AR_MA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3161052</th>\n",
       "      <td>1515369360</td>\n",
       "      <td>16167.98</td>\n",
       "      <td>16179.65</td>\n",
       "      <td>16154.59</td>\n",
       "      <td>16154.60</td>\n",
       "      <td>2.887541</td>\n",
       "      <td>46660.414604</td>\n",
       "      <td>16159.221330</td>\n",
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       "    <tr>\n",
       "      <th>3161053</th>\n",
       "      <td>1515369420</td>\n",
       "      <td>16178.07</td>\n",
       "      <td>16178.07</td>\n",
       "      <td>16142.13</td>\n",
       "      <td>16176.43</td>\n",
       "      <td>1.014694</td>\n",
       "      <td>16402.389804</td>\n",
       "      <td>16164.858364</td>\n",
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       "    <tr>\n",
       "      <th>3161054</th>\n",
       "      <td>1515369480</td>\n",
       "      <td>16131.19</td>\n",
       "      <td>16176.10</td>\n",
       "      <td>16114.30</td>\n",
       "      <td>16114.30</td>\n",
       "      <td>16.897225</td>\n",
       "      <td>272434.236630</td>\n",
       "      <td>16123.016402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3161055</th>\n",
       "      <td>1515369540</td>\n",
       "      <td>16169.82</td>\n",
       "      <td>16189.43</td>\n",
       "      <td>16114.49</td>\n",
       "      <td>16124.02</td>\n",
       "      <td>6.384757</td>\n",
       "      <td>103222.040090</td>\n",
       "      <td>16166.949978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3161056</th>\n",
       "      <td>1515369600</td>\n",
       "      <td>16173.98</td>\n",
       "      <td>16176.96</td>\n",
       "      <td>16102.05</td>\n",
       "      <td>16166.78</td>\n",
       "      <td>25.633791</td>\n",
       "      <td>413278.394640</td>\n",
       "      <td>16122.406640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "          Timestamp      Open      High       Low     Close  Volume_(BTC)  \\\n",
       "3161052  1515369360  16167.98  16179.65  16154.59  16154.60      2.887541   \n",
       "3161053  1515369420  16178.07  16178.07  16142.13  16176.43      1.014694   \n",
       "3161054  1515369480  16131.19  16176.10  16114.30  16114.30     16.897225   \n",
       "3161055  1515369540  16169.82  16189.43  16114.49  16124.02      6.384757   \n",
       "3161056  1515369600  16173.98  16176.96  16102.05  16166.78     25.633791   \n",
       "\n",
       "         Volume_(Currency)  Weighted_Price  \n",
       "3161052       46660.414604    16159.221330  \n",
       "3161053       16402.389804    16164.858364  \n",
       "3161054      272434.236630    16123.016402  \n",
       "3161055      103222.040090    16166.949978  \n",
       "3161056      413278.394640    16122.406640  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Users/User/Desktop/bitstampUSD_1-min_data_2012-01-01_to_2018-01-08.csv')\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('C:/Users/User/Desktop/bitstampUSD_1-min_data_2012-01-01_to_2018-01-08.csv')\n",
    "# 2017 jan\n",
    "# 2017 oct - 8th jan\n",
    "train=df[2490000:3017056] #from 01-10-2016 - 30-09-17\n",
    "test=df[3017057:] # 01-10-2017 - 08-01-2018\n",
    "df.Timestamp = pd.to_datetime(df.Timestamp, unit='s')\n",
    "df.index = df.Timestamp\n",
    "df = df.resample('D').mean() # aggregating on daily basis\n",
    "  \n",
    "#df = df.resample('M').mean()\n",
    "train.Timestamp = pd.to_datetime(train.Timestamp,unit='s') \n",
    "train.index = train.Timestamp # set timestamp as the index\n",
    "train = train.resample('D').mean() \n",
    "test.Timestamp = pd.to_datetime(test.Timestamp,unit='s') \n",
    "test.index = test.Timestamp \n",
    "test = test.resample('D').mean()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
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       "    <tr>\n",
       "      <th>Timestamp</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>2016-09-28</th>\n",
       "      <td>603.745625</td>\n",
       "      <td>603.758750</td>\n",
       "      <td>603.565625</td>\n",
       "      <td>603.755625</td>\n",
       "      <td>0.548430</td>\n",
       "      <td>330.769106</td>\n",
       "      <td>603.612671</td>\n",
