Rolling Mean & Standard Deviation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculating Simple Moving Average & Standard Deviation\n",
    "### Pandas Rolling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing data\n",
    "df = pd.read_csv('Desktop\\RELIANCE.NS.csv',index_col ='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11-10-2018</th>\n",
       "      <td>1064.000000</td>\n",
       "      <td>1114.750000</td>\n",
       "      <td>1043.000000</td>\n",
       "      <td>1087.800049</td>\n",
       "      <td>1087.800049</td>\n",
       "      <td>13224172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12-10-2018</th>\n",
       "      <td>1104.949951</td>\n",
       "      <td>1129.000000</td>\n",
       "      <td>1101.900024</td>\n",
       "      <td>1126.550049</td>\n",
       "      <td>1126.550049</td>\n",
       "      <td>9058536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-10-2018</th>\n",
       "      <td>1129.900024</td>\n",
       "      <td>1146.900024</td>\n",
       "      <td>1118.150024</td>\n",
       "      <td>1139.750000</td>\n",
       "      <td>1139.750000</td>\n",
       "      <td>8871776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16-10-2018</th>\n",
       "      <td>1146.000000</td>\n",
       "      <td>1167.400024</td>\n",
       "      <td>1140.000000</td>\n",
       "      <td>1163.800049</td>\n",
       "      <td>1163.800049</td>\n",
       "      <td>7478601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17-10-2018</th>\n",
       "      <td>1179.000000</td>\n",
       "      <td>1179.349976</td>\n",
       "      <td>1140.000000</td>\n",
       "      <td>1151.300049</td>\n",
       "      <td>1151.300049</td>\n",
       "      <td>12785646</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Open         High          Low        Close    Adj Close  \\\n",
       "Date                                                                          \n",
       "11-10-2018  1064.000000  1114.750000  1043.000000  1087.800049  1087.800049   \n",
       "12-10-2018  1104.949951  1129.000000  1101.900024  1126.550049  1126.550049   \n",
       "15-10-2018  1129.900024  1146.900024  1118.150024  1139.750000  1139.750000   \n",
       "16-10-2018  1146.000000  1167.400024  1140.000000  1163.800049  1163.800049   \n",
       "17-10-2018  1179.000000  1179.349976  1140.000000  1151.300049  1151.300049   \n",
       "\n",
       "              Volume  \n",
       "Date                  \n",
       "11-10-2018  13224172  \n",
       "12-10-2018   9058536  \n",
       "15-10-2018   8871776  \n",
       "16-10-2018   7478601  \n",
       "17-10-2018  12785646  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating 30 days moving average\n",
    "df['30_MA_Close'] = df['Close'].rolling(window=30).mean()\n",
    "#calculating 20 days rolling standard devtaion\n",
    "df['20_std_Close'] = df['Close'].rolling(window=20).std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>30_MA_Close</th>\n",
       "      <th>20_std_Close</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01-01-2018</th>\n",
       "      <td>922.700012</td>\n",
       "      <td>922.700012</td>\n",
       "      <td>907.500000</td>\n",
       "      <td>909.750000</td>\n",
       "      <td>904.174133</td>\n",
       "      <td>4321686</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02-01-2018</th>\n",
       "      <td>913.000000</td>\n",
       "      <td>919.549988</td>\n",
       "      <td>906.400024</td>\n",
       "      <td>911.150024</td>\n",
       "      <td>905.565552</td>\n",
       "      <td>4342815</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03-01-2018</th>\n",
       "      <td>925.000000</td>\n",
       "      <td>926.000000</td>\n",
       "      <td>913.049988</td>\n",
       "      <td>914.799988</td>\n",
       "      <td>909.193176</td>\n",
       "      <td>6175312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04-01-2018</th>\n",
       "      <td>918.150024</td>\n",
       "      <td>921.799988</td>\n",
       "      <td>915.700012</td>\n",
       "      <td>920.299988</td>\n",
       "      <td>914.659424</td>\n",
       "      <td>4118581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-01-2018</th>\n",
       "      <td>921.799988</td>\n",
       "      <td>926.900024</td>\n",
       "      <td>920.250000</td>\n",
       "      <td>923.250000</td>\n",
       "      <td>917.591370</td>\n",
