s&p.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import scipy\n",
    "import scipy.fftpack\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('./monthly.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = df.sort_values(by=\"Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# http://stackoverflow.com/questions/30132282/datetime-to-string-with-series-in-python-pandas\n",
    "df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
    "df[\"close_1yr\"] = pd.rolling_mean(df[\"Close\"], 12)\n",
    "df[\"close_2yr\"] = pd.rolling_mean(df[\"Close\"], 24)\n",
    "df[\"close_3yr\"] = pd.rolling_mean(df[\"Close\"], 36)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>close_1yr</th>\n",
       "      <th>close_2yr</th>\n",
       "      <th>close_3yr</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>794.000000</td>\n",
       "      <td>794.000000</td>\n",
       "      <td>794.000000</td>\n",
       "      <td>794.000000</td>\n",
       "      <td>7.940000e+02</td>\n",
       "      <td>794.000000</td>\n",
       "      <td>783.000000</td>\n",
       "      <td>771.000000</td>\n",
       "      <td>759.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>485.978299</td>\n",
       "      <td>501.992796</td>\n",
       "      <td>469.175214</td>\n",
       "      <td>488.300150</td>\n",
       "      <td>8.472439e+08</td>\n",
       "      <td>488.300150</td>\n",
       "      <td>480.952196</td>\n",
       "      <td>472.408615</td>\n",
       "      <td>464.506202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>559.956192</td>\n",
       "      <td>576.763892</td>\n",
       "      <td>540.683734</td>\n",
       "      <td>561.900927</td>\n",
       "      <td>1.519324e+09</td>\n",
       "      <td>561.900927</td>\n",
       "      <td>547.967422</td>\n",
       "      <td>530.355666</td>\n",
       "      <td>513.688322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.660000</td>\n",
       "      <td>17.090000</td>\n",
       "      <td>16.660000</td>\n",
       "      <td>17.049999</td>\n",
       "      <td>1.024300e+06</td>\n",
       "      <td>17.049999</td>\n",
       "      <td>18.430833</td>\n",
       "      <td>20.397917</td>\n",
       "      <td>21.810556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>83.932503</td>\n",
       "      <td>86.937500</td>\n",
       "      <td>81.487499</td>\n",
       "      <td>84.135002</td>\n",
       "      <td>8.476250e+06</td>\n",
       "      <td>84.135002</td>\n",
       "      <td>84.794999</td>\n",
       "      <td>87.103541</td>\n",
       "      <td>90.181528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>142.969993</td>\n",
       "      <td>149.620003</td>\n",
       "      <td>139.989998</td>\n",
       "      <td>146.680001</td>\n",
       "      <td>8.524640e+07</td>\n",
       "      <td>146.680001</td>\n",
       "      <td>146.346666</td>\n",
       "      <td>142.292083</td>\n",
       "      <td>140.995833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>962.012527</td>\n",
       "      <td>996.125000</td>\n",
       "      <td>902.275009</td>\n",
       "      <td>967.460007</td>\n",
       "      <td>8.896886e+08</td>\n",
       "      <td>967.460007</td>\n",
       "      <td>968.050418</td>\n",
       "      <td>998.278748</td>\n",
       "      <td>1030.870414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2108.639893</td>\n",
       "      <td>2134.719971</td>\n",
       "      <td>2067.929932</td>\n",
       "      <td>2107.389893</td>\n",
       "      <td>7.633306e+09</td>\n",
       "      <td>2107.389893</td>\n",
       "      <td>2053.950002</td>\n",
       "      <td>2006.952082</td>\n",
       "      <td>1906.211941</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close        Volume  \\\n",
