{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 0 - Loading the necessary libraries and setting them up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import mpld3\n",
    "mpld3.enable_notebook()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.set_option('display.max_rows', 2)\n",
    "pd.set_option('display.max_columns', 7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1 - Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-15</th>\n",
       "      <td>18251.970703</td>\n",
       "      <td>18272.720703</td>\n",
       "      <td>18215.070313</td>\n",
       "      <td>18272.560547</td>\n",
       "      <td>18272.560547</td>\n",
       "      <td>108220000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>23049.060547</td>\n",
       "      <td>23630.859375</td>\n",
       "      <td>22789.619141</td>\n",
       "      <td>23625.339844</td>\n",
       "      <td>23625.339844</td>\n",
       "      <td>472700000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1211 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Open          High           Low         Close  \\\n",
       "Date                                                                 \n",
       "2015-05-15  18251.970703  18272.720703  18215.070313  18272.560547   \n",
       "...                  ...           ...           ...           ...   \n",
       "2020-05-14  23049.060547  23630.859375  22789.619141  23625.339844   \n",
       "\n",
       "               Adj Close     Volume  \n",
       "Date                                 \n",
       "2015-05-15  18272.560547  108220000  \n",
       "...                  ...        ...  \n",
       "2020-05-14  23625.339844  472700000  \n",
       "\n",
       "[1211 rows x 6 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = pd.read_csv('A.csv', parse_dates=True, index_col=0)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-15</th>\n",
       "      <td>2122.070068</td>\n",
       "      <td>2123.889893</td>\n",
       "      <td>2116.810059</td>\n",
       "      <td>2122.72998</td>\n",
       "      <td>2122.72998</td>\n",
       "      <td>3092080000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>2794.540039</td>\n",
       "      <td>2852.800049</td>\n",
       "      <td>2766.639893</td>\n",
       "      <td>2852.50000</td>\n",
       "      <td>2852.50000</td>\n",
       "      <td>5641920000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1209 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low       Close   Adj Close  \\\n",
       "Date                                                                        \n",
       "2015-05-15  2122.070068  2123.889893  2116.810059  2122.72998  2122.72998   \n",
       "...                 ...          ...          ...         ...         ...   \n",
       "2020-05-14  2794.540039  2852.800049  2766.639893  2852.50000  2852.50000   \n",
       "\n",
       "                Volume  \n",
       "Date                    \n",
       "2015-05-15  3092080000  \n",
       "...                ...  \n",
       "2020-05-14  5641920000  \n",
       "\n",
       "[1209 rows x 6 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = pd.read_csv('B.csv', parse_dates=True, index_col=0); \n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-15</th>\n",
       "      <td>12.46</td>\n",
       "      <td>13.090000</td>\n",
       "      <td>12.350000</td>\n",
       "      <td>12.380000</td>\n",
       "      <td>12.380000</td>\n",
       "      <td>0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>35.16</td>\n",
       "      <td>39.279999</td>\n",
       "      <td>32.330002</td>\n",
       "      <td>32.610001</td>\n",
       "      <td>32.610001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1206 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open       High        Low      Close  Adj Close  Volume\n",
       "Date                                                                 \n",
       "2015-05-15  12.46  13.090000  12.350000  12.380000  12.380000       0\n",
       "...           ...        ...        ...        ...        ...     ...\n",
       "2020-05-14  35.16  39.279999  32.330002  32.610001  32.610001       0\n",
       "\n",
       "[1206 rows x 6 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C = pd.read_csv('C.csv', parse_dates=True, index_col=0); \n",
    "C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2 - Data wrangling/munging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = A.join(B, how='outer', lsuffix='_A', sort=True).join(C, how='outer', lsuffix='_B', rsuffix='_C', sort=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open_A</th>\n",
       "      <th>High_A</th>\n",
       "      <th>Low_A</th>\n",
       "      <th>...</th>\n",
       "      <th>Close_C</th>\n",
       "      <th>Adj Close_C</th>\n",
       "      <th>Volume_C</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-15</th>\n",
       "      <td>18251.970703</td>\n",
       "      <td>18272.720703</td>\n",
       "      <td>18215.070313</td>\n",
       "      <td>...</td>\n",
       "      <td>12.380000</td>\n",
       "      <td>12.380000</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>23049.060547</td>\n",
       "      <td>23630.859375</td>\n",
       "      <td>22789.619141</td>\n",
       "      <td>...</td>\n",
       "      <td>32.610001</td>\n",
       "      <td>32.610001</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1259 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open_A        High_A         Low_A    ...       Close_C  \\\n",
       "Date                                                    ...                 \n",
       "2015-05-15  18251.970703  18272.720703  18215.070313    ...     12.380000   \n",
       "...                  ...           ...           ...    ...           ...   \n",
       "2020-05-14  23049.060547  23630.859375  22789.619141    ...     32.610001   \n",
       "\n",
       "            Adj Close_C  Volume_C  \n",
       "Date                               \n",
       "2015-05-15    12.380000       0.0  \n",
       "...                 ...       ...  \n",
       "2020-05-14    32.610001       0.0  \n",
       "\n",
       "[1259 rows x 18 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3 - Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Open_A</th>\n",
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       "      <th>Low_A</th>\n",
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       "      <th>Close_C</th>\n",
       "      <th>Adj Close_C</th>\n",
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       "    <tr>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "</table>\n",
       "<p>0 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Open_A, High_A, Low_A, Close_A, Adj Close_A, Volume_A, Open_B, High_B, Low_B, Close_B, Adj Close_B, Volume_B, Open_C, High_C, Low_C, Close_C, Adj Close_C, Volume_C]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 18 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "merged_df[merged_df.isnull().all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "148"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df[['Close_A', 'Close_B', 'Close_C']].isnull().any(axis=1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_close_df = merged_df.dropna(subset=['Close_A', 'Close_B', 'Close_C'], how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_close_df[['Close_A', 'Close_B', 'Close_C']].isnull().any(axis=1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>Open_A</th>\n",
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       "      <th>Low_A</th>\n",
       "      <th>...</th>\n",
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       "      <th>Adj Close_C</th>\n",
       "      <th>Volume_C</th>\n",
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       "    <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",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-15</th>\n",
       "      <td>18251.970703</td>\n",
       "      <td>18272.720703</td>\n",
       "      <td>18215.070313</td>\n",
       "      <td>...</td>\n",
       "      <td>12.380000</td>\n",
       "      <td>12.380000</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>23049.060547</td>\n",
       "      <td>23630.859375</td>\n",
       "      <td>22789.619141</td>\n",
       "      <td>...</td>\n",
       "      <td>32.610001</td>\n",
       "      <td>32.610001</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1111 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open_A        High_A         Low_A    ...       Close_C  \\\n",
       "Date                                                    ...                 \n",
       "2015-05-15  18251.970703  18272.720703  18215.070313    ...     12.380000   \n",
       "...                  ...           ...           ...    ...           ...   \n",
       "2020-05-14  23049.060547  23630.859375  22789.619141    ...     32.610001   \n",
       "\n",
       "            Adj Close_C  Volume_C  \n",
       "Date                               \n",
       "2015-05-15    12.380000       0.0  \n",
       "...                 ...       ...  \n",
       "2020-05-14    32.610001       0.0  \n",
       "\n",
       "[1111 rows x 18 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_close_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_close_df.isnull().any(axis=1).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Open_A</th>\n",
       "      <th>High_A</th>\n",
       "      <th>Low_A</th>\n",
       "      <th>...</th>\n",
       "      <th>Close_C</th>\n",
       "      <th>Adj Close_C</th>\n",
       "      <th>Volume_C</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-18</th>\n",
       "      <td>18267.250000</td>\n",
       "      <td>18325.539063</td>\n",
       "      <td>18244.259766</td>\n",
       "      <td>...</td>\n",
       "      <td>12.730000</td>\n",
       "      <td>12.730000</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-01</th>\n",
       "      <td>24120.779297</td>\n",
       "      <td>24120.779297</td>\n",
       "      <td>23645.300781</td>\n",
       "      <td>...</td>\n",
       "      <td>37.189999</td>\n",
       "      <td>37.189999</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>165 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open_A        High_A         Low_A    ...       Close_C  \\\n",
       "Date                                                    ...                 \n",
       "2015-05-18  18267.250000  18325.539063  18244.259766    ...     12.730000   \n",
       "...                  ...           ...           ...    ...           ...   \n",
       "2020-05-01  24120.779297  24120.779297  23645.300781    ...     37.189999   \n",
       "\n",
       "            Adj Close_C  Volume_C  \n",
       "Date                               \n",
       "2015-05-18    12.730000       0.0  \n",
