Project - Time_Series.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem Statement: Pick up the following stocks and generate forecasts accordingly Stocks:\n",
    "\n",
    "1. NASDAQ.AAPL\n",
    "2. NASDAQ.ADP\n",
    "3. NASDAQ.CBOE\n",
    "4. NASDAQ.CSCO\n",
    "5. NASDAQ.EBAY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from pandas import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data_stocks.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DATE</th>\n",
       "      <th>SP500</th>\n",
       "      <th>NASDAQ.AAL</th>\n",
       "      <th>NASDAQ.AAPL</th>\n",
       "      <th>NASDAQ.ADBE</th>\n",
       "      <th>NASDAQ.ADI</th>\n",
       "      <th>NASDAQ.ADP</th>\n",
       "      <th>NASDAQ.ADSK</th>\n",
       "      <th>NASDAQ.AKAM</th>\n",
       "      <th>NASDAQ.ALXN</th>\n",
       "      <th>...</th>\n",
       "      <th>NYSE.WYN</th>\n",
       "      <th>NYSE.XEC</th>\n",
       "      <th>NYSE.XEL</th>\n",
       "      <th>NYSE.XL</th>\n",
       "      <th>NYSE.XOM</th>\n",
       "      <th>NYSE.XRX</th>\n",
       "      <th>NYSE.XYL</th>\n",
       "      <th>NYSE.YUM</th>\n",
       "      <th>NYSE.ZBH</th>\n",
       "      <th>NYSE.ZTS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1491226200</td>\n",
       "      <td>2363.6101</td>\n",
       "      <td>42.3300</td>\n",
       "      <td>143.6800</td>\n",
       "      <td>129.6300</td>\n",
       "      <td>82.040</td>\n",
       "      <td>102.2300</td>\n",
       "      <td>85.2200</td>\n",
       "      <td>59.760</td>\n",
       "      <td>121.52</td>\n",
       "      <td>...</td>\n",
       "      <td>84.370</td>\n",
       "      <td>119.035</td>\n",
       "      <td>44.40</td>\n",
       "      <td>39.88</td>\n",
       "      <td>82.03</td>\n",
       "      <td>7.36</td>\n",
       "      <td>50.22</td>\n",
       "      <td>63.86</td>\n",
       "      <td>122.000</td>\n",
       "      <td>53.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1491226260</td>\n",
       "      <td>2364.1001</td>\n",
       "      <td>42.3600</td>\n",
       "      <td>143.7000</td>\n",
       "      <td>130.3200</td>\n",
       "      <td>82.080</td>\n",
       "      <td>102.1400</td>\n",
       "      <td>85.6500</td>\n",
       "      <td>59.840</td>\n",
       "      <td>121.48</td>\n",
       "      <td>...</td>\n",
       "      <td>84.370</td>\n",
       "      <td>119.035</td>\n",
       "      <td>44.11</td>\n",
       "      <td>39.88</td>\n",
       "      <td>82.03</td>\n",
       "      <td>7.38</td>\n",
       "      <td>50.22</td>\n",
       "      <td>63.74</td>\n",
       "      <td>121.770</td>\n",
       "      <td>53.350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1491226320</td>\n",
       "      <td>2362.6799</td>\n",
       "      <td>42.3100</td>\n",
       "      <td>143.6901</td>\n",
       "      <td>130.2250</td>\n",
       "      <td>82.030</td>\n",
       "      <td>102.2125</td>\n",
       "      <td>85.5100</td>\n",
       "      <td>59.795</td>\n",
       "      <td>121.93</td>\n",
       "      <td>...</td>\n",
       "      <td>84.585</td>\n",
       "      <td>119.260</td>\n",
       "      <td>44.09</td>\n",
       "      <td>39.98</td>\n",
       "      <td>82.02</td>\n",
       "      <td>7.36</td>\n",
       "      <td>50.12</td>\n",
       "      <td>63.75</td>\n",
       "      <td>121.700</td>\n",
       "      <td>53.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1491226380</td>\n",
       "      <td>2364.3101</td>\n",
       "      <td>42.3700</td>\n",
       "      <td>143.6400</td>\n",
       "      <td>130.0729</td>\n",
       "      <td>82.000</td>\n",
       "      <td>102.1400</td>\n",
       "      <td>85.4872</td>\n",
       "      <td>59.620</td>\n",
       "      <td>121.44</td>\n",
       "      <td>...</td>\n",
       "      <td>84.460</td>\n",
       "      <td>119.260</td>\n",
       "      <td>44.25</td>\n",
       "      <td>39.99</td>\n",
       "      <td>82.02</td>\n",
       "      <td>7.35</td>\n",
       "      <td>50.16</td>\n",
       "      <td>63.88</td>\n",
       "      <td>121.700</td>\n",
       "      <td>53.380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1491226440</td>\n",
       "      <td>2364.8501</td>\n",
       "      <td>42.5378</td>\n",
       "      <td>143.6600</td>\n",
       "      <td>129.8800</td>\n",
       "      <td>82.035</td>\n",
       "      <td>102.0600</td>\n",
       "      <td>85.7001</td>\n",
       "      <td>59.620</td>\n",
       "      <td>121.60</td>\n",
       "      <td>...</td>\n",
       "      <td>84.470</td>\n",
       "      <td>119.610</td>\n",
       "      <td>44.11</td>\n",
       "      <td>39.96</td>\n",
       "      <td>82.03</td>\n",
       "      <td>7.36</td>\n",
       "      <td>50.20</td>\n",
       "      <td>63.91</td>\n",
       "      <td>121.695</td>\n",
       "      <td>53.240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 502 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         DATE      SP500  NASDAQ.AAL  NASDAQ.AAPL  NASDAQ.ADBE  NASDAQ.ADI  \\\n",
       "0  1491226200  2363.6101     42.3300     143.6800     129.6300      82.040   \n",
       "1  1491226260  2364.1001     42.3600     143.7000     130.3200      82.080   \n",
       "2  1491226320  2362.6799     42.3100     143.6901     130.2250      82.030   \n",
       "3  1491226380  2364.3101     42.3700     143.6400     130.0729      82.000   \n",
       "4  1491226440  2364.8501     42.5378     143.6600     129.8800      82.035   \n",
       "\n",
       "   NASDAQ.ADP  NASDAQ.ADSK  NASDAQ.AKAM  NASDAQ.ALXN  ...  NYSE.WYN  NYSE.XEC  \\\n",
       "0    102.2300      85.2200       59.760       121.52  ...    84.370   119.035   \n",
       "1    102.1400      85.6500       59.840       121.48  ...    84.370   119.035   \n",
       "2    102.2125      85.5100       59.795       121.93  ...    84.585   119.260   \n",
       "3    102.1400      85.4872       59.620       121.44  ...    84.460   119.260   \n",
