AR_MA_btc_ml.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import datetime\n",
    "import math\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from itertools import product\n",
    "import seaborn; seaborn.set()\n",
    "%matplotlib inline\n",
    "\n",
    "import json\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "endpoint = 'https://min-api.cryptocompare.com/data/histoday'\n",
    "res = requests.get(endpoint + '?fsym=BTC&tsym=USD&limit=2000')\n",
    "hist = pd.DataFrame(json.loads(res.content)['Data'])\n",
    "hist = hist.set_index('time')\n",
    "hist.index = pd.to_datetime(hist.index, unit='s')\n",
    "\n",
    "# Adding weighted price\n",
    "\n",
    "hist['Weighted_Price'] = (hist.close + hist.high + hist.low + hist.open)/4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "#hist = hist['2016-10-01':'2018-01-08']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>volumefrom</th>\n",
       "      <th>volumeto</th>\n",
       "      <th>Weighted_Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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>2013-11-24</th>\n",
       "      <td>795.01</td>\n",
       "      <td>854.99</td>\n",
       "      <td>745.21</td>\n",
       "      <td>832.50</td>\n",
       "      <td>25948.64</td>\n",
       "      <td>20776035.16</td>\n",
       "      <td>806.9275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-25</th>\n",
       "      <td>829.99</td>\n",
       "      <td>849.99</td>\n",
       "      <td>758.95</td>\n",
       "      <td>795.01</td>\n",
       "      <td>26381.41</td>\n",
       "      <td>21438670.96</td>\n",
       "      <td>808.4850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-26</th>\n",
       "      <td>969.96</td>\n",
       "      <td>974.96</td>\n",
       "      <td>825.08</td>\n",
       "      <td>829.99</td>\n",
       "      <td>31613.86</td>\n",
       "      <td>28285852.13</td>\n",
       "      <td>899.9975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-27</th>\n",
       "      <td>1079.89</td>\n",
       "      <td>1094.78</td>\n",
       "      <td>914.27</td>\n",
       "      <td>969.96</td>\n",
       "      <td>37859.08</td>\n",
       "      <td>37868643.04</td>\n",
       "      <td>1014.7250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-28</th>\n",
       "      <td>1101.38</td>\n",
       "      <td>1224.48</td>\n",
       "      <td>1032.06</td>\n",
       "      <td>1079.89</td>\n",
       "      <td>37396.83</td>\n",
       "      <td>42912383.44</td>\n",
       "      <td>1109.4525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              close     high      low     open  volumefrom     volumeto  \\\n",
       "time                                                                      \n",
       "2013-11-24   795.01   854.99   745.21   832.50    25948.64  20776035.16   \n",
       "2013-11-25   829.99   849.99   758.95   795.01    26381.41  21438670.96   \n",
       "2013-11-26   969.96   974.96   825.08   829.99    31613.86  28285852.13   \n",
       "2013-11-27  1079.89  1094.78   914.27   969.96    37859.08  37868643.04   \n",
       "2013-11-28  1101.38  1224.48  1032.06  1079.89    37396.83  42912383.44   \n",
       "\n",
       "            Weighted_Price  \n",
       "time                        \n",
       "2013-11-24        806.9275  \n",
       "2013-11-25        808.4850  \n",
       "2013-11-26        899.9975  \n",
       "2013-11-27       1014.7250  \n",
       "2013-11-28       1109.4525  "
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split(df, test_size=0.1):\n",
    "    split_row = len(df) - int(test_size * len(df))\n",
    "    train_data = df.iloc[:split_row]\n",
    "    test_data = df.iloc[split_row:]\n",
    "    return train_data, test_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(hist, test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train=hist[1475280000:1506729600] #from 01-10-2016 - 30-09-17\n",
    "#test=hist[1506816000:1533686400]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train=hist['2016-10-01':'2017-09-30'] #from 01-10-2016 - 30-09-17\n",
    "#test=hist['2017-10-01':] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>volumefrom</th>\n",
       "      <th>volumeto</th>\n",
       "      <th>Weighted_Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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>2013-11-24</th>\n",
       "      <td>795.01</td>\n",
       "      <td>854.99</td>\n",
       "      <td>745.21</td>\n",
       "      <td>832.50</td>\n",
       "      <td>25948.64</td>\n",
       "      <td>20776035.16</td>\n",
       "      <td>806.9275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-25</th>\n",
       "      <td>829.99</td>\n",
       "      <td>849.99</td>\n",
       "      <td>758.95</td>\n",
       "      <td>795.01</td>\n",
       "      <td>26381.41</td>\n",
       "      <td>21438670.96</td>\n",
       "      <td>808.4850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-26</th>\n",
       "      <td>969.96</td>\n",
       "      <td>974.96</td>\n",
       "      <td>825.08</td>\n",
       "      <td>829.99</td>\n",
       "      <td>31613.86</td>\n",
       "      <td>28285852.13</td>\n",
       "      <td>899.9975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-27</th>\n",
       "      <td>1079.89</td>\n",
       "      <td>1094.78</td>\n",
       "      <td>914.27</td>\n",
       "      <td>969.96</td>\n",
       "      <td>37859.08</td>\n",
       "      <td>37868643.04</td>\n",