       "...                 ...       ...  \n",
       "2020-05-01    37.189999       0.0  \n",
       "\n",
       "[165 rows x 18 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_close_df[valid_close_df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "valid_close_complete = valid_close_df.fillna(method='backfill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_close_complete.isnull().any(axis=1).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4 - Obtaining Descriptive Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Open_A</th>\n",
       "      <th>High_A</th>\n",
       "      <th>Low_A</th>\n",
       "      <th>...</th>\n",
       "      <th>Close_C</th>\n",
       "      <th>Adj Close_C</th>\n",
       "      <th>Volume_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1111.000000</td>\n",
       "      <td>1111.000000</td>\n",
       "      <td>1111.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1111.000000</td>\n",
       "      <td>1111.000000</td>\n",
       "      <td>1111.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22291.125036</td>\n",
       "      <td>22402.395046</td>\n",
       "      <td>22168.922744</td>\n",
       "      <td>...</td>\n",
       "      <td>16.417606</td>\n",
       "      <td>16.417606</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3771.056417</td>\n",
       "      <td>3784.558787</td>\n",
       "      <td>3755.740756</td>\n",
       "      <td>...</td>\n",
       "      <td>8.016538</td>\n",
       "      <td>8.016538</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>15676.259766</td>\n",
       "      <td>15897.820313</td>\n",
       "      <td>15370.330078</td>\n",
       "      <td>...</td>\n",
       "      <td>9.140000</td>\n",
       "      <td>9.140000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>18232.280274</td>\n",
       "      <td>18285.384766</td>\n",
       "      <td>18156.614258</td>\n",
       "      <td>...</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>12.300000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22762.029297</td>\n",
       "      <td>22872.890625</td>\n",
       "      <td>22634.449219</td>\n",
       "      <td>...</td>\n",
       "      <td>14.090000</td>\n",
       "      <td>14.090000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>25516.320312</td>\n",
       "      <td>25659.810547</td>\n",
       "      <td>25382.705078</td>\n",
       "      <td>...</td>\n",
       "      <td>17.665000</td>\n",
       "      <td>17.665000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29440.470703</td>\n",
       "      <td>29568.570313</td>\n",
       "      <td>29406.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>82.690002</td>\n",
       "      <td>82.690002</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open_A        High_A         Low_A    ...         Close_C  \\\n",
       "count   1111.000000   1111.000000   1111.000000    ...     1111.000000   \n",
       "mean   22291.125036  22402.395046  22168.922744    ...       16.417606   \n",
       "std     3771.056417   3784.558787   3755.740756    ...        8.016538   \n",
       "min    15676.259766  15897.820313  15370.330078    ...        9.140000   \n",
       "25%    18232.280274  18285.384766  18156.614258    ...       12.300000   \n",
       "50%    22762.029297  22872.890625  22634.449219    ...       14.090000   \n",
       "75%    25516.320312  25659.810547  25382.705078    ...       17.665000   \n",
       "max    29440.470703  29568.570313  29406.750000    ...       82.690002   \n",
       "\n",
       "       Adj Close_C  Volume_C  \n",
       "count  1111.000000    1111.0  \n",
       "mean     16.417606       0.0  \n",
       "std       8.016538       0.0  \n",
       "min       9.140000       0.0  \n",
       "25%      12.300000       0.0  \n",
       "50%      14.090000       0.0  \n",
       "75%      17.665000       0.0  \n",
       "max      82.690002       0.0  \n",
       "\n",
       "[8 rows x 18 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "valid_close_complete.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "prices_only = valid_close_complete.drop(['Volume_A', 'Volume_B', 'Volume_C'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>Open_A</th>\n",
       "      <th>High_A</th>\n",
       "      <th>Low_A</th>\n",
       "      <th>...</th>\n",
       "      <th>Low_C</th>\n",
       "      <th>Close_C</th>\n",
       "      <th>Adj Close_C</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-15</th>\n",
       "      <td>18251.970703</td>\n",
       "      <td>18272.720703</td>\n",
       "      <td>18215.070313</td>\n",
       "      <td>...</td>\n",
       "      <td>12.350000</td>\n",
       "      <td>12.380000</td>\n",
       "      <td>12.380000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>23049.060547</td>\n",
       "      <td>23630.859375</td>\n",
       "      <td>22789.619141</td>\n",
       "      <td>...</td>\n",
       "      <td>32.330002</td>\n",
       "      <td>32.610001</td>\n",
       "      <td>32.610001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1111 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open_A        High_A         Low_A     ...           Low_C  \\\n",
       "Date                                                     ...                   \n",
       "2015-05-15  18251.970703  18272.720703  18215.070313     ...       12.350000   \n",
       "...                  ...           ...           ...     ...             ...   \n",
       "2020-05-14  23049.060547  23630.859375  22789.619141     ...       32.330002   \n",
       "\n",
       "              Close_C  Adj Close_C  \n",
       "Date                                \n",
       "2015-05-15  12.380000    12.380000  \n",
       "...               ...          ...  \n",
       "2020-05-14  32.610001    32.610001  \n",
       "\n",
       "[1111 rows x 15 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices_only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 5 - Visual Inspection of the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9767bef60>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "         \n",
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28068.689452999995], [737389.0, 28080.75, 28121.679688, 28042.210938, 28146.019531], [737390.0, 28156.470702999995, 28164.0, 28075.230469, 28174.970702999995], [737392.0, 28103.160156, 28051.410156, 28042.529297000005, 28119.50976600001], [737395.0, 28109.740233999997, 27783.039063, 27782.349608999997, 28109.839844], [737397.0, 27634.630858999997, 27649.779297000005, 27612.080077999995, 27727.449219], [737398.0, 27736.050781, 27677.789063, 27562.800781, 27745.199219], [737399.0, 27839.679688, 28015.060547000005, 27839.679688, 28035.849608999997], [737402.0, 27987.050781, 27909.599608999997, 27906.140625, 28010.419922000005], [737404.0, 27867.310547000005, 27911.300781, 27801.800781, 27925.5], [737405.0, 27898.339844, 28132.050781, 28028.320313, 28224.949219], [737406.0, 28123.640625, 28135.380858999997, 28028.320313, 28290.730469], [737409.0, 28191.669922000005, 28235.890625, 28191.669922000005, 28337.490233999997], [737410.0, 28221.75, 28267.160156, 28220.560547000005, 28328.630858999997], [737411.0, 28291.439452999995, 28239.279297000005, 28239.279297000005, 28323.25], [737412.0, 28278.310547000005, 28376.960938, 28278.240233999997, 28381.480469], [737413.0, 28608.640625, 28455.089844, 28445.599608999997, 28608.640625], [737416.0, 28491.779297000005, 28551.529297000005, 28491.779297000005, 28582.490233999997], [737417.0, 28572.570313, 28515.449219, 28503.210938, 28576.800781], [737419.0, 28539.460938, 28621.390625, 28535.15039100001, 28624.099608999997], [737420.0, 28675.339844, 28645.25976600001, 28608.980469, 28701.660156], [737423.0, 28654.75976600001, 28462.140625, 28428.980469, 28664.689452999995], [737424.0, 28414.640625, 28538.439452999995, 28376.490233999997, 28547.349608999997], [737426.0, 28638.970702999995, 28868.800781, 28627.769531, 28872.800781], [737427.0, 28553.330077999995, 28634.880858999997, 28500.359375, 28716.310547000005], [737430.0, 28465.5, 28703.380858999997, 28418.630858999997, 28708.019531], [737431.0, 28639.179688, 28583.679688, 28565.279297000005, 28685.5], [737432.0, 28556.140625, 28745.089844, 28789.099608999997, 28866.179688], [737433.0, 28851.970702999995, 28956.90039100001, 28789.099608999997, 28988.00976600001], [737434.0, 28977.519531, 28823.769531, 28789.099608999997, 29009.070313], [737437.0, 28869.00976600001, 28907.050781, 28819.429688, 28909.910156], [737438.0, 28895.5, 28939.669922000005, 28872.269531, 29054.160156], [737439.0, 28901.800781, 29030.220702999995, 28897.349608999997, 29127.589844], [737440.0, 29131.949219, 29297.640625, 29131.949219, 29300.320313], [737441.0, 29313.310547000005, 29348.099608999997, 29289.910156, 29373.61914100001], [737446.0, 29263.630858999997, 29186.269531, 29172.25976600001, 29320.199219], [737447.0, 29111.019531, 29160.089844, 28966.980469, 29190.470702999995], [737448.0, 29230.390625, 28989.730469, 28843.310547000005, 29288.789063], [737451.0, 28542.490233999997, 28535.800781, 28440.470702999995, 28671.789063], [737453.0, 28820.529297000005, 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[737497.0, 21973.820313, 23185.619141, 21285.369141, 23189.759766], [737500.0, 20917.529297, 20188.519531, 20116.460938, 21768.279297], [737501.0, 20487.050781, 21237.380859, 19882.259766, 21379.349609], [737502.0, 20188.689453, 19898.919922, 18917.460938, 20489.330078], [737503.0, 19830.009766, 20087.189453, 19177.130859, 20442.630859], [737504.0, 20253.150391, 19173.980469, 19094.269531, 20531.259766], [737507.0, 19028.359375, 18591.929688, 18213.650391, 19121.009766], [737508.0, 19722.189453, 20704.910156, 19649.25, 20737.699219], [737509.0, 21050.339844, 21200.550781, 20538.339844, 22019.929688], [737510.0, 21468.380859, 22552.169922, 21427.099609, 22595.060547], [737515.0, 22208.419922, 21917.160156, 21852.080078, 22480.369141], [737516.0, 21227.380859, 20943.509766, 20784.429688, 21487.240234], [737517.0, 20819.460938, 21413.439453, 20735.019531, 21477.769531], [737521.0, 21693.630859, 22679.990234, 21693.630859, 22783.449219], [737522.0, 23537.439453, 22653.859375, 22634.449219, 23617.240234], [737523.0, 22893.470703, 23433.570313, 22682.990234, 23513.400391], [737528.0, 23698.929688, 23390.769531, 23095.349609, 23698.929688], [737529.0, 23690.570313, 23949.759766, 23683.439453, 24040.580078], [737530.0, 23600.720703, 23504.349609, 23233.320313, 23649.720703], [737531.0, 23543.660156, 23537.679688, 23211.380859, 23598.080078], [737532.0, 23817.150391, 24242.490234, 23817.150391, 24264.210938], [737535.0, 24095.099609, 23650.439453, 23339.599609, 24108.689453], [737537.0, 23437.339844, 23475.820313, 23339.599609, 23613.099609], [737538.0, 23543.089844, 23515.259766, 23483.349609, 23885.359375], [737539.0, 23628.240234, 23775.269531, 23417.679688, 23826.0], [737543.0, 