Model_tesla_time_series_night_trading.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tesla stock price over time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datetime import timedelta\n",
    "\n",
    "import statsmodels.api as sm \n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.tsa.stattools import acf  \n",
    "from statsmodels.tsa.stattools import pacf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock = pd.read_csv('TESLAHistoricalQuotes.csv')\n",
    "stock = stock.drop(0,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fix up the date column\n",
    "stock.date = pd.to_datetime(stock.date)\n",
    "stock = stock.sort_values('date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock.date = stock.date + timedelta(hours=16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = stock[['date', 'close']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df.columns = ['date', 'price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock.date = stock.date - timedelta(hours=6.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>2010-06-29 09:30:00</td>\n",
       "      <td>23.89</td>\n",
       "      <td>18751150.0000</td>\n",
       "      <td>19.00</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>17.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>2010-06-30 09:30:00</td>\n",
       "      <td>23.83</td>\n",
       "      <td>17165210.0000</td>\n",
       "      <td>25.79</td>\n",
       "      <td>30.4192</td>\n",
       "      <td>23.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1864</th>\n",
       "      <td>2010-07-01 09:30:00</td>\n",
       "      <td>21.96</td>\n",
       "      <td>8216789.0000</td>\n",
       "      <td>25.00</td>\n",
       "      <td>25.9200</td>\n",
       "      <td>20.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>2010-07-02 09:30:00</td>\n",
       "      <td>19.20</td>\n",
       "      <td>5135795.0000</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.1000</td>\n",
       "      <td>18.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>2010-07-06 09:30:00</td>\n",
       "      <td>16.11</td>\n",
       "      <td>6858092.0000</td>\n",
       "      <td>20.00</td>\n",
       "      <td>20.0000</td>\n",
       "      <td>15.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    date  close         volume   open     high    low\n",
       "1866 2010-06-29 09:30:00  23.89  18751150.0000  19.00  25.0000  17.54\n",
       "1865 2010-06-30 09:30:00  23.83  17165210.0000  25.79  30.4192  23.30\n",
       "1864 2010-07-01 09:30:00  21.96   8216789.0000  25.00  25.9200  20.27\n",
       "1863 2010-07-02 09:30:00  19.20   5135795.0000  23.00  23.1000  18.71\n",
       "1862 2010-07-06 09:30:00  16.11   6858092.0000  20.00  20.0000  15.83"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stock.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock.columns = ['date', 'close', 'volume', 'price', 'high', 'low']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.concat([new_df, stock[['date', 'price']]], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = new_df.sort_values('date')\n",
    "new_df.set_index(new_df.date, inplace=True)\n",
    "new_df = new_df.drop('date', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29 09:30:00</th>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-29 16:00:00</th>\n",
       "      <td>23.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30 09:30:00</th>\n",
       "      <td>25.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30 16:00:00</th>\n",