       "      <td>1014.7250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-11-28</th>\n",
       "      <td>1101.38</td>\n",
       "      <td>1224.48</td>\n",
       "      <td>1032.06</td>\n",
       "      <td>1079.89</td>\n",
       "      <td>37396.83</td>\n",
       "      <td>42912383.44</td>\n",
       "      <td>1109.4525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              close     high      low     open  volumefrom     volumeto  \\\n",
       "time                                                                      \n",
       "2013-11-24   795.01   854.99   745.21   832.50    25948.64  20776035.16   \n",
       "2013-11-25   829.99   849.99   758.95   795.01    26381.41  21438670.96   \n",
       "2013-11-26   969.96   974.96   825.08   829.99    31613.86  28285852.13   \n",
       "2013-11-27  1079.89  1094.78   914.27   969.96    37859.08  37868643.04   \n",
       "2013-11-28  1101.38  1224.48  1032.06  1079.89    37396.83  42912383.44   \n",
       "\n",
       "            Weighted_Price  \n",
       "time                        \n",
       "2013-11-24        806.9275  \n",
       "2013-11-25        808.4850  \n",
       "2013-11-26        899.9975  \n",
       "2013-11-27       1014.7250  \n",
       "2013-11-28       1109.4525  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>volumefrom</th>\n",
       "      <th>volumeto</th>\n",
       "      <th>Weighted_Price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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>2018-10-30</th>\n",
       "      <td>6309.11</td>\n",
       "      <td>6340.68</td>\n",
       "      <td>6286.30</td>\n",
       "      <td>6314.38</td>\n",
       "      <td>42701.22</td>\n",
       "      <td>2.695922e+08</td>\n",
       "      <td>6312.6175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-31</th>\n",
       "      <td>6342.61</td>\n",
       "      <td>6391.25</td>\n",
       "      <td>6241.49</td>\n",
       "      <td>6309.11</td>\n",
       "      <td>38823.41</td>\n",
       "      <td>2.453572e+08</td>\n",
       "      <td>6321.1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>6381.30</td>\n",
       "      <td>6402.59</td>\n",
       "      <td>6322.60</td>\n",
       "      <td>6342.81</td>\n",
       "      <td>31397.71</td>\n",
       "      <td>1.998612e+08</td>\n",
       "      <td>6362.3250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-02</th>\n",
       "      <td>6394.66</td>\n",
       "      <td>6420.93</td>\n",
       "      <td>6371.57</td>\n",
       "      <td>6381.30</td>\n",
       "      <td>27987.60</td>\n",
       "      <td>1.793856e+08</td>\n",
       "      <td>6392.1150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-03</th>\n",
       "      <td>6376.32</td>\n",
       "      <td>6398.09</td>\n",
       "      <td>6336.96</td>\n",
       "      <td>6394.65</td>\n",
       "      <td>28808.38</td>\n",
       "      <td>1.835653e+08</td>\n",
       "      <td>6376.5050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              close     high      low     open  volumefrom      volumeto  \\\n",
       "time                                                                       \n",
       "2018-10-30  6309.11  6340.68  6286.30  6314.38    42701.22  2.695922e+08   \n",
       "2018-10-31  6342.61  6391.25  6241.49  6309.11    38823.41  2.453572e+08   \n",
       "2018-11-01  6381.30  6402.59  6322.60  6342.81    31397.71  1.998612e+08   \n",
       "2018-11-02  6394.66  6420.93  6371.57  6381.30    27987.60  1.793856e+08   \n",
       "2018-11-03  6376.32  6398.09  6336.96  6394.65    28808.38  1.835653e+08   \n",
       "\n",
       "            Weighted_Price  \n",
       "time                        \n",
       "2018-10-30       6312.6175  \n",
       "2018-10-31       6321.1150  \n",
       "2018-11-01       6362.3250  \n",
       "2018-11-02       6392.1150  \n",
       "2018-11-03       6376.5050  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1801, 7)\n",
      "(200, 7)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "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>close</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>volumefrom</th>\n",
       "      <th>volumeto</th>\n",
       "      <th>Weighted_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1801.000000</td>\n",
       "      <td>1801.000000</td>\n",
       "      <td>1801.000000</td>\n",
       "      <td>1801.000000</td>\n",
       "      <td>1801.000000</td>\n",
       "      <td>1.801000e+03</td>\n",
       "      <td>1801.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2470.155875</td>\n",
       "      <td>2551.024947</td>\n",
       "      <td>2370.620272</td>\n",
       "      <td>2467.121899</td>\n",
       "      <td>65276.309489</td>\n",
       "      <td>2.449847e+08</td>\n",
       "      <td>2464.730748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3526.109140</td>\n",
       "      <td>3664.672429</td>\n",
       "      <td>3348.707837</td>\n",
       "      <td>3525.182048</td>\n",
       "      <td>57444.720884</td>\n",
       "      <td>5.151137e+08</td>\n",
       "      <td>3513.891181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>111.560000</td>\n",
       "      <td>160.150000</td>\n",
       "      <td>91.660000</td>\n",
       "      <td>111.560000</td>\n",
       "      <td>404.380000</td>\n",
       "      <td>2.101127e+05</td>\n",
       "      <td>118.822500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>384.690000</td>\n",
       "      <td>395.730000</td>\n",