24357.169922, 24101.550781, 24453.990234, 24512.240234], [737544.0, 24490.369141, 24633.859375, 24453.990234, 24764.769531], [737545.0, 24585.570313, 24345.720703, 24186.900391, 24585.570313], [737546.0, 24120.779297, 23723.689453, 23645.300781, 24120.779297], [737549.0, 23581.550781, 23749.759766, 23361.160156, 23769.560547], [737550.0, 23958.880859, 23883.089844, 23868.910156, 24169.720703], [737551.0, 23978.880859, 23664.640625, 23661.140625, 24054.589844], [737552.0, 23837.210938, 23875.890625, 23834.390625, 24094.619141], [737553.0, 24107.820313, 24331.320313, 24107.050781, 24349.900391], [737556.0, 24256.449219, 24221.990234, 24070.220703, 24366.210938], [737557.0, 24292.839844, 23764.779297, 23761.580078, 24382.089844], [737558.0, 23702.160156, 23247.970703, 23067.640625, 23708.900391], [737559.0, 23049.060547, 23625.339844, 22789.619141, 23630.859375]], \"data02\": [[0.013440860215053752, 0.954044117647059, 0.8998970247821353, 0.8457499319172114, 0.7916028390522877], [0.04330943847072878, 0.954044117647059, 0.8998970247821353, 0.8457499319172114, 0.7916028390522877]], \"data03\": [[0.010454002389486239, 0.7561785130718954], [0.11648745519713258, 0.7561785130718954], [0.11947431302270009, 0.7561785130718954], [0.11947431302270009, 0.7629867919389979], [0.11947431302270009, 0.9761710239651418], [0.11947431302270009, 0.982979302832244], [0.11648745519713258, 0.982979302832244], [0.010454002389486239, 0.982979302832244], [0.007467144563918754, 0.982979302832244], [0.007467144563918754, 0.9761710239651418], [0.007467144563918754, 0.7629867919389979], [0.007467144563918754, 0.7561785130718954], [0.010454002389486239, 0.7561785130718954]]}, \"id\": \"el298441827342285120\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "                 \n",
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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid_close_complete['Open_A'].plot(figsize=(12,6), linestyle='--', color='black', legend='Open_A')\n",
    "valid_close_complete['Close_A'].plot(figsize=(12,6), linestyle='-', color='grey', legend='Close_A')\n",
    "valid_close_complete['Low_A'].plot(figsize=(12,6), linestyle=':', color='black', legend='Low_A')\n",
    "valid_close_complete['High_A'].plot(figsize=(12,6), linestyle='-.', color='grey', legend='High_A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a976286208>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el2984418273422433203966075372\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el2984418273422433203966075372\", {\"width\": 864.0, \"height\": 432.0, \"axes\": [{\"bbox\": [0.125, 0.20000000000000007, 0.775, 0.6799999999999999], \"xlim\": [735641.7, 737650.3], \"ylim\": [-436.5, 366.5], \"xdomain\": [[2015, 1, 12, 16, 48, 0, 0.0], [2020, 7, 13, 7, 12, 0, 0.0]], \"ydomain\": [-436.5, 366.5], \"xscale\": \"date\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 6, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 10, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el298441827343458824\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 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0.982979302832244], [0.9895459976105138, 0.982979302832244], [0.8835125448028674, 0.982979302832244], [0.8805256869772998, 0.982979302832244], [0.8805256869772998, 0.9761710239651418], [0.8805256869772998, 0.7629867919389979], [0.8805256869772998, 0.7561785130718954], [0.8835125448028674, 0.7561785130718954]]}, \"id\": \"el298441827342243320\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el2984418273422433203966075372\", {\"width\": 864.0, \"height\": 432.0, \"axes\": [{\"bbox\": [0.125, 0.20000000000000007, 0.775, 0.6799999999999999], \"xlim\": [735641.7, 737650.3], \"ylim\": [-436.5, 366.5], \"xdomain\": [[2015, 1, 12, 16, 48, 0, 0.0], [2020, 7, 13, 7, 12, 0, 0.0]], \"ydomain\": [-436.5, 366.5], \"xscale\": \"date\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 6, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 10, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el298441827343458824\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el298441827343297784\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"5.550000000000001,2.4000000000000004\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el298441827345568936\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el298441827345636824\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"1.5,2.4749999999999996\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el298441827343735048\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"9.600000000000001,2.4000000000000004,1.5,2.4000000000000004\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el298441827343736448\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"5.550000000000001,2.4000000000000004\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el298441827343692520\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el298441827343733872\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"1.5,2.4749999999999996\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el298441827343835768\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"9.600000000000001,2.4000000000000004,1.5,2.4000000000000004\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el298441827343733480\", \"dasharray\": \"none\", \"alpha\": 0.8, \"facecolor\": \"rgba(255, 255, 255, 0.8)\", \"edgecolor\": \"rgba(204, 204, 204, 0.8)\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Date\", \"position\": [0.49999999999999994, -0.110329023821638], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827345256632\"}, {\"text\": \"Open_C\", \"position\": [0.9283154121863798, 0.9421296296296298], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827343735944\"}, {\"text\": \"Close_C\", \"position\": [0.9283154121863798, 0.8879825367647061], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827343695264\"}, {\"text\": \"Low_C\", \"position\": [0.9283154121863798, 0.8338354438997821], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827343736616\"}, {\"text\": \"High_C\", \"position\": [0.9283154121863798, 0.7796883510348585], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827343835320\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[735733.0, 12.46, 12.38, 12.35, 13.09], [735736.0, 13.08, 12.73, 12.55, 13.22], [735737.0, 12.95, 12.85, 12.55, 13.13], [735739.0, 13.03, 12.11, 12.09, 13.09], [735740.0, 12.37, 12.13, 11.82, 12.37], [735744.0, 13.45, 14.06, 13.34, 14.63], [735745.0, 14.16, 13.27, 13.05, 14.41], [735746.0, 13.49, 13.31, 13.31, 13.99], [735747.0, 13.59, 13.84, 13.4, 14.43], [735750.0, 13.92, 13.97, 13.47, 14.86], [735751.0, 14.72, 14.24, 13.59, 15.05], [735752.0, 13.73, 13.66, 13.4, 14.2], [735754.0, 15.01, 14.21, 14.21, 15.65], [735757.0, 14.84, 15.29, 14.67, 15.5], [735758.0, 15.18, 14.47, 14.47, 15.74], [735759.0, 14.24, 13.22, 12.96, 14.37], [735760.0, 13.04, 12.85, 12.56, 13.22], [735761.0, 13.31, 13.78, 14.91, 14.02], [735764.0, 15.48, 15.39, 14.91, 15.57], [735765.0, 15.62, 14.81, 14.81, 15.62], [735766.0, 14.66, 14.5, 14.07, 15.49], [735767.0, 14.03, 13.19, 12.54, 14.03], 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20.35], [736017.0, 19.75, 20.98, 19.540001, 21.16], [736018.0, 22.280001, 20.719999, 20.26, 22.870001], [736019.0, 20.540001, 19.110001, 19.1, 21.26], [736020.0, -400.0, 19.809999, 18.459999, 20.129999], [736023.0, 20.49, 20.549999, 18.379999, 20.809999], [736024.0, 19.84, 17.700001, 16.780001000000002, 20.17], [736025.0, 17.98, 17.09, 16.780001000000002, 18.41], [736026.0, 17.25, 16.700001, 16.32, 17.559998999999998], [736027.0, 16.48, 16.860001, 16.049999, 17.35], [736030.0, 17.98, 17.35, 16.870001000000002, 18.040001], [736031.0, 18.379999, 18.67, 17.82, 18.889999], [736032.0, 18.559999, 18.34, 18.309999, 19.110001], [736033.0, 18.17, 18.049999, 17.059998999999998, 19.59], [736034.0, 17.09, 16.5, 16.280001000000002, 17.27], [736037.0, 17.01, 16.92, 16.690001000000002, 17.67], [736038.0, 17.6, 16.84, 16.84, 17.85], [736040.0, 15.34, 14.44, 13.82, 15.38], [736044.0, 14.57, 13.79, 13.79, 14.73], [736045.0, 14.57, 14.17, 13.75, 14.76], [736047.0, 16.299999, 14.74, 330.0, 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36.59, 39.57], [737549.0, 39.130001, 35.970001, 35.529999, 40.32], [737550.0, 34.82, 33.610001000000004, 31.950001, 36.220001], [737551.0, 32.689999, 34.119999, 31.68, 35.689999], [737552.0, 32.119999, 31.440001, 30.370001, 32.279999], [737553.0, 30.139999, 27.98, 27.889999, 30.389999], [737556.0, 28.459999, 27.57, 26.969999, 31.459999], [737557.0, 28.469999, 33.040001000000004, 26.0, 33.040001000000004], [737558.0, 32.740002000000004, 35.279999, 30.77, 37.419998], [737559.0, 35.16, 32.610001000000004, 32.330002, 39.279999]], \"data02\": [[0.8864994026284349, 0.954044117647059, 0.8998970247821353, 0.8457499319172114, 0.7916028390522877], [0.9163679808841099, 0.954044117647059, 0.8998970247821353, 0.8457499319172114, 0.7916028390522877]], \"data03\": [[0.8835125448028674, 0.7561785130718954], [0.9895459976105138, 0.7561785130718954], [0.9925328554360812, 0.7561785130718954], [0.9925328554360812, 0.7629867919389979], [0.9925328554360812, 0.9761710239651418], [0.9925328554360812, 0.982979302832244], [0.9895459976105138, 0.982979302832244], [0.8835125448028674, 0.982979302832244], [0.8805256869772998, 0.982979302832244], [0.8805256869772998, 0.9761710239651418], [0.8805256869772998, 0.7629867919389979], [0.8805256869772998, 0.7561785130718954], [0.8835125448028674, 0.7561785130718954]]}, \"id\": \"el298441827342243320\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el2984418273422433203966075372\", {\"width\": 864.0, \"height\": 432.0, \"axes\": [{\"bbox\": [0.125, 0.20000000000000007, 0.775, 0.6799999999999999], \"xlim\": [735641.7, 737650.3], \"ylim\": [-436.5, 366.5], \"xdomain\": [[2015, 1, 12, 16, 48, 0, 0.0], [2020, 7, 13, 7, 12, 0, 0.0]], \"ydomain\": [-436.5, 366.5], \"xscale\": \"date\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 6, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 10, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el298441827343458824\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el298441827343297784\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"5.550000000000001,2.4000000000000004\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el298441827345568936\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el298441827345636824\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"1.5,2.4749999999999996\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el298441827343735048\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"9.600000000000001,2.4000000000000004,1.5,2.4000000000000004\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el298441827343736448\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"5.550000000000001,2.4000000000000004\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el298441827343692520\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el298441827343733872\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"1.5,2.4749999999999996\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el298441827343835768\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"9.600000000000001,2.4000000000000004,1.5,2.4000000000000004\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el298441827343733480\", \"dasharray\": \"none\", \"alpha\": 