
       "      <td>2542.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2142925.0</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>4.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-27</th>\n",
       "      <td>4.87</td>\n",
       "      <td>74.1861</td>\n",
       "      <td>2276546.0</td>\n",
       "      <td>2625.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>2142925.0</td>\n",
       "      <td>6</td>\n",
       "      <td>25</td>\n",
       "      <td>5.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-04</th>\n",
       "      <td>4.72</td>\n",
       "      <td>71.2182</td>\n",
       "      <td>2190270.0</td>\n",
       "      <td>2684.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1691813.0</td>\n",
       "      <td>7</td>\n",
       "      <td>26</td>\n",
       "      <td>4.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-11</th>\n",
       "      <td>4.61</td>\n",
       "      <td>73.7622</td>\n",
       "      <td>2190270.0</td>\n",
       "      <td>2761.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1691813.0</td>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>4.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-18</th>\n",
       "      <td>4.46</td>\n",
       "      <td>77.1073</td>\n",
       "      <td>2190270.0</td>\n",
       "      <td>2841.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1691813.0</td>\n",
       "      <td>7</td>\n",
       "      <td>28</td>\n",
       "      <td>4.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-25</th>\n",
       "      <td>4.69</td>\n",
       "      <td>76.5347</td>\n",
       "      <td>2190270.0</td>\n",
       "      <td>2893.0</td>\n",
       "      <td>255035.0</td>\n",
       "      <td>1691813.0</td>\n",
       "      <td>7</td>\n",
       "      <td>29</td>\n",
       "      <td>4.46</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-08-26</th>\n",
       "      <td>3.01</td>\n",
       "      <td>70.5362</td>\n",
       "      <td>3081754.0</td>\n",
       "      <td>2435.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2047353.0</td>\n",
       "      <td>8</td>\n",
       "      <td>34</td>\n",
       "      <td>3.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-02</th>\n",
       "      <td>2.98</td>\n",
       "      <td>71.5244</td>\n",
       "      <td>2954943.0</td>\n",
       "      <td>2504.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2050171.0</td>\n",
       "      <td>8</td>\n",
       "      <td>35</td>\n",
       "      <td>3.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-09</th>\n",
       "      <td>2.93</td>\n",
       "      <td>69.7597</td>\n",
       "      <td>2954943.0</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2050171.0</td>\n",
       "      <td>9</td>\n",
       "      <td>36</td>\n",
       "      <td>2.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-16</th>\n",
       "      <td>2.91</td>\n",
       "      <td>67.9809</td>\n",
       "      <td>2954943.0</td>\n",
       "      <td>2636.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2050171.0</td>\n",
       "      <td>9</td>\n",
       "      <td>37</td>\n",
       "      <td>2.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-23</th>\n",
       "      <td>3.02</td>\n",
       "      <td>65.9100</td>\n",
       "      <td>2954943.0</td>\n",
       "      <td>2722.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2050171.0</td>\n",
       "      <td>9</td>\n",
       "      <td>38</td>\n",
       "      <td>2.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-30</th>\n",
       "      <td>3.04</td>\n",
       "      <td>59.9781</td>\n",
       "      <td>2954943.0</td>\n",
       "      <td>2768.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2050171.0</td>\n",
       "      <td>9</td>\n",
       "      <td>39</td>\n",
       "      <td>3.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-07</th>\n",
       "      <td>3.25</td>\n",
       "      <td>57.8728</td>\n",
       "      <td>3108098.0</td>\n",
       "      <td>2866.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2346416.0</td>\n",
       "      <td>10</td>\n",
       "      <td>40</td>\n",
       "      <td>3.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-14</th>\n",
       "      <td>3.19</td>\n",
       "      <td>48.9546</td>\n",
       "      <td>3108098.0</td>\n",
       "      <td>2956.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2346416.0</td>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-21</th>\n",
       "      <td>3.26</td>\n",
       "      <td>48.1611</td>\n",
       "      <td>3108098.0</td>\n",
       "      <td>3037.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2346416.0</td>\n",