       "      <td>374.260000</td>\n",
       "      <td>384.690000</td>\n",
       "      <td>26952.730000</td>\n",
       "      <td>1.120408e+07</td>\n",
       "      <td>386.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>625.570000</td>\n",
       "      <td>637.600000</td>\n",
       "      <td>614.880000</td>\n",
       "      <td>625.570000</td>\n",
       "      <td>52093.800000</td>\n",
       "      <td>2.624220e+07</td>\n",
       "      <td>624.165000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3232.030000</td>\n",
       "      <td>3295.070000</td>\n",
       "      <td>2875.910000</td>\n",
       "      <td>2973.450000</td>\n",
       "      <td>87651.140000</td>\n",
       "      <td>2.597085e+08</td>\n",
       "      <td>3090.302500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>19345.490000</td>\n",
       "      <td>19870.620000</td>\n",
       "      <td>18750.910000</td>\n",
       "      <td>19346.600000</td>\n",
       "      <td>517733.910000</td>\n",
       "      <td>6.245732e+09</td>\n",
       "      <td>19258.460000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              close          high           low          open     volumefrom  \\\n",
       "count   1801.000000   1801.000000   1801.000000   1801.000000    1801.000000   \n",
       "mean    2470.155875   2551.024947   2370.620272   2467.121899   65276.309489   \n",
       "std     3526.109140   3664.672429   3348.707837   3525.182048   57444.720884   \n",
       "min      111.560000    160.150000     91.660000    111.560000     404.380000   \n",
       "25%      384.690000    395.730000    374.260000    384.690000   26952.730000   \n",
       "50%      625.570000    637.600000    614.880000    625.570000   52093.800000   \n",
       "75%     3232.030000   3295.070000   2875.910000   2973.450000   87651.140000   \n",
       "max    19345.490000  19870.620000  18750.910000  19346.600000  517733.910000   \n",
       "\n",
       "           volumeto  Weighted_Price  \n",
       "count  1.801000e+03     1801.000000  \n",
       "mean   2.449847e+08     2464.730748  \n",
       "std    5.151137e+08     3513.891181  \n",
       "min    2.101127e+05      118.822500  \n",
       "25%    1.120408e+07      386.670000  \n",
       "50%    2.624220e+07      624.165000  \n",
       "75%    2.597085e+08     3090.302500  \n",
       "max    6.245732e+09    19258.460000  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "close             False\n",
       "high              False\n",
       "low               False\n",
       "open              False\n",
       "volumefrom        False\n",
       "volumeto          False\n",
       "Weighted_Price    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_avg = test.copy()\n",
    "# SELECTING DATA FROM ROW NUMBERS\n",
    "y_hat_avg['moving_avg_forecast'] = train['Weighted_Price'].rolling(60).mean().iloc[-1]\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.plot(train['Weighted_Price'], label='Train')\n",
    "plt.plot(test['Weighted_Price'], label='Test')\n",
    "plt.plot(y_hat_avg['moving_avg_forecast'], label='Moving Average Forecast')\n",
    "plt.legend(loc='best')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12b19f9b0>"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u5fnnn6W+vo5bb/0Pzj33fO6//x40Gg3V1UexWq3ceONNXHTRJRG2bdq0kSeffAyv10tRUQn/9V/+mj8CwUg4vA7eOv5vpOyZVE4Zey+IRCHYuUuvH76W1o7DbeRmpWBJMUyUWRNOVB7tkiTpJUn6UJKkmBR2sVz/C/QzVoLO6N+gM6KfuRLL9b8Yt3PU1dXyyCOPUVpajiTN5amnnuW3v/1f/vznp2hoqOeFF57juuu+wJNPPsM111zLvn17sNmsfPTRh/zud//LM8+8wJlnns1LL/V3zMrNzePZZ19Akubw7LNP89BDj3L33T/h2Wf/GNqnra2Vxx57ikce+T2//e3DdHS0h97r6uriscce5Ve/epQ//vE5li9fye9/PzGJZILEp8HajKqqXDnj4qTus+z2+NDptMOGegb5aKc/CsjrU2jutNPUYePl9dVJkwcQrZH/j4G6KB17RLSpWWiMZv/oX2cAnweNwYw2dfxGwGVlFaSlpbF16xZcLievv/4qAE6nk2PHqjn99DN46KGfs3nzRlatOpOzzz4XnU7HPffcx7vvvs3x4zVs3rwxVBMfYOXKVQAUFBSSm5uHXq+nsLCIvr7+KejFF1+GXq8nP7+A005bGGofCbB//15aWpq54w5/q0VF8ZGRkTlu1yxIbmZmTeO+M36IMThoSlJsTi+WFP2IDzhXoMjb3upOjjf3hrb32j2kGBPfaTLuVyBJ0peATUBMy+Opjh4MledgmHs2ngMfoI7zom+wzr2i+Lj77p8iSXMA6OzsICMjE71ez/z5C/j44/X87W//x6ZNH3PjjTdz++238NnPXsvKlauYMiWHw4fl0DENhv4p5sCa/v3b+78yVVUiXiuKjwULFvLgg78GwOVyYbfbBx1DIBhIdc9xpmaUJ73wg7+EQ6ppZOkL7tPncEdsdydJ5c9ouH2uBi4DLgW+PcK+UcN8wR2krP4SupxyUlZ/CfMFd0TlPEuWLOPll18EoL29nS9/+XpaWpr50Y9+wP79+7jyyqu5+eZvIMsHOXhwP6WlZVx//ReorJzPpk0bUZSx/ZDee+8dfz3y5ib279/LwoWLQu9VVs5n37491NbWAPD003/gd797ePwuVpCUNFibeGjb7/l37UexNmVCcLi8pAwj/otm5Yb+Nuj98mhzRLp5gmsGic64j/xlWb4CQJKke4B/jvfx442vfvVr/OpXD/LFL34ORfF3siopKeWLX/wKDz54H3/60x/Q6XTcfvv/jyTN5aWXXuS6667GYDBQWTmf6uqjYzqfy+Xkppu+iMfj5nvfi1zMzcnJ5fvf/xE/+tEPUBQfeXkF/OhHPxnvSxYkGa8cfZMUfQqripfH2pSo4/UpuDy+YUf+UwszqChIZ/uhNjoCIZ9ub+QALVkqf466nr8kSRnARuBSWZaPB7Z9HrgLMAK/lmX5t6dgy1QmUT3/k0khv//+e1i8uIqLL74sClaNL6Kef2Igdx7hkZ3/y5UzLub8irOH3CcZrjNIc6edTfuaWTI7b1A72PDr3H20g7rWPi5eWcFrHx8PZQUDzC7LonLqlAm1+0REtZ6/JEkrgCeA2WHbSoD7gSrABWyUJOl9WZb3j97swQQuIoLWVi16fXLFHJ/M9Wg0GrRaTcLcixPZqdVqycuLbS/m8SCRr0FRFf654y1yU6dwzeKLMOqGD2tM5OsMZ/3eFiwWE4UFGeTlDdaa4HXmdjtp7XGSlW3BnNq/DqLRaEi1mE54Pw7VdqEoKnPi6AExFKN1+3wNuBV4JmzbecB7six3AkiS9CJwDXBKfoahRv6KoiRVsaWTHfn/8Ic/BhKj8NRI16goSsKPJhN9RNzl7MbqtHPx1PPp6XQCziH3S/TrDMekgw6bC4M6+PcXfp12qwurzUVtQzc2W3/Gb5rZQHuH7YT348OttQBkp+pHDCcdD8JG/mNiVOIvy/LNAJIkhW8uBprCXjcBye80FAiShOyULO5a8Z2kjukfiNPtIy/LPOI1BxPA7E5/sujCmblkpZnYvL+F+jYrcyqySTNHzpTaux1s2NMviVa7hwxL/EZPnYr/YKi7F/9DUoFAwLGeWpxeFzqtLmnLOAyF0+0jxTh0GHU4Bp3/nvTZ/eKfkWokO90USvCSa7sHLfzKdZHh5HWt1vEwOWqcyrfeABSGvS4CGk/NHIFAEG3sHge/3/0Uzx18MdamTDgujw+TYWTxD67v7j/eCYAp8MBYUVkAQF1rH69/cjziM1aH/0FRMCUVs0kf9y0hT0