       "count   794.000000   794.000000   794.000000   794.000000  7.940000e+02   \n",
       "mean    485.978299   501.992796   469.175214   488.300150  8.472439e+08   \n",
       "std     559.956192   576.763892   540.683734   561.900927  1.519324e+09   \n",
       "min      16.660000    17.090000    16.660000    17.049999  1.024300e+06   \n",
       "25%      83.932503    86.937500    81.487499    84.135002  8.476250e+06   \n",
       "50%     142.969993   149.620003   139.989998   146.680001  8.524640e+07   \n",
       "75%     962.012527   996.125000   902.275009   967.460007  8.896886e+08   \n",
       "max    2108.639893  2134.719971  2067.929932  2107.389893  7.633306e+09   \n",
       "\n",
       "         Adj Close    close_1yr    close_2yr    close_3yr  \n",
       "count   794.000000   783.000000   771.000000   759.000000  \n",
       "mean    488.300150   480.952196   472.408615   464.506202  \n",
       "std     561.900927   547.967422   530.355666   513.688322  \n",
       "min      17.049999    18.430833    20.397917    21.810556  \n",
       "25%      84.135002    84.794999    87.103541    90.181528  \n",
       "50%     146.680001   146.346666   142.292083   140.995833  \n",
       "75%     967.460007   968.050418   998.278748  1030.870414  \n",
       "max    2107.389893  2053.950002  2006.952082  1906.211941  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x112af8210>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5M5QXrVrRqm9fPK4JBnaPR9dZB88jnmhavFsjq+gF8wJSrVo1rK2tGTNmDImJiSQnJ3Py\n5MmXjmnRogW3b99m3bp1pKens2HDBm7evEnr1q158uQJ27dvJyEhAQ0NDfT09F5KFzdt2jSCgoKA\nzHyjmzZtemud0tPTSU5OJiMjg7S0NJKTk1GpVFnf9+7dmy1btrBmzRr69u2bj78NScq5DFUG1+NO\noTK9TennKazaDMeXKWjycyPiqlZD/P0v1A9l6ZmNVFlRFr8YXw4d6cWGw9ak/LUToz++oOnQMdSN\njsZr41/MuHcvz/Ms5h31pdGIlcw6soTuh6oTfOgnrp/YweNG9WnSpQutNqnoOCKBMr+XwWWeC2pa\nRTMki9fJbn9hEBoaKtq3by/MzMyEubm5GDZsmPD19RV16tTJOubEiRPC29tbGBkZicqVK4uTJ08K\nIYR49OiRqFevnjAyMhLGxsaiQYMG4saNG1nnrVq1Snh4eAhDQ0Nhb28v+vfv/9b69OvXTygUipc+\nK1aseOmYRo0aCWdn55f2HT16VNjb27+1/ML8LKTC78BtP6H8zk4MWva70B9cUkytjYjWMBbpEyeJ\n/qf3CvZtFeoH9gvz7TtEm9OnxZrISJGhUr3XOt2Oui00fjATnw3aLu42ai0Sq9cQE4KChKm/v/Ce\ns1B07jBFTKw4X+wzWSd+arRfTN4VlOOyL4UHiLIjhorzl1KFEEKkpqcKfMj8jDITq2p9KUJcSoth\n+/aLEgeOi/XtzonznudF4r3EXN0D8Nq/MHIGaDHXv39/bG1tmTRpUq7Plc9Celc3o27i/ps7AKaJ\ncGqhA871PUiav4TJIp4D0U+5drQzTqGa/Hp1OuJJOLedK6OeocS7sjnl21hjUNUANfX8aalO3raW\npFB3TqpNw2qVJhtCj3Jk8CAGNmpEy5ta9FguSLwTy26b3Vy1v4738yH0C9jDbbVuaHUxo+mCcqhp\nZl+Xe9H3KfVLSQAMAr7n/pKpHIvczoDZ0ynLWdr5VSOhYy3mt2vH0KdmNBn7AtOaRpRZWAalrjJX\n95Jd2jgZzIuxBw8eUKlSJQICAnB0dMz1+fJZSO9i/50DNF/TDJ6VZqzLItrMakR8iSG4+c+iR1AQ\nOkolyxWO3PjyKrFBUUQ66uFtEIlj9CVODhvEmROPaXhJiblQp8zCMpg0NHnnujyOf0LluW2IjL9J\nh+Bk+l5Rx8zciyk/TSbpjgbfr9JE/2EGTj86YdnLkucpL/h03Uh2hC/h3HxvxK8/cmyxETUfa1Fy\nrCNWvaxQ03g1qDf9vQ/nj1ly/ffvWNDVic/ueGAdeRmh0mR93x5MbtuRRppWDF2nQ8re55T+pTSW\nXS3f6Z6yC+YFKdt/QkiZjh8/LvT19V/5GBgYvPXccePGCX19fTFt2rR3vr58FtK7qP1ba4EPoknP\n60J8951QtW4j7sYnCKfTp8XUBw9E5MbH4oT5CRH2c5jwnNpC4INQ+7ySeO5cXoj160VEcrLwPH9e\nTPr9ijhld0oEfxssMlIz3nrdxNREcejiPRGfmCY2XNkq9L+tIixHIkZ6eohEu5JiS/t2otm6raLq\ncn+xvfFZcdLupAhfHP5K2c+TngvTSY7i63L9hGjfXky9f1/0X3ROXG54WZxyPCUe/v5QZCT/c05M\nUoxQ/1Ff/DQ/XoipU8UNa33RsgdC36eTsD90WtQ8d0H4zbwt/M38xZ3v7ojUmNQ8/X75gN0sbsAw\nwAzYDyx9QzB/tUKyNVhoyGch5ZZKqNCfZEGPmGsscdmKYt5c7h07RsOwML5zcKDjfjUeTHhA+R3l\nMahkQL0/WnL80V7sUhvh4VeJPcEr4JdfiOvUiSZXrtBdw5RGY18g0gXl/iqHukH2q3Z/9tcQlgf+\nBumaWCemMnOHBbWeZ7Bq0BjWN6iC9WNthq5SYnEhFccxDtgMtEGp/foujkmHZzJ/ZhjPzq0mIziY\nemFhtDYzY1CEMSGTQ4i6+Iyd7tFM2tGRT7YMYIt/EJF2PbBYMYuOs79hu6YWZlRkhYMTJb58hJqO\nGmUWl0HPTS/Pv+PsWubv49XpTeAroDvQ7D2UL0lSIZOQmsCKAxe5Gn4Lko0YII6gmDmDO3v3Uj8s\njDEODnS7qMX9H+5T8VBFDCplrjPSxbMVPKzGkIadOGwbzdgBnYj8/Auul6nAUhMHZr6I4PkKe7Sd\ntQmoG0DKo5Ssa849tIrePvv44a9lHPKPZdflTfy4tz3HV6bjv8ICv3Fz8dqyjZB2rVi00ozJQzPw\nrmNFjbvVsf/GPttADlDepjQJ1g9JbtgS9Y0b2VC2LIsiItjhnESFPRX4qt1AXO5Gcq7Oec5e3Mv6\n+2WxWDOPTzZt4rpVWQhdzcxrFTHrGIpFFws8j3rmSyB/k5y2zJcBrYAngMe/9jcH5gNKYAkw8+/9\nbYBBwJ/AlmzKlC3zQk4+CyknDtw6RrP19QGonjQR5YVTHA+4RIzfESrHxzPWwYGud3UJ7BSIxx4P\nDCsbvlLG+fDz1PmtPSlaEeikwuwD0O1FDc4f38bnd+5wwdublLmRRCyOoMKeCuwXh+i2YijpemGY\nJsI3J7QYeFGgWbcVS0cOYLquHn1LlGBYqDGRg++h761Pmd/KoG6Us3w8x0OO02L291wvOw5n3wlw\n9ixBCQnUvXyR2POTSY87hFqqBmN3DqPR9TrYmp7nwMi2XFVT8a2jGStW7qLl6Sq4THfGZoBNfv66\n89wyX05m4P43JfDr3/vLAj0A97+/2wm0ALLPcSRJUrEwbs8ciLdCLU2fsJiVrDt5Hpb+SV+Vik7m\n5vSMMiSwcyDua91fG8gBPKw8SNGKgLAaLGsYxLctDLgdk0Sz3xfxmbU1PW7cwGqUHc6TnQmoH8DB\nKSexv+lDoOc6Qn2tqKOqxrNdF/l2wTQ2mVtwzL48X0xLJ7xfMM5TnHFf5Z7jQA5gpmMGOs944NIY\nwsLg5k1Kaaljc3s96eWHoKUzgk1bVPQNn83R0gt42qc7sYcTGXhGh/Q1SfQ1aYr3Cc98D+RvktO7\n8wec/rOvKnAHePD39nqgHWAJdAS0gaN5rqEkSYWWSqi4+vwEP1oGknanA722nsW//FeEeHoS++wZ\nE9RsuNoygNK/lMa0sWm25Wir/70+0J3mdKnvTpTeLHpc38WdxX8ywaM8vVxdaX3tGtt6lCdG5wKu\nw53oHFOK9P2XeNTCF/N6bmxcFUGtEMEXj9SJCruKzQAbqgRVeWM/e3bMdM3I0HrG42fq0L07rFnD\nxFqaXAv7Hfv97XFt48W34/4g4uE0DC/0x7drHHPHuuNl+W4jVPJDXnKA2gL/XgnqIVANOPb3R5Kk\nYi7wSSAkmdEz7QDG227wdU1tHLtOYlX4bc6V8+R24xvYf2ufo2F4CWOS0fheC6US6pesxegyczld\ndxt1BjRnzf79DNDVpeKZE9yLnAkVOxKmXEuooRfXrW0IPBZKmTJGtGhrjZ67HjqldPI0m9JUx5Q0\nZQyPHwvo1QvRpQuL1NKpHrqVXT9WwLS2O1OP7mWu9TLSaioY7+hI5wIM5JC3YJ7nzlQfH5+sn+vX\nr0/9+vXzWuQHV5zTxklSdg5eC8BOrxSdNg3A9kpjSl8cya9T+rLxcjiWdrdZ7eZG8oRwtOy0sBtu\nl6MydbW0sn52NXMlVTuMI4lu1Pn5Z5S9ezNo1zJ8A35Bx+QbTAYZ4plRGkcrK9z11Bnl4EUF/dyt\nMvgmmkpNNBS6hDyJAS8vEgx1aHnxBf1HtMds+Ujo2pVxntUZo1LxPD0d8/e4aJ2fn1+OciXnJZiH\nA/b/2rYns3WeY/8O5lLuPH36lK+//prjx4+TkJBA+fLlmTt3LlWrVi3oqknFXHJ6Mk23VMrcCK/M\nEysd1Pr0pla7/ljbPeUrBxs8j2Vwd/szvC95v9OibhpKDWy0XNl95RTrFUvYrnTm/ogh6KV1IvoT\nNZR9e8LevVC5cj7f3T/stNwJeHAdFHVZ1KocPvP3Ydf9L1i/Hi5eBEBdTe29BnJ4taGbXYrIvAxN\nvACUJrMvXRPoBuzIQ3lSLsTHx1OtWjUuXbpETEwM/fr1o1WrViQkJOS6LJk2TsqNoKdBWT97Xv0M\ni90rUA0fzvinKuqVtGK4ypLbA25TdkNZNEw03vk65Uu4cd69KTdTj1I/sQv1DgSw9c4ZlIMGwP79\n7zWQA5SzLMdR864cORPFd5qbCDCvgXbfrrBhA1hZvddrv4ucBvN1wCmgDJn95J8C6cAQMicGBQEb\ngBu5ubiPj0+O/vlQWBSmtHHOzs588803WFlZoVAo+OKLL0hNTeX27dukpqZiamoq08ZJ78WR4JOo\nhdVjqXswl7XWw8iR/K6mxvP0dHwdShPUMRDHcY4YVn39yJWc+qv3sqyfH1U4SrNeSnTLeGe2ir28\n8nobbzWhyQjQf8zsXVsxeNQGx037ICUFatV679d+HT8/v0Lbm5HtVNXCKD09XVSoUEEMHz5cJCYm\nipSUFHHixAnh6+srateuLYQQ4tmzZ8LY2FisXr1aZGRkiHXr1gkTExMRHR0t4uPjhaGhobh9+7YQ\nQojIyEgRGBgohBBi27ZtwsXFRdy8eVNkZGSIKVOmiJo1a+aqfpcvXxba2toiLi5OCCHEoEGDxOjR\no7O+nz9/vmjbtq0QInPVRHV1dTFmzBiRmpoqkpKSXltmYX0WUsFy+6m2qNR1txATJwrRsKF4EB8v\nzPz9ReCLFyKwV6AI7BUoVPm0+iE+CLq3y/zfwe7i/Pl8KTbHnKdVFhYj6wv1RhNFYu4WN3xvyIf3\nlfkt24oWRqdOnRIWFhYiI+PldRyWL1+eFcxXrlwpqlWr9tL3NWrUEL6+viIhIUEYGxuLzZs3i8T/\n/FfRvHlzsXTp0qztjIwMoaurK0JDQ3NUt9jYWFG+fHkxY8aMrH1nzpwRDg4OWdve3t5i06ZNQojM\nYK6pqSlSUlLeWG5hfRZSwYlPiRfqP+qJ38dcFcLMTGSEhoqGly+Laffvi1sDb4mL1S+K9IT0fLse\nPgjqTBFXw2+KrYfD8q3cnGr5R1+BD8K2ycYPfu3skE0wz8sL0Dzz8fHJ9SgWRT51y4hcjpzJr7Rx\ns2fPpn///tSqVYs5c+bg6uqalTZuxIgRL50bHh6Ovb09b5KUlESbNm2oWbMmo0ePztr/77RxJUqU\nkGnjpDzzDzlJXd/a8KgyPW59D6NGMVcIUlQq+h3VIvzEUyqdqpTrJV3fJPirMDQ/t8LBRgOPDzf/\nJotbCWf2RICHtfvbD37P3jaqpcCDeW7lNgjnl3+njft/hqD/srW1ZcuWl1cvCAkJoUWLFkBm2rim\nTZuSkpLCDz/8wBdffMHx48dxcHBg/Pjx9OjRI1d1SklJoX379jg4OLB48eJXvv9/2jgrKyuZNk7K\nk8j4SBotbwYKKPdIE8PgiwR8sZKZQUGcsizH/VGBVDxU8Z0m6LyJi2XOhjW+L2VKZKZ8rOVWukDr\nAf+Mankfo1k+KoUtbVxaWhqdO3dGV1cXX1/f1x4j08ZJ+WXZpZWkKRIof2U9R0IFySNH0jM4mLkl\nS5I4JATbYbboV8y/cd6FhbFO5ktcb0+ttxxZ8GQwzyE1NTV27tzJnTt3cHBwwN7enk2bNqFQKLJa\nuWZmZuzatYs5c+Zgbm7O7Nmz2bVrF6ampqhUKubNm4etrS1mZmb4+/vz+++/A9C+fXtGjx5N9+7d\nMTIywsPDg/3797+xPqdOnWL37t0cPHgQY2NjDAwMMDAweOkPjL29PV5eXqipqVG7du2Xzpctcyk3\n/G9dw/bSH1y1PYmltSGjWrWigp4eDberSI9Ox2GMw9sLKYK6lOvC7+53qFevoGvydgWaaWjChAmv\n9JnLlfryl0wbJ+WFEIKohGjsfirNmoDRdL69nL0HDjAwPJyzpuW5W/sqnsc80Sv7fpd3lf7pM/+7\nm0WmjfuYyLRxUl68SHmBxQw7UsQL6vrX4sjV2zzzP4ZndDRrS7li0OYBlj0tsf/mzS/ppfz1IZNT\nSPnE398/q/vk3x9Dw7dPxhg/fjweHh6MGjXqnQK59HE7HXIBx8nVUYTUwztCsP74dRRrV9MnJYVP\nSpTAblYMmiU0sRtWsC8opX/IlrmULfksPk7pqnRKzaiK+pUvCOjshNaAXqTMX8ov9bzYGx3N5nB7\n7g4KpvKzdVvjAAAgAElEQVTlymiYvft0fendZNcyL9ChiZIkFT6/nPqTiHvGXOpSD4NB9WHnTs6W\nK8eCwEBOGZcj+PNAym0qJwN5IVPg48yL6tK3klQcHX/gz/DDg6jweDceU3rClCmEeXvT8/JllpuW\n4mmHWzhPdMa4jnFBV/Wj87ZJQ7KbRcqWfBYfl8fxj7Gf5YLWufE8a6CB5sE9hO/cSZ2AAL5VWlHt\nk6dYdLbAeaJzQVf1o5ZdN4sM5lK25LP4uHRd+RXbdyfxYMQkrFt6EX3yJHVjY/kqwphKQ6KwHWKL\n4xj5Mr2gyWAu5Zp8Fh+P8LhwnGd5MNn0DqOP9CO+alWatGpF9/1KKs+Lx3WpK+ZtzAu6mhJyaOJ7\n4+vrS506dQq6GpKUJ2N2zkI9qDffRs4jLCODpo2b88nsdKqvSMbzuKcM5EVAgQbzopacorBp0KAB\nlpaWGBoa4u7uzp9//lnQVZIKueuPbnP8XMxL+6ISo9hwcwWrNV05dv0S7XqP5ftB6dR4oY33WW/0\n3OTszsLgbckpZDdLHhVkQudr167h5uaGhoYG586do27duly5cgVXV9dclZOeno66+qsDm4ras5Cy\nd+txCFO3bmLV4+/gdiviFmyhymwn0hMeYaveB4fAGFQ1q6HzrA691qhRZnJJbL6ykWv4FEKymyUf\nFKa0cQAeHh5oaPwz1ldfXx9DQ0OZNk4CMtdVmbF7Lb1/XoTbIifunP+OfUusSFm/m4eeznxyrxpO\ndlMIq9SO5DJf0WtJHb66aki1M97YDrKVgVzKsWyzaBRGhTVtXKtWrYS2trbQ0dER27dvz9ov08Z9\n3KITo0XDeV8IfBB1PkGcsHUUkWXKiWmbNgmdvVuF6Zpton2nrWJXtZPikI6fCGgWIKKPRhd0taUc\nQKaNy5vCnDYuPT1dbNq0SZiYmIiQkBAhhEwb97GJeBYnMjJU4vqj26L5tMmi9Ph6on39RuJpxWri\nZiVv8fXWrcLk+HEx7pdL4njN88Lf2F8E9Q4ST7c/FemJ+ZfmTXr/KIxp496Fn8IvX8qpL+rn6vjC\nmjYOQKlU0rlzZ5YuXcrWrVsZNmyYTBv3ETkVdopayzIzxhskQ++rMOeEPsK9KqOmjGO7sTEDtS05\nMD0Z1Y1knKc4Y9bKDDUt2ctanBS5YJ7bIJxfCmPauP9KS0tDT++fkQcybVzx9uBxNBP2LOB0yEV0\nn5sz4awhg649Jc67AYc2Dma4ni4Dzaw5s0edJ7MeYtzTCuc15VBq51+OTqnwkEMTc6iwpY27desW\ne/fuJSkpibS0NFavXs2FCxdo2rRp1jEybVzx9cf55TgvMmNl6ESiE3dxaUkao5yroRZwiRlLZjNV\n14B91+xo1fwJycfiqHiwIi5zXWQgL8LeNjSxIGXbH1RYhYaGivbt2wszMzNhbm4uhg0bJnx9fUWd\nOnWyjjlx4oTw9vYWRkZGonLlyuLkyZNCCCEePXok6tWrJ4yMjISxsbFo0KCBuHHjRtZ5q1atEh4e\nHsLQ0FDY29uL/v37v7EuN27cENWqVRMGBgbC1NRU1KtXT5w4ceKV4xo1aiScnZ1f2nf06FFhb2//\n1vstzM/iY3Yp4rLAB4EPQrvqTHHFyUjMKdlO/BwWJspv8Bdzvjwr/K1OiMuNLovnJ58XdHWlfEY2\nfeZynHkxJ9PGFU3h0TEo1dS4FBSNuUMU9X7tTt34X9i7oCUD/hrFhtW63Fk1hLSBw1lTsSyrPevx\n6RKBl58K2z4lsP7SGj13OdmnOJJrs3yEZNq4omlr4C46/tUmc+NeIwZWr46T71xEkh0915yn6d7W\n1DnXhpTqauwsW44RVyypPS8J255WOPk4oWEs1xkvzuSkoSJIpo0rXlRCxd4LQaSkvPm4DZf2Ynzz\nazzSPqFbwmEmjZyK5VMzbMUTlC098X5Slq193SlvXY7DC61ptl6F996KlJ5fWgbyj5hsmUvZks8i\nfy2/tIrPdvbl87hQ/pzz+iGnkfGRWM+xxvv6ES5UPkfYvBmMbdwOh7Yjma7cjbpBJZwvX2eNTgdS\np0Rg/aU1Tj86oaYp22UfC5k2TpIK2M/HfAG4EnkdeH0w99m7AISCHz2AOXOwv3iV9lpafBUcjOnt\nCHRO/MnoHatRKZ9QYV8FDLwMPlj9pcJNBnNJ+gBikmIIen4eqxetiU2Ofe0xZ24HsyFwPf2e+tP2\nwCfELVzIyLA4UnfFsOW8LvFBLUmNaYhWxxJU/sMepa4cZij9Q+YAlaT37HLEFZos/IS0e/Vp5K5P\nzIUrENsCjIyyjolLiaPGujIATA3+k9g6jfltsw2dd0Vh1dwM688t0fd0Q72EBhqGGsg5Xx8fmQNU\nemfyWeSPGvM7cub+Fb4zXMsPvzbkSZoupdVSoHdvmDqVF7rqfLp2Fttj/RhwpwHoWFNtsxsqd226\nr/JCy1QuuyD9o8j0mZuYmMip5oWEiYlJQVehyHuS8ISL0Uf52T2Mr+9P5lJVV6qrteDh4k8I+GU5\n+8eMZHHjGiTaVaNNkB1Vz1hge98My5+cKPep3RvXApKkfyt0wTw6OrqgqyBJ+Sb4WTCaca40sTgD\ny5dzdPlw0q4YYRV0EeqVwv1WFOum+2J7vT5JGnWwG+eK7WA71A0L3f81pUJO/hcjSfksQ5XBmlNH\nOHbhKeU8E3G6a0KZ1T1J3vIXs0lBYXEZETgXvUvVGKb6GoOb0WjVFXhsaoiGiRwnLr0bGcwlKZ/9\neGga007/CIDOQTh7Qh/F9Dn8aGuLS1QIkRGjcT34Pb4vOqBlrKT0mdroV9Av4FpLRV2hewEqSUXZ\n3uD9tFzbHAD1DNi8VhMznboI/010CQpiv60tS7rOof6ldtRcXg6rXlbyHZGUK3I6vyR9AKvP7YTI\nCiytcYY5+0E/xpNyZ7fT+8YNFtu6kNL6CTqpOkyrYkmJ3iVkIJfyjWyZS1I+cphWCbe7CznQ7ikM\nG4bq0iW6RURgqa7OkKkqFEoFZj/bYKBpiLZ2QddWKoqKzNBESSqqrj8O4mFKIOOcbeDLDrB5Mz7P\nnxORksKcU8ZEXgzH+5w3Sj05c1PKf7JlLkn5pMKchtw/5cXTlAdoe7qxdPBgpoaE4KfhSljzIDyP\neaJXVq4xLuWN7DOXpPcoJimGm3EXWGbTCO3Ai2z/6ism3L/PTlGK8PY3cZnvIgO59F4V5L/3fP7/\ng5OTU8HVQpLy4EnCE4YuW05Q+ENun49g4b4NPPxlPm1TU/kr0oHk3vdwWeCCVXergq6qVMT5+fnh\n6+vLsWPHACb+93vZzSJJedBySW/2Xz+L0uAOB35xouaXXanbrSsDD2lRel4s5TeXx6iW0dsLkqQc\nKjJp4ySpKHiS8IQas3tyP+00I5/sptmhBmilNmDj4d+xnhNDg6MKKuzxQLe0bkFXVSpm5GgWScpH\ne27