       "      <td>23.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01 09:30:00</th>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price\n",
       "date                      \n",
       "2010-06-29 09:30:00  19.00\n",
       "2010-06-29 16:00:00  23.89\n",
       "2010-06-30 09:30:00  25.79\n",
       "2010-06-30 16:00:00  23.83\n",
       "2010-07-01 09:30:00  25.00"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.stattools import adfuller\n",
    "def test_stationarity(timeseries):\n",
    "    \n",
    "    #Determing rolling statistics\n",
    "    # This can be fine tuned.  There is some relationship, but hard to quantify.\n",
    "    rolmean = pd.rolling_mean(timeseries, window=5)\n",
    "    rolstd = pd.rolling_std(timeseries, window=5)\n",
    "\n",
    "    #Plot rolling statistics:\n",
    "    fig = plt.figure(figsize=(12, 8))\n",
    "    orig = plt.plot(timeseries, color='blue',label='Original')\n",
    "    mean = plt.plot(rolmean, color='red', label='Rolling Mean')\n",
    "    std = plt.plot(rolstd, color='black', label = 'Rolling Std')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean & Standard Deviation')\n",
    "    plt.show()\n",
    "    \n",
    "    #Perform Dickey-Fuller test:\n",
    "    print('Results of Dickey-Fuller Test:')\n",
    "    dftest = adfuller(timeseries, autolag='AIC')\n",
    "    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])\n",
    "    for key,value in list(dftest[4].items()):\n",
    "        dfoutput['Critical Value (%s)'%key] = value\n",
    "    print(dfoutput)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
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UDqmW4BwRlwN/BAwCH8jMd8/Q71XAJ4D/kplr6xhbkiRJ9WmH2kxotopQ22glJDyxd4xW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\nDMMwjAT8P2SR5rzgZcuNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e311cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(new_df.price, lags=100, ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(new_df.price, lags=100, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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E3Lp169buSylJkiRNsG6C8SZgWen2UmBz6fZhwOnATRFxH/ASYFXdB/Ay81OZuTIzVy5c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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127499588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(stock.close, lags=161, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:6: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=5,center=False).mean()\n",
      "  \n",
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=5,center=False).std()\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "image/png": 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UO4NSbZfQuuRrfSs3653Q9iiXZ52IZUEQ2jylIipKpR1CaVCRZw9dLkJEEEod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\nH2ltUC9g01T1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1273cfa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                  -61.752470\n",
      "p-value                           0.000000\n",
      "#Lags Used                        0.000000\n",
      "Number of Observations Used    3730.000000\n",
      "Critical Value (1%)              -3.432104\n",
      "Critical Value (5%)              -2.862315\n",
      "Critical Value (10%)             -2.567183\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "new_df['first_difference'] = new_df.price - new_df.price.shift(1)  \n",