X83wXOlSQpT5KkVPzx/cnV700gSELeqf0Au8cxbHRPsuJTFHw+BeMQ4q/Yu2l85u5QBeDUlEiPeDA0tCjHgl7XL5s9Nn8CmKKoON0+phZmcPq8QhRFpb7NSp89MkEsnjhp8ZdluQG4E3gf2Ak8J8vylvEyLBmQ5YN89rOXc9ttX4+1KQIB4F/kfb9uPUsLFlOWXhJrcyYUt8fvpjEMEYXm3vYKzroDoQrAUzJSIt7Xavu93OHhKH0B8Xe6vaiqSna6P/PfE0gE+/e2ejp6hl5ID8fl8U14E5kxJXnJsjx1wOvngOfG06BkYuPG9Zx33oXccsutsTZFIADgn9X/QlVVLpt+YaxNmXCONvYAYAwT/xNVANbNvRPfENm84dv6Aq6ej/c2A5Bi8s8qzl5Uwnvb6wHo6HWSk5nCcKiqypub/Fn561ZUhFxM0SbxqxNNMF6vl1/96gGqq4/S2dlJeXkF997739x77510dHQA/qxfnU7HSy/5fapGo5HGxgZ6enpoaKjjttu+RVbWFB555CFcLieZmVl873s/pLi4hIMHD/Dzn98HwIoVq3jnnbd48cXXYna9guTBp/jodvWwpuwMcszZsTZnwjlS7xf/8Cgdy/W/wPXJ83iPbwef218BeNoSTCuvw7fN7++vkvKHPabV4cHrU7A5+heGATIsRmaWZHKkoSfUPGY43GELxy1d9kHJZ9EiIcX/f7Y/NmjbkvwFnFW6CrfPze92PTXo/RVFSzm9aClWt40/7H1m0PtnlqykqmDRoO0D2bt3N3q9gccf/yOKonDHHd9g/foPKCws5he/eJjjx4/x+uuvcuut3+KKKz4DwFe+8jXuv/8eMjMz+fnPf42q+rjxxi/w4IO/prCwkM2bP+HBB+/n4Yd/x333/Yjbb/82K1aczlNP/e9J3B2BYGh0Wh23LboZRZ18QXlB3/ys0iwy0/r7EodXANboDaje8ArAfvHPSI0M6VyzqIQNuxtJMxuwOTz0Bo4dXOgNMn96DnVtVlxuHy1ddox6XcgtFI4rrFBccNF4IkhI8Y8lixYtISMjk7///QVqa49TX19HU1Mj69d/QHt7K6efvpobb7xpyM9WVs4HoLa2hsbGer7//f66dzabja6uLjo6Olix4nQALr/8Kt54Q4z6BadOg7WJVL2Z7JQsdJqJcSvEE80dNoBBsfnQXwE4f9UltG58fVAFYIM+8n5lp5u47Ixp7DjcRkunI+Tfl8oGl4xPMeg43twbKgl9/rKyQQ1iXO5+8Q+Glk4ECSn+/7HkG8O+Z9QZT/h+mtFywvdHYsOGD/nDHx7ns5+9josvvpzu7m4KC4t47rkX2bTpEz7++COef/5Z/vKXwWF0/WWgFYqLS3j6af9yic/no6urE5MpMiEkvFyzQHCyKKrCM/v/ilvxcvckS+pyuLy8vaU2NCIvzk0dtE+w4q8pL52U1V8a9L7RMHRcTIpBh9Ptxenyi3d4FFAQi9kQmnUA9FjdEeKvqCofBxLDstNNg6KD7E4vfXY3BVMG232qTJ7sjnFi69YtrF17Hpdccjk5OTns2rUDq9XKk08+ztq15/Gd73yfrq4urNbhEzwqKqbS29vLrl07AHj99Ve55547SU21UFExlQ0bPgTgnXdE5Kzg1NnaspM6ayMXTV07qYQf+hOtHC4vWo1mSIEeCZ126HtmCjR02XG4DRg6imhWqX82EFzwHVgh1BmWC5CfnYrN6cWn9Lvl/vVpLZ/sa44oLDdeiKHlGLnssqu49947ef/9dzEYjMybN5+WliZqa2v40peuRa/X89Wvfp309OEXbYxGIz/96QM8/PAvcbvdpKZauOuuewG46657eeCBn/Lkk49TUTFtoi5LkKR4FC+vVb9NWVoxS0exppVshFctNui1Y3r4VRSmU9PcN+xnBkblDPVgyU43sXJeIdlpJt7cXBMKNw0SXOxdUVmAT1FRVZWuXhe5WWaONfV3D3O5fRHrCeOBEP8xMmPGTP7857+Oat+bbrol9Pedd94T8d78+Qt44ok/D/pMaWkZjz7qX+htamrk9ttvGbSPQDBaPqrfSKezixsWXTOpyjgEcbj6R9q6MY76F8/KY/GsvGHfNw4Y6et1Qz8kCgMuG51Oi93ppdfmJsNixKcoOAP+foNOy5QMv9u3tdtBbpY5lF0M/h4CBn2g8fxJzF6GQoi/QJDEdLt6qMyRmDNlVqxNiQl2V/QWUHPCEsHSU40jzipSTfrQ4u/5y8r49GAr3X3+iqEGgw6TQYfZpMfl9uF0eyNqB3l9CjsPt1Pf5ndjFU5JZUpGCsW5lpMuHifEP44pKioWMf6CU+LqWZfhG2Or0GTC7uz3qY/3aodWq+GyM6bS0GajLH/kksrh8f4HjneFhB/6C8kZ9Fr6HB7e2uwvCz2rLIvDdd18sq854mHQ3GmnudPO/uOdzCrN4uyTKOk8+eaBAsEkoMvZTU1vHeCP75+MqKqKw+UNxfVrh1m4PRV0Wi3lBemjWktIM/ePtYMj+CDBSqOpJj2dvf3lIGaVZAKRrSMHjvSbOm1jN5wEEv/RtpsUxD/iu4w+r1W/za+3P4bNY4+1KTHD6fbhU9RQktZwUTsTxfK5BZyzOLKeksmgIz3VGHow5WWZQ++tXVKK0aCjJNBxzGI2cOHyctYuKWVaUYZ/n6pSzphfdFL2JITbR683YrP1YrFkTLpQtWRDVVVstl70+vhtcpHo1Pc1sqV5O+eVr8FiGP/48ESgqcPGoUB9/fRAyYX01OHbVE4Eep2W9LBR+/TiTGaVZkY8lKYVZ9DUYae8IC00wq+ancdSKS9C+yqnZjO1KIOMsAfHmO05yeuYULKz8+jqasNq7R555wRAq9WiKMmdYn+ia9TrjWRnDx9FITg1Xj76BmZ9ChdMspLN4Ww92BrysRdOScWSoo9KotRY0Wo0nFtVyr7jncwsyRgUvqnVaFi9IHIkP5S4G/Q6MvWn5s5LCPHX6fTk5p7c1CYeSaaeqMMxGa4xHjnQeYgDnYf4zMxLSZ2ko/7jzb0Ri6tmkz6u2immpxpZWVk48o5RJmF8/gKBYGS6nT0UWwo5q3RVrE2JGdWNvRGvh8q8FSTIyF8gEIyO04uXsaKoalImdAXx+vyjfq1Gw7lLS2NsTfwyeX8hAkES4fF52NW2D1VVJ7Xwgz+e32I2cPHpFYMqaAr6mdy/EoEgSfiwYSP/u+dPHO+tjbUpMcft9VGQnTpuZRCSFXF3BIIEx+ax89bx96jMkZiWWRFrc2KKoqqBOjhC2kZC3CGBIMF5u+Y9nF4nV864ONamTBiKquL2DC5bEcyEHVh0TTAYcYcEggSmw9HJh3Ufs6KoipK05AmHHgm5tps3NtXgGVAfP/hAECP/kRF3SCBIYLpcPeSYp3DptAtibcqEUtPszyE50hAZ1tkbaIMYzOoVDI8Qf4EggZmZNY27VnyH7JTB/WOTmWB9KLm2C6+vP5O8u8+FVqshwyKifEZCiL9AkKBsa9mJx+eZVKGdamBB1xXm77cFyja3dNo5XN9NhsWITjt57snJIu6QQJCAHOg4xFP7nmNT89ZYmzKhvL2ljtc/OR6xrcfqr4v/yb5moL82vuDEiLskECQYiqrw0tHXyUmZwsqiZbE2Z8Lw+hSc7v7mLMGyxseb+yIifyoKh++fLehHiL9AkGB82ryDBmsTl8+4CIM2uhVaFHs3jc/cjWKPfUVdm6O/JeOUjBQWzswlO92Ex6vQEWiAMq0og9K8sXe1mowI8RcIEgiPz8Nr1W9Tnl7KkvwFUT+fe9srOOsO4N72StTPNRLOsNF9sD1jpsVEn93N5v0tAMwqnVwL36eCKOwmECQQvW4rmaYMrphxUVTZqtKWAAAgAElEQVQXevue/Br4+kfangPv4znwPugMpN/0RNTOeyJcbr/4mww6Fs3KBQbH86eYJmfLypNBiL9AkEDkmLP5btWtUe9oZ7n+F7g+eR7v8e3gc4POiH7aEkwrr4vqeU+EMyD+5y8rC9Xt0ev674Nep0UrOv2NGuH2EQgShJ1te+lzWyeklak2NQuN0Qw+Dxq9wf9/gxltauzcKi6PD4NeG1GwLfxeBJugC0aHEH+BIAFod3Tyx71/4Z/H/jVh51QdPRgqz6H4xgcwVJ6D6uiZsHMPhcvtw2SIFPjwjl25Yc3PBSMj3D4CQQLwWvVbaDRa1k09d8LOab7gDgBMeemkrP7ShJ13OBxuL6ZhRveZaSZOmz5lgi1KbMTIXyCIc2p769naspO1ZWeSZcqMtTkxwen20tHjRAkb6QNYUvzj1+lFGSKrd4yIkb9AEMeoqspLR14nzWDh/Io1sTYnZjS22wEGrXeU5adhMujIzxYun7EixF8giGM8ipd0Yxrr8s/DrJ+8Ahcs3Xz6vMKI7RqNhoIpqbEwKeER4i8QxDFGnYGvzr8hVMVysuJ0+zAadKJO/zgi7qRAEKcc7DxMo9VfrGwiwjvjGa9PEQXbxhlxNwWCOMTt8/DMgRd47uCLsTYlLhB9eccfcTcFgjjkg/oNdLt6uGLGulibEnV6bG427m2KaMoyECH+44+4mwJBnGF123j7+PucljuXWdkzYm1O1Nkut9La5aA7UJd/IC6Pj45epxD/cUYs+AoEccZbNf/G5XNxxYyLY21K1HG4vPTY3ACDYvgB9h7r4Ei9P7PY4x1+ZiAYO0L8BYI4I0WXwlmlp1NkKYi1KVEnWKwNYONe/+L25WdMQ6vV4PL4QsIPkGkRTdnHEyH+AkGccen0C2JtwoThGcLP39nnxOdTsZgjm7BXThXlG8YTIf4CQZxQ39dIj7uPyimzJ01opyesQUuQDbubAEI1+wHmVmSj1U6OezJRiBUUgSAOUFWVFw+/yp/3P49b8Yz8gSShy+oa9kF3sKa/dWSJaM047gjxFwjigH0dBzncXc26aedh0iW/b1tVVQ7WdNHYZiNjGF++KzArOHtxCWkDXECCU0eIv0AQYxRV4eWjb5BnzmF18YpYmzMhuD0KB2u7sLu8ocqcAOtWVHDajBx0Wk2opIVeZPZGBXFXBYIYs6lpG022Fi6fsQ69dnIsw7m9/b7+FKOO1QuKWHVaESajjhnFmRFNWkR8f3QQd1UgiDFGrZ4FufNYnHdarE2ZMNye/iifFKOe3Ewz+WGduFZU9oe5ipo+0WFyDDMEgjhmaeFilhYujrUZE0r4yD8zbbDPPzymX0T5RAfxSBUIYoTVbWN9wyZ8yuBwx2QnPLkrb4jeuzox2o864g4LBDHizePv8lf5JdocHbE2ZcJxuLyhv7VDhHrqdWK0H22E20cgiAFt9g7WN2xiVfFyCi35sTZnwnG4/M3Yz19aNuT7oh9v9BF3WCCIAa9Vv4VOo+WSaefH2pSY4HD5SEsxiDDOGCLuvEAwwRzvrWVb6y7OLT+LTFNGrM2JCQ63F7NJOB5iibj7AsEEo6oqUvZMzitfE2tTYoKqqjhdXsw5lhPuN396TkQCmGB8EXdWIJhgpmVWcMfir8fajJjh9ij4FJUUk+6E+80syZwgiyYnwu0jEEwQPsXHOzUfYPc4Ym1KzHB5fPxrax0AqcLtE1