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       "    <tr>\n",
       "      <th>2016-09-29</th>\n",
       "      <td>603.928257</td>\n",
       "      <td>604.016049</td>\n",
       "      <td>603.805285</td>\n",
       "      <td>603.917486</td>\n",
       "      <td>2.856273</td>\n",
       "      <td>1724.681035</td>\n",
       "      <td>603.911641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-30</th>\n",
       "      <td>605.035618</td>\n",
       "      <td>605.128535</td>\n",
       "      <td>604.915674</td>\n",
       "      <td>605.013181</td>\n",
       "      <td>2.601640</td>\n",
       "      <td>1573.906429</td>\n",
       "      <td>605.017242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-01</th>\n",
       "      <td>611.691229</td>\n",
       "      <td>611.753257</td>\n",
       "      <td>611.641500</td>\n",
       "      <td>611.703965</td>\n",
       "      <td>1.979285</td>\n",
       "      <td>1209.742195</td>\n",
       "      <td>611.695313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-02</th>\n",
       "      <td>609.314528</td>\n",
       "      <td>609.422757</td>\n",
       "      <td>609.196382</td>\n",
       "      <td>609.317750</td>\n",
       "      <td>1.497508</td>\n",
       "      <td>911.680615</td>\n",
       "      <td>609.294611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                  Open        High         Low       Close  Volume_(BTC)  \\\n",
       "Timestamp                                                                  \n",
       "2016-09-28  603.745625  603.758750  603.565625  603.755625      0.548430   \n",
       "2016-09-29  603.928257  604.016049  603.805285  603.917486      2.856273   \n",
       "2016-09-30  605.035618  605.128535  604.915674  605.013181      2.601640   \n",
       "2016-10-01  611.691229  611.753257  611.641500  611.703965      1.979285   \n",
       "2016-10-02  609.314528  609.422757  609.196382  609.317750      1.497508   \n",
       "\n",
       "            Volume_(Currency)  Weighted_Price  \n",
       "Timestamp                                      \n",
       "2016-09-28         330.769106      603.612671  \n",
       "2016-09-29        1724.681035      603.911641  \n",
       "2016-09-30        1573.906429      605.017242  \n",
       "2016-10-01        1209.742195      611.695313  \n",
       "2016-10-02         911.680615      609.294611  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
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       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-09-30</th>\n",
       "      <td>4270.216074</td>\n",
       "      <td>4271.955490</td>\n",
       "      <td>4268.330188</td>\n",
       "      <td>4270.286637</td>\n",
       "      <td>5.323201</td>\n",
       "      <td>22772.951624</td>\n",
       "      <td>4270.099044</td>\n",
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       "    <tr>\n",
       "      <th>2017-10-01</th>\n",
       "      <td>4290.879264</td>\n",
       "      <td>4292.513625</td>\n",
       "      <td>4288.988646</td>\n",
       "      <td>4290.900590</td>\n",
       "      <td>5.205305</td>\n",
       "      <td>22284.395236</td>\n",
       "      <td>4290.509618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-02</th>\n",
       "      <td>4395.595528</td>\n",
       "      <td>4397.706764</td>\n",
       "      <td>4392.807063</td>\n",
       "      <td>4395.493111</td>\n",
       "      <td>5.838207</td>\n",
       "      <td>25652.542492</td>\n",
       "      <td>4395.341097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-03</th>\n",
       "      <td>4311.938333</td>\n",
       "      <td>4314.145292</td>\n",
       "      <td>4309.140639</td>\n",
       "      <td>4311.992896</td>\n",
       "      <td>8.717987</td>\n",
       "      <td>37447.731893</td>\n",
       "      <td>4311.533988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-10-04</th>\n",
       "      <td>4249.836465</td>\n",
       "      <td>4251.627278</td>\n",
       "      <td>4247.174708</td>\n",
       "      <td>4249.643597</td>\n",
       "      <td>6.008724</td>\n",
       "      <td>25445.996763</td>\n",
       "      <td>4249.380869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close  Volume_(BTC)  \\\n",