    "test_stationarity(new_df.first_difference.dropna(inplace=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:6: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=5,center=False).mean()\n",
      "  \n",
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=5,center=False).std()\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "image/png": 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EZSGEyCUJBvhZ2Nx3XzYPSojUWNpUlqME0NJEC4Ad6Z/KMmjO4iEWclWG95O/\nJCwLIUQu8U0d54blAFEWLIAPPsjicQmRArcNI0oAIhKWAe75Xbhf9nMKj/XLfvKZhGUhhMglCQb4\nWc7XtEVFWTsqIVJi6ShaBYgQhKi0YQAUY2YF0UpldD/78lFGt18IJCwLIUQuSdCGEcDpbczse6oQ\nGWMRRVvmA2Dm2w4GhuOOdAY6qsxFscZGYovB+J17Ljz6aMZ2m3ckLAshRA7xD/ArKfMqy3N4kWir\nzE8rBiZL29jKtGEoCcuAtzhLKKIy1mK1336wldEArGdXwHwev/9+OP30zOwzH0lYFkKIXOJMHffU\n0xb/+SPzEj2Lf/IixzLql9dk88iE6DMLMxtGhGDGV6wbKNxlvyNRxdSpmdnHF19AGWZZ7fFs4Die\no64uM/vKZxKWhRAihyinDWPMWEWw2LxE70Q1ANY7K7N2XEKkQtlRcAb4WdKzDHiVZYXO6H7csBzA\n5jnmsnmzuXzkyIzuNq9IWBZCiFzizIaBZUFxMQAjMKWgaETmWxYDk2VH0QFpw/ALRE1YHs52ishc\ni9V03os7v3mTeY0ZOzZju8w7EpaFECKHxJa2VgpVbKa/GEIjAC2RkmwdlhApUdqrLEsbhkN7H35n\nkrlvjb7LvXHnq79oA2DMmIztMu9IWBZCiFziqyx3DMtr1hVn66iESImlTWU5QhDLljYM8FquAI7k\n5Yzt520OjDtfvck8/hKWkydhWQghcohyq02+NowZvANACAnLYmCy7ChIG0Ycd5nrBoYwlVUZ208T\ng/mS8bHz9bUmLA8alLFd5h0Jy0IIkUvszm0YLgnLYqBS2oRlW+ZZjlHOt0iVNHImD2dsP0EiNFMR\nO6+dFRR1ZscV5hUJy0IIkUPceZaxLFRJfDh2FycRYiDR2nnuBqRnOY7dPwN2K2lgIzt7F0Tl8e8t\nCctCCJFDYm0YCSrLM4avy8IRCZGacNgLy9Kz7FG6Q1iOpP9x2Y9VTOUDnmUuYYIABLTZj1SWkydh\nWQghcol/gF9RMO6qUY2fyjucGHD8YTmqpGfZ1Wl+5ba2tO/ja8HXAHiYM7iAP5j9yuPfaxKWhRAi\nl9jeAL9AUMVdVRrZAVu2ZOGghOg7Nywrt2dZ2jAMp7L8C/7LnG9tTfsu9hhnAngjQ4gSAEBFpbLc\nWxKWhRAih/jbMAIB7/LP2MOc+PTT/j8oIVIQqywHnTYMWcEP8KaO28poc0EGwvKwQWZJ7XZKiDht\nGFJZ7j0Jy0IIkUP8A/zcsPw2B/ItHjBnmpqyc2BC9FE4DKW0YZeU0aQGU9rekO1Dyglu73AbpeaC\nDPQsB20TlkMUS2U5BRKWhRCyqgaEAAAgAElEQVQil/jaMCwLxrGRI3gF23257qcR9EKkSzhsFtaJ\nDhpMtRpNRcvWbB9STgjYZonrFpwJjzMwS0VRtJ2QKmbCBBWrLEsbTO9JWBZCiBzSsQ1jM+NooTxW\nFZJpn8RA8/mnNkNoIlQ6hGo1mkFt2yEUyvZhZV0wGiJMMBZiM/F/O2i3E1bF2Dax1xB3NhKpLCdP\nwrIQQuQS32wYQd9kGLGwLJVlMcDM/bppBahuKqNGOf251dVZPKLcELTb49ojMhKWoyHCqoRoFC+U\nS89yr0lYFkKIXOJbwa/YtyZJaZnzci2VZTHAWM6yzgQCVLtheau0YgTtEO2UZDQsByLthK0SwmFf\nZVl6lntNwrIQouDNnQtK9Xy7fuGrLPvD8pBh0oYhBiZ35UllWRKWfSI7QoQo9iq+GRjgF4i0Ew6U\n0N5O3GwY13ALcz78Vdr3l68kLAshBrRIJPUJIp57Lj3HkhbaG+DnhuUJEyBkOSPmM7BwgRCZ5FaW\ntRWgxpKwDLB8OWzf2o4dKOaEkzL3QVhHo2grwEUXeZXl9h0RbuFazvrnf6Z9f/lKwrIQYkA7+2wY\nMiTbR5E+KkEbRjgMrVa5ObNjR3YOTIg+civLBCx2WIPN6QJ/Hm/aZB6XoaOClJabEKsjmQjLGq0s\nbrkFTjzZVJb/7//Jt1O9JWFZCJFz2trglVeSu+2SJZk9lv6mE7RhhELQFpCwLAYmt7JcVh4gNnl4\ngbcThcPmcQkWW7HHJDNh2UZZTo+Zs58gsihMb0lYFkLknIsvhiOPhLVrs30k/WP7dqipMacTVZYj\nEWiznLlYm5v7/wCFSEGRMiHwsCMsAsUmsDVsK+ywHImYsKwsC22Zx8TOQFi2bQ3KiXrO9DqxSr9I\nmoRlIUTOefdd87NQiqjDh8NOOzlnElSWw2EoK7cIEyTcEs7KMQrRVxVB02dvDSpla62JHUseLOwp\nEN3KMgGvskyKYfm+++DxxztcGLXBqSyroNlPEfIa0lvBnm8ihBD9y12vwD8bRCKtrd5prXNoRosU\nqAQD/EIhuOoqiJ4TYPP6KLtn7/CE6LVS7fxHLSvzllwu8Ll+/ZVlNyzb4dQekwULzE//lHC2rcGK\nrywPY3tK+ylEUlkWQuScZMPyxo3e6XxZq8O/gp+/srzPPmbqp7qawg4ZYuAppXNYrigr7OexG5bj\nKsuZ6OP29SzXbDP7OY1H07+fPCdhWQiRc9ywHOzhuy//tKT5MsG+tr02DPc99KabTHEoSoDlL8vg\nHDGwlPkqy2GKABhSWtjLXbttGOmsLLviXhd9leXPIrsBcCJPp2U/hUTCshAi57hhuacA7L8+H8Ly\nM8/Et2GA+b2uvda0mOygnJHUZvEIhei9Ym2Wu6akBI1FK6UMKWrJ7kFlWSTi9A4HA7EBfumaDeOp\np8xP2wa0V1luVJVp2X4hkrAshMg5fQnL+dCG8Yc/wKr3vTYMv0gEPmYSE6jq/wMTIgWx2Recr4pa\nKfP6mAtUOAxj2YwaM8b7Ci1NbRjua2EoBAqvspyBBQILhoRlIUTO6iks2zaU0MZE1qSlsvzqq6lv\nIxXPPANFtlOF69CwHQpBDaPYh0+ycGRC9F3Hb0taGESwXSrLu/IF7Larrw0jtTRbST0XcVdsXuv2\ndvNBRQXM4x6Nwn18J7UDL1ASloUQOSuZyvLt/Jg1TEJv2Nj9jZNwxBFeVTtbSmgnTNAb9OMIh+Fz\n9pCR7GLA8VbwC1BWZirLwXBhV5Zpa2MnarB23SVtU8f9he9yFz9gyPoPABOWiwmhi8wH70gEfsmV\nKe2jUElYFkLkrGTC8mzeMmfWr09qm//1X3DJJV1fn+2vKktop52STpeHQmaAnywoIAYaf2V582bz\nPKbAp44btG2DObHrrmlbwc8dz1BUswkwH7CLCWEXmdeTaBTaKE1pH4VKwrIQIue47brJhOUR1AEQ\njSTXh3HrrbBoUfxlo6jmVq6mhLasr8LbU1i20PkxmlEUDOW0BRAIUFnphuU8GGSQgortX5gTu+zi\nDeaNpvaYrGYKAJWfrDTb005YDnqV5U5hWV5LkiJhWQiRkw7nFWhr6/Y2J59MbMBbpK3vJeEL+T1X\nczttlGFH+v9NPECE4U7oL6WNUIKwfPTRYLsv2fIGJwaQ2LchbihUFirbn0qzrKTZ/H9n1KhYT3Fs\n2sg+amEQAMP+/Wbssh7DcoH/HZIlYVkIkXPG2Rt4hSMZ/ZPvd3u7desg6LwRh9v6/qK/nt28Mx9+\n2Oft9NVivk0dIymmnTJaaVVlnW5TXg6HHOq8ZBd4VU4MLMU4AwGKzBzLdoG2YVRVwQsvmNNWyJtO\nL12VZXcZ68rNH5vtafNNlR3wBgt3Cstp6Dv705/Mt4FNTSlvKmdJWBZC5JygNi/6pa/9v25vt4C/\nxE6nEpY13jRt0VD/v4mfycOAaSkZy2a2MibxDZ0KlFSDcsuzz8KaNdk+itwVG5Q6bBgAURVAFeAH\nvjlz4LjjzMA7FfaWKY1VllMMy0FM8A2EzOBJtw0j6vQsP/CAGVwZJw1h+bzzzM8vv0x5UzlLwrIQ\nIucEtHkB39EQ7vI2K1bAX/he7HwqYTlu0FwWyyPl7GAcm9ikdk54vVbOqPkCDBq57BvfgEmTsn0U\nuUlrGM42c8YJy1pZBVlZdl9aPv4YiAvLzof1FP9fu2G5rG072DZam2qzDph5nPfaC26+NX5Z1NXv\np/Z38H8Rl8+f4SUsCyFyjm43byRRAl3e5sEH489H25OrkMxgJSfyZNxl/rCsW7vvk84E26lsl7OD\nndnIJjUu4e20kjYMMXC0tprC5TC2EyoaBKWmBcBWAVQBhuWtW83P994Dyw3LJSVmyWvSV1kORkNQ\nUwOYJbVRXtSbOSt+saPm+tQqy0Ff9pawLIQQ/cjuISz/z/90ntEi0t7zK3UkAiuZxZPMj3tljwvL\nKS4M0BduH+EYtjCYZjbRRWXZkrAsBo5Bg+DYY01lua1sWOxyu0DbMFzf/S6okLf4ULoG+Lk9ywCN\nNe1obcKy9oXlDguDpjQwGkzLtUvCshBC9CN3QFCEYMLrf/xj87OeStpxBq8k8Ur9yiu+M7W1sZOl\neNVkO9S/YbmlhdhUcXuzFoAtVg+V5Xx+VxJ5wX2KvvSSCcvb1fDYdRqr4CrLHT8bRFoy17MMsHl9\nCK3jl7uG2BhL77jau251S4Z/Yp58flmSsCyEyDluWO6uDSNAhKE08CQnmQuSGKjyyCO+M+53osAg\nvKV37VBqbx69VVvrVZbdpay3BLqoLCOVZTEwtLd7p4ezjaqGjpXlPE5WCbgrg36LxfyKKwjYvhlC\n3DaMFBcl8YdlK+yrLHcTljes2pbSPv0kLAshRD8aFOi+sgzeCPutjAZI6s33rbd8Z7Z7y0b7wzL9\n3IZRW+tVlnfFLFQw8/iRCW+rLRngN1C0tEB9fbaPInv8FcdhbGcbXmXZVgHQhfUcNkFSs5izuYJf\nUxJqJqSKQSnsEjNDhWpLbQlwf1h+903zGmp6lr3ei+Li+Pv88bZaVqzo+z6lsiyEEFkyKGhe6GOj\n6DvYlfXUsBMAn7CPubCHV+q2NjOwJsZXiS7De5Pq7zYMf1h2f9+rb0i8JK0M8Bs4Jk+OTf5Q8Iaz\njZYSXxuGsrAKrLIcjcIP+VXsfNOX22nXJrlGyyqIYqEaUvt05Q/L/3ubrw1DdV1ZLibE55/3fZ9u\nWC6jRcKyEEL0p712NWF5GPUJV6ubwurY6ec43pzo4ZX6qqviz4dbvTeWErzvjPt7gF9dndeG4Ybl\n8uGdV/ADCcsDSVVVto8gu/z/bYdSz6w5lbHzthVA6TxOVglEo/BtFsfOj2UzIWe8hRVQ1DMUqzG1\nsOwf4Lcb62ltDHdqwyguhjN5iJv4idk3NmWd10Dqlam8TwvlDH9paWobymESloUQOWeP8SHvzGef\ndbq+gmYAvjfpDf78V/OGo6Ldh9yOm2nbkXg2DML937PcsbIcN8Tcz5IBfmLguJQ7+YhJFBNClXrP\naZsAqgDbMA7k3dj543meEc7/d8siLWHZX1l+mDMpPuKgTlPHFRWZ655gvtl3imFZa5jJSgBG/PPZ\nvm8ox0lYFkLknEDUF5YTfEc4FPOmsmP4Ll4fbxJtGH6tjV4orqzw7mtnoWfZNYI6c6KLsGzLoiQ5\np6EBTuYxLuXObB9KTtEa7uRyJrGGYt/CGAC2srAKrLIcDkOIooTXKWXCskohLGttwnKLb4W+fZre\n6bJn2XbiXzoqy4Wg69EzQgiRJXFhOUGlt5IGAKzhQyFglsXqaYDfxx/Hn49sqo6dLglEaaeYEkL9\n3oZRX+9VhIrdr1E7jsJxSBtG7qmthcc41Tl3WVaPJddsYGfGsxEAHfSCok2g4HqWm5qgnXEMpZ6h\nzuuXy7JgO8MINGzv4t4988LyIAb5xmCU0I5d5H34dnuW3bAcIJpyZdk70/ft5DqpLAshck5PYXko\n9UQIUDx0kFex6qGyvGWL+dnAEAD0unXe/ojGWiHo56njbBuKrQ7H3lMbhoTlnJGgpV5gHpeP8dYA\njwvLKoCisJ7DjY1mILF/VpAL+R3gVZbT0bPcwqC4yzqGZfdzuDstp4Udtwpfb8UGEQK644oneUTC\nshAi54SafWHZmaB0yxZvnuSh1FPPUIZUKgiYF/3uepa3Oa3Ao9kS63cOfumFZUtHCTlhWScxX3M6\nuRWhT9nTu9BK/NIsi5LknvjKmiRnl9bQgDeoLz4sF14bxo4dJizXMCp22cscCZhBvvUMpXlj6m0Y\nOyiPu9xCJ6wsu2HZ3+ecsjx++ktYFkLknNYGLyyftyDMmjXwox/BGWeYpa4raaCeoVxyiTf38KLf\ndP3m++abZgT4FsYSwKaO4ZRu9sKywqZdOW8o/diGYdumcB4kwmqm9Hx76VnOOXH5eMeOrB1HLoqb\nZcbfs1yAbRjhsAnLr3FY7LJ/MxGA5mYTlsva09Gz7FWWdygTnKNBLyy7X1pVO1NvjmFLSp/xtPYG\nSPv/xvlGwrIQIue0NXphOdoaYtEibzaLH/8YBtOEXT6YiROJVZbjZrTo4NNP4+c4fYxTKN1SFTsf\n0FFCsbDcf20Y3/oW3H23OfaOFaGEpA0j58QtylBv+uejUTiGF7iTS72