OE+AsEE8Sm5q28fPQNDnUdibUpMaO2pQ9foIZ/ilGIfywR4i8QTAAun5vXq//FtIwKFubNj7U5MSM8pl+M/GOLEH+BYAJ4v249Pe4+rpp5yaRp1DIUwTLNZfnpmIwn9vkLoosQf4EgyvS5rbxT8wEL8+YzI2tqrM2JKQ6XD7NJT5WUF2tTJj1C/AWCKOP2eZiVPYMrpl8Ua1NijlPE98cN4lsQCKJMjjmbbyy4MdZmxBxFVbE5vWQNUcVTMPGIkb9AEEXer9tAm33yFW4bipZOO3anh+IRkrsEE4MQf4EgShzrqeXFw6+ypXlbrE2JC6wOf2P6gimpMbZEAEL8BYKooKoqLx15nXRjGueWnxVrc+ICm9OLyaATbRnjBPEtCARRYE/7fo72HOOSaeeTok+JtTlxgc3hIVXU6okbhPgLBOOMT/Hx8tE3KUjNY1XR8libEzfYHB4sKYZYmyEIIB7DAsE441G8zJkyiznZM9FpRSITQGevE7vLy/Q0U6xNEQQQ4i8QjDMpehOfm31FrM2YUGpb+jAadBQOs5jb1u1Ao9FQUZg2wZYJhkOIv0Awjmxq2kpOyhRmZU+PtSkTht3pZfuhNgCmF2cilWdhMvTPeJxuL4fqe8hINWDQi5lQvCB8/lHEG6heKJgc9Lr7eOHQy3xQvyHWpkwoVqcn9Hd1Yw/NHfbQ66ONPazf3YTPpzCteHJ2LYtXxMg/SrR2O9i4pwlLioHzlw3dpFqQXLx57F08ipfLZ6yLtSkTSnefK+K1JzDoqWnuY89Rf4JbTmYKUwuF+McTYuQfJZo7bADYwkZFguSlxdbKhsbNrC5eQUHq5ClaZnd62X+8M2KbJ1C5M3x7Wb7w9ccb4z7ylyRpAfBfgB14QZbld8b7HImA29Pv8lFVdVKX8Z0MvFr9FgatnnXTzou1KRNKS5ffxXPa9Bwa2m109jpxexVcbl+ofDOAQSfGmfFGNNw+acB3AS/wIDApxd/j7Rd/RVXRCfFPWlRVZXrmVGZkTSPDmB5rc8adPrt72PdsDg86nZbpxRnMKMnkna119NjcdPY5I/ZTo22kYMyM++NYluWNQDrwCvD2eB8/UVDU/p+7ooiffjKj0Wg4t/ws1padGWtTxp3WLjuvra+mvs065Pt9dg9pZkNoZmtzeOjsdbL/eBdajYZzq0opzUsbNgRUEDvGXfwlSVoKNMqyvAq4abyPnyjow6a5PiH+Scv+DplNTVtR1OSM7Oq1+9esOnsjF3V3H+1gy4EWWrrsWIYo2dBnd2MxG0hPNbJ0Tn7EvwdBfBANt08q8KQkSW3A61E4fkIQ7uUJH/m3dNrZdLCNZbNz0GnFP4hExqf4ePHwq4CGZQWLIak9e/2/4c5eJ9WNPaHXFQVDu7pEj974ZtTfjiRJGcBG4FJZlo8Htn0euAswAr+WZfm3six/BHwUBVsTCp+v/x9LV5+L1EBNk11H2tHodfTaPKSZ9ci13Ujl2aLSYQLyXvVGWuxt3HLal5O2jIPT5QWISM5qCovjB8jPNof+Ls610Njuj3SbN23KBFgoOFlGJf6SJK0AngBmh20rAe4HqgAXsFGSpPdlWd5/Kgbl5CRHSFiqpROLOxDyVtfDosoiACxpJuxOL1sPt5OXZaat20lOjpsFM5MvPDAvL/kWP4NYXTZe2PhP5ubNZO3cFUkZzeX2+GjqbgAgIyMl9H2qtd1YLP4aPWcuKiE/v/97vignjb++IwNQUZadcO6eZP7NDmS0I/+vAbcCz4RtOw94T5blTgBJkl4ErgF+cioGdXRYk2KBtKvHgc3W7ydtae1Fq9HQ1+dCZ9Bhs7lC72/e3YjWpyRVk4u8vHTa2vpibUZUUFSF+zc/RJ/LyiXzvkR7+9CLoYlOc6cdm82FxWKio9MW+j4bmnvJzUxh8aw8tFoGfc/B33VXp23CbT4VEvU3q9VqTmrQPCrxl2X5ZgBJksI3FwNNYa+bgFOuX6s4esGU+E9fn09Fr9OGSjzUNPcxrSgDn6KgY7CLoLbVmlTin2woqsLutn2clluJTqvjypkXM72wGIs3K9amRQ270xv62+NV6bW7eW9bPQAZFiNa7dCznfOWluF0e4d8TxA/nMqcbKhv/pRDHjx7kyMtwKeoEVPemmb/iGLgIu85i0vIyzJjPUEstSC2yJ1H+PnW3/DE3mfY0bYHgNNyK5manbxlO1RVZffR9tDrutY+dh7uf32i0M00s4HcTPOw7wvig1NZjm8AwgObi4DGUzMHvEc+wbntVdAZSL/piVM9XMzwKQpGvQ5nQNPTU/0LvqqqMqssiykWA063j8w0E2aTPtTfVBA/NFibePnoG+zvkMk2ZfHlyutYkr8g1mZNCG7v4HFcZ68/cev0eYWkpxon2iTBOHMq4v8ucI8kSXmADbga+PopW6QzoJ+5EtPK6075ULHE51PRm/onRz5FRVFV3F4Fk1FHRqqRjMDgyaDXRmQEC2KPqqr8af/zdDq7uWrmJawpWYVBN3m6UAVdPktm5+HTatl1sAWAKilfuCeThJMWf1mWGyRJuhN4H3+o5x9kWd5yyhb5vGgMZrSpietLVVV1kNvHp6i4PT5UVSXFGHnbdVpNUixyJzoOr4P3ateztvxMzHozN1ZeT6YpA4th8oldsCBhZpqJooKMkPiL2P3kYUzfpCzLUwe8fg54blwNmnU63va68TzkhOP1KaiqGhG77/UpHGnwJ8aYjJELvjqtBkX1zwy0SRgyGO94FS/rGzbx5vF3sXnsFFryqSpYRHFaYaxNixmOQHx/qkmP0dD/Ow66LwWJT9w9xv+RDhfO+zKJvFyk1WooL0gnNzMllPDi9SocqfeLf06mGYe1v/BVcBFYUVS0OiH+E4Wqqmxv3cWrR9+i3dmJlD2TK2deTHl6aaxNiyl9djfHGnsxGXUY9NqIGazRkJzJbJORuBP/w13VbGnczuUz1nFmyUq0msRKEgG/mC+ZnUevrT+Cx+nuL2+bZjZEiH8wZM4fHjpxdk52NBoNm5q3YdKbuHXhTcydMjsuk7UUVcXrVaIuvC63jw92NoRG/Ytn+RMPNRoNy+YWkJ0mFnmTibgT/9sW3swz+1/ghUMv82nzDu5Y/HWMCbrQFh4HHaxtXlE4OIdBFxR/4fePOo3WZl6rfpurZ11KrjmHGyuvx6xPietBxub9LbR1Obh01dRhY+tPhfZuB5/sb2FKuikk/CvnFUaEc5bkWsb9vILYEnfiP8WcxW2LbubTlh3U9TWEhF9Rlbj+BzoUlhQ95QXpdPW5QjXRh4qPDv6DDi8DLRhful09/LP6X2xq2kqK3kSTrYVcc05CLOa2dPpr6Tjd3lCNqFPF61PYebidwimpbJVbAWjrdmBJMXD24hJRa2oSEHfiD/5p5vLCJSwvXAJAfV8jT+17js/NvoI5U2bF2LrRo9FoWDI7j91HO0LiP1RHIzHyjy6vV/+Ld2o/RFUVzilbzYVT15JmSIyRbHgUmMc3fr+P40191LdZQ3X6jQYdM0syKcpJFcI/SYhL8R+IR/GgqD5+s/MJVhRW8ZmZl5JmTIx/vBA5ojcaB/ttQ+LvE7H+40X4TNHqsbMwbx6XTb+IXHNiVZoMT/6zOjw43V4Ksk99trL3WEfE6+Vz8snNSuQwC8FYSQjxn5ZZwQ+Xf5u3jv+bd2o/YG/HAT476wqWFS6OtWmjwhb2DzhjiMzIkNsnMMpTVRWvT4kooysYHf4Int28Wv0WX5z7OWZmTeOzsy9POJdhkIawDlqfHvDH2q+oLKCt20lJroWczJQxHzN8NrFyXiEpRh1ZaaZTN1aQUCSE+AMYdQYun3ERVQUL+b+Df6fF3hZrk0aNboRFOlMgiiMYEXS0oZe9xzq4cHk55jEk1Xi8Cq9/chyA85eVYRkn/3CicLirmpeOvk5Nbx3FlkI0gfJTiSr8AN4hXIGb9/sfAtWNPeRkpFAl5Y1pLSCYb7J8boForziJSRjxD1KSVsS3q76JGnCl7Os4SENfE+eWnxW3DTVG8tQGBT4o/sHm161djiGjg4ajtdsR+nv3kQ5On1/IriPtHGvq5ayFxVjMhtCDJtn48/6/srl5G1mmTL4w93OsKFyS0KIfxDuCK7Cj10lDu41ZpSNnxFsdHg7XdVPT4i8ymJc19lmDIHlIOPGHwEguMJje1yHzYf3HfNqyg8/PuYZpmeWxNW4Igl29ls0tGPJ9vU6DRqPBE/iHHnwY7DjchtPtRSrPHtV5XGG5BMFjHWvqBeCjXY2Bc2m5cHlZUriUelx9pBstaDVapmaUUZiaz9llZ2DUJU88utfnLwcSLJEcTB4szU9Dq9HwxqaaiNLLQRrabbR22lk0KzeUu7DrSDttgQHCrLKspPgNCE6ehBT/cD43+wqk7Bm8cOgVfrXtt5xVejqXTb8Isz5+RjVBIR6q0TX4o4L0Ok2ouFv4GsGBmi6mFmYMKgkxFA6XF51WQ1GOhfo2K3uqOwbt4/UpNHXYKctPC5070XB4nbxb+yH/rv2Iz8+5muWFSzirdFWszYoKXq9CilEXEv8lsyM7vqWa9NhdkeJf3dgbKsdc09JHilGPT1HweBVyM81UFKYLd48g8cUfYGHefGZnz+S16rf4qP4TKtLLWFFUFWuzQiyZlcuh+p4hF3uDGAKNX1q7HTQH4rpNBh0uj4+WLjvlwzTJDsfu8mI26TEHHjJHA77d6cUZzCjJxGTQ8c7WOrYfamP7oTYy00ysWVScMPWEfIqPDY2beePYO1g9NqryFzI9syImtij2bhqf+Tm6s74e1SKE/oX/4d1Xep2Wlk47H+1qpErKQ1UJCX9wxuB0ezHotcwoyWR2WVbSuv4EYyMpxB/ArE/hc7Ov5IziFRRb/AW59nfIFKcVkmXKjKltmWkmls3JP+E++kBZZ0fYFP7CFeW8/klNRJmI4bA7PTS0WcnJSME0YDo/rSgjtPgrlWWHxKHH6qLX5k6YSI/H9jzN/g6ZWVnTuWrmJVRkxK6ZinvbK3jqDmDY9gopZ345KueQa7vo6HVSnGth2Zz8IWvoB5/bnb1O3vk0siDiRSvKsTk9dPW5KMm1JOQsTxA9kkb8g5Sk+Rule3we/nzgr3h8Xq6cuY4zilfE9QJgcOQfHhmk1WgwG3UcaeihJC+N7PThRbq+zV9ATsVfQsLp9pKWaqCx3UaauT8SpCTPQm1rH9OLMth+qI0ea3yL/9Hu45SmF2PSGTmndDVrSlYxL2dOzISs78mvga/fLec58D6eA++Pe/MhVVU5UNMFQGleGsXDlFfo6HEOuT34O7KkGCZd1JdgdCSd+Acx6Ax8Z8mtPC//g+fll9jSvJ3rpatjXqZ3OHeBXq/11/8ZoGnZ6SasDg/Vjb1USXkMRzA7eMnsPAx6LfOn5wAwtTAjYj+TQcfZi0rwKQrbD7XFba/VZlsLrxx9i93t+7hq5iWcV76Gyhxp5A9GGcv1v8D1yfN4j28Hnxt0RvTTlox786FgJ63503OGFX7oTyBcs6gERVGZkmFChYRx5QliR/wOhceBvNQcblt0M1+aey0t9jYe+PRhOhydMbXJve0VnHUHcG97JWK7Qed3+wz8J3taQMQNev87Xp+Cx+tjIG6PD5NBFzHKPxE6rRZD8IETR/S4ennu4N+5b/NDHOo6wmXTL+KsktNjbVYIbWoWGqMZfB40eoP//1FoPhR09Y3UPCW4FpRhMZCTmYJGoxHCLxgVSTvyD6LRaFhRVMW8nDnsat9LTiC9v93ROaGp/iO5C/R67ZAx3UaDjlSTHo/XP8L7YEcDVoeHK8+cHrFfMKRzLKQY9VQ39uLxKkwvzjyhW2miePbg3zjYeZg1pau4aOq5pBvTYm3SIFRHD4bKc8hfdQmtG19HtXeP+zmONvZgMujIHSGDd9GsXOZWZId6QggEoyXpxT9ImtHCGcUrAH9j7gc+fZjlBUu4atYlE1LkayR3gUGnxetVGKqwp16vxaf4HwzBWi+OQGTPqRB0+dS1WqlrtXLRivJBLSajjU/x8XHjFhbkVZJlyuTqmZei1ejIT82dUDvGgvmCOwAw5aWTsvpLUTlHd5+bwimpI9bw12o0p/w7EExOJuVwIc+cw3nla9jSsp2fbvolW5q3hzKGo8VI7gK9ToNPUfEqg0f/et3gBu9vb6kN2RycMVROHdtMZuAxgyGmE4Gqquxs28t9W37FXw/512QACi0FcS38E4XH6xNdswRRZVKKv1Fn5IoZ6/j+sm+Ra87hT/uf5/E9T0f9ARB0FxTf+ACGynNQHT2h94Kt8oKCnBkWgdNtdYUyM8MJJvcEy0KkjCIRLJylkj/8NJhAtvNwOy+vrw6Vn44W1T3HeWj773hiz5/RouUbC27k/PKzo3rORMLrU/ApqiitLIgqk3q+WJJWxHeqvslHDZ/g9rlD4YPRahxzIneBThdc0PU/gFaElYIIVmGsb/VXeExPNdJnd2NzerGkGHAGHgIpY5z+B5tx52SkYHN46AksMm471MbZi0rGdKyxsKFhMx2OTj4vXc3KoqVxW5NporA5PZiN+lB11+AAILxxukAw3kxq8Qd/naCzS88Ivd7bfoBXq9/ihjnXTGgSUTBCI+jbD1+/Wzwrjx2H20Idl7LS/OJ/sKaLnAwTbYFY77GO/NMtRn8oYU4qZpMem9PLjkNtdPQ68fqUiMbdp0KPq483jr/DGUXLKc8o5TOzLsWgNWBKwBo8u460U99mpUrKJycnjdqWPkryLCe94OpweXnn0zqy00243D58qsq0QHiuUdTeEUSRSS/+A9FoNFjdNn6x9VHOLj2DS6dfQMoE1AnShTVxH4lgpmdnr5PXPj4e2m4e42KtVqNhZkl/9nOa2UBFYTodvU6cbh9p5lMTf6fXxb/rPuLd2g/xKl5KLEWUZ5QmTBetgSiKGoqq2rSvGUtaCtsPtbHvWCfrVp5cmYmjjX7XX1efK7TtYK0/ucsyyrBdgeBkEOI/gHk5c7h75Xd49ehbfFD/MTvb9nL9nKuZF+UEI+2AVo6asIj/kjwLOw739y/o6B2c1ZmeakSvO/X47uDsobnDzszSocti2J1e2rpPXG76g2Of8MzOf9DntrI47zQun3ER+anDJ6klAvtrInNENu1tAsDl8eHx+iKqZPba3Hy8twmTQUfl1ClDFlJze3wcqfeLv06rYXpJJlkWI58ebCUnI4WMVCH+gughxH8IzHoz10pXsaxwCc8dfJFe19hj6MeKLuBiUYZo3jHQ/VKal8b8aVPYtL8Fm8ODVJaFVJE9LiUPTIHZw95jHUzJMDElY/CsZ9P+ZnptbopzUzHodXS7eqjpraOmt57zys8i1ZBKm62DfHMut5z2ZabFqPjaeNPS6V90L5iSGmqqHqTX5iEns1/8G9qsuNw+XG4fOw+3c9bCYowGLR09TrqtLjxeJdRUZUVlAUU5/bOh7HQTRoNO1OIRRBUh/idgemYF31/2LXQa/z/qTxo/xat6o1InSBf4hz5UqCf4yzZsP+Qf/ednmzEZdKxdUoLbo4xrnLcpbJHR6vAMEn9VVXF7FHrVdv53zwc02Brocfubg2g1WublzGFG1lSuqlzHmvyzElLAWrvs7DvWyZpFJaEZGfhH5/nZZhbNzOXtLbWAv0fDpwda+PRgK2sWFYe+i/DGOk63l399WjvkucoLBpdXHktXLoHgZBHiPwJ6bf8t2tNxgF1te6NSJ0g70Oc/QDPLC9Kpa7XS1u0IuXd0Wi1m0/g+hMJjy7vtNlxdrdT01YdG9pdOvwCdLg8N0GpvZ3b2LCoySqnIKKM0rRijzi9cem3ijlx3HunA7vRgd3lD5TIUVaXP7mZacQZmk54zFxQza3ouNXV+V5DT7WXXkXZWzivE4fLS1edienEmGRYDOw+3Rxw/M83EjOIMstJMZFgSb9FbkBwI8R8DX5v/RTY3b+Mfh//JA58+zAUVZ3NhxVoMulMfqQVDPYdy+wRZPrcAq8MTlVR+j+KlwdqITqNnqZTPxoM1PH7ssdD7aboMZk2pIMOYjk2nJV2Ty7UlN485sSwRCC6+h5fb6LG68SlqqCdDTmYKOq0mIha/udNOR48z1IazMCeVdLO/5s78aTlotRrSzHpRikEQFwjxHwMajYaVRUuZlzOHvx/+J28e/zdS9kxmZc845WOHBCe04DsYg147rvV3Njdto7q3htreOhqszfhUH1X5C/nq/BswymZmaVcyO78Ma3sqRo2Z1eVF5Gaaaavzt4Q8VNeN26uQnWaiLD8twkWSyAQfxOG9DoKlkwfW2hm4HrN+d3+7zNzMFLQaDWcuKI62yQLBmBHifxKkG9O4cd51XFBxdsj1s6N1D7OzZ2AxnFx7vKCIjNSwe6yoqkqbo4Pa3jpq+upRVZVrZl8OwHt162l3dFKeUcrasjOpyChjaiC3YXpxJjQuwuQw4tb4k7/qWq3kZpojRP54Uy/HgSMNPZxbVTqutseKYM7F9kNtlBek09HjZO8xf0vMgesrumEeeLlZKaK6piCuEeJ/CgSFv9fdx9P7/w+zLoVrZl9OVf7CMfu79bpIV8PJ6IaqqvR5rGQY/SGYrx59i/UNn2D3+hcfDVo9M7P6q4HetuhmLIbUIRevF8zIoa61L1TqIdhXAAbXBALos7tRFDUpRv+pJj2d9K/DHKrvr9o58HvVaDScNiOHKekp9NndbD/URl6WmcWzEjusVZD8CPEfBzKM6Xyv6jaeO/h3/rjvOTY3beM66apQ+ejRoA3UYfcOIazDYffYOdZbFxjV+xdk+9xWfnnWvaToU8hOyWJx/mlUpJdRnlFGsaUgopTCSOWSDXpdSOgzUo3UtPThcvuwOjxYzAZsDg9mk565FdlsP9RGe6+T/CzzqO2PV8JnX6qqDvmwC2dGsT8fIjvdNKpeywJBPCDEf5woTS/mu0tv5cP6jbxW/RYPfPowP131g1FnB2s0GvR6Le5Qc5XIEabT66S2r4HavnqWFiwiy5TJlpYd/O3QK2jQkJ+ax5wps6hILyO4ZHxmycpxubbTZuT4k85a+nhzcw0As/IzaelysGBGTsjSTXubufj0inErCxErgol2iuIXfmcg6ufsxdGrdyQQTDRC/McRrUbLOWWrWZQ3n+qe4yHhb7N3kJeaM+Ln9VoN4fU02+wdvHn8XWr66mmxtaIGZD3PnEtWXiYLc+dRbCmgLL0UcxRKUAQjj4x6HSW5llDjd4CSvDSk8uzQ67kV2Ryo6eJoQw+zSse3qxJ43rkAAA6+SURBVNVEEz7yf2NTDRqNhhklGQn/UBMIwhHiHwWyU7KoSlkEwL6Og/x+1x85p2w1l0y7gBR9ZLSOT/HRbG+lpreO3e6DtHuaKdHOBaai0WjY3ylTkV5GVf4CKjLKKE8vDblrslOyyE6JntAGm71kpBrQajUsnJnLriPtGPTaQe0ii3IsHKjp4kBNF712DxcXZAx1yITAO6C+kqqqmERtfUGSIcQ/ykzPrGB1yUreq1vPjtY9XDXzYuYappFKJh6fh//acC8un3+8b9AYSdfkYdSkoNFATko2Pzvj7pglS00vzqS6sSdUSC4Y5jitaLCwW8z9P6WGNitdfYPrDyUCNqdnyH4GosKmINkQ4h9lzHoz10lXsaxgMc/Jf+epfc+xsGMuX6/8CgadgQsq1jIlJYuKjDIOH/HQ3tMvmrHOkJ0/fQqVU7NDUS/pqUYuXlkxZJORgYlLb248zvlLEs9H3tnrr65pNOjITjNhNumxuzwU555cCK9AEK8I8Z8gZmRN5QfLvsWBzkPMKi4j6Ny/aOra0D7H9S0xsm5otBoN2gGVQk/UWnDNohJ0Og3rd/kTnVRVndAHWI/NTWevc8iZyWhxBRbc1y4pmfB+xgLBRCJWsCYQvVbPabmVlGUOnfE5HiWZY0l2uomMVCNSmX8heDQJa43ttogiaKfCxj1N7DrSjsfro6HdhsvtG/lDA7A7veh1WuHjFyQ9QvzjiPBokkRODtV7eqk8+AT1NY0n3M/l8bHlQAsb9zSFyiecCsHSGIfqe/j0QAtb5dYx92WuDjRXibXLTSCINkL844hkCSVMO/w26dYanNtePmGCVLjg7zraPux+oyXYiOZwnT8jt63bwab9o3elBWcKyfI9CAQnQjg144hEL43Q9+TXwOchGMxa2r0N51NfxakzoP/C7yPq4tidHrYc6BfmYLXMk8XrU7AFyk+Af23C7fHR0mkf1GVrOILVOJfMzj0lWwSCREAMceKIcO3XDFnXM76xXP8L9DNWogYas/s0elylS1k//fZQ85MgNqc/hyDNbCAr3UR9m5XeIUIsR8uuI5Ezh1XzC1k+twCAj3Y1jej+sTu9bA7MEnIzE79EhUAwEkL8BeOGNjULjdGMxucBnQGt6sOjMeHWD64h5Ay4WFZUFtAdaF6+63A7NqcnlFw2Wtq6HdS1WgE4b2kZ5y8rIyvNFEpE67O7aR7QdnGoY4C/ameiz8AEgtEgxD+OiFhkTFD9UR09GCrPoeQrD9CYXYXWOXT/Y7vT76JJMepC3aysTg/vfFrHht1NozqXT1GwOT3UNPvbSFpSDKSZDVgCbRDTUw1USfloNJpQ/93h6LH5Zx2ifo9gsiB8/oJxxXzBHQCY8tI5VnYZzlwLNPU/AHYfbcftUei2ukgzGzDodZy9qIRXPz4WWnC1Ojwoqhqqh6+qKqo6eE3kYE03h8PKLZ8+P7KtpkajoSw/jbrWPrptriHt7bG6eH9HAwB5WWYR4imYNAjxjyOSYOAfgU6riViE9SkK1Y39D4KCbH/WrFarIT3VGFFWwerwkJFqxO7sb36+bG4B3VYXUwvTsaQY6Lb2C7pUljWo3lAQo16HPbDG0NJp55N9zaxZVEJ2uonth9pC+00vTtx6RALBWBHiH0ckW2y5RqsJLewCfHqwNeJ9fViZiLMXF2NzePF4FdbvbuRofQ8+RaW+zdr/+UB00PGmXs5fWoZPUdFpNZQXpDPtBMKt0fgfJl6fwoHaLgA+3NnA1KKMkLtnZmkmRTmWU79ogSBBEOIfR4RLfzI8CLQaDX2O/tF8c0f/ouv04gxmlvRXJNVptSHff1aaiZqWvohjFU5JpbnTTmaaiR6r6/+1d+8xUp1lHMe/u8vCAku5LuVWSwF5KIgUERt6MTW9xNYWqdaSlrTVpBIT