       "Timestamp                                                                      \n",
       "2017-09-30  4270.216074  4271.955490  4268.330188  4270.286637      5.323201   \n",
       "2017-10-01  4290.879264  4292.513625  4288.988646  4290.900590      5.205305   \n",
       "2017-10-02  4395.595528  4397.706764  4392.807063  4395.493111      5.838207   \n",
       "2017-10-03  4311.938333  4314.145292  4309.140639  4311.992896      8.717987   \n",
       "2017-10-04  4249.836465  4251.627278  4247.174708  4249.643597      6.008724   \n",
       "\n",
       "            Volume_(Currency)  Weighted_Price  \n",
       "Timestamp                                      \n",
       "2017-09-30       22772.951624     4270.099044  \n",
       "2017-10-01       22284.395236     4290.509618  \n",
       "2017-10-02       25652.542492     4395.341097  \n",
       "2017-10-03       37447.731893     4311.533988  \n",
       "2017-10-04       25445.996763     4249.380869  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(367, 7)\n",
      "(101, 7)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume_(BTC)</th>\n",
       "      <th>Volume_(Currency)</th>\n",
       "      <th>Weighted_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>367.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1768.433039</td>\n",
       "      <td>1769.630966</td>\n",
       "      <td>1767.060537</td>\n",
       "      <td>1768.416072</td>\n",
       "      <td>7.505846</td>\n",
       "      <td>15695.012743</td>\n",
       "      <td>1768.322545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1178.022648</td>\n",
       "      <td>1179.164353</td>\n",
       "      <td>1176.595672</td>\n",
       "      <td>1177.956286</td>\n",
       "      <td>5.016457</td>\n",
       "      <td>17419.789097</td>\n",
       "      <td>1177.865228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>603.745625</td>\n",
       "      <td>603.758750</td>\n",
       "      <td>603.565625</td>\n",
       "      <td>603.755625</td>\n",
       "      <td>0.548430</td>\n",
       "      <td>330.769106</td>\n",
       "      <td>603.612671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>863.116208</td>\n",
       "      <td>863.799983</td>\n",
       "      <td>862.605733</td>\n",
       "      <td>863.164142</td>\n",
       "      <td>4.081938</td>\n",
       "      <td>3769.836589</td>\n",
       "      <td>863.198405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1192.429083</td>\n",
       "      <td>1192.846611</td>\n",
       "      <td>1192.103542</td>\n",
       "      <td>1192.545056</td>\n",
       "      <td>6.357870</td>\n",
       "      <td>7669.417184</td>\n",
       "      <td>1192.500536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2557.112472</td>\n",
       "      <td>2560.104007</td>\n",
       "      <td>2555.293427</td>\n",
       "      <td>2557.028503</td>\n",
       "      <td>9.348044</td>\n",
       "      <td>21981.409659</td>\n",
       "      <td>2557.067577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4801.826097</td>\n",
       "      <td>4804.446132</td>\n",
       "      <td>4797.498319</td>\n",
       "      <td>4801.706236</td>\n",
       "      <td>41.870791</td>\n",
       "      <td>141124.036343</td>\n",
       "      <td>4801.471753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close  Volume_(BTC)  \\\n",
       "count   367.000000   367.000000   367.000000   367.000000    367.000000   \n",
       "mean   1768.433039  1769.630966  1767.060537  1768.416072      7.505846   \n",
       "std    1178.022648  1179.164353  1176.595672  1177.956286      5.016457   \n",
       "min     603.745625   603.758750   603.565625   603.755625      0.548430   \n",
       "25%     863.116208   863.799983   862.605733   863.164142      4.081938   \n",
       "50%    1192.429083  1192.846611  1192.103542  1192.545056      6.357870   \n",
       "75%    2557.112472  2560.104007  2555.293427  2557.028503      9.348044   \n",
       "max    4801.826097  4804.446132  4797.498319  4801.706236     41.870791   \n",
       "\n",
       "       Volume_(Currency)  Weighted_Price  \n",
       "count         367.000000      367.000000  \n",
       "mean        15695.012743     1768.322545  \n",
       "std         17419.789097     1177.865228  \n",
       "min           330.769106      603.612671  \n",
       "25%          3769.836589      863.198405  \n",
       "50%          7669.417184     1192.500536  \n",
       "75%         21981.409659     2557.067577  \n",
       "max        141124.036343     4801.471753  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open                 False\n",
       "High                 False\n",
       "Low                  False\n",
       "Close                False\n",
       "Volume_(BTC)         False\n",
       "Volume_(Currency)    False\n",
       "Weighted_Price       False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.isnull().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24d2423ff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_hat_avg = test.copy()\n",
    "y_hat_avg['moving_avg_forecast'] = train['Weighted_Price'].rolling(60).mean().iloc[-1] # SELECTING DATA FROM ROW NUMBERS\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": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x24d22803828>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24d22803470>"
      ]
     },
     "metadata": {},
     "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": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: pd.ewm_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.ewm(com=200,min_periods=0,adjust=True,ignore_na=False).mean()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24d22fde438>"
      ]
     },
     "metadata": {},
     "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.ewma(weighted_Price, com=200) # center of mass : \n",
    "\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": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 4270.09904362  4290.50961825  4395.34109711  4311.53398815\n",
      "  4249.38086932  4255.14117063  4359.02571159  4351.11523003\n",
      "  4495.68274331  4643.25831011  4811.40362944  4786.87981491\n",
      "  5114.22674297  5617.72855388  5665.83325869  5609.70521025\n",
      "  5673.30299676  5625.28140199  5401.84142245  5653.87709158\n",
      "  5788.39517381  6072.98628534  5909.35466505  5868.55444039\n",
      "  5658.96718301  5547.13609711  5826.22649318  5822.06714383\n",
      "  5749.52172552  5883.18983378  6122.31554456  6239.75223108\n",
      "  6501.9271264   6951.55011034  7239.98579691  7257.38299011\n",
      "  7457.70186427  7227.18831946  7102.41136848  7386.435501\n",
      "  7244.03997836  6961.08064607  6555.82914952  6113.8012755\n",
      "  6346.71495066  6590.1457225   7045.89127116  7459.01140241\n",
      "  7826.66995447  7696.52895975  7825.42031561  8089.82798336\n",
      "  8157.12061593  8194.20787954  8175.02189417  8167.54723751\n",
      "  8428.59055039  8971.59499367  9555.69705504  9832.17904349\n",
      " 10402.34334151  9887.5433868  10193.94721008 10937.45981194\n",
      " 11290.71944172 11345.14458862 11655.13542927 12577.9303519\n",
      " 14775.0590484  15429.64789883 14804.00010606 14132.60171064\n",
      " 16200.09235479 16729.84828177 16539.76072436 16340.21688416\n",
      " 17364.19043025 18173.93275387 19186.16961026 18716.81145948\n",
      " 18318.28161549 16831.69035647 16219.34143154 13759.47920588\n",
      " 14765.47055603 13652.62840629 13964.73595414 15242.6077966\n",
      " 15579.67456149 14246.32680928 14533.09224733 13207.97774421\n",
      " 13337.51323221 13424.16341043 13877.57991055 14962.37907754\n",
      " 14816.62546784 15790.44614532 16665.26880774 16516.38596171\n",
      " 16122.40664   ]\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": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lag: 17\n",
      "Coefficients: [ 1.40579501e+00  1.28416421e+00 -3.59431363e-01  1.55264852e-03\n",
      "  1.15429062e-01 -9.35948809e-02  2.33268318e-02  1.60494782e-01\n",
      " -1.75698848e-01  2.56073311e-02  6.66696261e-02 -6.24868195e-02\n",
      "  5.80841061e-02 -5.11266516e-02  1.24065400e-03  2.84771150e-03\n",
      "  9.89608734e-03 -1.73333988e-03]\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": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x26012b409b0>"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26012ae9438>"
      ]
     },
     "metadata": {},
     "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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}