0.8, \"facecolor\": \"rgba(255, 255, 255, 0.8)\", \"edgecolor\": \"rgba(204, 204, 204, 0.8)\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Date\", \"position\": [0.49999999999999994, -0.110329023821638], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827345256632\"}, {\"text\": \"Open_C\", \"position\": [0.9283154121863798, 0.9421296296296298], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827343735944\"}, {\"text\": \"Close_C\", \"position\": [0.9283154121863798, 0.8879825367647061], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827343695264\"}, {\"text\": \"Low_C\", \"position\": [0.9283154121863798, 0.8338354438997821], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827343736616\"}, {\"text\": \"High_C\", \"position\": [0.9283154121863798, 0.7796883510348585], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827343835320\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[735733.0, 12.46, 12.38, 12.35, 13.09], [735736.0, 13.08, 12.73, 12.55, 13.22], [735737.0, 12.95, 12.85, 12.55, 13.13], [735739.0, 13.03, 12.11, 12.09, 13.09], [735740.0, 12.37, 12.13, 11.82, 12.37], [735744.0, 13.45, 14.06, 13.34, 14.63], [735745.0, 14.16, 13.27, 13.05, 14.41], [735746.0, 13.49, 13.31, 13.31, 13.99], [735747.0, 13.59, 13.84, 13.4, 14.43], [735750.0, 13.92, 13.97, 13.47, 14.86], [735751.0, 14.72, 14.24, 13.59, 15.05], [735752.0, 13.73, 13.66, 13.4, 14.2], [735754.0, 15.01, 14.21, 14.21, 15.65], [735757.0, 14.84, 15.29, 14.67, 15.5], [735758.0, 15.18, 14.47, 14.47, 15.74], [735759.0, 14.24, 13.22, 12.96, 14.37], [735760.0, 13.04, 12.85, 12.56, 13.22], [735761.0, 13.31, 13.78, 14.91, 14.02], [735764.0, 15.48, 15.39, 14.91, 15.57], [735765.0, 15.62, 14.81, 14.81, 15.62], [735766.0, 14.66, 14.5, 14.07, 15.49], [735767.0, 14.03, 13.19, 12.54, 14.03], 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11.78, 12.42], [737441.0, 12.21, 12.1, 11.75, 12.48], [737446.0, 12.45, 12.91, 12.31, 13.01], [737447.0, 13.26, 12.98, 12.94, 14.15], [737448.0, 12.75, 14.56, 12.62, 15.98], [737451.0, 17.42, 18.23, 16.82, 19.02], [737453.0, 15.68, 16.389999, 14.94, 16.65], [737454.0, 17.82, 15.49, 15.3, 18.389999], [737455.0, 16.25, 18.84, 16.18, 19.99], [737458.0, 18.639999, 17.969998999999994, 17.1, 18.879999], [737459.0, 16.450001, 16.049999, 15.63, 16.459999], [737460.0, 16.290001, 15.15, 15.02, 16.32], [737461.0, 15.13, 14.96, 14.7, 15.66], [737462.0, 15.07, 15.47, 14.81, 16.16], [737465.0, 15.88, 15.04, 15.01, 16.43], [737467.0, 14.86, 13.74, 14.0, 14.88], [737468.0, 14.43, 14.15, 14.0, 15.44], [737469.0, 14.12, 13.68, 13.38, 14.54], [737473.0, 14.98, 14.83, 14.53, 15.49], [737474.0, 14.66, 14.38, 14.21, 14.74], [737475.0, 14.54, 15.56, 14.49, 17.209999], [737476.0, 17.33, 17.08, 16.190001000000002, 18.209999], [737479.0, 22.25, 25.030001, 22.0, 26.35], [737480.0, 23.280001, 27.85, 22.620001, 30.25], [737483.0, 42.02, 40.110001, 39.369999, 49.48], [737486.0, 38.540001, 33.419998, 31.5, 40.41], [737487.0, 33.650002, 36.82, 24.93, 41.060001], [737488.0, 34.439999, 31.99, 30.299999, 35.580002], [737489.0, 33.610001000000004, 39.619999, 33.540001000000004, 42.84], [737490.0, 48.93, 41.939999, 40.84, 54.389999], [737493.0, 41.939999, 54.459999, 41.939999, 62.119999], [737494.0, 49.68, 47.299999, 43.560001, 55.66], [737495.0, 52.240002, 53.900002, 49.98, 55.82], [737497.0, 65.07, 57.830002, 55.169998, 77.57], [737500.0, 57.830002, 82.690002, 57.830002, 83.559998], [737501.0, 82.690002, 75.910004, 70.370003, 84.83000200000002], [737502.0, 69.370003, 76.449997, 69.370003, 85.470001], [737503.0, 80.620003, 72.0, 68.57, 84.260002], [737504.0, 67.860001, 66.040001, 57.419998, 69.510002], [737507.0, 74.08000200000002, 61.59, 60.459999, 76.739998], [737508.0, 58.759998, 61.669998, 36.240002, 61.880001], [737509.0, 61.439999, 63.950001, 58.029999, 68.860001], [737510.0, 65.66999799999999, 61.0, 57.66, 67.059998], [737515.0, 56.689999, 53.540001, 50.880001, 58.75], [737516.0, 57.380001, 57.060001, 52.759998, 60.59], [737517.0, 54.459999, 50.91, 43.450001, 57.240002], [737521.0, 44.169998, 45.240002, 43.450001, 45.73], [737522.0, 44.830002, 46.700001, 43.509998, 47.509998], [737523.0, 45.900002, 43.349998, 42.529999, 47.279999], [737528.0, 44.599998, 41.169998, 41.169998, 45.040001], [737529.0, 40.240002, 37.759998, 37.310001, 40.57], [737530.0, 42.57, 40.84, 40.049999, 43.23], [737531.0, 41.919998, 40.110001, 39.869999, 43.02], [737532.0, 39.5, 38.150002, 37.630001, 40.259998], [737535.0, 40.68, 43.830002, 39.880001, 43.830002], [737537.0, 44.91, 41.98, 41.41, 45.07], [737538.0, 41.299999, 41.380001, 39.060001, 42.470001], [737539.0, 38.799999, 35.93, 35.599998, 40.169998], [737543.0, 33.209999, 33.57, 30.540001, 35.389999], [737544.0, 32.48, 31.23, 30.709999, 33.189999], [737545.0, 30.99, 34.150002, 30.93, 35.939999], [737546.0, 38.169998, 37.189999, 36.59, 39.57], [737549.0, 39.130001, 35.970001, 35.529999, 40.32], [737550.0, 34.82, 33.610001000000004, 31.950001, 36.220001], [737551.0, 32.689999, 34.119999, 31.68, 35.689999], [737552.0, 32.119999, 31.440001, 30.370001, 32.279999], [737553.0, 30.139999, 27.98, 27.889999, 30.389999], [737556.0, 28.459999, 27.57, 26.969999, 31.459999], [737557.0, 28.469999, 33.040001000000004, 26.0, 33.040001000000004], [737558.0, 32.740002000000004, 35.279999, 30.77, 37.419998], [737559.0, 35.16, 32.610001000000004, 32.330002, 39.279999]], \"data02\": [[0.8864994026284349, 0.954044117647059, 0.8998970247821353, 0.8457499319172114, 0.7916028390522877], [0.9163679808841099, 0.954044117647059, 0.8998970247821353, 0.8457499319172114, 0.7916028390522877]], \"data03\": [[0.8835125448028674, 0.7561785130718954], [0.9895459976105138, 0.7561785130718954], [0.9925328554360812, 0.7561785130718954], [0.9925328554360812, 0.7629867919389979], [0.9925328554360812, 0.9761710239651418], [0.9925328554360812, 0.982979302832244], [0.9895459976105138, 0.982979302832244], [0.8835125448028674, 0.982979302832244], [0.8805256869772998, 0.982979302832244], [0.8805256869772998, 0.9761710239651418], [0.8805256869772998, 0.7629867919389979], [0.8805256869772998, 0.7561785130718954], [0.8835125448028674, 0.7561785130718954]]}, \"id\": \"el298441827342243320\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "valid_close_complete['Open_C'].plot(figsize=(12,6), linestyle='--', color='black', legend='Open_C')\n",
    "valid_close_complete['Close_C'].plot(figsize=(12,6), linestyle='-', color='grey', legend='Close_C')\n",
    "valid_close_complete['Low_C'].plot(figsize=(12,6), linestyle=':', color='black', legend='Low_C')\n",
    "valid_close_complete['High_C'].plot(figsize=(12,6), linestyle='-.', color='grey', legend='High_C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 6 - Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "no_outlier_prices = prices_only[(np.abs(stats.zscore(prices_only)) < 6).all(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9767be2b0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
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       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el2984418273455357767958764243\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
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\"data03\": [[0.010454002389486239, 0.7561785130718954], [0.11648745519713258, 0.7561785130718954], [0.11947431302270009, 0.7561785130718954], [0.11947431302270009, 0.7629867919389979], [0.11947431302270009, 0.9761710239651418], [0.11947431302270009, 0.982979302832244], [0.11648745519713258, 0.982979302832244], [0.010454002389486239, 0.982979302832244], [0.007467144563918754, 0.982979302832244], [0.007467144563918754, 0.9761710239651418], [0.007467144563918754, 0.7629867919389979], [0.007467144563918754, 0.7561785130718954], [0.010454002389486239, 0.7561785130718954]]}, \"id\": \"el298441827345535776\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el2984418273455357767958764243\", {\"width\": 864.0, \"height\": 432.0, \"axes\": [{\"bbox\": [0.125, 0.20000000000000007, 0.775, 0.6799999999999999], \"xlim\": [735641.7, 737650.3], \"ylim\": [5.544999950000001, 71.87500105], \"xdomain\": [[2015, 1, 12, 16, 48, 0, 0.0], [2020, 7, 13, 7, 12, 0, 0.0]], \"ydomain\": [5.544999950000001, 71.87500105], \"xscale\": \"date\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 6, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 9, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el298441827348931248\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el298441827343298176\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"5.550000000000001,2.4000000000000004\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el298441827343927728\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el298441827343980640\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"1.5,2.4749999999999996\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el298441827343961728\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"9.600000000000001,2.4000000000000004,1.5,2.4000000000000004\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el298441827344021600\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"5.550000000000001,2.4000000000000004\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el298441827344470200\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el298441827344471264\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"1.5,2.4749999999999996\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el298441827344472328\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"9.600000000000001,2.4000000000000004,1.5,2.4000000000000004\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el298441827344020704\", \"dasharray\": \"none\", \"alpha\": 0.8, \"facecolor\": \"rgba(255, 255, 255, 0.8)\", \"edgecolor\": \"rgba(204, 204, 204, 0.8)\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Date\", \"position\": [0.49999999999999994, -0.110329023821638], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827342263128\"}, {\"text\": \"Open_C\", \"position\": [0.05525686977299879, 0.9421296296296298], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827344021376\"}, {\"text\": \"Close_C\", \"position\": [0.05525686977299879, 0.8879825367647061], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827344023224\"}, {\"text\": \"Low_C\", \"position\": [0.05525686977299879, 0.8338354438997821], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827344470816\"}, {\"text\": \"High_C\", \"position\": [0.05525686977299879, 0.7796883510348585], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, 