l2wqM1h3CcoG3YdihCEVE\naKCSJziJy7mDhQtN20JZmQnL5bSk9HwJEkGP9CrX5dp8eLOLvbAcCMDq1VDHCKJYjKKmz/tzuSuu\n2oHEAxjzgYRlIURO0Rram7w3jCLCbN4cH0qKCRFRpvnOrWYEiXTZnRCNwhscBMBrHEoDlQRam2PX\nW0QJu9vrxzaMJUvMzwARwl2MlPeTAX65x/+8/P1C85xqa4NL+Q2X8lv44Q+zdGTZ54YoAF1U2G0Y\ndrv5EB6imJN5gml/upwrrzTXlZZCqzuLRcdpe5KktXmtbC0Z2vm6YPyA4cmTARRhiiginHJlOXa6\n75vJeRKWhRA5Zds2CNjem2wxIZYuBW1rdnGWgy4iTEibN18V9CrLXRVltPaWkj6c1whThBXxKsgB\nHcVWAcIE+325a3CCvtND2B3pWc49/rCw4nlTWbZtX7/ua69l4aiyr2NlmY5tGAUWlrUzcHinsZ1b\nFYJBb3aKVJJrkEhcBd+1I5S4PSJEcdwHmr6IO9w8/hAvYVkIkVM2b46vSLmrUu258kG+YDdu4icc\nwLvUB0cCXs/yxdzVZVi2bTiFx2PnwxShol5YtnQUmwARgnHLYPeXIBGz7x5IZTn3+MNC3bpGwPx5\nBtFiLqxPbYaDgSyustyxDaPAFiWxQ+Z1JWp1DrNxYbmP/7fdnmXbKuIA3uF+zoldN3RY4qj3KXtx\nEk+i2vtWzXa5s2GoPH5dkrAshMgpmzebgBxRJjxeyO95k9lM5N8A/IRfMIx6nj70NgCUZfr+xrKF\n0PbEA6y0hjVMpJlyRo50wrLWsQqthVtZLur3FfzAVLaTCsuWDPDLNf6wfHvgGiIRWLAAyjHPxXBN\n3+fOHcjc8BZT4G0Y7vzttpW4sqxxpl3r4f+2bUN7e+fL3cdbB4L8iwP4gl1j103dP/G3Vgv5EXuw\njtJ1HyX5WyTerxuW83k2GAnLQoicsnmzefHVzqCUiXzCbP4ZW7UPYBvD2PlrZiS5f2pP/cnahNu0\nbdjGcN7gIP7xD7z+YCcYu20Y/VlZ9u8mSLh3bRgSlnOG1tDsDM6c1voW778b5YknvMpyUVtzwbbN\n+AfddpoNo9DCstOGEXUGwflft3rThnHJJabHuePNYpVl53H2v56oQOKo57YKqRSen3FhOY9flyQs\nCyFyypYtZqL8jqvYzeat2OlaRnLssQnu/MknCbfpTm8UIci4cZ3DsltZ7s+wXOMMQg8SZrBujFus\noCtuWNbR/H1TGmi855YJJ3bTDkZRHT/LQB6HiK74pxQDUMXShgGgnBVA4j4s96IN43e/S3wz23Za\n1pywHDdgOMG87a+/nnrrRydSWRZCiMx66y0zPVxbm1Op6PACP4XVsdPh0sHOiO74Co36NHFYrq01\nbyRRAgwbliAsaxttWU5Y7p82jNpa83MUNVhotjCm5ztZMsAv17ihsInBAEQbd/AR+zIR33OxQP9e\nlm96uEBpobdhON9ilZgw6x9fsddeybdhuDo+pWzbqSw7veFtlHpXJgjLpaXpCctSWRZCiH701a86\nbxraeZPtsHRqBV4/8tq2XRJuI7Dhi4SX//KXMJ4NbGIcRUUQVeYNJdQcorER2lvNAL8wRah+qiy7\nb3ZjMOtwJxOWZVGS3FFfb56rblhudJZRVw31jKQu/sYF+PfqVFn2/Xe2VSG2YZjXFfdDg7/vePJk\nGD6id7NhdBWWccY1xC17nSAsK+WF5VSr/BKWhRCin8UqFQle4N3e0AMmtsTdPna6LcHIF2AIDexE\nDZ+yFwBt2rR43HtPmLPPdiqDrf3bhuEetz8sH8lLzPK1m3S6T5bbMDZvhquv9t6oC7Fgeu21JmgM\nGwZ/+5uZ0jCATaTEPDdr/13X+U4F+EB1DMtUDomdjKhigrr/B9Jmi21DS6N5XQmWmspyx0F6Y8Yl\nV+X9Ch/TQhn2Z+viLrdtU2Rwe5Vj8zYDjBzZaTv+sJzK64m/smzncXuYhGUhRE5xX/Q7VpaB2Ajv\nIWWJ54jTXcwdtydm+b9zbjRh2W3DaKwL8/TT5k09NnVctH+njvOH5Vc4kpXM6vrGWV7B76ST4Pbb\n4Wc/g7/8xfRaXnZZwj9V3rrlFu/0O+94QSMUMOHk7lu9sPwxXzEn8rji1h0Lm0c5lWN5ntCMQ2KX\nR1QRQbtwVja8/HJYeKv5cBAs61xZBggUJddidR5/pIw2rCeWxi6bO9d8eLOwqfrC/GeMC8uzEr+m\npKOyHLeCZSh/n+cSloUQOaWmBix0wgT2OXsAMHjNyoT31e2J34CnlJqwPGV+fFgO7XD6CIkSdQb4\nqX7qWTZvMpozMcv4JdOGke1FSVauhD34jD/cup2//MVc9pvfZOVQcsJuuxH7W4QsE06Gsw2ABfyF\nP3ChuWEBV5YbqORFjqWk1Pv/HLaKCehowXyI+POfvWn0xu5iKss77xx/G1XqLEndw3LXbhW3rd2L\nb889511Xu81cHheWBw/utB3bTt8AP/eYohEZ4CeEEP2iqsqtLFtUMyruuntZAEDb3vvFXT6Xv5sT\nXbzRHL7XJgDULuMBLyyHd5jbu5XlMEVUb+y/yvJZPMixvAhAu39AThfceZazORvGZ+zF8vAsdiSe\n0rqgbNxILAi3KNMj6oblWkbGZsgolFDYUYBorC1g4kTvcnep+mzMaZ4N7lLUAEcfV8Rzz8F558Xf\nRpU5//97WO7aHTTZ1Ny5mGBhM2q0xaWXQjsmfL/H9ITbaW/3XgdVuO9Vfn8bRiScv89zCctCiJyi\nNVhKg6U4glc4l3tj1y3lNE7mMdb/4YW42z/HXF7noMSz9QOW7QRgZ2EE941Ehc3tA0RRQVNZ3tJP\nYVlrGNFxIFgPogEnZPTwhpo55k1xbz6lbUfhVUv9buInrPnlM2yvdSrLgfjKchODvbluCzAsu5Xl\nRPOHh92w3EMVNV/4F2hRRUGOO67zF2fWoOTCciyY2h3jm8ZCU1pmceedUM9QAL4k8WDotjZvnuVA\nc0Mvfpt40ahvgJ/0LAshRP+wbfPiGyy22P34SdTve0jc9U9wMlRWdrpfiGJUF2++lnYCsDPHqS4x\nwaYkYgYKWtiooJk6zq0A9Qd3BoVktZabgTqqrjYTh9OjwTTFTk/b/lJWjiEXWET5Cb/gGU5g7RoT\nlgcNjw/LzVR4X3MXYBsGmP9X06ZbrOzQNRWi8MKy+7pilXRe7hq8sFy/JbnKcqwly+EG1uJSc/k/\nmcW13MwP1F0JtxMflvu+JHsk4l/BT8KyEEL0C62hRLWjgkGefRbmn1Hc6TYJJsowb8DO14lbt5o5\nm2O3t52w4oTlasv0Bzes2cxkVrM/77GbvY4QxezDJzR9XkOmae2bWzVJO8p3Mvetrs7EIfXIDYIA\nleH4qnger0cQ52dcT9S3NLkblouGmLDsflvQTIVUlolSMTTAjBnx11VtMoFx/drCCctuZdkqTrys\nvRuWf3xpa7fbcoNpx9cON0RbQWeGCyxu4Vo2MD7hdtrbvepzMMXKckwevwhIWBZC5IwS2thpyyp2\n0+thVzPzRceV/KDrsOz23o0ZY+Zsjt3ebcMImPCy3RoBQPvmbXyfPwGwT/tqwhSxO1VE9vI1WGZQ\nb8NyuKSCVkpRNZkP84mcfvT22OnQ9uasHEO2Xc/P485/+m+TFqLFnSvL+0ws7MpygGhs3l+/iPNh\no762f2eeyZbYHMh0XVlWg8zzZ3NV15XlxYu9sLy1Jv5FsKuKc1f5dcoU2EE5EQIpVZb9bRj5vCqj\nhGUhRM5YxtEsXj2NffQa2HNPAEKlnVsVEk1VFqKY7VsTV6pKIjtoVyWxlB1xFiUpK4lSgRf63LaI\nYXp7542kWZ+KMEpRwyjIUmV5z2FeZXkwTYxhc+x8HheV4tQyIu58XXV8WP46ywD4clsFu+9V2JVl\nCxs7QVh2PyQqVRhPmrg2jC4qy+GAqSyXkjgsb90KZ5/theK/PaD4xLdIZCwsO69x++xjLn/wwcTH\ntOee8OKLigYqU6osx7dh5O/fU8KyECLrTJbQHMLrAGYFtD3MNHE7bG8KpIoK89NfWXaKxYQoJtKS\nOCyXRZpoCXjTJ9mWecMaXBKO68N9gvkp/ia9U4w53vv4TtL3qWYnnv9rdVYmEihu9x6rG/gZmxnH\nXqzt/wPJEq29r65dn39qwnK4pCLucmtwedzy5LfcYj7kFUqRObYoieocM9ywbJG/4cpPaxiEMz6i\nYlDC27Sr7sNyk/Nfzw3FCs2rr3rXx5YWdx5v93Vx8uSuj8uyzPM5KJXlHklYFkJkXTQK49gUf6ET\nltvaFY8zn8eP/R27OAO7m30dAE4bMiGKY+Gzo/ovG9kW8SrUUWXuVBoIx+6zsXRPHuBbAHzEpFR/\npR5pTayq/UN+ldR9lIIaRjGKGi64IJNH18X+o15CH4Z5gx3rVJefeKL/j6e/ae0NinIdycsAtFbE\nT3NIMIgKePPY/vSn5mQ/LRCZdQ0NJiwXlXZdWbYKpLJs25o7uAKAYGV5wtu06u7D8q23mp9uMC0i\nHFfI7diG0eq0Pg9KnM1j2ilBhRLPIpQM/4c/lcffoEhYFkJkXTQKJXR4wXbaMNra4BQe5/2vXsjd\nd8O++8Lee3s3c8NyOyWU0B73BnLbbeZNZhAttOC9a7hVnGK7jSLCfMEunHvwWiIUsZIZtI+dkIlf\ns5NKzNefjQxh9WooL4eZM7u/TzU7sRPVWQmnVoLVDd3H9YUXOl2Vd7SGbQwH4BFOA2AxZwMQKa3o\nfAenvKcj0VgnRh5/Ux2nutqE5UGDu2nDKJDK8tTAR+yEGWegKhKH5Z4qy+7EIW4oLiYU193jVZbN\nY+vOg55gPZIYpdy/Rd//Dv42jHyuLCdunhFCiH7k7+mL2X13wJs6uaQEDj0UPvww/mbO1MmxynJL\ni3fdNdeYn3+nLTa3MsCO9iA7GESl1chotvIR+2IFzJtMhCDFOvPflbu/s40iWBJk8uT4inlX2iil\nhHbq+/7NaZ+pBGE5tsBLAawv4bYWvMphnMHDaBL0A/m4lWU74oWIQgnLoRAEsAmUdH5c3Cn1Aip/\nw5Xf+PLt0OicKU8cltvoPix3HOfcMSzHZslwKsvua0lFgs9wfhqV0pMybp7lPH5yS2VZCJF1WsN0\n3ou/cKSZU9gNy6VdLHDXsQ2jrg52Yivnc3fsNiW0UzbU20Brq1k0YqjVxBi2sJXRsfbSCEFv9owM\nCxIhTBGvv57c7d1KkEJn5X1p29bOiTiA07NbIGG5hHbng5fit/zAuzIQYHjHRWacAG2HvQ9fefxN\ndZzYKpPd9CwXSmV5p0EmuX6TB71P9x24H+b/hx8nvN69m1tBLqWNxkbv+o4D/B55BI48EsrK6FKs\nspxiWO70rWAeSktYVkpVKaU+UEq9p5R627lsuFLqH0qptc7PYenYlxAi/9g2LOGb8Rc6Xye6C1r1\nJiw/yUnczQWMYyNgXswHDfMqy7Nnm7BcqRoZzjbqGBErDEYIEuiHsOzOvRoh2O0gnE73c8JyNnz0\nQefHpYgwx/ACkVD+p0DbNhU9d1EN/7cVKhhgu9OiEbvMqSxHw4VXWVbu3OYJKu6FFpZ3GWbC8sHn\nT+nyNiG7+y/63bDsBtPruInnr/EWBuo4wO8b34CXXko8c5CfjZVSr3Ek4lXDVT98I5ct6awsH6W1\nnq61dqcfvwZYprXeG1jmnBdCiE66CxD+NoxEOrZh1NXBJD4293HeWEpoxy72NvDIIyYsl0abY9Vd\nV4Sgt+JfhpXSRojiropNCdlYWQsZ7lyxY9nEN3gGgPk8wQscx7EfJzdIcaD64gtTpSsizN6Tirjt\nNqjzTyPnhMKhbPcqzKhKBLoAACAASURBVL6eZVehVZZ1N1PHFcoAv/FOWL7s2q57Inp6XkyaBKew\nlJl4yyG+xNdip7uaZ7kn6agsx8JyHj+5M9mGcRJwn3P6PujnOZmEEAOG1vAsxye87vTTzc9DD018\nX39lOYBNU300FibLMaNcSmkDX1gePNhUBbdtaiNA1Ftpjf6tLA+lnuLRwxIustLl/VCMopa5/D1z\nB9cFNyy3UxLrsRyJWXp7wrZ/9fvx9KelS3195oEiLAve5KveDZxg3ODEZQAVlMpyoie327NcKGG5\nuD3JBmJXgvkFx42DpZzGPqylmlGdro9VlnvxYpKOAX5xYVkqyz3SwItKqXeUUv/hXDZaa70ZwPm5\nU6I7KqX+Qyn1tlLq7ZosrUolhMgudx7SFRzc6bq5c8317iT7HY0fDwcfjPfVeGN7LPxeyS9Zw0RK\naKeh3evjUMobKGdhEyWAbZv9lFUECfRDZdkNy9GKoT3f2NHc7FXl/s68TB1al/bcxTQmRwjGVmFz\nWxHqN+0wU9vl6cu425NdTIhosJjx4+MryyVDOn/1oQq4suwGPt1NG0Ys4OW54rATlrsY3Oe6Gmd+\nOP8oZYf/Q5b7QdXPfSznn9KHyrKd2ocWr7IsYbknh2itDwCOB36glDo82Ttqre/WWs/QWs8YNarz\npyUhRP7TGobQGJuWqzeCQVixAoKDTFgONYdicwAv4D4m8gmVNBCyfP2lyoS8UtoIYGNjxea/jaj+\na8MYxnYivQjLjY29XyI7ndwPEff8pSj2gWRnpy+8WpvX7zfeyM6xZZo7dZdbWT711PgFSg66cBrn\nnRd/n0SzYRRKWPYqy9KGURJqJorV9cALx8fu/O6rVvHuu+Z1at06c5E/LLsrjfq5YXlwZe8qy6bK\nn6awnMdTx6UlLGutNzk/q4HHgVnAVqXUWADnZ3bWZxVC5Dz/YLe++spkc9+25s5BdzTVjJ0QX/lr\no5QyzMz9UQKxbz6jyrRhPP+86W3OFLeyHK5IfuxztsOyO0vImd8OcuWPzeM9ypk/ttr58jCZ6e8G\nIn9Y1oEiiorgf//oLVBSssfO3HOPOb3ffs6F7mwYBTh1XOxTgQzwoyTcTItV0e1oO63hLWYDEHr1\nTe52JvN5/vnOt22ljGu52Zxx5pDsSxsGOAOGU/wEVwiV5ZTnWVZKlQOW1rrJOX0M8N/AU8C5wK3O\nzydT3ZcQIj/ZtpmCLJWw7C5hrWq76AMo7lxZdpe6DlMUqyxHnQF+xzst1JkMN0Op73Vl2c7ijJ8B\n2+lFCAQYPMxZBdF5o3T/dvkalv1tGGFlvsUYuXvnFR9qa71V02Ir+EULrw3DDU6JBpy5z+HY9HJ5\nriTcTKtVQTfrgwBQzWjWsysj33qXducztDuw2f9Y2VhsYhwAjeu3A0NTCsupvMhp7b0GxF4f8lA6\nXnVHA8uVUu8D/wT+rrV+HhOS5yil1gJznPNCCNGJW1mOEmAbw1jDxN5vw/m6d/CGjxJfX+pNOOqG\n5QmsB6CZiliesVWgX3qWd+zofVgOh7PfhhHFAsuKPd7ujCPucbkrh+UbNywXEYZiM31JcVnnqumI\nEb65ba3OobBQKsuvv9b11HHu7DMqkr/hyk+3tNIe6GbCY7yHqZ6hhOt3dJoFyP9Y2Vg0OdE7VNfE\n11jGp+zl3DD514d0DPADX1iO5u/fM+XKstb6c2BagsvrgKNT3b4QIv+5K6NFCMZmV+htzcmtLJfW\nbUp4fXSIF0qVgkNYETvfTIVXWVb9MxvGBd8Ls44d/Ds6jN2SvI/W2W/DiFpFBAAdiK8su1+p52tl\n2X1+FBNi6gFOWHZWVXuh9ESOTXAfFSzcRUn+cFeE6/CeJ36FFJYffxzaam0CCZb99nNDcZgiQi2R\nWNtPSQk8+ig8/XCIk53b+sMyTU38hkspcmaqSWdlORo1m+spf7uvAZZUloUQInP8lWWNFb+McLLb\ncCqdbbWJ05pdGd8bvJRTY6d3UB7Xs9wfA/yaN5peQ/8gsZ5Eo9kNy0EdJqpM+HFDkNv37U4rl6+V\nZVcRYYrKTUouLgaLKOeNeCLxjQu4shz7xqG48ywh7sw1ugCWfTz/fFMIiLXkdMENyxGChNsiscpy\ncbGZPvPpx73HqpWyWFjWjU3m2w5XL8JyIGCCt+5iNoy5c838zm5w70osLOdxZVnCshAi6/yV5b5y\nK8ufreoiLA/xwnJxMVzNbazCjMRqo5SdnMktTVjO/ECVoc6MHS0lyYdlt7fb9a9+nto4oCPYblh2\nPpwMwkxz9VNuZjxf5m1lGWA6/zIVvCKvsqzppvRWwFPHuQEqWtR5BohYZbmnFJYHxo83g+9GjEou\nLDdTgWps6NSG4X4YBdjCGK+y3NgY+8AK9Cosm+evgi56x198Ef79b3j55a634e9ZtqL9M4tQNkhY\nFkJkXXOzV1kuL4c99uj9NqwiE0wqSJzW9FAvLJuso2jAzGZwwkkB/vpXc11U9c88y+6CKe2B7ude\n9Ssujh/gd8AB6SlTtrTAnXf2HOQCOkLEcpYbDMZXlgGO4qW8Dsv/4gBzoii+DaMriZa7LoSw3Nrq\nVZajRZ0ry7EVMwugsmxZsPMYm+LS7tsw3FnlNjOW0u2bY9Vc9znmrx5vZTRbGIONIrD247gg3Zew\nbPcwz7Ib3Ls8drcVSyrLQgiROTU1pmI657gADQ2wdm3vt2EVm/DWVViusztP0eYuqHHM8QFGOOtL\nRFUQHcr8i747ej1sd/8m6ldeDhP5t3ee9PQ8XH01XH45PPVU97cL2KFYBd/9Wea8UYKZgi9fw3Lc\nompOWHYHZXXZ0+ncoG2Hd+dCaMP43//1VZaDnSvLI8cUTs+y1s78wz2E2PPOg+OPhyZVSXF7E8Ed\nDfyS/6TYNo+jPxDXMpI6RvIqh1P+9EPxrVl9CMtdtWH4f4fuSBuGEEL0g9pa82YwaHCQQKDXY1SA\nnivLbaN2iTt/1FFeWA4qL8y0q9LYi38mBZUJyxGd/C9bXk5syigwi5qkw9at5mdPFaSSUDPtRc6S\nvb5ZDtqcx7GM1rwNy3Hzd48fD/QcItzKcqitsCrLbW1eZdlOUFn+jx8UTlgG54NxDy9qw4bBs8/C\noOGlBCNtfH/99fwnv2KnfywG4sOyO87hZY6k5PM18Ssh9jIsd9ez7Eo2LOfzbBgSloUQWedWlkvL\nk6+ydqSKTKXTnTvZdQvXcCivERo+Ju7yZcvgC3Y19x3kTesUskopIYTK8FK8RQEnLEeTH7B35ZVw\nBXfwAGcBXt9zqmLTohV1f7vScBOhEmdgkW+Wg1bM4xckwgsv5GcgDNf7qvjz5wMw1Gk3P/PMLu7k\nfKAItRZWZRl8828nqCzbgcJpw9AaMwYiyRAbDpRSFG1jSMjMF28HTR+GPyy77WPuh/1UwrJG9Tjf\ndXfP2bh5lnX+/j0lLAshsq6mxrwZlA3u+wA/d5qujpXlX3AtKzi08+0V/Iwb+AG/JXrYkbHL2y0T\n/DJVXW5vN/uORMw70E6jk38ZnjIFho0bxJ/5HpC+sOxOixbs4eEvizQRKe06LLt9lfffn5bDyin/\nn73zDpOiyN/4p2dmA7vLLgKSJQgoIodZMZ8opjuznOEUxXiG0+M8RE/BBN6Zfnoq6qGCioeKCeMZ\ngVNAEEERyUhmgYVd2Dw7oev3R031dM/05IEN9Ps8++xMh+qaTvXWW299v11L5H31JpcYWUdKSqC8\nHP4ZI4uAOd11PvVcwWR0b8uf1AZhL3sw1ya+sBqVCLbcjG9m5OgN4Zl6CbC+LB83Ot7K0DBPqOcZ\nOcHvzDPDiYAyJssZKMtaMIAnNOlYa8E9QYcsO3DgoNGhlOWcBJNg4kF5aCPJcg3SNmD3Hq8p7MRz\n3EJOXvhVuKN295JlZVNQDdypQ1KMiyrCw7D7sDMrSmUyynIwCB7hQ+SFlEKTDaMOSR7v5h8AbN2a\neZ2aGg7uJaN+fMzvLcvbtrXNuwGEybIIBLmPB5jMMFp9N3231rOpIF40DCOrXwsmVwpCpEaWVcdz\nI9I2VrBmMRAmy18whBeWn8K0aeEQfJbQcSkkJYlHlrduhQ5sQ6DRec47Mctw+83vyZZ7PR2y7MCB\ng0aHUpaVlSIdBIRkLP1Zig8z69O44AIYMiR6n1695P9WJvGrXsjGfRBzOYa5adcnFhQ/UGQ5UfxV\nO+xETlZsw67dRpYXLYIRI8L1XbpUXaNQog1X+FpVUQxAF7ZwNp+0SMFQ+WsVQUkKodEOEdTpEcoW\nqe3Kjs+8KUPTwspyIMdGWVaEriX6dWyQCln2It8/ihwXrfqRzpTyItcD8Ax/5oADNfLyoKSHfA8U\nm61nWVKWdR16sRaAg966P2YZiix7yTMSE7VEZJzBz4EDBw4yxY7tQg7lxZLokoBfqIxy1llq8cjk\nxx/D9OlyOF0hiKzDp/xOlZB2neLBIMuu1JKMZFtZ3rlTngOwkuVDD5X/H3oIiopg4EBYSMCIOmL2\nbOi4qKI1xVTzCb/n+5eGwj1TM6tYE