       "      <td>10</td>\n",
       "      <td>42</td>\n",
       "      <td>3.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-28</th>\n",
       "      <td>3.27</td>\n",
       "      <td>49.8180</td>\n",
       "      <td>3108098.0</td>\n",
       "      <td>3095.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2346416.0</td>\n",
       "      <td>10</td>\n",
       "      <td>43</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-04</th>\n",
       "      <td>3.26</td>\n",
       "      <td>47.7930</td>\n",
       "      <td>3180213.0</td>\n",
       "      <td>3143.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2311449.0</td>\n",
       "      <td>11</td>\n",
       "      <td>44</td>\n",
       "      <td>3.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-11</th>\n",
       "      <td>3.54</td>\n",
       "      <td>37.9105</td>\n",
       "      <td>3180213.0</td>\n",
       "      <td>3208.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2311449.0</td>\n",
       "      <td>11</td>\n",
       "      <td>45</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-18</th>\n",
       "      <td>4.30</td>\n",
       "      <td>34.1366</td>\n",
       "      <td>3180213.0</td>\n",
       "      <td>3247.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2311449.0</td>\n",
       "      <td>11</td>\n",
       "      <td>46</td>\n",
       "      <td>3.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-25</th>\n",
       "      <td>4.70</td>\n",
       "      <td>37.1451</td>\n",
       "      <td>3180213.0</td>\n",
       "      <td>3113.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2311449.0</td>\n",
       "      <td>11</td>\n",
       "      <td>47</td>\n",
       "      <td>4.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-02</th>\n",
       "      <td>4.50</td>\n",
       "      <td>36.9052</td>\n",
       "      <td>3180213.0</td>\n",
       "      <td>3054.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2311449.0</td>\n",
       "      <td>11</td>\n",
       "      <td>48</td>\n",
       "      <td>4.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-09</th>\n",
       "      <td>4.51</td>\n",
       "      <td>28.6403</td>\n",
       "      <td>3134786.0</td>\n",
       "      <td>2991.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2158676.0</td>\n",
       "      <td>12</td>\n",
       "      <td>49</td>\n",
       "      <td>4.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-16</th>\n",
       "      <td>4.31</td>\n",
       "      <td>35.5015</td>\n",
       "      <td>3134786.0</td>\n",
       "      <td>2914.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2158676.0</td>\n",
       "      <td>12</td>\n",
       "      <td>50</td>\n",
       "      <td>4.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-23</th>\n",
       "      <td>3.70</td>\n",
       "      <td>37.9515</td>\n",
       "      <td>3134786.0</td>\n",
       "      <td>2773.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2158676.0</td>\n",
       "      <td>12</td>\n",
       "      <td>51</td>\n",
       "      <td>4.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-30</th>\n",
       "      <td>3.25</td>\n",
       "      <td>33.7773</td>\n",
       "      <td>3134786.0</td>\n",
       "      <td>2725.0</td>\n",
       "      <td>341133.0</td>\n",
       "      <td>2158676.0</td>\n",
       "      <td>12</td>\n",
       "      <td>52</td>\n",
       "      <td>3.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-06</th>\n",
       "      <td>2.80</td>\n",
       "      <td>32.0436</td>\n",
       "      <td>3272022.0</td>\n",
       "      <td>2705.0</td>\n",
       "      <td>464292.0</td>\n",
       "      <td>2276313.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-13</th>\n",
       "      <td>2.95</td>\n",
       "      <td>32.3805</td>\n",
       "      <td>3272022.0</td>\n",
       "      <td>2614.0</td>\n",
       "      <td>464292.0</td>\n",
       "      <td>2276313.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-20</th>\n",
       "      <td>3.43</td>\n",
       "      <td>30.8203</td>\n",
       "      <td>3272022.0</td>\n",
       "      <td>2533.0</td>\n",
       "      <td>464292.0</td>\n",
       "      <td>2276313.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-27</th>\n",
       "      <td>3.13</td>\n",
       "      <td>27.8937</td>\n",
       "      <td>3272022.0</td>\n",
       "      <td>2370.0</td>\n",
       "      <td>464292.0</td>\n",
       "      <td>2276313.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>3.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-03</th>\n",
       "      <td>2.82</td>\n",
       "      <td>29.6499</td>\n",
       "      <td>3233430.0</td>\n",
       "      <td>2197.0</td>\n",
       "      <td>464292.0</td>\n",
       "      <td>2688953.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>3.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-10</th>\n",
       "      <td>2.62</td>\n",
       "      <td>27.4735</td>\n",
       "      <td>3233430.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>464292.0</td>\n",
       "      <td>2688953.0</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>2.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-17</th>\n",
       "      <td>2.61</td>\n",
       "      <td>30.4407</td>\n",
       "      <td>3233430.0</td>\n",
       "      <td>1882.0</td>\n",
       "      <td>464292.0</td>\n",
       "      <td>2688953.0</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>2.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-24</th>\n",
       "      <td>2.73</td>\n",
       "      <td>29.1303</td>\n",
       "      <td>3233430.0</td>\n",
       "      <td>1705.0</td>\n",
       "      <td>464292.0</td>\n",
       "      <td>2688953.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>2.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-03</th>\n",
       "      <td>2.89</td>\n",
       "      <td>29.8650</td>\n",
       "      <td>3233430.0</td>\n",
       "      <td>1539.0</td>\n",
       "      <td>464292.0</td>\n",
       "      <td>2688953.0</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>2.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-10</th>\n",
       "      <td>2.90</td>\n",
       "      <td>31.3371</td>\n",
       "      <td>3351564.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>464292.0</td>\n",
       "      <td>2969569.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-17</th>\n",
       "      <td>2.86</td>\n",
       "      <td>36.5454</td>\n",
       "      <td>3351564.0</td>\n",
       "      <td>1390.0</td>\n",
       "      <td>464292.0</td>\n",
       "      <td>2969569.0</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>2.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>481 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Henry Hub Price $/MMBtu     Temp  Production MMCF  Storage BCFE  \\\n",
       "DateTime                                                                      \n",
       "2010-01-03                     5.82  24.2249        2197157.0        3117.0   \n",
       "2010-01-10                     6.56  23.2831        2197157.0        3117.0   \n",
       "2010-01-17                     5.66  35.7195        2197157.0        2850.0   \n",
       "2010-01-24                     5.66  37.1215        2197157.0        2607.0   \n",
       "2010-01-31                     5.32  28.3624        2197157.0        2521.0   \n",
       "2010-02-07                     5.51  31.3111        2144157.0        2406.0   \n",
       "2010-02-14                     5.48  28.8482        2144157.0        2214.0   \n",
       "2010-02-21                     5.40  33.4098        2144157.0        2026.0   \n",
       "2010-02-28                     4.84  31.8510        2144157.0        1853.0   \n",
       "2010-03-07                     4.76  37.3725        2196325.0        1738.0   \n",
       "2010-03-14                     4.44  41.5551        2196325.0        1627.0   \n",
       "2010-03-21                     4.19  42.8659        2196325.0        1614.0   \n",
       "2010-03-28                     4.01  43.6955        2196325.0        1626.0   \n",
       "2010-04-04                     3.72  50.1082        2210163.0        1638.0   \n",
       "2010-04-11                     3.92  49.7275        2210163.0        1670.0   \n",
       "2010-04-18                     3.97  52.7891        2210163.0        1754.0   \n",
       "2010-04-25                     4.02  53.5033        2210163.0        1828.0   \n",
       "2010-05-02                     4.18  52.4570        2210163.0        1912.0   \n",
       "2010-05-09                     3.91  55.0015        2047815.0        1994.0   \n",
       "2010-05-16                     4.26  56.2390        2047815.0        2091.0   \n",
       "2010-05-23                     4.12  61.2489        2047815.0        2165.0   \n",
       "2010-05-30                     4.22  64.7964        2047815.0        2268.0   \n",
       "2010-06-06                     4.46  67.4317        2276546.0        2357.0   \n",
       "2010-06-13                     4.68  67.8670        2276546.0        2456.0   \n",
       "2010-06-20                     5.14  68.9569        2276546.0        2542.0   \n",
       "2010-06-27                     4.87  74.1861        2276546.0        2625.0   \n",