2yZVrGhrMN5rY2nUxhsRaRukCbSSVokVaDQEpbUWxWKeGgJeKFVAbbmzwPrHe4YOC8vO7HnP7Oyc3ychmTnMefd59sw+c+Y95zyH9X/YyeGjx5g8dggTzzt7Z9PCdNGzG3ecsnxHMh116dSRtNTATWhEyqHiX016fr0/RUefX1PHDWX86IEdrvfWDW2Cd9twxgxv5kRbG3XA6g3bOXz0GEMHNjF+dOdTNdPGDzt5UBg42Zq6f1Mjs94xosPpIpFapuJfRXriuf1nU/j20qex4WTDNIDmfmcvtqNbmk8eyG2or2NUS5iOKRwAvmTqSHbtOYC9bdBp3UTPpPhA8ezLLqC+ri5V8zeRWqDiX0VqYKbnVMmFVUdajzPn8nEcPNzK/kOtnU6xTDp/EONGnXPKFcHFhg/qW/a9ght71dN67MTJDxCRvFPxryK1VpcKB1ML+jU10q+p8ymWhvp6+vaJewnKlTPGcLSTG7GL5ImKfxWptWmfgsljh3R3CDT17kVTuvZBIjVFV/hK5iaM6fjgroh0DxV/yUyhNbLm2UWqj6Z9JDNXzhjDoSPlNWkTkcpQ8ZfM9O3Tq8MzdkSke2naR0Qkh1T8RURySMVfRCSHVPxFRHJIxV9EJIdU/EVEckjFX0Qkh1T8RURySMVfRCSHVPyr0PnnDujuEESkxqn4i4jkkIq/iEgOqfiLiOSQir+ISA6p+IuI5JCKv4hIDqn4V5GBzeEO4y2D+3ZzJCJS66LfZsnMpgALgTeA1939K7F/Rq0a1NyHD8waS2MvfSaLSLayqDLDgAXufhdwSQbj1zQVfhGphOh7/u7+awAz+yywIvb4IiKSXhbTPk3Aw8Bqd/9l7PFFRCS9LOYYFgEzgNvMbEkG44uISEol7/mb2TnARuB6d9+RLLsVeADoDSx290fd/XNZBCoiIvHUtbW1dfoiM7sY+BEwCZjo7jvMbDSwgbCXf4TwwXCLu2/tYixjge1dXFdEJO8uAHaU+uJS9/w/DnwSeLxo2VXAenf/D4CZrQRuAr5U6g8/k71793PiROcfSD1ZS8sAdu/e191hZEo51g7lWd3q6+sYOrS57PVKKv7ufieAmRUvHgXsKnq+C3hP2RG8pQFCInmQhzyVY+1QntWrKOaGctZLc7bPmX5LJ1KMNxJg8OD+KYboObrySd3TKMfaoTx7hJHAtlJfnKb47wQub/eDX0sx3ovJeLuA4ynGERHJkwZC/X2xnJXSFP+1wBfNrAU4AHwYmJ9ivCOEA8giIlKekvf4C7p8nr+77wTuB54HNgPL3f2Fro4nIiKVU9KpniIiUlvURUxEJIdU/EVEckjFX0Qkh1T8RURySMVfRCSHovfzb8/MFgE3J09/7u73mdlVhJ7/fYEn3f2BdussA553958kzy8HHiF0D90O3OHu/8069nLEyLNo+XTgd+7eJ/vISxdpW94OPAj8q2ic+ysRf6ki5TkSWEJog3IQmFfohlsN0uZoZsOB54r+eyDQ4u5VdYlspG05FngMOAf4H6H+/K0yGWQn0z3/5Jd8DTAduAiYYWa3AD8GPghcCMw0s2uT148ys2eAj7Qbailwm7tPBbYCn8ky7nJFzBMz6wd8l/BBVzUi5jgT+JS7X5T8q7bCHyvPx4Fn3H168vjBCqXQqRg5uvu/C9sQeBehm2Saizyji7gtvwz8NMl1FfDVCqWQqaz3/HcBn3b3owBm9hdgIvBXd9+eLHuC8MteA8wDVgN7241zobu3mlkjMBr4U8ZxlytWngDfAhZTffc/jpXjTGCCmS0EtgB3V9m3uNR5mtkwYBpwdbJoKbCuUgmUIOb7FeBjwEF3X5514GWKlWcDYa8foD9wKPvQs5dp8Xf3VwqPzeztwFzg25zeDXRM8vqHktde1m6cVjObSmgp0Qp8Psu4yxUrTzObDfRz95XtOqh2u1g5Jq/5BvAC8DXCt5x5mQVepkh5jgf+Diw2s/clj+/KNvLSRdyWmFkD4YZOszMMuUsi5vkFYKOZ3UP4Rj4rw7ArpiIHfM1sCvArYAFn7kHRaTdQd9/i7ucSvoI9GTfCONLkaWYjCH9Ed2cTXRxpt6W73+jum9y9DfgmcF38KNNLmWcvwlTDWnefRtibXBY9yJRi/F0C7wdedfctMWOLKUKey4D57j4a+ATwtJn1vN7P7WRe/M3sUsJX3oXuvozQDXRE0UvO2g3UzJrMbE7RoieAd2YRaxpp8wSuB4YCvzGzzcmYm81sQEYhly3CthxoZvcWLaojfJOrKhG25evAPnd/Nnm+nHT3uoguQo4Fc4AV8SOMI8J7tgWY5O6rAdx9VbL+sMyCrpBMp33M7DzgZ8Bcd1+fLN4U/ssmEM7cuZVwAKYjrcCjZvYPd3+JcOS+qrp/xsjT3ZcQzg4pjNmWHGCqCpG25X7gPjPb6O6bCFMhT2cYdtkibcttZrbTzK519zXADcBLGYdeskjbsmAWVXQwu1ikPPcAh83sMnffkHyY7HP33VnGXglZH/BdADQBDxfNYX8f+CjhqHkT8AtgZUcDuPtxM5sL/DCZX9wJ3JlhzF2ROs8eINa2vBn4npn1BV4Fbs8w5q6ItS1vBH5gZg8BbwJ3ZBFsF8V8v44D/hk/xChivGfbzOxDwHeS9+w+Qvv6Hk9dPUVEckhX+IqI5JCKv4hIDqn4i4jkkIq/iEgOqfiLiORQ5l09RaqZmT1HONf7MWCBu2/t5pBEKkLFX/LuagB3r8o2EyJZ0Xn+kltmtpRwwc+fgcnAxUAz8HXCJf9TCL34FwH3AAascvd7k/VvIPRj6p28boG7/7ayWYh0jYq/5JqZtQEtwO+BmwjFfy0w091fNrM1hBuVXEFo6/saMJbQ2vcp4Ap335s0D1sLTHD3A5XOQ6RcmvYROd12d385ebwNeCPpCb/HzN4EhgDvJTQFW1fUOuAEMAH4Y4XjFSmbir/I6Y60e36mzqMNwDp3n1tYkDQSK6UTpki306meknfHgcYurLceuMbMJgGY2XWEO8w1RYxNJDPa85e8e4rQIrysG4+7+ytmNh9YkdzY4xgwW/P90lPogK+ISA5p2kdEJIdU/EVEckjFX0Qkh1T8RURySMVfRCSHVPxFRHJIxV9EJIdU/EVEcuj/28Dn4YeBtBEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Resampling at lower frequency\n",
    "#plot the average of price in the previous year and price at the end of the year\n",
    "weighted_Price = train['Weighted_Price']\n",
    "#print(weighted_Price)\n",
    "weighted_Price.plot(alpha=0.5, style='-')\n",
    "weighted_Price.resample('BA').mean().plot(style='*') # avg of previous year\n",
    "weighted_Price.asfreq('BA').plot(style='--') # value at the end of the year\n",
    "plt.yscale('log')\n",
    "plt.title(\"logarithmic scale\")\n",
    "plt.legend(['Weighted_Price', 'resample', 'asfreq'], \n",
    "           loc='upper left')\n",
    "# 'resample'-- average of the previous year\n",
    "# 'asfreq' -- value at the end of the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#The Exponentially Weighted Moving Average (EWMA) is a statistic for monitoring the process that averages the data in a way \n",
    "#that gives less and less weight to data\n",
    "rolling = pd.DataFrame.ewm(weighted_Price, com=200)\n",
    "# center of mass : \n",
    "data = pd.DataFrame({'input': weighted_Price, \n",
    "                     '200days rolling_mean': rolling.mean(), \n",
    "                     '200days rolling_std': rolling.std()}) # moving avg : smoothning the data due to fluationi\n",
    "\n",
    "ax = data.plot(style=['-', '--', ':'])\n",
    "ax.set_yscale('log')\n",
    "ax.set_title(\"EMA on log scale\")\n",
    "ax.lines[0].set_alpha(0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6312.6175 6321.115  6362.325  6392.115  6376.505  6423.42   6445.82\n",
      " 6452.155  6512.195  6489.4225 6408.6175 6392.6125 6386.2825 6392.385\n",
      " 6354.815  5981.5675 5613.955  5599.545  5570.655  5613.1275 5199.4675\n",
      " 4593.31   4509.995  4462.9125 4310.1925 4092.6825 3893.5575 3878.14\n",
      " 3774.9425 4077.0125 4281.32   4135.9725 4114.85   4184.505  3992.365\n",
      " 3918.3625 3841.74   3643.515  3432.465  3444.835  3552.2475 3530.565\n",
      " 3425.185  3453.115  3389.3175 3263.7    3227.32   3258.7775 3420.5675\n",
      " 3618.3725 3778.915  3953.135  4028.845  3963.405  4035.7275 4099.8875\n",
      " 3936.535  3838.575  3751.545  3807.5225 3880.86   3844.8125 3812.34\n",
      " 3815.8075 3914.26   3885.2875 3848.8625 3874.375  3983.1225 4071.805\n",
      " 4059.4125 4048.75   3858.1625 3670.8625 3659.225  3604.765  3637.1375\n",