       "4    102.0600      85.7001       59.620       121.60  ...    84.470   119.610   \n",
       "\n",
       "   NYSE.XEL  NYSE.XL  NYSE.XOM  NYSE.XRX  NYSE.XYL  NYSE.YUM  NYSE.ZBH  \\\n",
       "0     44.40    39.88     82.03      7.36     50.22     63.86   122.000   \n",
       "1     44.11    39.88     82.03      7.38     50.22     63.74   121.770   \n",
       "2     44.09    39.98     82.02      7.36     50.12     63.75   121.700   \n",
       "3     44.25    39.99     82.02      7.35     50.16     63.88   121.700   \n",
       "4     44.11    39.96     82.03      7.36     50.20     63.91   121.695   \n",
       "\n",
       "   NYSE.ZTS  \n",
       "0    53.350  \n",
       "1    53.350  \n",
       "2    53.365  \n",
       "3    53.380  \n",
       "4    53.240  \n",
       "\n",
       "[5 rows x 502 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DATE'].dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DATE'] = pd.to_datetime(df['DATE'], unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41261   2017-08-31 19:56:00\n",
       "41262   2017-08-31 19:57:00\n",
       "41263   2017-08-31 19:58:00\n",
       "41264   2017-08-31 19:59:00\n",
       "41265   2017-08-31 20:00:00\n",
       "Name: DATE, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['DATE'].tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df['DATE']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('DATE',axis = 1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>SP500</th>\n",
       "      <th>NASDAQ.AAL</th>\n",
       "      <th>NASDAQ.AAPL</th>\n",
       "      <th>NASDAQ.ADBE</th>\n",
       "      <th>NASDAQ.ADI</th>\n",
       "      <th>NASDAQ.ADP</th>\n",
       "      <th>NASDAQ.ADSK</th>\n",
       "      <th>NASDAQ.AKAM</th>\n",
       "      <th>NASDAQ.ALXN</th>\n",
       "      <th>NASDAQ.AMAT</th>\n",
       "      <th>...</th>\n",
       "      <th>NYSE.WYN</th>\n",
       "      <th>NYSE.XEC</th>\n",
       "      <th>NYSE.XEL</th>\n",
       "      <th>NYSE.XL</th>\n",
       "      <th>NYSE.XOM</th>\n",
       "      <th>NYSE.XRX</th>\n",
       "      <th>NYSE.XYL</th>\n",
       "      <th>NYSE.YUM</th>\n",
       "      <th>NYSE.ZBH</th>\n",
       "      <th>NYSE.ZTS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:56:00</th>\n",
       "      <td>2472.22</td>\n",
       "      <td>44.72</td>\n",
       "      <td>164.11</td>\n",
       "      <td>155.090</td>\n",
       "      <td>83.67</td>\n",
       "      <td>106.565</td>\n",
       "      <td>114.49</td>\n",
       "      <td>47.150</td>\n",
       "      <td>142.425</td>\n",
       "      <td>45.130</td>\n",
       "      <td>...</td>\n",
       "      <td>99.675</td>\n",
       "      <td>99.53</td>\n",
       "      <td>49.485</td>\n",
       "      <td>40.955</td>\n",
       "      <td>76.360</td>\n",
       "      <td>32.285</td>\n",
       "      <td>62.110</td>\n",
       "      <td>76.88</td>\n",
       "      <td>114.31</td>\n",
       "      <td>62.7250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:57:00</th>\n",
       "      <td>2471.77</td>\n",
       "      <td>44.73</td>\n",
       "      <td>164.12</td>\n",
       "      <td>155.160</td>\n",
       "      <td>83.65</td>\n",
       "      <td>106.590</td>\n",
       "      <td>114.52</td>\n",
       "      <td>47.150</td>\n",
       "      <td>142.450</td>\n",
       "      <td>45.130</td>\n",
       "      <td>...</td>\n",
       "      <td>99.730</td>\n",
       "      <td>99.63</td>\n",
       "      <td>49.480</td>\n",
       "      <td>40.960</td>\n",
       "      <td>76.370</td>\n",
       "      <td>32.295</td>\n",
       "      <td>62.100</td>\n",
       "      <td>76.90</td>\n",
       "      <td>114.33</td>\n",
       "      <td>62.7100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:58:00</th>\n",
       "      <td>2470.03</td>\n",
       "      <td>44.74</td>\n",
       "      <td>164.01</td>\n",
       "      <td>155.065</td>\n",
       "      <td>83.62</td>\n",
       "      <td>106.520</td>\n",
       "      <td>114.47</td>\n",
       "      <td>47.150</td>\n",
       "      <td>142.330</td>\n",
       "      <td>45.135</td>\n",
       "      <td>...</td>\n",
       "      <td>99.735</td>\n",
       "      <td>99.64</td>\n",
       "      <td>49.495</td>\n",
       "      <td>40.940</td>\n",
       "      <td>76.315</td>\n",
       "      <td>32.290</td>\n",
       "      <td>62.090</td>\n",
       "      <td>76.88</td>\n",
       "      <td>114.31</td>\n",
       "      <td>62.6850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:59:00</th>\n",
       "      <td>2471.49</td>\n",
       "      <td>44.71</td>\n",
       "      <td>163.88</td>\n",
       "      <td>154.960</td>\n",
       "      <td>83.58</td>\n",
       "      <td>106.400</td>\n",
       "      <td>114.33</td>\n",
       "      <td>47.135</td>\n",
       "      <td>142.170</td>\n",
       "      <td>45.065</td>\n",
       "      <td>...</td>\n",
       "      <td>99.700</td>\n",
       "      <td>99.63</td>\n",
       "      <td>49.485</td>\n",
       "      <td>40.925</td>\n",
       "      <td>76.300</td>\n",
       "      <td>32.275</td>\n",
       "      <td>62.075</td>\n",
       "      <td>76.83</td>\n",
       "      <td>114.23</td>\n",
       "      <td>62.6301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 20:00:00</th>\n",
       "      <td>2471.49</td>\n",
       "      <td>44.74</td>\n",
       "      <td>163.98</td>\n",
       "      <td>155.160</td>\n",
       "      <td>83.69</td>\n",
       "      <td>106.470</td>\n",
       "      <td>114.46</td>\n",
       "      <td>47.150</td>\n",
       "      <td>142.410</td>\n",
       "      <td>45.120</td>\n",
       "      <td>...</td>\n",
       "      <td>99.670</td>\n",
       "      <td>99.64</td>\n",
       "      <td>49.490</td>\n",
       "      <td>40.940</td>\n",
       "      <td>76.320</td>\n",
       "      <td>32.270</td>\n",
       "      <td>62.070</td>\n",