vIyQsnQluSxm3rDex38xkZONWuI56Sv1wbSqdqoCmhVztUPpwZMtckl7jYdxDxv52luY1bYmM\nSuHTBnUxNVXwJOEJP23ej1/kTsKPVmSrxS3+bNYMf00HZkxUo6SbIeWXu6NuINtJ0vshu1kkKYci\nXkRgO9c2a7veffhhny1e1racaV2aqcGHEDYWpLkOI7aEBV+rXPEc9Ayn7xyw+9ZOzuqU3is5NFGS\ncsg/xB+eOzHe+hR3DFZwaI8FK2qG85nbORTbzqHjMYLwupMZbGrMidTqeH35DPc/XLEfbi8DuVRg\nZMtckv6jx5qB7FhRkvh5rVHUrwcHD3JUU42GO5tB+eloXrEhZlRDouZH8HD+Qzx2eMjEytIHI1+A\nSlIOhMWGseXmJgZWv4xi0ncwejR4eqIVG4tx9bUY37zF7/Ze3GkXREp4Cl6nvdC2l4usSAVPdrNI\nxV5UYhS/bL6ASvX2Ywf9NR7Nq18xs3s6HD4MAwaw7vFj2l+7xrpUFw4/aIr+tzcxrmuM9zlvGcil\nQkO2zKVib+2VjQzzW8DT6KVUsHehc/PXz8bcf+cg+28eZ367i2gvHI/44gtm3XvKgz8j+Gu/Ei21\nCFStzah8sTLajjKIS4WL7DOXir0+a4eyOvhXAEzjNJl6pQ89BvfEqH0D+NcLy6aLe3J9dz3C/+xA\narnyLOq7A5elyVi3saDUIDsMaxjKF5xSgZNDE6WPVrlZ9XgQmoHFQw0unrrBQYvH1H3kgr65Bvo/\nz0eteVMAHKdVovz9P5mkt5dLR6uhq9SlyXpPLMvIlG5S4SGDufRRmnNkGaN3TWNdjQt0+a09NGlC\nDePLnEtWo4RhGUw0ShPuYI+WlhYxj4P5ZJszrbdlkNTHgE7zvVFqyNdKUuHyoYN5O6AVYAgsBQ6+\n5hgZzIuJB9FhXLqsomMjx4KuyksePH9Aqbll6Rzrz4bHsxCJiSz7/Xe+Dr5BYvQldINv0XGXK2MC\nnxLh5E18sBYmmo+p9JXAaMpXBV19SXqtgmqZGwOzgc9f850M5sVAhioD3clGpCZqsbHaI7p0LPj1\nSG49vcuxE6nolrzMZ7M288ylJtp7NvDNihX4xcUx6UEGp2ZtpllQG67YnOeZfggDbtxFQxWK7Ypx\nKHr2KOhbkKRs5UcwX0Zma/sJ4PGv/c2B+WSujb4EmPmv72YDq4GA15Qng3kRlpaRRujTaD5dO4Yb\nN84z9uYLHB5o07xdB/S9ykDt2lCmzAetU0JqIvHx0NC3CUEvTmGXXoduO1yYFHOINhs3YhqvzZhZ\nkP4gBcMeZlj3s8BkpTEAmrHWVIlcyom1LT5onSUpt/JjOv9yMgP3vymBX//eXxboAbj/faGZwF5e\nH8ilAnI3+i4Wo+px8uS7l3Es+ALWkz1wWVyCoOD9BOxKpa9DNZa11ORrEmj8MBDvI0dodcCPJRER\npORkgPe/RCVG8dmvS4mMzF293Od5U2KWDbdiruAdDrM3neOzlJu0X7eOqle1+aZPIma1jKlyvQru\nPi4YOxvxZMQzZtRYSqrRI1xtXHN3QUkqRHIzztwfcPrPvqrAHeDB39vryewvbww0IrPP3AVYnJdK\nSvnnpyN/EKV3nAFzd1Al4Ar1zD/jk2622R4vhGDo2nl0tB9Mw7paPE14SotVrXEIG8F3hmEM3ryF\n7ROG8V2lSiQlRJEcGUC7Her09dfD5sVznjjcZorXPTr0d8GrYYlsr3PkeiBrLmxn1609xCpP0uQe\n7Dxvzxd/1gfNV7tu1p8/SPJDVz7p4ABA4OMbhCXfRF8Bv/iWwVPxlPEt+3C1fWcmLtemzP5Eyq4v\ni3E945fKsdA3ZVSTTzHVN6BNKad3+p1KUmGQ2z5zJ2An/3SzdAaaAV/8vd0bqAYMzUFZspvlA3qW\nGM28HQeYeX0QdeK+pOOZmfS9AiJDC8OObVFMmwYuLq+cdzL0FLWX14LNqzm1rh6Lrs4k8ef7rE2P\ngoRYvvn5Z44YG7PCvjRWq+O4O+MBaRU1GeMwjClMpea2SRzrNIW0PQJ7d0PcJpR8JaDW/a0D/lHb\nIE0br/DW/L5/J+nKFPQS7HBJi2FPnXZEDulFbNmy1DIywlktEef5dnCrFWs+/4FBBzvhHBVHh4NN\n6JhwkSUNv+WBTXmqntWkVrASy1ZmlJpVSq4vLhUL+fUC1ImXg3knMrtY3imYT5gwIWujfv361K9f\nP5fVkbKTmpHKpE1bsDayoIxxOTrs80KV/oguWzoxO/0qg1pXYG+1niRYGGKQLPC4qsI5VQ3bMlZ8\n0bQkLmb6CCGo/UsnLl5LIMXuALVCYNYBNcwTnLBd8hMdXVwwihJMPm9M1M8RGNU2wvFHR/Q99FFM\nVMCNDhws24XGvw5j/u79BO1I4tO1amjbaWE30g7TpmaoaSpQm6SG9oElTOmjxze/TkPZqQMb+3ej\n2/k9KAzdMYyNpvWlu5SNtuSx0g0eZuB0L4OytzRRZoACFRnqgjRNNZK0lajpqWFf0wS7RuYYNzRG\nx1mnoB+HJL0zPz8//Pz8srYnTpwI7yGYVyczMfP/+9LHAipefgmaHdkyf0cPY55w5X4YItaO1g1e\nnpoe8SKC3vP+5KjwAcAiHuqeqkKTmAg+D3nOvu49+LxXLzrFm9IsUMH5tVuocsebJyZhlErR5IWB\nHRqPtUh310K9wgt2XPejtW43UsJP4Rx6j0OlH6NmP5JUoxc43ge7OwLztmbYj7BHv8I/k2u+XDOJ\nTReOkPBch3D9GpjH3KbX99+jL9SYdNGYHeP2YPusFI7DzWiQ2IZNil+ov+Er+OQT/ho6lMHBwYy3\nt+LA5m143tbDdrsC2+e6OKdeJNrwCSnGSkKdHnPe1YGLrrVo36oxHia61DYywkRD40M+Dkn6oN5X\ny1wduEVm/3gEcI7Ml6A3clCWDOa5oBIqnj5PQl8P9Kfro6aCck+g9OVu/DH9e8waVADg2x3jmX95\nCgBDb41g0o6F+NukcEmtPYa9xpDkn0TNSwq0jTUwrmPMd8/HcDLDCE3PG5hfbcqF438QtnUHM4PV\nESeekB4BNWqZgP8RkivVIeahiliFAi9XU1pUtcG0gTFKbeVr65yakYrpZHu6R+5nycaGvLh8mZpP\nnlBeM4312+pQxW8JHZNjqHixNLbcRKddFe47WrA19hl91MzQD83gxaUXaJfUZpLDQnaWWYVVtBUN\n7//Imnl1+fmJP21KdqGkrUyYLH088iOYrwPqAWZkDk/8kcwRLi34Z2jiUmB6DssTEyZMkN0rObD1\nyiF6/dWHJPVImr0YTOXDfvg8jmWPkznranqhp26No60VJVo1Y9j2YZg/s6HF9bZ4h6Rg8tQCo8fw\n3ERBkrc2Ndrb4tDCAm2HlxeK+uHAJKadnkDPvZVZGROP4uB+9LZ9irvGT7QM34rK0xOTqlWx1tSk\ngYkJtlpaOar7gM0jWbEqnedmqWg7luD52LF4+m0nJDUdgrdTImwDAb/akzFqJZc0LNkT8pSeJhY4\n2RugXUobfQ/9rEWtGvzeCb8nW/hBGc2UcSb5/nuWpMLs/90t+dXNkp9ky/wtgp+E8cnyiZyN3UyD\nW9qMvfiMyo/S2FWjL5unfsl5NTW0zh/F66AzzWPVUTxWYhxpiJqGgkTzh2g2tsKytyeWbvrYGeig\nq3x9CxpgRcAKPtn+CYRVJUy/M7orJzGolpLfIqpgZqcL27a9tChVToXHheMy25OxD5fx474BcPMm\nlj9Xo2TaH5ytfB33x1BF24grbqVJUqlY4+5OZUPDbMuavvI8Mz9tj55erqsiScWCXJuliJlz9E9G\nHv8S/aDGHLqThndMOJe+/YwvraoSbqPN95rWNJqdyPOD0RywP4yDbTceJ46n9o14GnQtDX36QLVq\nOb6eSqg4ePUqbTY3YLbLZQ7sc2bkLhfqjO6PcvRIUH/31ZKrzm9D6tnPCNDazj31RNyMAtjT8iY2\na3txPE4TtblTcDcwoJaREWpyVUJJeiMZzIsIlVDRf+33rL6yhiFR3zD90EK0mzdi+Q8/MPrhQ3pa\nWjL0jB6Rw+9jM8gGh1EOlPmlBvdeBAIKfjS6x8RRlu90bSEEJpMcMHlRl4g7ZjxduYBsGsm5MnDz\nKNYuMyZu6Wc8L+PMKLc+/NHeCZYuhbNnwdw87xeRpI9EoUwb5+PjI/vM/6PNks84cP4+W01n03rn\nENIWLOArb2+OPX7MQVcPdHwiebovlIqHKqJfMXP0SGXbSty7d54FlkkM/erdkyYoFApq2DRgX8Qq\n6uqfzJdADlC1pDtL9Y8QrVmC7t2MWLnpAJwoC0eOyEAuSTn03yGK/yVb5oVEYmoSNX7uwNWnFzn2\nfAp1t/9Iyl9/0cXYmHQhWPrCnvAhd9Fx0cF1iSvqRv/8HU5KS+LA+fs08yqLdh4T4PxyZiFf7x/M\ndylJ/DQtf7LpnHl4hnq/dsM6phNhJRYyzzyBr4dm338vSVL28mNtFukdzTmwDrt+P3D37qt/vNIy\n0pi/ezd603W5mrifhce6UufAdFKOHaOzviHuh1KZ/FU6Ib1vYTvElrIby74UyAF0NHRoVzPvgRyg\njVtLrBIa0b1z/qVFczN3I1UnlBCbeajizXEpJQO5JOW3Am2ZfwxDE48F3qTphpqkK2JocB/6Vgym\n74h/ps2P2j2ZWRd+BGD3jbm0vPAb8dsO8tPKp3ivS8bOQR+H4faYtzdHoSy6LwcVE/+p+41uAje3\nAqyMJBVBcmhiAbkZEU7PJeO4LHwp9cCGc0e1eZwYjtULHUw3roQ2bdh3/TTtN7SlavgaBno9QW33\nAeLNB1BiRxqPa2nRYbw7pjWM336xIkAxUQFxtkyquI7ve9XhDaMkJUl6Azma5QN6GPcQx9muqJSJ\nON/8jmuX9qA3sD/9bcO5ulmDKS+uENy1K0N1boOOLc7x5ei1IomaZ3R43sUQp2H21PCwKOjbyFeK\niQp4VAmx6FJBV0WSirRCOZqluBqycTL6QUMI+uNbbIYOBy8PdvbuzYWjl8EjhZUl2mLrd4qJIQK3\nByUpEZaAbXOB/d2aaJoXz5X9RnvP5MxO2bciSe+LbJm/g3l+voyec5WLC79m7Z4QJn5aL2vJ7XRV\nOvqTzBkibjDr+QyORUezqMEQ6v2RQulbKm7p3sPdoDzhKbcxfppMVaNjmI+ojnL44IK9KUmSioRC\n2TIviuPM5x5dxojj/aEyDPSZR4tgiDzng+2njUiqUI2A6Iso8QT1LXhWrEfXreZ8/kMa7lNLY9BZ\njybzGmSV1S4igDaLX5ceVZIk6WVynHk+SctIY9G+44w81YvWCSsZfHkEZS7cY1nlREo8bo6uWQhr\nWvfkZPmKaKamM3h7FA33uGHTuwTOk5xRN8z8u/nvUR0zDRMY9a1uQd2SJElFUKFsmRclEw/OZerh\n3yin6sfKlAPoaVnDo5OE7p/NhKRYsBiI2c0/6eb7G61PTMBSswLldpfHsNrL0yiVqJNxdDxbJ3ej\nVXUZyCVJyh+yZZ4DQghKTauF7b0f8P/UCLp1g6tXuaypSafAQDw01PksqhQpJ/xJXpiKm2cZPNe5\noWn56svMtIx0UCnR0Ci6Y8YlSSo4smX+GkLkbFXXtQGbCXv6nFX96sHA6jB/PnuBT65e5bdEW8qu\nSOTZzuuYNCyJ9WprTJub/v8X/goNpXrmyu+SJEn56KN7ARqdFM2cdRc59Px3zvl2RnWl51sD+g97\nZ+AVNYdap35F5eDAVK/KnF12g407tdG8/wj9YbaU/rk0GmYyXZkkSe+HfAH6H1OOzGa8/3eZGxFe\nbGu0ncYe0RxXPKHlhiY4XFlMyJYvs47fcGkPPTd8xl73M3iNa85vHZfgsSUDaytdnL+xx7KbJWqa\ncokbSZI+jI9+BqhKqPj2r1lsDdrOQ9VpvjkD35wBsySI1dDguosDy5u34ZqdM55uVWlU3pG6xsa0\nmtMCj9PD+SIijrQ7tjxrqEur0a6Y1jDKtitFkiTpffmog/nj+MfsCNrPl3v7gQC/zVUolfqCec17\n8LvyNEnVq6Bu4E3nZxqoHf2ZRjdqkqRTCa2H+tiFKIi0gaBKsTQe50nTSk4fpM6SJEmv81EHc5Np\nljxPewrAutt96XLrBsoTxzmclESfGzdQRBzG45YZy+w6cvGrC0QbG1C7bDBPrvyGiXo8mqaalFy9\nGipV+iD1lSRJys5HO5olOT2ZFynxtI8+Q63Y03Q/OA2OH2djXBzf3LnDKnd3GlSpzt3hdwlbEcK+\nH0+wMGVm5skulbjR6xQlPTTAuHisXihJUvFU7IN5QGQAyhg3furlQumOHWDjRgLt7RkcEMCBChWo\noNTlervroACvc144Kp1YODczmM9r8CdudYrX6oWSJBVPxX5o4oqz20i9X4VSC4dD166k161L30uX\nmO7sjIfQ4Wrzq2g7auO6zBU1DTVKYEXC9wmkZaRjpJ1PSTAlSZLy6KMdmpiSqmLsutXMe9CPfhcX\n4HthOty+zc/Pn7MtKopDZT243uo62s7alFlUBoWaHJkiSVLh99H1mffz9WHTvUU01JjNctUBGDOG\nMHV1Jj94wEkvL+4Ov4tCXUHphaVlIJckqcgrli1zIQSGE23ok36chW2ioXNnCA6mw507eOrr8/ke\nDcJ/DcfrtNcryZElSZIKs4+qZX7r2S0yUrRpVqUkfNMXJkxge3w8QQkJLAq35s6km3idlIFckqTi\no1jOQx+weRRJIeWp+3AtAC/69mVocDCLcOBO35uU21gOnVI6BVxLSZKk/FPsgvmtyBCOR+5EM9IN\nk7nj4aefmBAaSgsM0e0dSsnpJTGuK8eMS5JUvBSrYC6EwG2xEwD3vG3Aw4PLnp6sfxhJ/zEpmLc3\nx/oz64KtpCRJ0ntQrMaZLz6d2a1S4c5obA7MIP7QIXoHBfHHH3roGKlTckbJfLmOJEnSh/bRjDNP\nSU/BbIoDpW/+wYWSF+DRQ7oNH07DeUlUC1RS8VBF1A3kC09Jkoq2Yj+aZX/wYdIiy7D2y6rQ7XMG\n7t1LxXnxeJ9XUuFYBRnIJUkq1opcy7zF4v+1d+/hUVR3GMe/JBASFJDQKKCQEEAEuYmCRRFTFW+o\nQGuVW7VAlYpQtV642BbRYq3lAatIWlFENIqoteVS9FFrqihVi0aRi1KrVSsEEfAulJD+8ZtxhyTA\n5rK7Zybv53l82JndxPNmZs6eOXPmzBg+fLEfxbMuoWVLW7d7z26OmXk2X5ecxaqdK7l88DBOfLQN\n/T5qTK8VPcjIqfwsThGRMAp1y7y8vJxrixaSn9WbJzbfy6HNH2feqK+YPLU39OrFvHUPs/bD97mx\nSzpDssYy5VdNaV+QTeeiTmqRi0i94HzLfMMHW3j2o2WMf2Is2V/BpMXHMvGT9Sxpl836bn0gszmr\nOufyQYvejHqsnBNLsun6x6PI+b5mOxSR6Aldy7y0FM5bdC4v71hGk11w/6IChm56iWeG9mPElYUs\n+2w7u7e+yLH/LGPMPcfQbUtT2oxuRduF+TRu3TjVxRcRSSonK/NLF09h3prZNEjbyYg3YfJK2J17\nOI3VbK8AAArVSURBVLOWvMLtO3bwy8NaMXvd4WyYUc7mHZtoM7IXJ844gvQm6akuuohISjjVzfL0\nxucYeNNNpLd7hSFfXsPM5UW0O6QZpddcz4hObclMT2duVi6fjnuXsq/KyJuWR/aZ2XqwsojUG84/\nA/SWp+5iyovjAGhd2oJ/L21J5ujhbLjuOoasXcvA7Gxmd+jAmoLXyT4zm3aT2tEgXZW4iNQvzlfm\nHW4+gbL159Ck7XKK7nqD7hMns/qqiQxes4YZ+fmMbW234e/ZtYe0jEjNQiAiEjenK/OPv9hGm1vz\nWHjEWwy773zKuxwFd82jf0kJ1+fmMsgfUC4iUs85WZlPmzaNo/sczfBnr6FsU1d2bssgI68N3Hkn\npKVRVl5OuvrDRUS+nZtl+vTp4FplPmjueJrTlodfWMWWI/uQvWwJrFwJGbpjU0SkKk6OM1++ZS4A\nBRmLyF46ExYvVkUuIlIDKW2ZcwOw62A2jN5G5w4NQV0qIiL75WTL/MHj32FoQT6ZmakshYhI+Dkx\nmkVEROKzr5a5BmyLiESAKnMRkQhQZS4iEgGqzEVEIkCVuYhIBKgyFxGJAFXmIiIRoMpcRCQCVJmL\niERAIirz9sDdwCMJ+N0iIlKFRFTm7wI/ScDv/VZxcXEif31SRSVLVHKAsrgoKjkgcVnircznA6XA\nmgrrzwQ2ABuBSXVYrv3ShnVPVHKAsrgoKjkg9ZX5vVjFHZQOzPHWdwWGA13qrmgiIhKveCvz54Ht\nFdb1Bf4FvAf8D1gEDAaygT8AvUhia11EpD6rzhS4ecBSoLu3fD5wBnCJtzwKOB6YGOfvKwF6VuP/\nLyIi8DrWWN5LbR5OUdvJyCsVRkREaqY2o1n+C7QNLLcFPqxdcUREJNHy2Hs0S0PgHW99BtZtogug\nIiIOewj4CNgJfACM9tafBbyFXQidkqD/d1TuUo1KDlAWF0UlR1SeCByVHLXWAziX8P9BopIDlMVF\nUckB0Af4EzYS7lRs6HMYpSyHa3+wQ4BZwBXYtAC9sZb/jlQWqgaikgOUxUVRyQE2ou4WLMtCbGDF\nQOAF4IsUlqu6Up6jNqNZEmESsAsbstgCuB/4OqUlqpmo5AC4luhkicp2uQ7r8gx7DrBK73ngVmAb\n0AaYDXyaykLVQDnwd8Kfo1ZOJ3Z3afB08UJgNXZnaW6yC1UDPyQ2xj7MOQB+ANzhvW4SWB/GLL2J\nXZgP83ZpDxzkvT4osD5sOQBGADdiNxkGDQD+A7wMLMAyuawA+G4V608iXDlqrRt212gx1s8UdDLw\nCjAeeACYCxyRzMJVw8FYH9lL2E7agNjNWGHKAXA08CDwGrAHOCzwXtiy5APLgVXYtjk18F6YsrQH\nVgB/Ax4DOgfe+x7hyQF2XFyG7V9jgLexwRTNvPe7A6d4r0djs68emeQyxqMpdsxvx6Y6yfbW+xeh\nuxGOHLXiV3ItgU+Awjh+pis20Vf/RBWqBoJ3znbBDqKq3gtyMQfEyjsA69/7mbc8Gxiyj59xNUvQ\n3cAM7/VU7EuqKq5nmQNM915PwKaW7l7F51zP4bsPGOa9Pg0oAgZReTROR6zCbJ28osWtMXYWfja2\nj43z1ld17CctR7KHM2V5/34CzMT+KAA/xqYG6OAtB1u364AcbEikK4Kn6z2JtYYuB6ZhLT8/q3+R\n2cUcECvnOqzL63bsvoFOWOscLENwR3U9SyPsotNub7kZNrtnsFXr7/suZvFz+Ne01nr/zsHmRBpO\n7KzJ5eME4CLsePBbr+uBw7FsT2P3rvT31gWdiu1/XyanmAd0Edat0gK7XjEPK//bwLFYy7ucyhV6\n0nIkqzIfiAW/lVj/0W3AccBmrO/sbODPWCUCtrEHA88Am7CLCqnm5/gd1qUC8CqWYT7QDxtRMBW4\nGKtU9mAtXJdywN5ZhgFbsR0uC7tI+AYw0vtsGbaTurhNYO8sF2ITvy0B2mGn9GdhX0gPYI0G/8vJ\ntSzBHBdgX0bbgWOwRkNP4E2sb9yvHF3cJg2wC4DFWENtJPZF1By7SzwHa7GCdbV2Ab7jLZ+BXQMY\nBPwC+CxJZa5KxRwjgDux8n+DHSergI+x/Q6sQs/EzjpexY0cdaYj1mc5GLsYVQRc7713HvZH8s0H\nfu29Hoj1B+7rVD/ZKuZ4ELgaO5hmYTtgI++zP8K6Xlpi38wu5YDKWR7AvoDAWuVgrZAibMcF++I/\nBfezPERsts4uWKXu+yXWfQR2sLmUpWKORVhfeFOs3MuwbrA+2L43wfs513L4ZxOdsf3HXzcXG7KX\nAdyDtXSbe+/fR6wryR87n2r7yjEH6zYJGorl64hV5I2wrjAXctRaGrFW/yj27lMeiw3XOazC58FG\nURRSvdkcE+lAOXZgp10DgGeJtWR7YmcZLo3j31+WMVTeJgOxCqQhse0Rlu3yKXAo1qKdT2w0y0nA\no7hzt2Q8+9eh3nKHwHsTiD3Ny5Us6cBvgN9iDYFzsUo6+P4WbIK907AWrt+AmA+ck6yCHkA8OUqx\nrqOgqdj0JqXY9YtIGIOd7t3sLffAThXbe8vjsFbsQm/ZryAuxvrPXGlhxJOjBOs7A2tRrQYmY/2X\n11K5rzlVqrtNfFXttKkWT5bXsDvwmmCtwELswu5abDRFmLaJ3ypMC6x/FeundcXJ2LFQiE2J/Tw2\n3Ph9rI/fdznwpPe6Bzba6CXgcWxUWKrFm+MyrOvFdwHWRXk3sS/f0DsY+AtwJXZAHeWtvw07bXwB\n2zm7A3/FWoItsYuhxVQeopgq1c3hX6nuC/wUOCGZhT2A6mZp5b2fAVxKrHJxQXWyrPA+3xHbJvdQ\n9ZjgVKjJcdIAuArrUumLWwZgXYu+QqzCG419IYE1bFphZ0b+PtWCyhc+U6k6OR4hlmOA91/ktPP+\nvQV42HudjlXaJwU+swA7hW+EzbzomurkyMJt1cni+jwf8Wbx+2ddVZ1t4o/6Ct4s5JIsbL/xuxVH\nYl0VYC1df7jrcdg1DVdFJUeda4W1Is7wloP9xzOwbz3XphOoSjw5XOob3x9lcU9UjpOgBdjgALCL\nuYVYl8pGrPsxLBYQjRx1YhzwXGC5Lza6INg1EQZRyQHK4qKo5GiIfRmtIDb0sCPWndIft+9ODQpl\njkReCGqAjbl8DJsLfRc2hnYjNv95WEQlByiLi6KSw5eJDQp4HBuRsxW7WzJs46yjkqPONMGuCG/F\npoYMq6jkAGVxUVRygN04twdYiVWCYRWVHHXmauD3xC7ghFVUcoCyuCgqOcC6IKbi9sXneEQlR51x\n5YaG2opKDlAWF0Ulh4iIiIiIiIiIiIiIiIiIiIiIiEg9UobNUPgmNmnSzznwHdC51IMnq4uIhMnn\ngdc5wFPADQf4mQJgaYLKIyIiNfB5heX22O3zYFMwP4fNWb0au5Ub4B/Y035ew26zT8Oez/ky8Do2\n37uIiCRRxcoc7Mk+Odg81v6t9J2wKWnBnjwTbJlfSuz5tY29z+XVdUFFaiJs8ySLJEIG9qDenljf\neidvfcU+9dOxp/+c7y03w6ZGfS/xRRTZP1XmUl/lYxX3x1jf+SbskWHpwDf7+bkJWH+7iFM0wY/U\nRznYA5/v8JabAZu91xcRe9rP50DTwM89CYwn1gg6Epu+VkREkmQ3+x6a2BG7oFmCPZPTfwBBQ+AZ\nb/0V3udnAG8Aa7z3miWn+CIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiUq/8Hw8a7V8a68buAAAA\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114033a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y=[\"Open\", \"Close\", \"close_1yr\", \"close_2yr\", \"close_3yr\"], x=\"Date\", logy = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x1142f2990>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1142a0050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y=\"Open\", bins=150, kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x114414f50>"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lgIuBXxDmmZIkNZgkgaMd+D3KN+4tIdw9/odZZUqSVLuSTDlyGmHuqD5Fks+OK0kaZZLU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113850e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y=\"Close\", bins=20, kind=\"hist\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.94420667946068837"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Open\"].autocorr(lag=24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x11498ee90>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VtVkem7wkZcxMfkSYeSpl1me1zOQlqaZs8hkz91SqrM3y2OQlKWNm8iPCzFMpsz6rZSYv\nSTVlk8+YuadSZW2WxyYvSRkzkx8RZp5KmfVZLTN5Saopm3zGzD2VKmuzPDZ5ScqYmfyIMPNUyqzP\napnJS1JN2eRHRItGOJQZ4FYMuH8ruT/sNCoGrc9Ba9P6XDib/Iho0Ap/qw5y2759oP0b+LewFmbg\n+hywNq3PhUvto9FMvgczT6XM+qyWmbwk1ZRNPmNei6xUWZvlWVb1BNS/xhKHayefvLQ/X3mzPtNk\nJp8xM0ylytocLjN5Saqpspv8WmA3sAf4csnvXUNF1ROQeiiqnkBtlNnkjwP+ltDoVwNXAeeW+P41\n9GzVE5B6sDbLUmaTvxDYC+wHjgD/CKwr8f1r6GdVT0DqwdosS5lX16wCfty2fQC4qMT3z1bjGJc1\nNBpf7TruCW6VYSG1CdbnMJV5JO//akuk1Wp1va1fv77nc1IZFlKb1udwlXkJ5SeAKUImD7ABeAe4\npW2fZ4GPlTgnScrBc8BE1ZNYBuwDxoH3Exq6J14lKSOXAS8TTsBuqHgukiRJklSO04DvAq8Q/hra\nBBxf6Yyk4BTCZdF7gWeAh4CzgReqnJQ0ShrADmB93H4fcCfw9cpmJAUN4PvAn7aNnQ/8JjZ5qW+f\nAv69Y+wk4H+APyMc4W8nHOV/pW2fq4GngZ3A3zF32exbwF8RToB/H/iVpZq4svdJjq5NCBdZzDb5\nXwTuAZ4H/gtoxvHzmKvP54Az43ivutWA/IcbHecBP+gYexP4b8KVSx8Hfg/4KPAHwBrC1UtXAL8B\nXEC4ZPWP42s/QGjuE8B/AJ9b2ukrY7/G0bXZ6QvAzwn1eRUwDfwC8HlgM6E+1wAzHLtuNSDXkx8d\n831D5DHgcHz8LcKfyj8n/OI8E8dPAA7Gx/9HyE0h/IJ+emgzVd308+2li4Hb4uOXgR8B5wD/CfwF\n4XzTtwiZ/qfoXbcakE1+dOwCPtsx9svAh4G3ee8vWqNtexq4ucvPO9L2+B2sBS3cSxxdm910fvmy\nBWwFngJ+B3gYuC4+16tuNSDjmtHxBCFiuSZuHwfcSsg5/5dwJH4y4ahnHfC9+JrPAh+Kr1lO+FCQ\nhunfCNFLe+T3UeD0tu0nmYtcziHU4cvArwKvAn9DOK90Ptatauw04AHmLqHcTPj28Hrg24RftleA\nv2x7zRXMndR6hrAaKMAbbfv8PnD3Uk5c2VsJ/BOhLl8EHgTOIpxohfAhcDdzJ14vieNfjvvvJBzJ\nj8XxXnUr1dIk4UhIkt7DuCYPLVzlU5IkSZIkSZIkSZIkSZIkSZIA/h9HVHFXcmTFvwAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11431ecd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.boxplot([\"Open\", \"Close\"], return_type=\"axes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes.AxesSubplot at 0x114a05450>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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qW2N1/nxryQNcfbUNYFq2DEaNik95JbkoXSOSQBUVNrK0osK6Ptakd2/4+mvf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\nuvg/3pCgoQnV/FgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1143a6b90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(y=[\"Open\"], x=\"Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.85866769e+05 +0.00000000e+00j,\n",
       "         1.28194830e+05 +2.08011296e+05j,\n",
       "        -1.22501143e+04 +1.12845214e+05j,\n",
       "         1.00891175e+04 +4.31676956e+04j,\n",
       "         3.55709210e+04 +4.26686055e+04j,\n",
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     "execution_count": 68,
     "metadata": {},
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   "source": [
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