       "2010-07-04                     4.72  71.2182        2190270.0        2684.0   \n",
       "2010-07-11                     4.61  73.7622        2190270.0        2761.0   \n",
       "2010-07-18                     4.46  77.1073        2190270.0        2841.0   \n",
       "2010-07-25                     4.69  76.5347        2190270.0        2893.0   \n",
       "...                             ...      ...              ...           ...   \n",
       "2018-08-26                     3.01  70.5362        3081754.0        2435.0   \n",
       "2018-09-02                     2.98  71.5244        2954943.0        2504.0   \n",
       "2018-09-09                     2.93  69.7597        2954943.0        2567.0   \n",
       "2018-09-16                     2.91  67.9809        2954943.0        2636.0   \n",
       "2018-09-23                     3.02  65.9100        2954943.0        2722.0   \n",
       "2018-09-30                     3.04  59.9781        2954943.0        2768.0   \n",
       "2018-10-07                     3.25  57.8728        3108098.0        2866.0   \n",
       "2018-10-14                     3.19  48.9546        3108098.0        2956.0   \n",
       "2018-10-21                     3.26  48.1611        3108098.0        3037.0   \n",
       "2018-10-28                     3.27  49.8180        3108098.0        3095.0   \n",
       "2018-11-04                     3.26  47.7930        3180213.0        3143.0   \n",
       "2018-11-11                     3.54  37.9105        3180213.0        3208.0   \n",
       "2018-11-18                     4.30  34.1366        3180213.0        3247.0   \n",
       "2018-11-25                     4.70  37.1451        3180213.0        3113.0   \n",
       "2018-12-02                     4.50  36.9052        3180213.0        3054.0   \n",
       "2018-12-09                     4.51  28.6403        3134786.0        2991.0   \n",
       "2018-12-16                     4.31  35.5015        3134786.0        2914.0   \n",
       "2018-12-23                     3.70  37.9515        3134786.0        2773.0   \n",
       "2018-12-30                     3.25  33.7773        3134786.0        2725.0   \n",
       "2019-01-06                     2.80  32.0436        3272022.0        2705.0   \n",
       "2019-01-13                     2.95  32.3805        3272022.0        2614.0   \n",
       "2019-01-20                     3.43  30.8203        3272022.0        2533.0   \n",
       "2019-01-27                     3.13  27.8937        3272022.0        2370.0   \n",
       "2019-02-03                     2.82  29.6499        3233430.0        2197.0   \n",
       "2019-02-10                     2.62  27.4735        3233430.0        1960.0   \n",
       "2019-02-17                     2.61  30.4407        3233430.0        1882.0   \n",
       "2019-02-24                     2.73  29.1303        3233430.0        1705.0   \n",
       "2019-03-03                     2.89  29.8650        3233430.0        1539.0   \n",
       "2019-03-10                     2.90  31.3371        3351564.0        1390.0   \n",
       "2019-03-17                     2.86  36.5454        3351564.0        1390.0   \n",
       "\n",
       "            Reserves BCFE  Consumption MMCF  MOY  WOY  one_back  \n",
       "DateTime                                                         \n",
       "2010-01-03       255035.0         1666520.0    1   53      5.82  \n",
       "2010-01-10       255035.0         1666520.0    1    1      5.82  \n",
       "2010-01-17       255035.0         1666520.0    1    2      6.56  \n",
       "2010-01-24       255035.0         1666520.0    1    3      5.66  \n",
       "2010-01-31       255035.0         1666520.0    1    4      5.66  \n",
       "2010-02-07       255035.0         1776171.0    2    5      5.32  \n",
       "2010-02-14       255035.0         1776171.0    2    6      5.51  \n",
       "2010-02-21       255035.0         1776171.0    2    7      5.48  \n",
       "2010-02-28       255035.0         1776171.0    2    8      5.40  \n",
       "2010-03-07       255035.0         2491864.0    3    9      4.84  \n",
       "2010-03-14       255035.0         2491864.0    3   10      4.76  \n",
       "2010-03-21       255035.0         2491864.0    3   11      4.44  \n",
       "2010-03-28       255035.0         2491864.0    3   12      4.19  \n",
       "2010-04-04       255035.0         2809788.0    4   13      4.01  \n",
       "2010-04-11       255035.0         2809788.0    4   14      3.72  \n",
       "2010-04-18       255035.0         2809788.0    4   15      3.92  \n",
       "2010-04-25       255035.0         2809788.0    4   16      3.97  \n",
       "2010-05-02       255035.0         2809788.0    4   17      4.02  \n",