       "      <td>76.81</td>\n",
       "      <td>114.28</td>\n",
       "      <td>62.6800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 501 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       SP500  NASDAQ.AAL  NASDAQ.AAPL  NASDAQ.ADBE  \\\n",
       "DATE                                                                 \n",
       "2017-08-31 19:56:00  2472.22       44.72       164.11      155.090   \n",
       "2017-08-31 19:57:00  2471.77       44.73       164.12      155.160   \n",
       "2017-08-31 19:58:00  2470.03       44.74       164.01      155.065   \n",
       "2017-08-31 19:59:00  2471.49       44.71       163.88      154.960   \n",
       "2017-08-31 20:00:00  2471.49       44.74       163.98      155.160   \n",
       "\n",
       "                     NASDAQ.ADI  NASDAQ.ADP  NASDAQ.ADSK  NASDAQ.AKAM  \\\n",
       "DATE                                                                    \n",
       "2017-08-31 19:56:00       83.67     106.565       114.49       47.150   \n",
       "2017-08-31 19:57:00       83.65     106.590       114.52       47.150   \n",
       "2017-08-31 19:58:00       83.62     106.520       114.47       47.150   \n",
       "2017-08-31 19:59:00       83.58     106.400       114.33       47.135   \n",
       "2017-08-31 20:00:00       83.69     106.470       114.46       47.150   \n",
       "\n",
       "                     NASDAQ.ALXN  NASDAQ.AMAT  ...  NYSE.WYN  NYSE.XEC  \\\n",
       "DATE                                           ...                       \n",
       "2017-08-31 19:56:00      142.425       45.130  ...    99.675     99.53   \n",
       "2017-08-31 19:57:00      142.450       45.130  ...    99.730     99.63   \n",
       "2017-08-31 19:58:00      142.330       45.135  ...    99.735     99.64   \n",
       "2017-08-31 19:59:00      142.170       45.065  ...    99.700     99.63   \n",
       "2017-08-31 20:00:00      142.410       45.120  ...    99.670     99.64   \n",
       "\n",
       "                     NYSE.XEL  NYSE.XL  NYSE.XOM  NYSE.XRX  NYSE.XYL  \\\n",
       "DATE                                                                   \n",
       "2017-08-31 19:56:00    49.485   40.955    76.360    32.285    62.110   \n",
       "2017-08-31 19:57:00    49.480   40.960    76.370    32.295    62.100   \n",
       "2017-08-31 19:58:00    49.495   40.940    76.315    32.290    62.090   \n",
       "2017-08-31 19:59:00    49.485   40.925    76.300    32.275    62.075   \n",
       "2017-08-31 20:00:00    49.490   40.940    76.320    32.270    62.070   \n",
       "\n",
       "                     NYSE.YUM  NYSE.ZBH  NYSE.ZTS  \n",
       "DATE                                               \n",
       "2017-08-31 19:56:00     76.88    114.31   62.7250  \n",
       "2017-08-31 19:57:00     76.90    114.33   62.7100  \n",
       "2017-08-31 19:58:00     76.88    114.31   62.6850  \n",
       "2017-08-31 19:59:00     76.83    114.23   62.6301  \n",
       "2017-08-31 20:00:00     76.81    114.28   62.6800  \n",
       "\n",
       "[5 rows x 501 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_AAPL = df[['NASDAQ.AAPL']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>NASDAQ.AAPL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:56:00</th>\n",
       "      <td>164.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:57:00</th>\n",
       "      <td>164.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:58:00</th>\n",
       "      <td>164.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:59:00</th>\n",
       "      <td>163.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 20:00:00</th>\n",
       "      <td>163.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     NASDAQ.AAPL\n",
       "DATE                            \n",
       "2017-08-31 19:56:00       164.11\n",
       "2017-08-31 19:57:00       164.12\n",
       "2017-08-31 19:58:00       164.01\n",
       "2017-08-31 19:59:00       163.88\n",
       "2017-08-31 20:00:00       163.98"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_AAPL.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NASDAQ.AAPL    41266\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_AAPL.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ded3e5b278>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_AAPL.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stationary means mean,variance and covariance is constant over periods.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "plot_acf(df_AAPL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting series to stationary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>NASDAQ.AAPL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DATE</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:30:00</th>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:31:00</th>\n",
       "      <td>143.6800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:32:00</th>\n",
       "      <td>143.7000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:33:00</th>\n",
       "      <td>143.6901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:34:00</th>\n",
       "      <td>143.6400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:35:00</th>\n",
       "      <td>143.6600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:36:00</th>\n",
       "      <td>143.7800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:37:00</th>\n",
       "      <td>143.8640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:38:00</th>\n",
       "      <td>143.8100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:39:00</th>\n",
       "      <td>143.8150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:40:00</th>\n",
       "      <td>143.8000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:41:00</th>\n",
       "      <td>143.8900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:42:00</th>\n",
       "      <td>143.9700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:43:00</th>\n",