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41.939999, 62.119999], [737494.0, 49.68, 47.299999, 43.560001, 55.66], [737495.0, 52.240002, 53.900002, 49.98, 55.82], [737508.0, 58.759998, 61.669998, 36.240002, 61.880001], [737509.0, 61.439999, 63.950001, 58.029999, 68.860001], [737510.0, 65.66999799999999, 61.0, 57.66, 67.059998], [737515.0, 56.689999, 53.540001, 50.880001, 58.75], [737516.0, 57.380001, 57.060001, 52.759998, 60.59], [737517.0, 54.459999, 50.91, 43.450001, 57.240002], [737521.0, 44.169998, 45.240002, 43.450001, 45.73], [737522.0, 44.830002, 46.700001, 43.509998, 47.509998], [737523.0, 45.900002, 43.349998, 42.529999, 47.279999], [737528.0, 44.599998, 41.169998, 41.169998, 45.040001], [737529.0, 40.240002, 37.759998, 37.310001, 40.57], [737530.0, 42.57, 40.84, 40.049999, 43.23], [737531.0, 41.919998, 40.110001, 39.869999, 43.02], [737532.0, 39.5, 38.150002, 37.630001, 40.259998], [737535.0, 40.68, 43.830002, 39.880001, 43.830002], [737537.0, 44.91, 41.98, 41.41, 45.07], [737538.0, 41.299999, 41.380001, 39.060001, 42.470001], [737539.0, 38.799999, 35.93, 35.599998, 40.169998], [737543.0, 33.209999, 33.57, 30.540001, 35.389999], [737544.0, 32.48, 31.23, 30.709999, 33.189999], [737545.0, 30.99, 34.150002, 30.93, 35.939999], [737546.0, 38.169998, 37.189999, 36.59, 39.57], [737549.0, 39.130001, 35.970001, 35.529999, 40.32], [737550.0, 34.82, 33.610001000000004, 31.950001, 36.220001], [737551.0, 32.689999, 34.119999, 31.68, 35.689999], [737552.0, 32.119999, 31.440001, 30.370001, 32.279999], [737553.0, 30.139999, 27.98, 27.889999, 30.389999], [737556.0, 28.459999, 27.57, 26.969999, 31.459999], [737557.0, 28.469999, 33.040001000000004, 26.0, 33.040001000000004], [737558.0, 32.740002000000004, 35.279999, 30.77, 37.419998], [737559.0, 35.16, 32.610001000000004, 32.330002, 39.279999]], \"data02\": [[0.013440860215053752, 0.954044117647059, 0.8998970247821353, 0.8457499319172114, 0.7916028390522877], [0.04330943847072878, 0.954044117647059, 0.8998970247821353, 0.8457499319172114, 0.7916028390522877]], \"data03\": [[0.010454002389486239, 0.7561785130718954], [0.11648745519713258, 0.7561785130718954], [0.11947431302270009, 0.7561785130718954], [0.11947431302270009, 0.7629867919389979], [0.11947431302270009, 0.9761710239651418], [0.11947431302270009, 0.982979302832244], [0.11648745519713258, 0.982979302832244], [0.010454002389486239, 0.982979302832244], [0.007467144563918754, 0.982979302832244], [0.007467144563918754, 0.9761710239651418], [0.007467144563918754, 0.7629867919389979], [0.007467144563918754, 0.7561785130718954], [0.010454002389486239, 0.7561785130718954]]}, \"id\": \"el298441827345535776\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el2984418273455357767958764243\", {\"width\": 864.0, \"height\": 432.0, \"axes\": [{\"bbox\": [0.125, 0.20000000000000007, 0.775, 0.6799999999999999], \"xlim\": [735641.7, 737650.3], \"ylim\": [5.544999950000001, 71.87500105], \"xdomain\": [[2015, 1, 12, 16, 48, 0, 0.0], [2020, 7, 13, 7, 12, 0, 0.0]], \"ydomain\": [5.544999950000001, 71.87500105], \"xscale\": \"date\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 6, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 9, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el298441827348931248\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el298441827343298176\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"5.550000000000001,2.4000000000000004\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el298441827343927728\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"data\", \"id\": \"el298441827343980640\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"1.5,2.4749999999999996\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data01\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"data\", \"id\": \"el298441827343961728\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"9.600000000000001,2.4000000000000004,1.5,2.4000000000000004\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"id\": \"el298441827344021600\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"5.550000000000001,2.4000000000000004\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"axes\", \"id\": \"el298441827344470200\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 3, \"coordinates\": \"axes\", \"id\": \"el298441827344471264\", \"color\": \"#000000\", \"linewidth\": 1.5, \"dasharray\": \"1.5,2.4749999999999996\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 4, \"coordinates\": \"axes\", \"id\": \"el298441827344472328\", \"color\": \"#808080\", \"linewidth\": 1.5, \"dasharray\": \"9.600000000000001,2.4000000000000004,1.5,2.4000000000000004\", \"alpha\": 1, \"zorder\": 1000002.0, \"drawstyle\": \"default\"}], \"paths\": [{\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"axes\", \"pathcodes\": [\"M\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"L\", \"S\", \"Z\"], \"id\": \"el298441827344020704\", \"dasharray\": \"none\", \"alpha\": 0.8, \"facecolor\": \"rgba(255, 255, 255, 0.8)\", \"edgecolor\": \"rgba(204, 204, 204, 0.8)\", \"edgewidth\": 1.0, \"zorder\": 1000000.0}], \"markers\": [], \"texts\": [{\"text\": \"Date\", \"position\": [0.49999999999999994, -0.110329023821638], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827342263128\"}, {\"text\": \"Open_C\", \"position\": [0.05525686977299879, 0.9421296296296298], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827344021376\"}, {\"text\": \"Close_C\", \"position\": [0.05525686977299879, 0.8879825367647061], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827344023224\"}, {\"text\": \"Low_C\", \"position\": [0.05525686977299879, 0.8338354438997821], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827344470816\"}, {\"text\": \"High_C\", \"position\": [0.05525686977299879, 0.7796883510348585], \"coordinates\": \"axes\", \"h_anchor\": \"start\", \"v_baseline\": \"auto\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 1000003.0, \"id\": \"el298441827344471880\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}], \"data\": {\"data01\": [[735733.0, 12.46, 12.38, 12.35, 13.09], [735736.0, 13.08, 12.73, 12.55, 13.22], [735737.0, 12.95, 12.85, 12.55, 13.13], [735739.0, 13.03, 12.11, 12.09, 13.09], [735740.0, 12.37, 12.13, 11.82, 12.37], [735744.0, 13.45, 14.06, 13.34, 14.63], [735745.0, 14.16, 13.27, 13.05, 14.41], [735746.0, 13.49, 13.31, 13.31, 13.99], [735747.0, 13.59, 13.84, 13.4, 14.43], [735750.0, 13.92, 13.97, 13.47, 14.86], [735751.0, 14.72, 14.24, 13.59, 15.05], [735752.0, 13.73, 13.66, 13.4, 14.2], [735754.0, 15.01, 14.21, 14.21, 15.65], [735757.0, 14.84, 15.29, 14.67, 15.5], [735758.0, 15.18, 14.47, 14.47, 15.74], [735759.0, 14.24, 13.22, 12.96, 14.37], [735760.0, 13.04, 12.85, 12.56, 13.22], [735761.0, 13.31, 13.78, 14.91, 14.02], [735764.0, 15.48, 15.39, 14.91, 15.57], [735765.0, 15.62, 14.81, 14.81, 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14.73], [735806.0, 15.6, 15.6, 15.03, 16.27], [735807.0, 14.87, 13.44, 13.32, 15.62], [735808.0, 13.57, 12.5, 11.85, 13.59], [735809.0, 12.72, 12.13, 11.82, 13.42], [735810.0, 12.03, 12.12, 11.82, 12.63], [735813.0, 12.85, 12.56, 12.32, 13.55], [735814.0, 12.66, 13.0, 12.29, 13.22], [735815.0, 12.02, 12.51, 10.88, 12.72], [735816.0, 12.2, 13.77, 12.16, 14.25], [735817.0, 13.57, 13.39, 13.29, 14.58], [735820.0, 12.73, 12.23, 12.18, 12.78], [735821.0, 13.24, 13.71, 13.02, 14.33], [735822.0, 15.19, 13.61, 13.45, 16.280001000000002], [735823.0, 13.87, 13.49, 13.06, 14.33], [735824.0, 13.69, 12.83, 12.8, 13.87], [735827.0, 14.32, 13.02, 13.01, 14.52], [735828.0, 13.41, 13.79, 13.17, 13.94], [735829.0, 14.84, 15.25, 13.73, 15.96], [735830.0, 16.549999, 19.139999, 16.129998999999998, 19.24], [735831.0, 22.549999, 28.030001, 20.799999, 28.379999], [735834.0, 28.030001, 40.740002, 28.030001, 53.290001], [735835.0, 31.129999, 36.02, 28.08, 38.060001], [735836.0, 31.129999, 30.32, 28.67, 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12.61], [737416.0, 12.81, 12.61, 12.56, 12.9], [737417.0, 12.65, 12.67, 12.56, 12.84], [737419.0, 12.74, 12.65, 11.72, 12.75], [737420.0, 12.61, 13.43, 13.44, 13.72], [737423.0, 13.74, 14.82, 13.44, 15.14], [737424.0, 14.84, 13.78, 13.75, 15.39], [737426.0, 13.46, 12.47, 13.13, 13.72], [737427.0, 15.01, 14.02, 13.13, 16.200001], [737430.0, 15.45, 13.85, 13.54, 16.389999], [737431.0, 13.84, 13.79, 13.39, 14.46], [737432.0, 15.16, 13.45, 12.83, 15.24], [737433.0, 12.95, 12.54, 12.53, 13.24], [737434.0, 12.42, 12.56, 12.09, 12.87], [737437.0, 12.84, 12.32, 12.32, 13.09], [737438.0, 12.72, 12.39, 12.05, 13.82], [737439.0, 12.79, 12.42, 11.95, 12.83], [737440.0, 12.2, 12.32, 11.78, 12.42], [737441.0, 12.21, 12.1, 11.75, 12.48], [737446.0, 12.45, 12.91, 12.31, 13.01], [737447.0, 13.26, 12.98, 12.94, 14.15], [737448.0, 12.75, 14.56, 12.62, 15.98], [737451.0, 17.42, 18.23, 16.82, 19.02], [737453.0, 15.68, 16.389999, 14.94, 16.65], [737454.0, 17.82, 15.49, 15.3, 18.389999], [737455.0, 16.25, 18.84, 16.18, 19.99], [737458.0, 18.639999, 17.969998999999994, 17.1, 18.879999], [737459.0, 16.450001, 16.049999, 15.63, 16.459999], [737460.0, 16.290001, 15.15, 15.02, 16.32], [737461.0, 15.13, 14.96, 14.7, 15.66], [737462.0, 15.07, 15.47, 14.81, 16.16], [737465.0, 15.88, 15.04, 15.01, 16.43], [737467.0, 14.86, 13.74, 14.0, 14.88], [737468.0, 14.43, 14.15, 14.0, 15.44], [737469.0, 14.12, 13.68, 13.38, 14.54], [737473.0, 14.98, 14.83, 14.53, 15.49], [737474.0, 14.66, 14.38, 14.21, 14.74], [737475.0, 14.54, 15.56, 14.49, 17.209999], [737476.0, 17.33, 17.08, 16.190001000000002, 18.209999], [737479.0, 22.25, 25.030001, 22.0, 26.35], [737480.0, 23.280001, 27.85, 22.620001, 30.25], [737483.0, 42.02, 40.110001, 39.369999, 49.48], [737486.0, 38.540001, 33.419998, 31.5, 40.41], [737487.0, 33.650002, 36.82, 24.93, 41.060001], [737488.0, 34.439999, 31.99, 30.299999, 35.580002], [737489.0, 33.610001000000004, 39.619999, 33.540001000000004, 42.84], [737490.0, 48.93, 41.939999, 40.84, 54.389999], [737493.0, 41.939999, 54.459999, 41.939999, 62.119999], [737494.0, 49.68, 47.299999, 43.560001, 55.66], [737495.0, 52.240002, 53.900002, 49.98, 55.82], [737508.0, 58.759998, 61.669998, 36.240002, 61.880001], [737509.0, 61.439999, 63.950001, 58.029999, 68.860001], [737510.0, 65.66999799999999, 61.0, 57.66, 67.059998], [737515.0, 56.689999, 53.540001, 50.880001, 58.75], [737516.0, 57.380001, 57.060001, 52.759998, 60.59], [737517.0, 54.459999, 50.91, 43.450001, 57.240002], [737521.0, 44.169998, 45.240002, 43.450001, 45.73], [737522.0, 44.830002, 46.700001, 43.509998, 47.509998], [737523.0, 45.900002, 43.349998, 42.529999, 47.279999], [737528.0, 44.599998, 41.169998, 41.169998, 45.040001], [737529.0, 40.240002, 37.759998, 37.310001, 40.57], [737530.0, 42.57, 40.84, 40.049999, 43.23], [737531.0, 41.919998, 40.110001, 39.869999, 43.02], [737532.0, 39.5, 38.150002, 37.630001, 40.259998], [737535.0, 40.68, 43.830002, 39.880001, 43.830002], [737537.0, 44.91, 41.98, 41.41, 45.07], [737538.0, 41.299999, 41.380001, 39.060001, 42.470001], [737539.0, 38.799999, 