       "2010-05-09       255035.0         2480950.0    5   18      4.18  \n",
       "2010-05-16       255035.0         2480950.0    5   19      3.91  \n",
       "2010-05-23       255035.0         2480950.0    5   20      4.26  \n",
       "2010-05-30       255035.0         2480950.0    5   21      4.12  \n",
       "2010-06-06       255035.0         2142925.0    6   22      4.22  \n",
       "2010-06-13       255035.0         2142925.0    6   23      4.46  \n",
       "2010-06-20       255035.0         2142925.0    6   24      4.68  \n",
       "2010-06-27       255035.0         2142925.0    6   25      5.14  \n",
       "2010-07-04       255035.0         1691813.0    7   26      4.87  \n",
       "2010-07-11       255035.0         1691813.0    7   27      4.72  \n",
       "2010-07-18       255035.0         1691813.0    7   28      4.61  \n",
       "2010-07-25       255035.0         1691813.0    7   29      4.46  \n",
       "...                   ...               ...  ...  ...       ...  \n",
       "2018-08-26       341133.0         2047353.0    8   34      3.01  \n",
       "2018-09-02       341133.0         2050171.0    8   35      3.01  \n",
       "2018-09-09       341133.0         2050171.0    9   36      2.98  \n",
       "2018-09-16       341133.0         2050171.0    9   37      2.93  \n",
       "2018-09-23       341133.0         2050171.0    9   38      2.91  \n",
       "2018-09-30       341133.0         2050171.0    9   39      3.02  \n",
       "2018-10-07       341133.0         2346416.0   10   40      3.04  \n",
       "2018-10-14       341133.0         2346416.0   10   41      3.25  \n",
       "2018-10-21       341133.0         2346416.0   10   42      3.19  \n",
       "2018-10-28       341133.0         2346416.0   10   43      3.26  \n",
       "2018-11-04       341133.0         2311449.0   11   44      3.27  \n",
       "2018-11-11       341133.0         2311449.0   11   45      3.26  \n",
       "2018-11-18       341133.0         2311449.0   11   46      3.54  \n",
       "2018-11-25       341133.0         2311449.0   11   47      4.30  \n",
       "2018-12-02       341133.0         2311449.0   11   48      4.70  \n",
       "2018-12-09       341133.0         2158676.0   12   49      4.50  \n",
       "2018-12-16       341133.0         2158676.0   12   50      4.51  \n",
       "2018-12-23       341133.0         2158676.0   12   51      4.31  \n",
       "2018-12-30       341133.0         2158676.0   12   52      3.70  \n",
       "2019-01-06       464292.0         2276313.0    1    1      3.25  \n",
       "2019-01-13       464292.0         2276313.0    1    2      2.80  \n",
       "2019-01-20       464292.0         2276313.0    1    3      2.95  \n",
       "2019-01-27       464292.0         2276313.0    1    4      3.43  \n",
       "2019-02-03       464292.0         2688953.0    1    5      3.13  \n",
       "2019-02-10       464292.0         2688953.0    2    6      2.82  \n",
       "2019-02-17       464292.0         2688953.0    2    7      2.62  \n",
       "2019-02-24       464292.0         2688953.0    2    8      2.61  \n",
       "2019-03-03       464292.0         2688953.0    2    9      2.73  \n",
       "2019-03-10       464292.0         2969569.0    3   10      2.89  \n",
       "2019-03-17       464292.0         2969569.0    3   11      2.90  \n",
       "\n",
       "[481 rows x 9 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = dfw.pop('Henry Hub Price $/MMBtu').values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfw.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = int(len(X) * 0.75)\n",
    "X_train, X_test, y_train, y_test = X[0:size], X[size:len(X)], y[0:size], y[size:len(y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: 0.474\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestRegressor()\n",
    "\n",
    "param_grid = {\n",
    "    'bootstrap': [True, False],\n",
    "    'max_depth': [80, 90], #, 100, 110\n",
    "    'max_features': [2, 3, 4, 5, 8],\n",
    "    'min_samples_leaf': [3, 4, 5, 10], #, 50, 90\n",
    "    'min_samples_split': [8, 10, 12],\n",
    "    'n_estimators': [100, 200] #, 300, 1000, 2000\n",
    "}\n",
    "clf = GridSearchCV(model, param_grid, cv=3, n_jobs=-1)\n",
    "clf.fit(X_train,y_train)\n",
    "print('Accuracy score: {:.3}'.format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=90,\n",
       "           max_features=8, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=4,\n",
       "           min_samples_split=10, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=100, n_jobs=None, oob_score=False,\n",
       "           random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 0.09188\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test \n",