       "      <td>143.9199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:44:00</th>\n",
       "      <td>144.0239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:45:00</th>\n",
       "      <td>144.0500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:46:00</th>\n",
       "      <td>144.0638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:47:00</th>\n",
       "      <td>144.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:48:00</th>\n",
       "      <td>144.0200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:49:00</th>\n",
       "      <td>143.9700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:50:00</th>\n",
       "      <td>144.0300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:51:00</th>\n",
       "      <td>143.9800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:52:00</th>\n",
       "      <td>143.9000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:53:00</th>\n",
       "      <td>143.9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:54:00</th>\n",
       "      <td>143.9100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:55:00</th>\n",
       "      <td>143.8600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:56:00</th>\n",
       "      <td>143.8600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:57:00</th>\n",
       "      <td>143.8935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:58:00</th>\n",
       "      <td>143.9300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-03 13:59:00</th>\n",
       "      <td>143.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:31:00</th>\n",
       "      <td>164.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:32:00</th>\n",
       "      <td>164.0700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:33:00</th>\n",
       "      <td>164.0750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:34:00</th>\n",
       "      <td>164.1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:35:00</th>\n",
       "      <td>164.1300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:36:00</th>\n",
       "      <td>164.1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:37:00</th>\n",
       "      <td>164.1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:38:00</th>\n",
       "      <td>164.2050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:39:00</th>\n",
       "      <td>164.2350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:40:00</th>\n",
       "      <td>164.2900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:41:00</th>\n",
       "      <td>164.2800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:42:00</th>\n",
       "      <td>164.2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:43:00</th>\n",
       "      <td>164.2634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:44:00</th>\n",
       "      <td>164.2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:45:00</th>\n",
       "      <td>164.2700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:46:00</th>\n",
       "      <td>164.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:47:00</th>\n",
       "      <td>164.1350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:48:00</th>\n",
       "      <td>164.1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:49:00</th>\n",
       "      <td>164.2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:50:00</th>\n",
       "      <td>164.2100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:51:00</th>\n",
       "      <td>164.1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:52:00</th>\n",
       "      <td>164.1600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:53:00</th>\n",
       "      <td>164.0600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:54:00</th>\n",
       "      <td>164.1900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:55:00</th>\n",
       "      <td>164.2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:56:00</th>\n",
       "      <td>164.1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:57:00</th>\n",
       "      <td>164.1100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:58:00</th>\n",
       "      <td>164.1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 19:59:00</th>\n",
       "      <td>164.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-31 20:00:00</th>\n",
       "      <td>163.8800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>41266 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     NASDAQ.AAPL\n",
       "DATE                            \n",
       "2017-04-03 13:30:00          NaN\n",
       "2017-04-03 13:31:00     143.6800\n",
       "2017-04-03 13:32:00     143.7000\n",
       "2017-04-03 13:33:00     143.6901\n",
       "2017-04-03 13:34:00     143.6400\n",
       "2017-04-03 13:35:00     143.6600\n",
       "2017-04-03 13:36:00     143.7800\n",
       "2017-04-03 13:37:00     143.8640\n",
       "2017-04-03 13:38:00     143.8100\n",
       "2017-04-03 13:39:00     143.8150\n",
       "2017-04-03 13:40:00     143.8000\n",
       "2017-04-03 13:41:00     143.8900\n",
       "2017-04-03 13:42:00     143.9700\n",
       "2017-04-03 13:43:00     143.9199\n",
       "2017-04-03 13:44:00     144.0239\n",
       "2017-04-03 13:45:00     144.0500\n",
       "2017-04-03 13:46:00     144.0638\n",
       "2017-04-03 13:47:00     144.0200\n",
       "2017-04-03 13:48:00     144.0200\n",
       "2017-04-03 13:49:00     143.9700\n",
       "2017-04-03 13:50:00     144.0300\n",
       "2017-04-03 13:51:00     143.9800\n",
       "2017-04-03 13:52:00     143.9000\n",
       "2017-04-03 13:53:00     143.9100\n",
       "2017-04-03 13:54:00     143.9100\n",
       "2017-04-03 13:55:00     143.8600\n",
       "2017-04-03 13:56:00     143.8600\n",
       "2017-04-03 13:57:00     143.8935\n",
       "2017-04-03 13:58:00     143.9300\n",
       "2017-04-03 13:59:00     143.9500\n",
       "...                          ...\n",
       "2017-08-31 19:31:00     164.0700\n",
       "2017-08-31 19:32:00     164.0700\n",
       "2017-08-31 19:33:00     164.0750\n",