35.93, 35.599998, 40.169998], [737543.0, 33.209999, 33.57, 30.540001, 35.389999], [737544.0, 32.48, 31.23, 30.709999, 33.189999], [737545.0, 30.99, 34.150002, 30.93, 35.939999], [737546.0, 38.169998, 37.189999, 36.59, 39.57], [737549.0, 39.130001, 35.970001, 35.529999, 40.32], [737550.0, 34.82, 33.610001000000004, 31.950001, 36.220001], [737551.0, 32.689999, 34.119999, 31.68, 35.689999], [737552.0, 32.119999, 31.440001, 30.370001, 32.279999], [737553.0, 30.139999, 27.98, 27.889999, 30.389999], [737556.0, 28.459999, 27.57, 26.969999, 31.459999], [737557.0, 28.469999, 33.040001000000004, 26.0, 33.040001000000004], [737558.0, 32.740002000000004, 35.279999, 30.77, 37.419998], [737559.0, 35.16, 32.610001000000004, 32.330002, 39.279999]], \"data02\": [[0.013440860215053752, 0.954044117647059, 0.8998970247821353, 0.8457499319172114, 0.7916028390522877], [0.04330943847072878, 0.954044117647059, 0.8998970247821353, 0.8457499319172114, 0.7916028390522877]], \"data03\": [[0.010454002389486239, 0.7561785130718954], [0.11648745519713258, 0.7561785130718954], [0.11947431302270009, 0.7561785130718954], [0.11947431302270009, 0.7629867919389979], [0.11947431302270009, 0.9761710239651418], [0.11947431302270009, 0.982979302832244], [0.11648745519713258, 0.982979302832244], [0.010454002389486239, 0.982979302832244], [0.007467144563918754, 0.982979302832244], [0.007467144563918754, 0.9761710239651418], [0.007467144563918754, 0.7629867919389979], [0.007467144563918754, 0.7561785130718954], [0.010454002389486239, 0.7561785130718954]]}, \"id\": \"el298441827345535776\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "no_outlier_prices['Open_C'].plot(figsize=(12,6), linestyle='--', color='black', legend='Open_C')\n",
    "no_outlier_prices['Close_C'].plot(figsize=(12,6), linestyle='-', color='grey', legend='Close_C')\n",
    "no_outlier_prices['Low_C'].plot(figsize=(12,6), linestyle=':', color='black', legend='Low_C')\n",
    "no_outlier_prices['High_C'].plot(figsize=(12,6), linestyle='-.', color='grey', legend='High_C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Open_C</th>\n",
       "      <th>Close_C</th>\n",
       "      <th>Low_C</th>\n",
       "      <th>High_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1095.000000</td>\n",
       "      <td>1095.000000</td>\n",
       "      <td>1095.000000</td>\n",
       "      <td>1095.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>16.147571</td>\n",
       "      <td>16.072648</td>\n",
       "      <td>15.223635</td>\n",
       "      <td>17.214539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.764147</td>\n",
       "      <td>6.773569</td>\n",
       "      <td>5.995822</td>\n",
       "      <td>7.588690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.010000</td>\n",
       "      <td>9.140000</td>\n",
       "      <td>8.560000</td>\n",
       "      <td>9.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.420000</td>\n",
       "      <td>12.285000</td>\n",
       "      <td>11.865000</td>\n",
       "      <td>12.890000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.160000</td>\n",
       "      <td>14.060000</td>\n",
       "      <td>13.440000</td>\n",
       "      <td>14.940000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17.625000</td>\n",
       "      <td>17.500000</td>\n",
       "      <td>16.550000</td>\n",
       "      <td>18.929999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>65.669998</td>\n",
       "      <td>63.950001</td>\n",
       "      <td>58.029999</td>\n",
       "      <td>68.860001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Open_C      Close_C        Low_C       High_C\n",
       "count  1095.000000  1095.000000  1095.000000  1095.000000\n",
       "mean     16.147571    16.072648    15.223635    17.214539\n",
       "std       6.764147     6.773569     5.995822     7.588690\n",
       "min       9.010000     9.140000     8.560000     9.310000\n",
       "25%      12.420000    12.285000    11.865000    12.890000\n",
       "50%      14.160000    14.060000    13.440000    14.940000\n",
       "75%      17.625000    17.500000    16.550000    18.929999\n",
       "max      65.669998    63.950001    58.029999    68.860001"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', None)\n",
    "no_outlier_prices[['Open_C', 'Close_C', 'Low_C', 'High_C']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 7 - Advanced Visualization Techniques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "close_prices = no_outlier_prices[['Close_A', 'Close_B', 'Close_C']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "delta_close_prices = (close_prices.shift(-1) - close_prices).fillna(0)\n",
    "delta_close_prices.columns = ['Delta_Close_A', 'Delta_Close_B', 'Delta_Close_C']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delta_Close_A</th>\n",
       "      <th>Delta_Close_B</th>\n",
       "      <th>Delta_Close_C</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-15</th>\n",
       "      <td>26.320312</td>\n",
       "      <td>6.469971</td>\n",
       "      <td>0.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-18</th>\n",
       "      <td>13.509766</td>\n",
       "      <td>-1.369873</td>\n",
       "      <td>0.120000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-13</th>\n",
       "      <td>377.369141</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>-2.669998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-05-14</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1095 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Delta_Close_A  Delta_Close_B  Delta_Close_C\n",
       "Date                                                   \n",
       "2015-05-15      26.320312       6.469971       0.350000\n",
       "2015-05-18      13.509766      -1.369873       0.120000\n",
       "...                   ...            ...            ...\n",
       "2020-05-13     377.369141      32.500000      -2.669998\n",
       "2020-05-14       0.000000       0.000000       0.000000\n",
       "\n",
       "[1095 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta_close_prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delta_Close_A</th>\n",
       "      <th>Delta_Close_B</th>\n",
       "      <th>Delta_Close_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1095.000000</td>\n",
       "      <td>1095.000000</td>\n",
       "      <td>1095.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.888383</td>\n",
       "      <td>0.666457</td>\n",
       "      <td>0.018475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>268.137091</td>\n",
       "      <td>29.218995</td>\n",
       "      <td>1.938761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-2848.310547</td>\n",
       "      <td>-294.049805</td>\n",
       "      <td>-9.120001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-70.894532</td>\n",
       "      <td>-7.140076</td>\n",
       "      <td>-0.760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>15.539063</td>\n",
       "      <td>1.770020</td>\n",
       "      <td>-0.080000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>113.290039</td>\n",
       "      <td>12.255005</td>\n",
       "      <td>0.540001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1351.619141</td>\n",
       "      <td>154.510009</td>\n",
       "      <td>20.010001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Delta_Close_A  Delta_Close_B  Delta_Close_C\n",
       "count    1095.000000    1095.000000    1095.000000\n",
       "mean        4.888383       0.666457       0.018475\n",
       "std       268.137091      29.218995       1.938761\n",
       "min     -2848.310547    -294.049805      -9.120001\n",
       "25%       -70.894532      -7.140076      -0.760000\n",
       "50%        15.539063       1.770020      -0.080000\n",
       "75%       113.290039      12.255005       0.540001\n",
       "max      1351.619141     154.510009      20.010001"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta_close_prices.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9763ab048>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
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       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el2984418273439589848562886644\", {\"width\": 864.0, \"height\": 432.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3058.307031399997, 1561.615625399999], \"ylim\": [0.0, 165.9], \"xdomain\": [-3058.307031399997, 1561.615625399999], \"ydomain\": [0.0, 165.9], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 10, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, 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       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el2984418273439589848562886644\", {\"width\": 864.0, \"height\": 432.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [-3058.307031399997, 1561.615625399999], \"ylim\": [0.0, 165.9], \"xdomain\": [-3058.307031399997, 1561.615625399999], \"ydomain\": [0.0, 165.9], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 10, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "delta_close_prices['Delta_Close_A'].plot(kind='hist', bins=100, figsize=(12,6), color='black', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9765d8198>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el2984418273492635289159174157\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el2984418273492635289159174157\", {\"width\": 864.0, \"height\": 432.