       "      <td>3401905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-01-2018</th>\n",
       "      <td>926.099976</td>\n",
       "      <td>931.000000</td>\n",
       "      <td>923.500000</td>\n",
       "      <td>928.549988</td>\n",
       "      <td>922.858887</td>\n",
       "      <td>4035417</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09-01-2018</th>\n",
       "      <td>928.150024</td>\n",
       "      <td>943.900024</td>\n",
       "      <td>924.000000</td>\n",
       "      <td>940.950012</td>\n",
       "      <td>935.182922</td>\n",
       "      <td>6534997</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-01-2018</th>\n",
       "      <td>943.000000</td>\n",
       "      <td>947.400024</td>\n",
       "      <td>935.500000</td>\n",
       "      <td>942.349976</td>\n",
       "      <td>936.574280</td>\n",
       "      <td>5361502</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11-01-2018</th>\n",
       "      <td>941.799988</td>\n",
       "      <td>942.650024</td>\n",
       "      <td>935.000000</td>\n",
       "      <td>937.750000</td>\n",
       "      <td>932.002502</td>\n",
       "      <td>3588377</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12-01-2018</th>\n",
       "      <td>943.000000</td>\n",
       "      <td>952.799988</td>\n",
       "      <td>938.250000</td>\n",
       "      <td>949.000000</td>\n",
       "      <td>943.183533</td>\n",
       "      <td>6890028</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15-01-2018</th>\n",
       "      <td>950.000000</td>\n",
       "      <td>958.500000</td>\n",
       "      <td>945.200012</td>\n",
       "      <td>949.150024</td>\n",
       "      <td>943.332642</td>\n",
       "      <td>5084113</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16-01-2018</th>\n",
       "      <td>947.700012</td>\n",
       "      <td>947.700012</td>\n",
       "      <td>920.000000</td>\n",
       "      <td>922.950012</td>\n",
       "      <td>917.293213</td>\n",
       "      <td>4948895</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17-01-2018</th>\n",
       "      <td>926.000000</td>\n",
       "      <td>929.000000</td>\n",
       "      <td>907.000000</td>\n",
       "      <td>924.500000</td>\n",
       "      <td>918.833740</td>\n",
       "      <td>6036432</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18-01-2018</th>\n",
       "      <td>929.000000</td>\n",
       "      <td>929.599976</td>\n",
       "      <td>915.099976</td>\n",
       "      <td>919.700012</td>\n",
       "      <td>914.063171</td>\n",
       "      <td>4289053</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19-01-2018</th>\n",
       "      <td>923.000000</td>\n",
       "      <td>934.400024</td>\n",
       "      <td>922.099976</td>\n",
       "      <td>931.299988</td>\n",
       "      <td>925.592041</td>\n",
       "      <td>4559564</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22-01-2018</th>\n",
       "      <td>948.000000</td>\n",
       "      <td>974.299988</td>\n",
       "      <td>940.000000</td>\n",
       "      <td>971.500000</td>\n",
       "      <td>965.545654</td>\n",
       "      <td>20892838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23-01-2018</th>\n",
       "      <td>975.000000</td>\n",
       "      <td>990.950012</td>\n",
       "      <td>975.000000</td>\n",
       "      <td>983.250000</td>\n",
       "      <td>977.223633</td>\n",
       "      <td>10298500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24-01-2018</th>\n",
       "      <td>981.750000</td>\n",
       "      <td>981.750000</td>\n",
       "      <td>962.650024</td>\n",
       "      <td>966.349976</td>\n",
       "      <td>960.427185</td>\n",
       "      <td>6532672</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25-01-2018</th>\n",
       "      <td>967.900024</td>\n",
       "      <td>972.000000</td>\n",
       "      <td>956.150024</td>\n",
       "      <td>965.900024</td>\n",
       "      <td>959.979980</td>\n",
       "      <td>6249419</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29-01-2018</th>\n",
       "      <td>966.200012</td>\n",
       "      <td>977.299988</td>\n",
       "      <td>959.299988</td>\n",
       "      <td>964.500000</td>\n",
       "      <td>958.588562</td>\n",
       "      <td>4524605</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.979359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-01-2018</th>\n",