      " 3659.9825 3645.245  3652.8725 3659.795  3705.115  3645.3525 3568.6675\n",
      " 3570.855  3587.3    3583.35   3582.0625 3608.6025 3569.715  3497.155\n",
      " 3426.015  3442.5775 3455.9875 3443.3175 3482.665  3474.2875 3445.2175\n",
      " 3435.6125 3416.03   3386.465  3533.445  3650.5275 3659.545  3648.3125\n",
      " 3612.91   3617.295  3595.42   3599.7025 3610.63   3650.6275 3799.3375\n",
      " 3932.56   3947.3775 3959.4775 3958.595  4068.3725 3974.4275 3818.43\n",
      " 3826.9875 3799.9925 3840.4875 3835.91   3827.885  3824.1425 3769.935\n",
      " 3804.8675 3876.21   3880.6375 3871.9075 3912.9175 3930.9025 3899.0425\n",
      " 3873.8475 3879.58   3876.78   3904.9475 3987.045  4006.59   3997.5375\n",
      " 4004.375  4035.3325 4022.375  3999.995  4002.47   3995.4925 3952.9075\n",
      " 3928.1225 3993.8275 4035.89   4075.485  4110.815  4114.76   4127.635\n",
      " 4577.5925 5012.7025 4940.425  4983.0825 5070.6675 5142.1825 5246.09\n",
      " 5237.1525 5289.8625 5174.6175 5044.0725 5086.8275 5116.0675 5091.87\n",
      " 5127.8175 5227.66   5269.195  5287.6175 5315.69   5303.32   5352.725\n",
      " 5482.9775 5504.415  5292.6375 5215.72   5234.7475 5259.51   5243.1825\n",
      " 5296.3675 5376.0125 5452.175  5657.695  5772.7425 5798.365  5752.5325\n",
      " 5836.8625 5900.9025 6088.5575 6277.0075 6825.4025 7125.9525 7446.29\n",
      " 7928.7125 8069.2925 8022.31   7375.0025]\n"
     ]
    }
   ],
   "source": [
    "#autoregression: \n",
    "from statsmodels.tsa.ar_model import AR\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "test = test['Weighted_Price'].values\n",
    "\n",
    "print(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pr = train['Weighted_Price'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lag: 25\n",
      "Coefficients: [ 6.25114175e+00  1.46861161e+00 -6.65423642e-01  2.37682327e-01\n",
      " -2.25056656e-02  2.50425573e-02 -2.26259402e-02 -7.79930430e-02\n",
      "  1.39904427e-01 -6.78578033e-02  6.80165322e-02 -1.47957323e-01\n",
      "  1.90448290e-02  2.16639284e-02 -5.02778854e-02  7.78420886e-02\n",
      " -7.87418426e-02  1.50985843e-01 -8.93171759e-02  9.98227236e-02\n",
      " -3.03155396e-03 -8.02117708e-02  4.02654866e-04 -6.31805537e-03\n",
      " -3.09615410e-02  3.23814449e-02]\n",
      "Test MSE 2543200.917\n"
     ]
    }
   ],
   "source": [
    "#train and fit autoregression\n",
    "model = AR(train_pr)\n",
    "model_fit = model.fit()\n",
    "\n",
    "print(\"Lag: %s\" % model_fit.k_ar)\n",
    "print(\"Coefficients: %s\" % model_fit.params)\n",
    "\n",
    "pred = model_fit.predict(start=len(train), end=len(train_pr)+len(test)-1, dynamic=False)\n",
    "mse = mean_squared_error(test, pred)\n",
    "print(\"Test MSE {0:.3f}\".format(mse))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12b402470>"
      ]
     },
     "execution_count": 186,
     "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": [
    "plt.plot(test, label='true value')\n",
    "plt.plot(pred, color='red', label='prediction')\n",
    "plt.title(\"Autoregressive model\")\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-1.182925491529332\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-189-4943f3ee00d6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Weighted_Price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "\n",
    "print(r2_score(test, pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "\n",
    "# data params\n",
    "window_len = 7\n",
    "test_size = 0.1\n",
    "zero_base = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "shapes (1776,26) and (200,) not aligned: 26 (dim 1) != 200 (dim 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-194-babe779a836c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/test_jupyter/lib/python3.7/site-packages/statsmodels/tsa/ar_model.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, params, start, end, dynamic)\u001b[0m\n\u001b[1;32m    225\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m         \u001b[0;31m# just do the whole thing and truncate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 227\u001b[0;31m         \u001b[0mfittedvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    228\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    229\u001b[0m         \u001b[0mpv_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mk_ar\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: shapes (1776,26) and (200,) not aligned: 26 (dim 1) != 200 (dim 0)"
     ]
    }
   ],
   "source": [
    "preds = model.predict(test).squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'preds' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-193-7779601f2902>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mwindow_len\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mpreds\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpreds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mline_plot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtargets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'actual'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'prediction'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlw\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'preds' is not defined"
     ]
    }
   ],
   "source": [
    "preds = test[:-window_len] * (preds + 1)\n",
    "preds = pd.Series(index=targets.index, data=preds)\n",
    "\n",
    "line_plot(targets, preds, 'actual', 'prediction', lw=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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