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [0.5, 1.5], \"ylim\": [-316.47779569999966, 176.93799970000006], \"xdomain\": [0.5, 1.5], \"ydomain\": [-316.47779569999966, 176.93799970000006], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 1, \"tickvalues\": [1], \"tickformat_formatter\": \"fixed\", \"tickformat\": [\"Delta_Close_B\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el298441827346940312\", \"lines\": [{\"data\": \"data01\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el298441827347062008\", \"color\": \"#000000\", \"linewidth\": 1.0, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el298441827347140680\", \"color\": \"#000000\", \"linewidth\": 1.0, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data02\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el298441827347141576\", \"color\": \"#000000\", \"linewidth\": 1.0, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el298441827347142472\", \"color\": \"#000000\", \"linewidth\": 1.0, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el298441827347143368\", \"color\": \"#000000\", \"linewidth\": 1.0, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data04\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el298441827347144264\", \"color\": \"#000000\", \"linewidth\": 1.0, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2.1, \"drawstyle\": \"default\"}], \"paths\": [], \"markers\": [{\"data\": \"data05\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el298441827347173896pts\", \"facecolor\": \"none\", \"edgecolor\": \"#000000\", \"edgewidth\": 1.0, \"alpha\": 1, \"zorder\": 2, \"markerpath\": [[[0.0, 3.0], [0.7956093000000001, 3.0], [1.5587396123545605, 2.683901074764725], [2.121320343559643, 2.121320343559643], [2.683901074764725, 1.5587396123545605], [3.0, 0.7956093000000001], [3.0, 0.0], [3.0, 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-36.160155999999915, 41.07983400000012]], \"data03\": [[0.9625, -36.160155999999915, 41.07983400000012], [1.0375, -36.160155999999915, 41.07983400000012]], \"data04\": [[0.925, 1.770020000000386], [1.075, 1.770020000000386]], \"data05\": [[1.0, -43.850097000000005], [1.0, -43.8801269999999], [1.0, -64.83996500000012], [1.0, -77.68005399999993], [1.0, -58.33007799999973], [1.0, -37.2800299999999], [1.0, -49.56994600000007], [1.0, -39.85998500000005], [1.0, -47.17004400000019], [1.0, -48.400024999999914], [1.0, -41.51001000000019], [1.0, -36.34997599999997], [1.0, -36.41992100000016], [1.0, -75.910034], [1.0, -53.489990000000034], [1.0, -38.100097000000005], [1.0, -59.85009700000046], [1.0, -113.18994199999952], [1.0, -100.65991200000008], [1.0, -39.09008799999992], [1.0, -68.23999099999992], [1.0, -55.429931000000124], [1.0, -45.92993199999955], [1.0, -58.99023400000078], [1.0, -58.37011699999994], [1.0, -37.899902999999995], [1.0, -37.80981499999962], [1.0, -151.96997099999953], [1.0, 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[1.0, -114.09008799999992], [1.0, -62.69995199999994], [1.0, -51.40014699999983], [1.0, -81.71997099999999], [1.0, -60.0698239999997], [1.0, -50.12011700000039], [1.0, 48.29003899999998], [1.0, 72.90002500000014], [1.0, 47.1500239999998], [1.0, 48.19006300000001], [1.0, 63.23010299999987], [1.0, 46.02001999999993], [1.0, 46.11999600000013], [1.0, 46.34008799999992], [1.0, 46.19995199999994], [1.0, 43.34008799999992], [1.0, 47.600097000000005], [1.0, 70.29003899999998], [1.0, 62.81005800000048], [1.0, 43.710205000000315], [1.0, 42.89990299999954], [1.0, 58.41992200000004], [1.0, 49.4699700000001], [1.0, 81.81005899999991], [1.0, 42.699951000000056], [1.0, 61.62011699999994], [1.0, 116.59985300000017], [1.0, 84.05004800000006], [1.0, 61.8198239999997], [1.0, 58.820068999999876], [1.0, 64.51000999999997], [1.0, 54.110107999999855], [1.0, 43.62011699999994], [1.0, 48.67016599999988], [1.0, 136.01000900000008], [1.0, 126.75], [1.0, 135.66992100000016], [1.0, 154.51000900000008], [1.0, 56.39990200000011], [1.0, 136.7800299999999], [1.0, 90.57006799999999], [1.0, 84.4301760000003], [1.0, 75.01000999999997], [1.0, 76.12011699999994], [1.0, 48.610107999999855]]}, \"id\": \"el298441827349263528\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "         \n",
       "         mpld3.draw_figure(\"fig_el2984418273492635289159174157\", {\"width\": 864.0, \"height\": 432.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [0.5, 1.5], \"ylim\": [-316.47779569999966, 176.93799970000006], \"xdomain\": [0.5, 1.5], \"ydomain\": [-316.47779569999966, 176.93799970000006], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 1, \"tickvalues\": [1], \"tickformat_formatter\": \"fixed\", \"tickformat\": [\"Delta_Close_B\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, 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\"drawstyle\": \"default\"}, {\"data\": \"data03\", \"xindex\": 0, \"yindex\": 2, \"coordinates\": \"data\", \"id\": \"el298441827347143368\", \"color\": \"#000000\", \"linewidth\": 1.0, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2, \"drawstyle\": \"default\"}, {\"data\": \"data04\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el298441827347144264\", \"color\": \"#000000\", \"linewidth\": 1.0, \"dasharray\": \"none\", \"alpha\": 1, \"zorder\": 2.1, \"drawstyle\": \"default\"}], \"paths\": [], \"markers\": [{\"data\": \"data05\", \"xindex\": 0, \"yindex\": 1, \"coordinates\": \"data\", \"id\": \"el298441827347173896pts\", \"facecolor\": \"none\", \"edgecolor\": \"#000000\", \"edgewidth\": 1.0, \"alpha\": 1, \"zorder\": 2, \"markerpath\": [[[0.0, 3.0], [0.7956093000000001, 3.0], [1.5587396123545605, 2.683901074764725], [2.121320343559643, 2.121320343559643], [2.683901074764725, 1.5587396123545605], [3.0, 0.7956093000000001], [3.0, 0.0], [3.0, 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-36.160155999999915, 41.07983400000012]], \"data03\": [[0.9625, -36.160155999999915, 41.07983400000012], [1.0375, -36.160155999999915, 41.07983400000012]], \"data04\": [[0.925, 1.770020000000386], [1.075, 1.770020000000386]], \"data05\": [[1.0, -43.850097000000005], [1.0, -43.8801269999999], [1.0, -64.83996500000012], [1.0, -77.68005399999993], [1.0, -58.33007799999973], [1.0, -37.2800299999999], [1.0, -49.56994600000007], [1.0, -39.85998500000005], [1.0, -47.17004400000019], [1.0, -48.400024999999914], [1.0, -41.51001000000019], [1.0, -36.34997599999997], [1.0, -36.41992100000016], [1.0, -75.910034], [1.0, -53.489990000000034], [1.0, -38.100097000000005], [1.0, -59.85009700000046], [1.0, -113.18994199999952], [1.0, -100.65991200000008], [1.0, -39.09008799999992], [1.0, -68.23999099999992], [1.0, -55.429931000000124], [1.0, -45.92993199999955], [1.0, -58.99023400000078], [1.0, -58.37011699999994], [1.0, -37.899902999999995], [1.0, -37.80981499999962], [1.0, -151.96997099999953], [1.0, 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[1.0, -114.09008799999992], [1.0, -62.69995199999994], [1.0, -51.40014699999983], [1.0, -81.71997099999999], [1.0, -60.0698239999997], [1.0, -50.12011700000039], [1.0, 48.29003899999998], [1.0, 72.90002500000014], [1.0, 47.1500239999998], [1.0, 48.19006300000001], [1.0, 63.23010299999987], [1.0, 46.02001999999993], [1.0, 46.11999600000013], [1.0, 46.34008799999992], [1.0, 46.19995199999994], [1.0, 43.34008799999992], [1.0, 47.600097000000005], [1.0, 70.29003899999998], [1.0, 62.81005800000048], [1.0, 43.710205000000315], [1.0, 42.89990299999954], [1.0, 58.41992200000004], [1.0, 49.4699700000001], [1.0, 81.81005899999991], [1.0, 42.699951000000056], [1.0, 61.62011699999994], [1.0, 116.59985300000017], [1.0, 84.05004800000006], [1.0, 61.8198239999997], [1.0, 58.820068999999876], [1.0, 64.51000999999997], [1.0, 54.110107999999855], [1.0, 43.62011699999994], [1.0, 48.67016599999988], [1.0, 136.01000900000008], [1.0, 126.75], [1.0, 135.66992100000016], [1.0, 154.51000900000008], [1.0, 56.39990200000011], [1.0, 136.7800299999999], [1.0, 90.57006799999999], [1.0, 84.4301760000003], [1.0, 75.01000999999997], [1.0, 76.12011699999994], [1.0, 48.610107999999855]]}, \"id\": \"el298441827349263528\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el2984418273492635289159174157\", {\"width\": 864.0, \"height\": 432.0, \"axes\": [{\"bbox\": [0.125, 0.125, 0.775, 0.755], \"xlim\": [0.5, 1.5], \"ylim\": [-316.47779569999966, 176.93799970000006], \"xdomain\": [0.5, 1.5], \"ydomain\": [-316.47779569999966, 176.93799970000006], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 1, \"tickvalues\": [1], \"tickformat_formatter\": \"fixed\", \"tickformat\": [\"Delta_Close_B\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": true, \"color\": \"#B0B0B0\", \"dasharray\": \"none\", \"alpha\": 1.0}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "delta_close_prices['Delta_Close_B'].plot(kind='box', figsize=(12,6), color='black', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001A9765EC3C8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A97665EE80>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A97669B128>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001A9766D1358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A9767045C0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A976738828>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001A97676DA90>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A97684FCC0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001A97684FD30>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/html": [
       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el2984418273473023284072921763\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
       "  s.async = true;\n",
       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el2984418273473023284072921763\", {\"width\": 720.0, \"height\": 720.0, \"axes\": [{\"bbox\": [0.125, 0.6283333333333334, 0.25833333333333336, 0.2516666666666666], \"xlim\": [-2953.308789199997, 1456.617383199999], \"ylim\": [-0.00013169111176202506, 0.002765513347557042], \"xdomain\": [-2953.308789199997, 1456.617383199999], \"ydomain\": [-0.00013169111176202506, 0.002765513347557042], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 6, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": false}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": [0.00016612073228615122, 0.0004946074626821765, 0.0008230941930782019, 0.0011515809234742273, 0.001480067653870253, 0.001808554384266278, 0.002137041114662303, 0.0024655278450583288], \"tickformat_formatter\": \"fixed\", \"tickformat\": [-2500, 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       "   }(mpld3);\n",
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       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
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[14.9071477967968, 2.3011360648245653e-08], [14.93630695795796, 1.7296579025828398e-08], [14.965466119119121, 1.295389861666183e-08], [14.994625280280282, 9.66634290425774e-09], [15.023784441441446, 7.186946550173164e-09], [15.052943602602607, 5.324100696460523e-09], [15.082102763763768, 3.929767646880347e-09], [15.111261924924928, 2.89005037352356e-09], [15.14042108608609, 2.11768355580795e-09], [15.16958024724725, 1.5460838166062061e-09], [15.19873940840841, 1.1246581905737953e-09], [15.227898569569572, 8.151224626818327e-10], [15.257057730730732, 5.886257214637196e-10], [15.286216891891893, 4.2351516121371405e-10], [15.315376053053058, 3.0360669735949766e-10], [15.344535214214218, 2.1685314155344538e-10], [15.37369437537538, 1.543232701368072e-10], [15.40285353653654, 1.0942279572690272e-10], [15.4320126976977, 7.730262891457205e-11], [15.461171858858862, 5.4411435378521006e-11], [15.490331020020022, 3.815882332051754e-11], [15.519490181181183, 2.666294172583215e-11], [15.548649342342344, 1.8562176219746602e-11], [15.577808503503508, 1.2875286829371366e-11], [15.60696766466467, 8.897983523940578e-12], [15.63612682582583, 6.1267819973639485e-12], [15.66528598698699, 4.2031888186476694e-12], [15.694445148148152, 2.872966250104927e-12], [15.723604309309312, 1.9565313976963625e-12], [15.752763470470473, 1.3275391176640245e-12], [15.781922631631634, 8.974527379607727e-13], [15.811081792792795, 6.044761371082857e-13], [15.840240953953955, 4.0564826557513657e-13], [15.86940011511512, 2.7122053495879144e-13], [15.89855927627628, 1.8067481634284238e-13], [15.927718437437441, 1.199152666236168e-13], [15.956877598598602, 7.929631035719898e-14], [15.986036759759763, 