    "history_X = X_train\n",
    "history_y = y_train\n",
    "predictions2 = list()\n",
    "rfr = RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=90,\n",
    "           max_features=8, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "           min_impurity_split=None, min_samples_leaf=4,\n",
    "           min_samples_split=10, min_weight_fraction_leaf=0.0,\n",
    "           n_estimators=100, n_jobs=None, oob_score=False,\n",
    "           random_state=None, verbose=0, warm_start=False)\n",
    "rfr_fit = rfr.fit(X_train, y_train)\n",
    "for t in range(len(y_test)):\n",
    "    output = rfr_fit.predict(X_test[t].reshape((1,8)))\n",
    "    yhat = output[0]\n",
    "    predictions2.append(yhat)\n",
    "#     obs = y_test[t]\n",
    "#     history_X = np.append(history_X, X_test[t].reshape((1,6)), axis=0)\n",
    "#     history_y = np.append(history_y,obs)\n",
    "    #print('{} of {} predicted={}, expected={}'.format(t, len(y_test), yhat, obs))\n",
    "pred = np.array(predictions2)\n",
    "error = mean_squared_error(y_test, predictions2)\n",
    "print('Test MSE: %.5f' % error)\n",
    "\n",
    "# plt.plot(test)\n",
    "# plt.plot(predictions, color='red')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(25, 5))\n",
    "axs.plot(y_test, color='black')\n",
    "axs.plot(predictions2, color='red', alpha = 0.8)\n",
    "#axs.set_xlim([300, 600])\n",
    "#axs.set_ylim([-0.2, .2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Without the one week back feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Temp', 'Production MMCF', 'Storage BCFE', 'Reserves BCFE',\n",
       "       'Consumption MMCF', 'MOY', 'WOY', 'one_back'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb = ['Temp', 'Production MMCF', 'Storage BCFE', 'Reserves BCFE',\n",
    "       'Consumption MMCF', 'MOY', 'WOY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = dfw[nb].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "size = int(len(X) * 0.75)\n",
    "X_train, X_test, y_train, y_test = X[0:size], X[size:len(X)], y[0:size], y[size:len(y)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score: -1.04\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestRegressor()\n",
    "\n",
    "param_grid = {\n",
    "    'bootstrap': [True, False],\n",
    "    'max_depth': [80, 90], #, 100, 110\n",
    "    'max_features': [2, 3, 4, 5, 7],\n",
    "    'min_samples_leaf': [3, 4, 5, 10], #, 50, 90\n",
    "    'min_samples_split': [8, 10, 12],\n",
    "    'n_estimators': [100, 200] #, 300, 1000, 2000\n",
    "}\n",
    "clf = GridSearchCV(model, param_grid, cv=3, n_jobs=-1)\n",
    "clf.fit(X_train,y_train)\n",
    "print('Accuracy score: {:.3}'.format(clf.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=80,\n",
       "           max_features=2, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=10,\n",
       "           min_samples_split=10, min_weight_fraction_leaf=0.0,\n",
       "           n_estimators=200, n_jobs=None, oob_score=False,\n",
       "           random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test MSE: 0.31595\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test \n",
    "history_X = X_train\n",
    "history_y = y_train\n",
    "predictions2 = list()\n",
    "rfr = RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=80,\n",
    "           max_features=2, max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
    "           min_impurity_split=None, min_samples_leaf=10,\n",
    "           min_samples_split=10, min_weight_fraction_leaf=0.0,\n",
    "           n_estimators=200, n_jobs=None, oob_score=False,\n",
    "           random_state=None, verbose=0, warm_start=False)\n",
    "rfr_fit = rfr.fit(X_train, y_train)\n",
    "for t in range(len(y_test)):\n",
    "    output = rfr_fit.predict(X_test[t].reshape((1,7)))\n",
    "    yhat = output[0]\n",
    "    predictions2.append(yhat)\n",
    "#     obs = y_test[t]\n",
    "#     history_X = np.append(history_X, X_test[t].reshape((1,6)), axis=0)\n",
    "#     history_y = np.append(history_y,obs)\n",
    "    #print('{} of {} predicted={}, expected={}'.format(t, len(y_test), yhat, obs))\n",
    "pred = np.array(predictions2)\n",
    "error = mean_squared_error(y_test, predictions2)\n",
    "print('Test MSE: %.5f' % error)\n",
    "\n",
    "# plt.plot(test)\n",
    "# plt.plot(predictions, color='red')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(figsize=(25, 5))\n",
    "axs.plot(y_test, color='black')\n",
    "axs.plot(predictions2, color='red', alpha = 0.8)\n",
    "#axs.set_xlim([300, 600])\n",
    "#axs.set_ylim([-0.2, .2])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}