       "2017-08-31 19:34:00     164.1500\n",
       "2017-08-31 19:35:00     164.1300\n",
       "2017-08-31 19:36:00     164.1400\n",
       "2017-08-31 19:37:00     164.1450\n",
       "2017-08-31 19:38:00     164.2050\n",
       "2017-08-31 19:39:00     164.2350\n",
       "2017-08-31 19:40:00     164.2900\n",
       "2017-08-31 19:41:00     164.2800\n",
       "2017-08-31 19:42:00     164.2600\n",
       "2017-08-31 19:43:00     164.2634\n",
       "2017-08-31 19:44:00     164.2400\n",
       "2017-08-31 19:45:00     164.2700\n",
       "2017-08-31 19:46:00     164.2000\n",
       "2017-08-31 19:47:00     164.1350\n",
       "2017-08-31 19:48:00     164.1800\n",
       "2017-08-31 19:49:00     164.2300\n",
       "2017-08-31 19:50:00     164.2100\n",
       "2017-08-31 19:51:00     164.1850\n",
       "2017-08-31 19:52:00     164.1600\n",
       "2017-08-31 19:53:00     164.0600\n",
       "2017-08-31 19:54:00     164.1900\n",
       "2017-08-31 19:55:00     164.2000\n",
       "2017-08-31 19:56:00     164.1400\n",
       "2017-08-31 19:57:00     164.1100\n",
       "2017-08-31 19:58:00     164.1200\n",
       "2017-08-31 19:59:00     164.0100\n",
       "2017-08-31 20:00:00     163.8800\n",
       "\n",
       "[41266 rows x 1 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_AAPL.shift(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28886\n",
      "12380\n"
     ]
    }
   ],
   "source": [
    "X = df_AAPL.values\n",
    "train = X[0:28886] # 27 data as train data\n",
    "test = X[28886:] # 9 data as test data\n",
    "print(train.size)\n",
    "print(test.size)\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ARIMA model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARIMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import itertools\n",
    "p=d=q=range(0,6)\n",
    "pdq=list(itertools.product(p,d,q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 0, 0) 170326.9720446082\n",
      "(0, 0, 1) 131018.03599865185\n",
      "(0, 0, 2) 95780.67244459334\n",
      "(0, 1, 0) -64708.71200636144\n",
      "(0, 1, 1) -64706.8773153409\n",
      "(0, 1, 2) -64718.28609667219\n",
      "(0, 1, 3) -64719.67999909342\n",
      "(0, 1, 4) -64724.746502435286\n",
      "(0, 1, 5) -64723.273957570884\n",
      "(0, 2, 0) -44617.132207470175\n",
      "(0, 2, 1) -64678.284053556345\n",
      "(1, 0, 0) -64703.10601142913\n",
      "(1, 0, 1) -64701.25996832439\n",
      "(1, 0, 2) -64712.567383946094\n",
      "(1, 0, 3) -64714.013818526466\n",
      "(1, 0, 4) -64719.00741382557\n",
      "(1, 0, 5) -64717.51647348693\n",
      "(1, 1, 0) -64706.87029558887\n",
      "(1, 1, 1) -64714.631574454295\n",
      "(1, 1, 2) -64725.38695945205\n",
      "(1, 2, 0) -52566.60024359099\n",
      "(1, 2, 1) -64686.65932985763\n",
      "(1, 2, 2) -64694.1307696757\n",
      "(1, 2, 3) -64695.3638634479\n",
      "(1, 2, 4) -64699.685139195164\n",
      "(1, 2, 5) -64699.39715244861\n",
      "(2, 0, 0) -64701.25345517087\n",
      "(2, 0, 1) -64709.05736396497\n",
      "(2, 0, 2) -64719.70639660023\n",
      "(2, 0, 3) -64717.71698031505\n",
      "(2, 0, 4) -64716.79946580622\n",
      "(2, 0, 5) -64715.50400855095\n",
      "(2, 1, 0) -64717.930868098265\n",
      "(2, 1, 1) -64725.40319961759\n",
      "(2, 2, 0) -56350.178772023486\n",
      "(2, 2, 1) -64659.76997015065\n",
      "(2, 2, 2) -64677.49948177354\n",
      "(2, 2, 3) -64699.14262573434\n",
      "(2, 2, 4) -64685.84999647424\n",
      "(2, 2, 5) -64698.457178656376\n",
      "(3, 0, 0) -64712.215473133896\n",
      "(3, 0, 1) -64719.725561353756\n",
      "(3, 0, 2) -64717.73653529119\n",
      "(3, 0, 3) -64716.71698770177\n",
      "(3, 0, 4) -64716.65501509764\n",
      "(3, 0, 5) -64715.34579434866\n",
      "(3, 1, 0) -64719.3189699363\n",
      "(3, 1, 1) -64723.405831671334\n",
      "(3, 1, 2) -64722.54804937482\n",
      "(3, 1, 3) -64730.2310793838\n",
      "(3, 2, 0) -58063.13991574208\n",
      "(3, 2, 1) -64689.83522723992\n",
      "(3, 2, 2) -64694.61157124773\n",
      "(3, 2, 3) -64683.62286452322\n",
      "(3, 2, 4) -64690.80762995382\n",
      "(3, 2, 5) -64691.41253928283\n",
      "(4, 0, 0) -64713.65383272787\n",
      "(4, 0, 1) -64717.72973973822\n",
      "(4, 0, 2) -64716.80280754084\n",
      "(4, 0, 3) -64724.48089452804\n",
      "(4, 0, 5) -64736.66348071818\n",
      "(4, 1, 0) -64724.01865458551\n",
      "(4, 1, 1) -64723.51089426143\n",
      "(4, 1, 2) -64722.38263317883\n",
      "(4, 1, 3) -64773.09216688924\n",
      "(4, 1, 4) -64744.94423435074\n",
      "(4, 2, 0) -59269.368500527824\n",
      "(4, 2, 1) -64707.707801326134\n",
      "(4, 2, 2) -64639.31863184688\n",
      "(4, 2, 3) -64710.81107096466\n",
      "(4, 2, 4) -64688.6980056342\n",
      "(4, 2, 5) -64693.359770768046\n",
      "(5, 0, 0) -64718.28359237622\n",
      "(5, 0, 1) -64716.7328895785\n",
      "(5, 0, 2) -64716.61860454251\n",
      "(5, 0, 3) -64740.76050197336\n",
      "(5, 0, 4) -64730.98575269655\n",
      "(5, 0, 5) -64737.16828471894\n",
      "(5, 1, 0) -64722.57419426138\n",
      "(5, 1, 1) -64721.56022781554\n",
      "(5, 1, 2) -64721.03844537416\n",
      "(5, 1, 3) -64726.31980702064\n",
      "(5, 1, 4) -64742.966683099236\n",
      "(5, 2, 0) -60006.914815066804\n",
      "(5, 2, 1) -64703.733026427406\n",
      "(5, 2, 2) -64691.069055184445\n",
      "(5, 2, 3) -64625.27930101279\n",
      "(5, 2, 4) -64647.30826462338\n",
      "(5, 2, 5) -64675.00365865482\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "for param in pdq:\n",
    "    try:\n",
    "        model_arima = ARIMA(train, order=param)\n",
    "        model_arima_fit = model_arima.fit()\n",
    "        print(param,model_arima_fit.aic)\n",
    "    except:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-64744.94423435074\n"
     ]
    }
   ],
   "source": [
    "#p,d,q \n",
    "#p -> Periods taken for auto regressive model\n",
    "#d -> Integrated order, difference\n",
    "#q -> Periods in moving average model\n",
    "model_arima = ARIMA(train, order=(4,1,4))\n",