5.224363026105044e-14], [16.015195920920924, 3.42938879261887e-14], [16.044355082082085, 2.2428829014340328e-14], [16.073514243243245, 1.4615456996075757e-14], [16.102673404404406, 9.48983369519662e-15], [16.131832565565567, 6.140558523149422e-15], [16.16099172672673, 3.9611351691483375e-15], [16.190150887887892, 2.5497520821092978e-15], [16.219310049049053, 1.641572815189765e-15], [16.248469210210214, 1.063245917777238e-15], [16.277628371371375, 7.025733109891144e-16], [16.306787532532535, 4.885324405343203e-16], [16.335946693693696, 3.7842658319758525e-16], [16.365105854854857, 3.5005229061832014e-16], [16.394265016016018, 3.975776782413496e-16], [16.423424177177182, 5.304349989224717e-16], [16.452583338338343, 7.750351019028877e-16], [16.481742499499504, 1.1795853476176544e-15], [16.510901660660664, 1.8228075024555313e-15], [16.540060821821825, 2.8279843811547124e-15], [16.569219982982986, 4.3846048323749346e-15], [16.598379144144147, 6.781043807017058e-15], [16.627538305305308, 1.0453366115246306e-14], [16.65669746646647, 1.6057696592664226e-14], [16.68585662762763, 2.45769568096325e-14], [16.715015788788794, 3.74775463174155e-14], [16.744174949949954, 5.693832150917224e-14], [16.773334111111115, 8.618379195044858e-14], [16.802493272272276, 1.29966901745603e-13], 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[18.11465552452453, 2.9588261782838154e-07], [18.14381468568569, 3.760606292063794e-07], [18.17297384684685, 4.7619160541765154e-07], [18.202133008008012, 6.007462283268023e-07], [18.231292169169173, 7.55067576368322e-07], [18.260451330330334, 9.455098140215534e-07], [18.289610491491494, 1.1795917092835625e-06], [18.318769652652655, 1.4661649491554846e-06], [18.347928813813816, 1.8155967853958315e-06], [18.37708797497498, 2.2399660104399242e-06], [18.40624713613614, 2.7532706327013722e-06], [18.435406297297302, 3.371644890874792e-06], [18.464565458458463, 4.113582421685057e-06], [18.493724619619623, 5.000161482864302e-06], [18.522883780780784, 6.055267146520665e-06], [18.552042941941945, 7.305804337183834e-06], [18.581202103103106, 8.781894519963941e-06], [18.610361264264267, 1.0517047780667485e-05], [18.639520425425427, 1.254830102149762e-05], [18.66867958658659, 1.4916312069872154e-05], [18.697838747747753, 1.7665398716505763e-05], [18.726997908908913, 2.0843511119570645e-05], 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\"el298441827347302328\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el2984418273473023284072921763\", {\"width\": 720.0, \"height\": 720.0, \"axes\": [{\"bbox\": [0.125, 0.6283333333333334, 0.25833333333333336, 0.2516666666666666], \"xlim\": [-2953.308789199997, 1456.617383199999], \"ylim\": [-0.00013169111176202506, 0.002765513347557042], \"xdomain\": [-2953.308789199997, 1456.617383199999], \"ydomain\": [-0.00013169111176202506, 0.002765513347557042], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 6, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": false}, {\"position\": \"left\", \"nticks\": 8, \"tickvalues\": [0.00016612073228615122, 0.0004946074626821765, 0.0008230941930782019, 0.0011515809234742273, 0.001480067653870253, 0.001808554384266278, 0.002137041114662303, 0.0024655278450583288], \"tickformat_formatter\": \"fixed\", 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\"rotation\": -90.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827347350472\"}], \"collections\": [], \"images\": [], \"sharex\": [], \"sharey\": []}, {\"bbox\": [0.38333333333333336, 0.6283333333333334, 0.25833333333333336, 0.2516666666666666], \"xlim\": [-305.26380034999966, 165.7240043500001], \"ylim\": [-2953.308789199997, 1456.617383199999], \"xdomain\": [-305.26380034999966, 165.7240043500001], \"ydomain\": [-2953.308789199997, 1456.617383199999], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": false}, {\"position\": \"left\", \"nticks\": 10, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": null, \"scale\": \"linear\", \"fontsize\": 8.0, \"grid\": {\"gridOn\": false}, \"visible\": false}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el298441827347492480\", \"lines\": [], \"paths\": [], \"markers\": [], \"texts\": [{\"text\": \"Delta_Close_B\", \"position\": [0.5, -2.496688741721855], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"hanging\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827347540064\"}, {\"text\": \"Delta_Close_A\", \"position\": [-1.483870967741935, 0.5], \"coordinates\": \"axes\", \"h_anchor\": \"middle\", \"v_baseline\": \"auto\", \"rotation\": -90.0, \"fontsize\": 10.0, \"color\": \"#000000\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827347541912\"}], \"collections\": [{\"offsets\": \"data02\", \"xindex\": 0, \"yindex\": 1, \"paths\": [[[[0.0, -0.25], [0.066300775, -0.25], [0.12989496769621336, -0.22365842289706042], [0.1767766952966369, -0.1767766952966369], [0.22365842289706042, -0.12989496769621336], [0.25, -0.066300775], 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       "         });\n",
       "}\n",
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      ],
      "text/plain": [
       "<Figure size 720x720 with 9 Axes>"
      ]
     },
     "metadata": {
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     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(delta_close_prices, figsize=(10,10), color='black', alpha=0.75, diagonal='kde', grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Delta_Close_A</th>\n",
       "      <th>Delta_Close_B</th>\n",
       "      <th>Delta_Close_C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Delta_Close_A</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.976104</td>\n",
       "      <td>-0.785566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delta_Close_B</th>\n",
       "      <td>0.976104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.817788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delta_Close_C</th>\n",
       "      <td>-0.785566</td>\n",
       "      <td>-0.817788</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Delta_Close_A  Delta_Close_B  Delta_Close_C\n",
       "Delta_Close_A       1.000000       0.976104      -0.785566\n",
       "Delta_Close_B       0.976104       1.000000      -0.817788\n",
       "Delta_Close_C      -0.785566      -0.817788       1.000000"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delta_close_prices.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a9779ab358>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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       "\n",
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       "\n",
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       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
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       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
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       "   }(mpld3);\n",
       "}else if(typeof define === \"function\" && define.amd){\n",
       "   // require.js is available: use it to load d3/mpld3\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "         \n",
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       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.2.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el2984418273701131207077281976\", {\"width\": 432.0, \"height\": 432.0, \"axes\": [{\"bbox\": [0.125, 0.19249999999999995, 0.6200000000000001, 0.6200000000000001], \"xlim\": [0.0, 3.0], \"ylim\": [3.0, 0.0], \"xdomain\": [0.0, 3.0], \"ydomain\": [3.0, 0.0], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"bottom\", \"nticks\": 3, \"tickvalues\": [0.5, 1.5, 2.5], \"tickformat_formatter\": \"fixed\", \"tickformat\": [\"Delta_Close_A\", \"Delta_Close_B\", \"Delta_Close_C\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"left\", \"nticks\": 3, \"tickvalues\": [0.5, 1.5, 2.5], \"tickformat_formatter\": \"fixed\", \"tickformat\": [\"Delta_Close_A\", \"Delta_Close_B\", \"Delta_Close_C\"], \"scale\": \"linear\", \"fontsize\": 10.0, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el298441827367727960\", \"lines\": [], \"paths\": [], \"markers\": [], \"texts\": [{\"text\": \"1\", \"position\": [0.5, 0.5], \"coordinates\": \"data\", \"h_anchor\": \"middle\", \"v_baseline\": \"central\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#262626\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827367640032\"}, {\"text\": \"0.98\", \"position\": [1.5, 0.5], \"coordinates\": \"data\", \"h_anchor\": \"middle\", \"v_baseline\": \"central\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#262626\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827370256760\"}, {\"text\": \"-0.79\", \"position\": [2.5, 0.5], \"coordinates\": \"data\", \"h_anchor\": \"middle\", \"v_baseline\": \"central\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#FFFFFF\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827370297776\"}, {\"text\": \"0.98\", \"position\": [0.5, 1.5], \"coordinates\": \"data\", \"h_anchor\": \"middle\", \"v_baseline\": \"central\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#262626\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827370297104\"}, {\"text\": \"1\", \"position\": [1.5, 1.5], \"coordinates\": \"data\", \"h_anchor\": \"middle\", \"v_baseline\": \"central\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#262626\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827370298616\"}, {\"text\": \"-0.82\", \"position\": [2.5, 1.5], \"coordinates\": \"data\", \"h_anchor\": \"middle\", \"v_baseline\": \"central\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#FFFFFF\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827370299176\"}, {\"text\": \"-0.79\", \"position\": [0.5, 2.5], \"coordinates\": \"data\", \"h_anchor\": \"middle\", \"v_baseline\": \"central\", \"rotation\": -0.0, \"fontsize\": 10.0, \"color\": \"#FFFFFF\", \"alpha\": 1, \"zorder\": 3, \"id\": \"el298441827370328528\"}, {\"text\": \"-0.82\", \"position\": [1.5, 2.5], \"coordinates\": \"data\", \"h_anchor\": \"middle\", 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{\"data01\": [[0.0, 0.0]]}, \"id\": \"el298441827370113120\", \"plugins\": [{\"type\": \"reset\"}, {\"type\": \"zoom\", \"button\": true, \"enabled\": false}, {\"type\": \"boxzoom\", \"button\": true, \"enabled\": false}]});\n",
       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "sn.heatmap(delta_close_prices.corr(), annot=True, square=True, linewidths=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Special Python Libraries for EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"475\"\n",
       "            src=\"http://JIRIPIK-PC:40000/dtale/iframe/1\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x1a97bcca048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import dtale\n",
    "dtale.show(valid_close_df, ignore_duplicate=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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