       "      <td>965.799988</td>\n",
       "      <td>965.799988</td>\n",
       "      <td>949.000000</td>\n",
       "      <td>950.400024</td>\n",
       "      <td>944.575012</td>\n",
       "      <td>4198139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.004921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31-01-2018</th>\n",
       "      <td>950.000000</td>\n",
       "      <td>964.500000</td>\n",
       "      <td>941.549988</td>\n",
       "      <td>961.299988</td>\n",
       "      <td>955.408142</td>\n",
       "      <td>5738209</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.248821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01-02-2018</th>\n",
       "      <td>963.250000</td>\n",
       "      <td>972.599976</td>\n",
       "      <td>936.599976</td>\n",
       "      <td>943.849976</td>\n",
       "      <td>938.065125</td>\n",
       "      <td>7554587</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.099536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02-02-2018</th>\n",
       "      <td>935.000000</td>\n",
       "      <td>943.950012</td>\n",
       "      <td>901.000000</td>\n",
       "      <td>904.349976</td>\n",
       "      <td>898.807190</td>\n",
       "      <td>13242095</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.462498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05-02-2018</th>\n",
       "      <td>894.000000</td>\n",
       "      <td>912.500000</td>\n",
       "      <td>881.799988</td>\n",
       "      <td>902.150024</td>\n",
       "      <td>896.620728</td>\n",
       "      <td>8773525</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.071599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06-02-2018</th>\n",
       "      <td>877.000000</td>\n",
       "      <td>898.000000</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>892.099976</td>\n",
       "      <td>886.632263</td>\n",
       "      <td>11000567</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.677522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07-02-2018</th>\n",
       "      <td>902.549988</td>\n",
       "      <td>905.400024</td>\n",
       "      <td>890.000000</td>\n",
       "      <td>894.400024</td>\n",
       "      <td>888.918213</td>\n",
       "      <td>7100838</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.802586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08-02-2018</th>\n",
       "      <td>897.900024</td>\n",
       "      <td>910.650024</td>\n",
       "      <td>892.349976</td>\n",
       "      <td>904.549988</td>\n",
       "      <td>899.005981</td>\n",
       "      <td>6320734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.853988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09-02-2018</th>\n",
       "      <td>889.000000</td>\n",
       "      <td>902.000000</td>\n",
       "      <td>886.000000</td>\n",
       "      <td>897.849976</td>\n",
       "      <td>892.347046</td>\n",
       "      <td>5294279</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.190319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12-02-2018</th>\n",
       "      <td>903.000000</td>\n",
       "      <td>918.200012</td>\n",
       "      <td>902.049988</td>\n",
       "      <td>915.500000</td>\n",
       "      <td>909.888855</td>\n",
       "      <td>5429792</td>\n",
       "      <td>931.446665</td>\n",
       "      <td>29.302752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14-02-2018</th>\n",
       "      <td>923.000000</td>\n",
       "      <td>942.500000</td>\n",
       "      <td>921.000000</td>\n",
       "      <td>933.650024</td>\n",
       "      <td>927.927673</td>\n",
       "      <td>10170959</td>\n",
       "      <td>932.243333</td>\n",
       "      <td>29.064867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close   Adj Close  \\\n",
       "Date                                                                     \n",
       "01-01-2018  922.700012  922.700012  907.500000  909.750000  904.174133   \n",
       "02-01-2018  913.000000  919.549988  906.400024  911.150024  905.565552   \n",
       "03-01-2018  925.000000  926.000000  913.049988  914.799988  909.193176   \n",
       "04-01-2018  918.150024  921.799988  915.700012  920.299988  914.659424   \n",
       "05-01-2018  921.799988  926.900024  920.250000  923.250000  917.591370   \n",
       "08-01-2018  926.099976  931.000000  923.500000  928.549988  922.858887   \n",