    "model_arima_fit = model_arima.fit()\n",
    "print(model_arima_fit.aic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([150.61361658, 150.61508213, 150.61485932, ..., 153.58414297,\n",
       "       153.58438291, 153.58462284])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions = model_arima_fit.forecast(steps=12380)[0]\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1ded5e859b0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FZLVdd1KIa/YRkXkiskpEXhCR5rXbpaQ1z8wrZecBd5XQPz8PH7umTU4WmRnC1bYnazKn0niopzxzf3aG79//YBNJrbm63BII9fXGNTJpNFTV+u51X1Ut/1myidca6LUd6mWiqRPtXc0EpjgLRGQicAZwmDFmKHCfXT4EOA8Yavf5h4i4BWC/B7jfGDMA2AlcGssNKEq8GX1Ie9fyTvnWO0umI94NJEdlZIyhvt54Q26MG9Ap5msN6JzPRUcdwj9+cri37EB1aoP1ubFi027WVOzjacem8VV2mJB7ZkeXGMgNzwph98Eav2t78DxnNzwrqcxmqjuKym7KGDNXRIoDiq8CZhhjquw2HputM4Dn7fJ1IrIaGAt87uko1n/S8cD5dtHTwO3AQzHdhaLEkWP6d/LmEHAyeUhX7v7RcO+5Z0owSVgjDL/9PSYMKvIKn4xGTEgZGcLvzhjmV3YwDYPdnfrXT13L91fV8sjHa/3KiqfN4rbThnDJMX2C2gemGPUE9htxx3uu19+2L3TioXpb29RYa690pTHrnoHAOFvt87GIePz/ewBO+60yu8xJR2CXMaY2TBtFSQ0hXvlH9Wrnd56sl8TiabPYV1XLW0s38/7XWwGorY9v/KEDKRIIlTV1FE+bxQI7nPe8tduZ+b91VIZRYQ29zT10t3OfwEmgVdXdjthUbvz+jKEh6/LzshjctYA/njMi7DWaKo3xrMgC2gNHAmOAF0WkL/6+Gx4C/8OiaWM1FLkcuBygd+/eMQ9WUTzsOhA+9aSb5RHAOSU9XcvXb0te+GtPlNNW2fFNg5mqPYTfvmFtS5798Occ078j/1ttTd63h5jcY6F/Z/8gdO99vZUVm3aHbH/hUcUh6zIzhHebcRjsxqwQyoBXjcV8oB7oZJc7A4n3BDYF9N0GtBORrDBtADDGPGqMKTHGlBQVhY5KqSjRsqh0V9j6QLPTOb+ewEMXHB4kKDznry92/dONC+V73C1pRgasVhpLqlRGLy4o8x57hEG03DRlcFTt3NRrTnXUiUO6xGyx1dxojEB4HWsfABEZCORgTfRvAueJSK6I9AEGAPOdHY1lKjEHONsuuhh4oxFjUZSo8aSSPKRja9f6wPmjT6c2nDy8W6KHBVibx05LolCJbK6a0N+1PFa+2byHZWW7Y7bzj4WGWkydM9p/hXbVhH5cdqz/nsFuF8sxj+/GH84cHlQ3bkAnHjzf2lz/69RRPNDCYz1Fa3b6HNam8CARKRORS4Engb62KerzwMX2amEF8CLwNfAu8AtjTJ19nbdFxBMr+CbgenvTuSPwRDxvTFFCkW3HuQ/cWPXQNoXxfvpMf5vbHLF5auuCrYqO7tcx7puad876htMe/JTxf0xOKOzq2npufHlp1O3vOH0oF7mocjrm+8d22umiDvSkO3WLA3Xd5IHeF4TTR3Tnh6Na9lZmVALBGDPVGNPNGJNtjOlpjHnCGFNtjPmJMWaYMeZwY8yHjvZ3GWP6GWMGGWPecZSfYozZZB+vNcaMNcb0N8ac47FWUpoH97//HcXTZqV6GK68vNCyedhX6W5qef7Y1OxVbdhxALD8IDwbnx4zU+dkeMuph8btMx+7qMTv3JP0J9EM/M07vLSwzLXu9tOGcPoIX46Je886jIuPLmZYj8KgtrUByX1q6w2ry/dRPG0W17+wmHeXb+G8R78AfC8CTrq3TV6k2qZA8/SuUFLOXz5YBSQ3zk+0fPCNZSHdrnW2a32qTArHORLVPDJ3La8tKvMKBM9bLMDQ7m3j9pnH9g/2Zwi1bxEvIqmlTji0C28u8e3L/NjObey22f/W0s1+53X1hhnvWD4Kry7ayJX/XuitcwtJUlTQuOixzY3mF79VSStq641rALlUsn2/pVYY26dDxLapCE/h4boXlnD+EdZqJTtT+PSmiUF5kRtLq5xga6VfPb+I5y8/KmSfcx/5nHl2kp3ld5xEfgMTBFUHvNV/+/sp5GVnUl9vqK6r92Z7i4Zt+/wVC7X19QzpVsB/v9ka1NYtJWZz9SeIFV0hKAmlti79VggeIgWxA2jtMmE6Gdy1gCHdCln4/c6E5CZ+dp6VlGfXgRp6tm9NZzvnbyLx6NxDMc+Rce2NxQ0L+QD+aUhzszK8AiAjQ7zHh/W0VkGPXDjar+/HN0zgximD+O5OK43ZdQECu67ekBfimS0q9Xc2HNEzfiut5oKuEJSEYjlQxddmPl6E8jdwcuph4a2Lvt1iZdc666HP6FyQy/xbJsVlbIFMOrRLQq7rxvKNe6isqSNDxE9V5YYzHlK0vLXEp+apqnUXop7wEj0CstEd0rENVzssrC44ojcrNu1mz8FaZi3bzCsLy3g6RCyqgV0LWD/jVPZX1TLxvo+4MUqz1ZaErhCUhJKO64PJQ7p4I36G4r/XH8ezlx1BtwZsOpbvTZxdRKSJOd4cdvt7DPzNO7yzbHPYdje+Er2lkIdXHYHkHnbEU3LiEbSRVl0iwt0/OsxrhRVKGAAcN8DyY2qTm8X8WyZxjMv+SUtHBYKSUEz8tSiNprq2ntwIE2z/zvkcnaIJ4+RhXZl/8wne81SoNjx6fk8wuXCsCZOFLBxH9u3AlGHhV2CltuVVJDwCJBwZul8QERUISkJJRuC3hrLrYE3S37gbQqvsTDoX5rHgN5b66YHzRqV0PJEsxXbuDx8KJBTRpPIsyItOq72n0j2UOcDqu05m2e0nRj2ulkz6/lcozYJ0sjo1xlA8bRZLNuziy/XB0UzThemnWH4GnfJzWT/j1KCUnYlidogYPa98FX7jONBqKFoK89zNfgH+c82x9OrQivEDogtX062t/2b7hEFFDO5awC8m9iMrM4OCMJ+l+FCBoCSUROrVG0oiojKcMLiz33m4N9VITDq0M707tE6ZbfygEPsqv35pifc40FIHYo+D1CaMuerwnm355MbjXU1F3bh8fD+/84d/Mpp3rx3PDSfpxnFDUIGgJJSXFmyI3ChJJMIs9OqJ/hNRYOz9hiFhJ8l04Hdv+aKQXnmcde+xhs6O58qnbSvfCuA/1xzbIF8GxYcKBCWhVNamT+KVPQdjf3sPRU6m/8TTuNzExjUufKJZevuJrLjDNdOtl9Xl1qbtmGLLme+K8X2ZOtbyIP7N68tD9nMjLzuDy8f3jWGk0TFc/QtiRgWCklDSKXf77gQIhEAz/OoQdvU79ldzz7vfhhUYxiQv6Y6Twrxs78rk3rMPc20z6c9z+dfn6+lrv9VffHSxN6z07oM1EXNMOKk3jcv4For83Cx6ddDYRI1BBYKSUNIpllGs5pHhCLy9xz5Z69ru1L9+wkMfreEDl5AKTlKdqvfHJb28OYUnD/F3hrv1jRVeL+Y2uVl0dOQeHvm796P+jOra+oTc55LbTuSjX0+M/4VbECoQlIQSyo68fE8lb0dweoonX2/aw5X/jmxT31Dysv3/hQKDrXnYbO8tbA2zyZ4uovNndo6Be846LMh6xxN0rk1OJq1zGr7f4clX8NBHaxo5ymAyM0RjEzUSFQhKQuhqx9wJlQXrgsfncfUzX4XNnRtPlm30ZUm7dtIAXrj8yLhcN5rwF07yXPwfHvxwFV9v2oMxBknJLoI/Vx3Xj29/P4UObXK8gszD0jIr9aSb9c+SDeEz0UFi1HZK/FCBoCSEvkXhLUhWlVvqm2UbQ+e2bSzleyu9ZqBOA6NrJw3kiL4dE/a54RjQxd+080B1Lfe99x0XPP4FhtSrjMAScrFY6Zzx9/9FbJObrVNOOqNPR0kI0W4arowi5EAsHKyuY+xdH3DY7e8BsNWO8f+vS8cm5PM8AvC8Mb3CtguMDbTMfuP2RIVNA3nQIAKjwXqis4bC83fxmzgm+VHihwoEJSFEihvjyWf86lfuWbMay/b9/rp6T8KecVF6vkZL7w6tGTegE388ewTtQyTccfLxdxWAlR3t1teXM/3VZQDsrapNK6/uaJl/yyRvzgaAv9rfcyg8oUxy1U8gLVGBoCQEZ06c/VW1FE+bxd/nrPaWVdVYOpyvSiPrnWPh282+lUciU3lmZ2bwr0uPYPQh7dl5oIbnv3R3xCuwzTo9QdjG3TuHf33xPWu37QfgjJHdrakyHXRGDiIF1svPzeJKh5fwlkjZ1myhl153qXhQgaAkBOdm68ZdVp7eP85eSfneSj5aWe43cextRLiHUFz2zwVxv2a07HAJ9rbXkXTGLdTDlt2V9qZyehFNiOje9moPImcg8yyC0kzuKTYqEJSE4Px/v2/2Su/xjv3V/PSpL/3axnuVEMo5bOYlY+L6OaG45tnw5q2H/vbdoDJPFrJ0myj/78RBDOySH3X7unoT1vfEeFcIaXajChCFQBCRJ0WkXESWO8puF5GNIrLY/jnFLr/AUbZYROpFZKTLNV37K82TFZv2eI/d5go3U8xY2bavyptk3ck3v5vChEGdXXrEj6HdCwH4bI27qW00pNs0mZkhvHnNsRHb/fnHI7zH0ZiWppvgUyyi8SyZCTwI/DOg/H5jzH3OAmPMM8AzACIyHHjDGLM4xHWD+ivNE2dYg/0u+XrjucF46dMLvPbwv/3BELbureTHJb1ck8nHG6fgc1IfZfyO/VW1aeOc5iTQBPXtX40LavOjw3ty/YtWVNTKmtBBBNMxP4biI+KrmTFmLrAjUjsXpgLPxdBPaUZkZgiHH9Lee37nrG+C2tTVxy8KqdM5alX5PqaffCj9iqJXeSSCxz91D2dxZN8Ofudfle5iUYI22ePJEHslFIpwUWWNbiqnNY1Zq18jIkttlVJ7l/pzCS8QIvVXmgGBwdwWOybsZy87AoDq2vi8NQaqKtLF1n3ZRveVw8xLxjJ1bG9G9mqX5BHFTjQe3u9/HTpek24qpzexCoSHgH7ASGAz8CdnpYgcARwwxoSKixu2f8C1LheRBSKyoKKiIsbhKqnkk1XbXMs9XquxZtwKJDDiZipzC9zy2jJuf3MFFz4xL+TbcF52Jnf/aDhLy9J/VeAhmhwGX6y19lD++fl6iqfN4sNvfQKi1n7WCUhNocSBmASCMWarMabOGFMPPAYEun+eR5jVQRT9nW0fNcaUGGNKiori61SkpJZsOx5OTQirIA+VNXUUT5vFDY7MXW4cdMRFmn/LCWFaJoZxA3wmms/MK2XmZ+v5ZNU217fhL6b7xpdOIcIjkZsVeS9mcDdLpfTbN1YA8LOZPhPgZ+dbnsxOIaGkDzEJBBHp5jg9E3BaIGUA5wDPx9JfaTl4Et1HWiGU77G8jl9aWMaDH4b2hC3d7ous2rkgL2S7RPGvS4/guIHBLy1uK4R8R/L4m0/xpXn0BAVMV3KisAgr7tiaunpDK8dm9MLvrW1IT0a5NRX7EzNApVFEY3b6HPA5MEhEykTkUuBeEVkmIkuBicB1ji7jgTJjzNqA6zwuIiX2abj+Sgtg4W8meVcIc78LrwrMdLg93/fedyHbXf6vhQDM+NHwOIwwNgLDYYP7CiDfoc5y5gN+99pgC550IhqBcLCmjmfnl/qt2M566HMASmwDg7tT+IyU0ERUshpjproUPxGm/UdA0M6TMeYyx/GFUY5PaaZ0zM/15uJ9/ssNzDjLPVNXLLRrnRO5UYL4fntw/gdPDgGAH5f0ZGAX92T2QEw5BpJJNPkGKmvqeeB9d8HtEY4DOqfW8ktxJ73/+pRmTTRvmwB1df6v2PurasNuGJ80tEvIukSzIURCIA/3nj0ibH12ZtM1vxl9SHsWfr+T/VW1fqE6PBhj2LbPUv9pcLv0RENXKCkjxyXJihu1AX4KQ2+bHdbZq6FJa+LJfpc4RQ0hlWMPx1FR5I946YqjEIGq2jp6d2gdVD97xRb+9qEV4DA3jt7pSvzQp6IkjX4BSXOyHZPC3W9/w7H3fOjazy0x/c+e/tKlZdPllauO4p8/S0yuhngw82djWPzbyWHbZGQIeVmZVNfWu6ZOdaYwzdJUl2mJCgQlKRTmZfH6L44B4Ck7yJxTPfLI3LWU7Tzo2remLlggHKgKfhPvUpjLuSXhE9QkmoK80KqsooLckHWjD+nAeBcLpXQhNyszqr2ZgzV1bNvn8wdZddfJru3SdSXU0lGBoCSFo/p1pCAvm/UzTmWiHWQuWpWR2wrBLTZRXX3kxDwJJ4xfLmB4AAAgAElEQVRPQbT329R5bdFG73F2ZgaPXDjar37Soanb41HC0zL+QpWUc/3kQUFl0b4lBu4hgC/zmJO6+vqUqyIKW1lZ0x67qIQlt53ol1KzvimmRIsDowJCc/z3G3VKS1dUIChJYVDX0KaWTtxi6butENza1tWbqMwiE0nnQkstNKxHIW1bZftFCr16Yv9UDStpDOtRGGRS2rkwjxFNKF5TS0YFgpJWuIWQrg0hEKoCQl7UG18S91TxyIWjufesw+jWthXgv08yIY33COJFUX6uqznxG784xhts8JJjipM8KiVaVCAoCcE5hT/505KQ7QL5wd8+DSqrddlUBrj19eVs3HWQ1xaV8ZPH57GvqpZUq+k7F+TxY4eaKNsxoJawj5oh4g1PEYjHOCDVaj0lNOqYpiSM4T3a8thFJXRtGzo+z5ji9ny5fqdf2aqtexng8OZ120MAK7bRSwvL/Moak60sEbz6lW+DtSVY1ogI211ySgMcqLac1dLdG7sloysEJaGEEwZAkDAAmHz/XL9zzx7Ca1cfzVOXjOG7O91NGQG/gGrpwJY9vrfl5i8OINzLv0cgRuuhriQfFdVKWlJVW+cNtexxcsrJyvCarIZiwqD01dO3gAWC3x5OoKPdz8f1oWJvFRcfXZzkUSnRoqJaSUue/NRKrjJv7Xbu+M/XAOyrDI6PE8iBRoaOSCTSAtYIGY4ZJSsgLlNBXjZ3/2i4X6RXJb3QJ6OkJfe8+y0A5z76hbcsVPiiAZ3zOaRjG84Y2Z0fHNbNvVGK6N85n9Xl+4CWsULY7NhQzsrQ982mhgoEJaWcPqK7X3jocJgQbsDvX39cPIcUV9o4PKpbgDxgUakvHWit5slscqgIV1KK02HtrV8eG7atM4/A+hmn8tQlY/jw/9JXGADcfMqhqR5CygjlP6KkLyoQlJTiDIM8rEdb/jft+JBt29lhITxMHNSZvkXpnWhltJ0hDGgZSwQHoTzMlfRFBYKSUgIT3fRo1ypk26Zox5/ldExrARLBGTqkb0C4cyX9UYGgpJS+nYInjX9ccLhr21THKWosTVCeNZhDu/nUeod0VIHQ1FCBoKQUt6Bn7R1x95tTmIPmcydKc0UFgpJS8rIzGVPc3m9V0CnfEgjnjO7JRzdMSNHI4k91C7C6CRV3SmkaRBQIIvKkiJSLyHJH2e0islFEFts/p9jlxSJy0FH+cIhrdhCR90Vklf27vVs7pWXw0pVHc8pwn//AgC4FvHb10fzhR8ObvJrISaf80BnTmgu/PtHKe9GhTeTsakr6Ec0KYSYwxaX8fmPMSPvnbUf5Gkf5lSGuOQ34wBgzAPjAPlcUL6N6tyc7M8ObZezY/p1SPKLGk53qUKxJYNKQLvz9/MMjmhAr6UlExzRjzFwRKY7z554BTLCPnwY+Am6K82cozYCO+bk8dckYDu+ti8imwqlp5i2uRE9jXlmuEZGltkrJ+d/aR0QWicjHIjIuRN8uxpjNAPbvkBHLRORyEVkgIgsqKoLTJirNn4mDOtM2wAdBUZT4E6tAeAjoB4wENgN/sss3A72NMaOA64FnRaSwMQM0xjxqjCkxxpQUFaVvJEtFCcW0kwf75VZWlHQlplhGxhhvlmwReQx4yy6vAqrs44UisgYYCCwIuMRWEelmjNksIt2A8ljGoaQvbrmRWypXHtcv1UNQlKiIaYVgT+IezgSW2+VFIpJpH/cFBgBrXS7xJnCxfXwx8EYs41DSm5bgiKUozYmIKwQReQ5rA7iTiJQBtwETRGQkVurc9cAVdvPxwO9EpBaoA640xuywr/M48LAxZgEwA3hRRC4FSoFz4nlTiqIoSsOJxspoqkvxEyHavgK8EqLuMsfxduCEKMeoKIqiJIHmbxitKIqiRIUKBEVRFAVQgaAoiqLYqEBQFEVRABUIiqIoio0KhCZOfb1RJzBFUeKCCoQmzMHqOvre/DZ/+3B1qoeiKEozQAVCivnX5+vZuOtgUPmdb31N8bRZFE+bRWVNnWvf3QdrAPjz+99RPG0WW/dUJnKoiqI0c1QgpJDlG3dz6xsrOGbGh0F1j3+6zntcsbeKbfuq+Pi7Csr3VHLza8sonjaLhd/v9Otz2dOBIaMURVGiJ6bgdkrjeGnBBm54eSl3nD40qvYvLtjgqhb6xbNf+Z0v27g7LuNTFKVlogIhBdzw8lIAbntzhbespq4+ZEataPcILjzykMYPTlGU1GEM7NoFGzZAaan14zm++WYYGt1LZKyoQEgTBtzyDvecNZxzx/SO+RoffVdO+d5KOhfkxXFkiqLEjaoqKCsLnuydx/v2+ffJzoZevaC8XAVCS+KmV5Zx+ogetMrJjKn/hh0HGXvXB6yfcWqcR6YoSkTq661JO9REX1oKW7cG9+vSxZrwBw+GE0+0jnv3tn569bLqM5Kz3asCIcnU1tWHrf/V84t47KIS17rjBhaRITBnZQX3nzuC29/82mtp5OSMBz/ljWs0ybmixJW9e0NP9KWl1pt/dbV/nzZtfJP7iBH+E33v3tCzJ+Slz4peBUKSqakL70T2/tcubxA2My8Zg4iw+2ANbVtlc+vrK1zbLSnTzWVFaRA1NbBpU3hVzq5d/n0yM6FHD2tyP+IIOOec4Lf79u2bVKYoFQhJpjpghXDGyO7cOGUwlTV1nPCnjwF4bVFZUL/ld5yE2H9YnoTzL15xFKf89ZMEjzg21HdaSRuMge3bw6tyNm+2VD5OOnSwJvbiYhg3zjfReyb7bt0gq3lNoc3rbpoANQ6BMKJnW/5y3ijAPwfxdS8sCeqXnxv8qIZ0L/QeX3BEb56ZV0r/zvkU5qXHY20670VKk+bgwfCqnA0brDZOcnN9k/vkycGqnF69LHVPCyM9Zo4WhEcgXDG+L9NPOdRbLiL06tCKDTv8/3C/u/NkcrIibyj96oQB3HXmcH4280vK96rHstJMqKuDLVtCT/SlpbBtm38fEevtvVcvS29/2mnBqpyioialykkWKhCSTHWtJRAGdikIqpt97XiG/Ha293xU73YRhUHHNjls31/tVSPlZGZ4P0NR0hpjYPfu8KqcjRuhtta/X2Ghb3IfMyb47b5HD8jJSc09NXFUICSZ/yzZBMDcVRWcNbqnX13rHP/Hsag0YBPLhWd+fgTz1+0gL9syVc3JyqBKBYKSDlRVWRN6uI3avXv9+2RlWRN7r17+enunKqdt29TcTwsgokAQkSeBHwDlxphhdtntwM+BCrvZzcaYt0VkMjADyAGqgRuMMUGBekL1b9ytNA1a2ZN+v6J81/r1M06leNqsqK83uGshg7v69hJys3SFoCSB+nqoqAivytmyJbhfUZE1sQ8cCJMmudvcZ8bmh6M0nmhWCDOBB4F/BpTfb4y5L6BsG3CaMWaTiAwDZgM9QlzXrX+zp7hja8DyKQjFs5cdwfmPz4vp+jkqEJR4sG9feFVOWZm1AnDSurVvch8+3N3mvlWr1NyPEhURBYIxZq6IFEdzMWPMIsfpCiBPRHKNMVWh+rQ06uota6LMjNAbWkf378RTPx0TchURDhUISkRqa0Pb3HvOd/pH0iUjw2dzP2YMnHVW8Nt9hw66UdvEacwewjUichGwAPg/Y0zAXxBnAYvCCINI/ZsltjwgI8I/zsTBnWO6fk5WBlURvKGVZowxsGNHeFXOpk3BNvft21sT+yGHwLHHBr/dd+/e7GzulWBifcIPAb/H8j/6PfAn4GeeShEZCtwDnBhLfycicjlwOUDv3rEHfksXKvZZ8jFRoUmyMzIihsdQmjAem/tQqpwNG+DAAf8+OTm+yf2EE9w3avMbvhpVmh8xCQRjjDe+gog8BrzlOO8JvAZcZIxZ09D+Lm0fBR4FKCkpafIOsLe+vhyAqprETNo1dfXUG9h9oIa2rbMT8hlKgqiv99nch9LfV1QE9/PY3A8fDqee6m5zn6TgaErTJiaBICLdjDGb7dMzgeV2eTtgFjDdGPO/hvZvSRyodk+L2VgembsWgBP+/BELfjM5IZ+hxIibzb3zvKws2OY+P99S4/TuDaNHu9vc5+am5n6UZkc0ZqfPAROATiJSBtwGTBCRkVgqn/XAFXbza4D+wK0icqtddqIxplxEHgceNsYsAO4N0b9ZU1XrEwJ52Yl9Y9u2rzpyIyV+VFf729y7veHv2ePfJyvLsrzp1QuOOcZ/onfa3OtGrZIkorEymupS/ESItncCd4aou8xxfGG0A2xObNntCykRTTiKWOjRrhUbdx3kR6NCWfsqDcYYn819KFXOli1WOyedOlkT+4ABcPzxwcHRunZVm3slrVCzgQTzwpel3PTKMs4/ojftHTr9Id0Kw/SKHU+QvIwwZq1KAPv2Rd6oDbS5b9XKN7GffLK7zX3r1qm5H0WJERUICWRvZQ03vbIMgGfnlXLJMcUAPHDuSG8o63hTbedbyM5UgQD4bO7DTfY7dvj3yciwzCx79bL09meeGazK6dhRVTlKs0MFQgK5IMDb+I3FVhyj00Z0T9hnDuySz7Z9VXRs0wI2Go2xHKjCqXI2bgy2uW/Xzje5H320u819tlpoKS2PFi0Q6uoNj8xdw8VHFdPGJd9AY1kakLlsx35rozecl3Jjueeswxh37xy6FDYDgVBZGazKCZz43WzuPcHRJk50t7kvCI40qyhKCxcIby/bzL3vrmTr7kruOGNYwj4nPzeLfVW1kRvGAY9gq093j436eivheDirnPLy4H5dulgT+9ChMGVK8EZt585qc68oMdKiBYIn5s+eyvhP1qXbfW+uX9x8AsNus/IcfHrTxLh/lhPP4qM+0OIlyeQe2EfvTWXw9h53VU5ZmZXH1kl+vm9iP/xw941atblXlITRogWCR3WTiMnzYI3P58CZ/rJHu8RGe/RsVid0heCxuQ+TtvCRQJv7zEyfzf1RR7nb3Ldrpxu1ipJCWpRAqK837DxQTcd86y1TvG/T8f8sT6rMqWN7AVaeg2TgWSGYWIWcMVZKwnCRMDdvDra579jRmtj79YMJE3h+K2zIL+KGyyZZ5d26qc29oqQ5LUogPPHpOu56+xt+MbEfkw7t4o04Wh9HiWCM4Z53V3on5FOGd4vbtaPBe0+hBML+/b5J3u0Nf8MGazPXSV6e7y3+pJPcN2oDbO7feXI+uw7WcMPRRyfiNhVFSQAtSiA89okV5+fvc9bw9zlr+MOZwwGYv35HuG4NYv66HTz8sS+mX2YyVSC1tWSWlXF42Tf0+e9q+KIqeNLfvt2/jychee/eMGoUnHFGsCqnUydV5ShKC6BFCYTiTm0o3+vzOL35NctprGJv/PL3nPvoF37nJcUd4nNhY2DXrvCqnI0byaur41Vnv7ZtfZP7kUe6B0dTm3tFUWhhAqF/53zmr3NfDeyvqqVNbhaLSnfSt1N+XEJHFxXkRh+zqLLSsrwJl7Zw/37/PtnZPpv7446D3r2p7d6Dy+aUs7GwiPfv/wkUJiZEhqIozY8WJRB2H6wJWffO8i1MGdaVM//xGeDbBDbG8PRn6/nR6J4U5jVMSORk2sLAY3MfxirH1ea+c2frLf7QQ+HEE4Pf7rt0Cba5r6vno9J3rGMVBoqiNIAWJRBmLd0csu7XLy2hf2df1qjiabMAeO7nR3L7f75m1rLNvHRliA3SPT5b+6mL36X7ngq67ylnWN1ueGyXVRdoc9+mjW9yHznS3eY+L6/B95ioGEmKojR/WoxA8EzwHv437XiOmfGhX9kP/x6c0+fChz+l577t8MlyKFjn/oa/2xei4m6gVjLY16kL+f37QPERcM45wRu17dsnZKNWg5wqihIrLUIgVDqcxLq1zePz6ScAMKJHIaWrNtB9TwU99lTQfU8F3fZus4/L6bZnG1327SAD24TzWfsiHTpYE3ufPjB+vHei35jfkbPf3sje9kUsvys5fgeB6ApBUZRYaREC4fV/z+bcJbPpvmcbvxqYC5NnQGkpr5eWIgE291WZ2WwsLGJzYSc+LR7FpsJObCosYlNhEf+6w37Tb9PG9XO2fL+DzZ9W8/dzD0/GbSmKosSVFiEQqv/2D+5ZNAsjgqzrar3RjxiBnHYaB7v24NrPtrGxsDObCovY0aowtCpn8OCwn3PWQ58D0C7Nktsv3rCLhd/v5NJj+6R6KIqipDEtQiBkT7uJcR+eyVv3TqVtob9HbVZdPbO3vRPUp6ggN2b/hPwEhNJuDJ69ERUIiqKEo0XECZ7643F88vClQcIAIDsz+Cv49KaJfHzDBHp3sNqfPqK799jDotKdFE+bxbdbrCBuzthBBXnpIxDq0j4OtqIo6UKLEAjR8vzlR7L8jpPo2b41rXOymHvjRNbPOJWsDMHgP7Ge/bClHprywCeAf4C8ggb6KySSz9dsj9zIpr7eqABRlBZMVAJBRJ4UkXIRWe4ou11ENorIYvvnFEfddBFZLSIrReSkENfsIyLzRGSViLwgIjmNv53YmDq2NwBH9Ongru6R4CyMJw/r6nf+0UqfY1mqVwhTx/b2ekg7/dac1lZuHHffHPrd/HYih6YoShoT7QphJjDFpfx+Y8xI++dtABEZApwHDLX7/ENE3OIe32P3HwDsBC5t6ODjxV0/HMbKO6eENNnMCCi/7Y3lvOVwcntufimXPr3Ae56Xndowz8/NL6W6tp79VbU+b2lg8K3vho3sumHHwWQMT1GUNCUqgWCMmQtEGxL0DOB5Y0yVMWYdsBoY62wg1sx7PPCyXfQ08MMorx93MjKE3KzQk/j7X29l466DVNbUsftgDU9//r1f/fRXlyV6iDFRXVvPu8u3+JUt37SbF74spXjaLIqnzeLd5Zs5WF3nJygirSQURWmeNFa3cY2IXAQsAP7PGLMT6AE4Q36W2WVOOgK7jDG1YdqkDZ4YSINvfTdi28Fd0yeB+0fflfP4p+v8yu6a9Q3zHAH+rvz3VwD0LfL5VqzYtIfRh7Rv1GfXG6Ne04rSxGjMpvJDQD9gJLAZ+JNd7jYNBOopomljNRS5XEQWiMiCioqKWMeaEAKzoHVok8O7145P0Wh8XDWhHwDXvbAkqG5eiGivayt8kVTbtmr8pvgnq7axZMOuRl9HUZTkEfMKwRiz1XMsIo8Bb9mnZUAvR9OewKaA7tuAdiKSZa8S3Np4PudR4FGAkpKSlJjATD95MHe/861r3Te/m8KWPZX06eTuvZwKGp+3uXFf84Fqa+GnBkuK0rSIeYUgIs7ckGcCHgukN4HzRCRXRPoAA4D5zr7GMtqfA5xtF10MvBHrWBLNmD7BSW5uP20IAK1yMtNKGEDwJng4/jp1FEO7+4fJbuxEfrBa9yAUpSkS1QpBRJ4DJgCdRKQMuA2YICIjsV4n1wNXABhjVojIi8DXQC3wC2NMnX2dt4HLjDGbgJuA50XkTmAR8EQc7yuuZAfkHPjRqB789Jj09fqNVnfvUXmdPKwrA27xeWuHSsccLZW19ZEbKYqSdkQlEIwxU12KQ07gxpi7gLtcyk9xHK8lwPooXckMmGF7dwz2eE4nMhq4mxvorV3fSIngWSFMOrRLo66jKEpyUU/lKMjK9J9g092bNxpxcMkxxSHrausad38es9Ufl/Rs1HUURUku6RN0J43JCnjjTrXjWSTmrtrmd37TlMHc8+63PH/5kQztXhgxtMZfP1zFYxeVhKzfub+adq2zXR35ynYe4Ad/+xSw9lcURWk6qECIgizHHsKvTxyY9lFDA3NHnzq8m9cUNRRXT+jHPz5aA4QXeOV7Kxl71wcAnH9Eb24+5VC/cB/H3jPHe9wqzQWnoij+qMooCjIdKqNrjh+Q9iuE2jrfpu6pw7tFtedx4xRfrofjBhaFbOfMS/3svFJueMnn6xAYFuOAWhspSpNCBUIUZDcxl1vnHsc5DdDjv3LVUYC1CghFYPTU97/eSl294bS/fcpjn6z1qxsSYM6qKEp6oyqjKAi0Mkp3nKr9I/t2jLpfd9uh7d53VzKwcwHfle/l6gn9w/aprTesrdjHso27WbZxt19dp/zc6AetKErKUYEQBZ5Q0iN6tk3xSKLDaTXaEPWWOOyTLvunFb11f1Ut54zuRbHtfPfe11uD+k2+f26MI1UUJZ1QgRAFBXnZPHFxCcObiECIFbdN4L/PWcPz8zew8NbJLPx+Z8RrHNO/I3ecPoy9lTUR2yqKkl6oQIiSE5qQk9WALvkhg9iFo23rbIZ2L2TFpj1+5dv3V3OgupZfPPNV2P7f3XmydzWlKErTQ/97myG/mBhe7x+OgV3cw3cP+e1stuwJvdkMqDBQlCaOrhCaIVkZsU/Mry3aGHXbvkVtWFuxn4d/cjhj+0S/ea0oSnqir3TNkMYYRU06tHPENh/+33FWYDx787pfUT4d2qQsJbaiKHFCBUIzpDFmskO6t414jb5F+QCcPrI7AEUFal6qKM0BVRk1Q9xiDEWLx8v5ukkDGNS1kGfnfc+clb5MdcN7+CytfnX8AC45pk9cMqwpipJ6dIXQDGnMCqGNHZeoS2Eek4d04XdnDPOrf+7yI73HGRmiwkBRmhG6QmiGNGYP4bJxfejYJoezDrdCXvTq0JpubfPYvNuyMHIGslMUpXmhK4RmSENSaAaSm5XJeWN7+yXZee+68QCMKW7f6LEpipK+6OteM6QxAsGNgrxsZl4yhpG92sX1uoqipBcqEJohiYjFN2FQZHNURVGaNqoyaoZ4VgitNWOZoigNQFcIzZCMDOGWUw5lwqDQiW4URVECibhCEJEnRaRcRJa71P1aRIyIdLLPbxCRxfbPchGpE5EOLv1misg6R9uR8bkdxcPPx/dlQIi4RIqiKG5EozKaCUwJLBSRXsBkoNRTZoz5ozFmpDFmJDAd+NgYEyrs5g2etsaYxQ0fuqIoihJPIgoEY8xcwG1Svx+4EW9EmyCmAs/FPjRFURQlmcS0qSwipwMbjTFLQtS3xlpVvBLmMneJyFIRuV9EQgbDEZHLRWSBiCyoqKgI1UxRFEVpJA0WCPZkfwvw2zDNTgP+F0ZdNB0YDIwBOgA3hbqQMeZRY0yJMaakqEg3SRVFURJFLCuEfkAfYImIrAd6Al+JSFdHm/MIoy4yxmw2FlXAU8DYGMahKIqixJEGm50aY5YBXi8lWyiUGGO22edtgeOAn4S6hoh0M8ZsFiss5w+BIAsmRVEUJblEY3b6HPA5MEhEykTk0ghdzgTeM8bsD7jO2yLS3T59RkSWAcuATsCdDR+6oiiKEk/EmFBGQulHSUmJWbBgQaqHoSiK0qQQkYXGmJKI7ZqSQBCRCuD7GLt3ArbFcTipoDncAzSP+9B7SA/0HqLjEGNMRKucJiUQGoOILIhGQqYzzeEeoHnch95DeqD3EF80uJ2iKIoCqEBQFEVRbFqSQHg01QOIA83hHqB53IfeQ3qg9xBHWswegqIoihKelrRCUBRFUcLQIgSCiEwRkZUislpEpqV6PE5EpJeIzBGRb0RkhYj8P7u8g4i8LyKr7N/t7XIRkb/a97JURA53XOtiu/0qEbk4yfeRKSKLROQt+7yPiMyzx/KCiOTY5bn2+Wq7vthxjel2+UoROSmZ47c/v52IvCwi39rP46gm+Byus/+OlovIcyKSl+7PQlxyrsTzexeR0SKyzO7zVztCQrLu44/239NSEXlNRNo56ly/41DzVajnGFeMMc36B8gE1gB9gRxgCTAk1eNyjK8bcLh9XAB8BwwB7gWm2eXTgHvs41OAdwABjgTm2eUdgLX27/b2cfsk3sf1wLPAW/b5i8B59vHDwFX28dXAw/bxecAL9vEQ+9nkYsXKWgNkJvlZPA1cZh/nAO2a0nMAegDrgFaOZ/DTdH8WwHjgcGC5oyxu3zswHzjK7vMOcHIS7+NEIMs+vsdxH67fMWHmq1DPMa73kIw/1FT+2H8Isx3n04HpqR5XmPG+gZV4aCXQzS7rBqy0jx8Bpjrar7TrpwKPOMr92iV4zD2BD4Djgbfsf7xtjn8E7zMAZgNH2cdZdjsJfC7Odkm6h0KsyVQCypvSc+gBbLAnxSz7WZzUFJ4FUBwwkcble7frvnWU+7VL9H0E1J0JPGMfu37HhJivwv1PxfOnJaiMPP8kHsrssrTDXrKPAuYBXYwxm8GKDosvoGCo+0nlfT6AlSyp3j7vCOwyxtS6jMU7Trt+t90+1c+pL1ABPGWrvh4XkTY0oedgjNkI3IeVxXAz1ne7kKb3LCB+33sP+ziwPBX8DGuFAg2/j3D/U3GjJQgEN31h2plWiUg+VkKha40xe8I1dSkzYcoTioj8ACg3xix0FocZS1qN30EW1nL/IWPMKGA/lqoiFGl3H7ae/QwsFUR3oA1wcpjxpN09REFDx5wW9yIitwC1wDOeIpdmKb+PliAQyoBejvOewKYUjcUVEcnGEgbPGGNetYu3ikg3u74bUG6Xh7qfVN3nMcDpYoVBfx5LbfQA0E5EPOHVnWPxjtOub4uVojXVz6kMKDPGzLPPX8YSEE3lOQBMAtYZYyqMMTXAq8DRNL1nAfH73svs48DypGFvcP8AuMDY+h4afh/bCP0c40ZLEAhfAgPsHfocrM2zN1M8Ji+2xcMTwDfGmD87qt4EPJYSF2PtLXjKL7KtLY4EdttL6tnAiSLS3n5TPNEuSyjGmOnGmJ7GmGKs7/ZDY8wFwBzg7BDj99zX2XZ7Y5efZ1u+9AEGYG0GJgVjzBZgg4gMsotOAL6miTwHm1LgSBFpbf9dee6hST0Ll7HF/L3bdXtF5Ej7O7nIca2EIyJTsDJCnm6MOeCoCvUdu85X9nMJ9RzjRyI3itLlB8sy4Tus3ftbUj2egLEdi7X0Wwostn9OwdIZfgCssn93sNsL8Hf7XpZhJSfyXOtnwGr755IU3MsEfFZGfe0/8NXAS0CuXZ5nn6+26/s6+t9i39dKEmQJEmH8I4EF9rN4HctapUk9B+AO4FuspFP/wrJiSetngZVdcTNQg/WGfGk8v3egxP4+1tZIMbkAAABhSURBVAAPEmA4kOD7WI21J+D533440ndMiPkq1HOM5496KiuKoihAy1AZKYqiKFGgAkFRFEUBVCAoiqIoNioQFEVRFEAFgqIoimKjAkFRFEUBVCAoiqIoNioQFEVRFAD+P0L2wUbIscxtAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(test)\n",
    "plt.plot(predictions, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.55078259648602"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "mean_squared_error(test,predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above forecasting done for NASDAQ.AAPL. Similar can be done for other stocks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}