       "09-01-2018  928.150024  943.900024  924.000000  940.950012  935.182922   \n",
       "10-01-2018  943.000000  947.400024  935.500000  942.349976  936.574280   \n",
       "11-01-2018  941.799988  942.650024  935.000000  937.750000  932.002502   \n",
       "12-01-2018  943.000000  952.799988  938.250000  949.000000  943.183533   \n",
       "15-01-2018  950.000000  958.500000  945.200012  949.150024  943.332642   \n",
       "16-01-2018  947.700012  947.700012  920.000000  922.950012  917.293213   \n",
       "17-01-2018  926.000000  929.000000  907.000000  924.500000  918.833740   \n",
       "18-01-2018  929.000000  929.599976  915.099976  919.700012  914.063171   \n",
       "19-01-2018  923.000000  934.400024  922.099976  931.299988  925.592041   \n",
       "22-01-2018  948.000000  974.299988  940.000000  971.500000  965.545654   \n",
       "23-01-2018  975.000000  990.950012  975.000000  983.250000  977.223633   \n",
       "24-01-2018  981.750000  981.750000  962.650024  966.349976  960.427185   \n",
       "25-01-2018  967.900024  972.000000  956.150024  965.900024  959.979980   \n",
       "29-01-2018  966.200012  977.299988  959.299988  964.500000  958.588562   \n",
       "30-01-2018  965.799988  965.799988  949.000000  950.400024  944.575012   \n",
       "31-01-2018  950.000000  964.500000  941.549988  961.299988  955.408142   \n",
       "01-02-2018  963.250000  972.599976  936.599976  943.849976  938.065125   \n",
       "02-02-2018  935.000000  943.950012  901.000000  904.349976  898.807190   \n",
       "05-02-2018  894.000000  912.500000  881.799988  902.150024  896.620728   \n",
       "06-02-2018  877.000000  898.000000  871.000000  892.099976  886.632263   \n",
       "07-02-2018  902.549988  905.400024  890.000000  894.400024  888.918213   \n",
       "08-02-2018  897.900024  910.650024  892.349976  904.549988  899.005981   \n",
       "09-02-2018  889.000000  902.000000  886.000000  897.849976  892.347046   \n",
       "12-02-2018  903.000000  918.200012  902.049988  915.500000  909.888855   \n",
       "14-02-2018  923.000000  942.500000  921.000000  933.650024  927.927673   \n",
       "\n",
       "              Volume  30_MA_Close  20_std_Close  \n",
       "Date                                             \n",
       "01-01-2018   4321686          NaN           NaN  \n",
       "02-01-2018   4342815          NaN           NaN  \n",
       "03-01-2018   6175312          NaN           NaN  \n",
       "04-01-2018   4118581          NaN           NaN  \n",
       "05-01-2018   3401905          NaN           NaN  \n",
       "08-01-2018   4035417          NaN           NaN  \n",
       "09-01-2018   6534997          NaN           NaN  \n",
       "10-01-2018   5361502          NaN           NaN  \n",
       "11-01-2018   3588377          NaN           NaN  \n",
       "12-01-2018   6890028          NaN           NaN  \n",
       "15-01-2018   5084113          NaN           NaN  \n",
       "16-01-2018   4948895          NaN           NaN  \n",
       "17-01-2018   6036432          NaN           NaN  \n",
       "18-01-2018   4289053          NaN           NaN  \n",
       "19-01-2018   4559564          NaN           NaN  \n",
       "22-01-2018  20892838          NaN           NaN  \n",
       "23-01-2018  10298500          NaN           NaN  \n",
       "24-01-2018   6532672          NaN           NaN  \n",
       "25-01-2018   6249419          NaN           NaN  \n",
       "29-01-2018   4524605          NaN     21.979359  \n",
       "30-01-2018   4198139          NaN     21.004921  \n",
       "31-01-2018   5738209          NaN     20.248821  \n",
       "01-02-2018   7554587          NaN     19.099536  \n",
       "02-02-2018  13242095          NaN     20.462498  \n",
       "05-02-2018   8773525          NaN     22.071599  \n",
       "06-02-2018  11000567          NaN     24.677522  \n",
       "07-02-2018   7100838          NaN     26.802586  \n",
       "08-02-2018   6320734          NaN     27.853988  \n",
       "09-02-2018   5294279          NaN     29.190319  \n",
       "12-02-2018   5429792   931.446665     29.302752  \n",
       "14-02-2018  10170959   932.243333     29.064867  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22221e37b00>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22221eb92e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['Close','30_MA_Close']].plot(figsize=(10,5))"
   ]
  }
 ],
 "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
}