4MrlNUxlVjgWoi46P6gcV+JQMuNRWtGXLLs2ruUZU8KZLlt\n53zYEh4hK1i/jKe5jaOZD1jvv9rCDtEFZGmCX06OzFYJ4PHGnrXr8oXIstaKfJFghnAzhqMsO3Dg\noNHhbwgpTIlMs3GglOVU0KMHDB8eUQ7pE/ZkoFTXTJRlRZb/yV3M+SYz8jVsmPxfRLWtDcPMZ9wE\nDbJs9izruCzq1tHr3s6oTk0SwdTJspGUJKgbZJlAC5TdI2Ce9BXXhrEXKMup2jAOPESeL0WWXX6f\nEVkETDGqgcqirtEFpECWPZ74SUnUZGnzKFIkNOO5yGnRyrJDlh04cNDo0PQQgchAWU6HLNvBH9wz\nZFk1RFpR8klJQDa+eoh4tcKL76XMZtNt2gRDmUo1xbRe+3PU+jCfEXRkG6JYEnWVwQ+siVJaKrSQ\nIuzyJH9/uDzhCX4GWW6JHpUIeL2ODcOMVMiynivJsvF+CAYsBNncWdtS2Ce6gBTIsqaBpmkIG4Vf\nCCimSn52xyHLIYIc1NwynnQLRct/wzlw4KDJwyDLGSjLQS07rrLqut37WlSj8F3ZDICnh406lALy\nRGYTEd1uOBeZjaRg5U9R6xWf6c4GOlJG3YCjAGkfCYaaEFuyHJrJuG2bnAS3p1NzZxtKQXv5tdSV\nZYImG8ZeQJbr6sKTzmxVyb3MhuESetJCgApx2ZN1coEetBBk8+e45zZZuOw9y0KECXtNfZx7PnQN\ndVyOsuzAgQMHuxPKD5qJsmze95uul6ZdTBCzYpqanzip8kNcyRhabWWjvMVBJL9QyVjSRWmptFfY\nlQ3h6B1H8z0A/kOPNtaphnvAQBcLVCpohYULAfjsM+mLfvLJjKrZ6FAdOk9+Cp7lkMVm6S96mOTs\nBTaMujqpOAZx2YvHe5myrCWRlERB79gZgE7IkDJaMGDpjJrJsnl0x0CKZFlzaRBjgp9Slst2JkGW\nNbfsFLRQOGTZgQMHjY5sK8ul597EFUzm0j4/pFxOCZXG591hL1DKshEbNZMOAuGh/nTh31nDZbwp\ny/LWGcs1dO7gca48V56P3vwql/c/CICNG8Mdi8JCjT/xgrXgUM7yYOYOmyYBpSxrKdyjKvb33DnB\n8L20F0zwq6uT97dAsxeP9zJlOZl01woLF8l7RnWmXQGfMdEOoEPn8P2nuV3RHfqUlWWXbafFrCyr\ncHa2MCnLTug4Bw4cONiNcIvMlWWzyiJcbv7DFSwvPCLlclRYNkBG6Mhygx4MwqOM5GpekQtSbNwi\nq+POzYyFdvWuNj6b0w8fy3c8zkj+/JNMgKIa7PwS6b1cvDiscrnbteEHjuIFbgwX7JPbq3Y4nRTm\nTQnG6EcqZDmkLLvQKURmANwL+CGdO0tlWU+oLLd8lR2SS3etUFlrjeqj6bolK+mHn4bvP5fLOhIm\nd0htNCwZz3JchPYVjg3DgQMHDnYv0iEikTD79+pby5BKKbYbALzDxdzKMzzCnXKBL7sZ1wIBGMnj\nHMoiuSDFDkK1NZs3hcWZkWVlwQAsk89qkRMP+7EckGQ5iItWRfJ4Xq9Jee/bFwjNrFeIIMstRVlO\niSyHlGU3wTDxyPL91BRx8snxleVAnry3XLWxQ5K1FKSqLB9xdPSDYh7tMoes2X//cMprA4WpTRjW\n4niW1T1reUdE7h8iyH4tFxeixaYwd8iyAwcOGh0F/lBjUFycdhlVRV2Yz5FcyWuUt+2bdjkCF+O5\nlWVIu0Hf/A3cckvaxUUhan5XipKrN2I+nyc3s9e4JVWuqXLmBvK112QosAbyaCODYVBfD21UI963\nL+eeay1L+PyWIpu7shy2YSTP+s3KskF4Ai2TTJghhPzNsZTlQEExXvJwl2/b85VrBLhSIMtDL4vu\njBUQtkeZO2tjx0IFba0b77MPKcGl2Q53mG0YbdvE9iIror3dIycmUlaW2vGbCZr568uBAwctAUX+\nUFazVF/0JjTktuZo5vM6VxqEMh1lWaH7kH6AVFafey79ciIRZVlNUXK9556IBRmO68dSllUGMQ3B\nVVdBm5xa/LmFFBVFbQoHHMAHH1jJsr9OksKpodwkzV1ZFv5Q6LjcVKJhyCbWoiy3UOXNDCHCNgzb\n21PT2EZHPDtaPlmW5yJ5smynDCv/8mP8DfqEw8UVFVltY4AMPZNK/XChJbBhxJu4p5TlMncXuaC0\nNKXjNxc4ZNmBAweNjtb+CvkhxRd9LESqr6lg4kSZGntXpzBZziaCvghpOUXrydixcOWVpgUZki8L\nWTbJgGq5kXaXWmP4HOC++0yFhGwY5gmR3ippN5g5U35v7spyfbU8DwXFyV8vFZPZhc4+yA6hthfY\nMJSyLNBiBrzYRse9QlmWGfx8ST/nWqt8IySjQg5+5nAsd/JYVK8zavKdOR1pMvVDk2Tept5GrOd4\n3vIQ0a7ID5HlLVtSOn5zQTN/fTlw4KAloHUgc2XZrCJnoiwPHw5ffAHe/DaUuTpmnSzr9REpYTNk\nkVowfbIsRDgeLlgbRUWS27ALgAJRiy8nTJYt/Zpu3QCoJNxQN9RY69XcleW6anlutJwUOjeha1tC\nJX1C0UT2BhvG6NHxlWVNY69RlouDO2kVqDGekUTQXBrVtLYsy8FvmzlS08BHrnVhig+a0LQklOU4\nEVxC+/o7hOLFO2TZgQMHDrKPYBC2r8qcLJuRDRuGpsEubR/LTPRsIOjPPBap+XdlolQGgxhRGowF\nIRx/tGwg27MDgFaizkKWLec2RArNZNlX6zeGoP/E8+QFTb7LZoigL/UJfipCy2jGGsu0vcCGsXx5\nWFmONVi0jY549gJluZtvjfyw//5Jba9p0WqxnFzrjrr1bMlyytCwC/lm7kh7gg1R6y0bAlWFnWQY\nO8eG4cCBAwfZRyCAMUTdFJRlBZdLptCONxM8HeiB7Abu1zJQKv1+LJ0B3R/+rTku+dmF4HomUCBq\n8cciyyHM4Tjjs6/WR10dzOZ4nudmfj/77rTr2RTgSicahju6idX8Ld+GATLdtacon1NPtV+/jY64\nd25v8YlJ9vOHyHLv3kltr2nR4eCUshyZvygbZDmesqz8yLnB2L42leJad+WwQ9vXUJYDAbj6ali2\nLKPqNRk4ZNmBAweNCiEkWa6myBIWKROkGD3JFpq2e8hytpXlTCb4BQJQRDh8V32tbl0ZwgRupACr\nDcPlguFM5Ez+ayybySn0YB3ltMVf56eiAo5lLgC6J1MFrHGRVpZJu233AmUZpH3Hs69951fToIpi\ntGAwswkGzQCGstyrV1LbxyPL+fnR22dKlmMlEzGT5Rw9zjVS7x9Nk+Em6+sBObrw6qtw0UUZVa/J\nwCHLDhw4aFQIAW2piJ7VnSb694fLL5efM7VhNGVl+VaekR8yJMuGqg94a8O/VUV/UCjQa2jwWJXl\nVxjO55xp2W4DPfCTQ7Dez86d8ClnAVDWYUDa9WwKSCsWuM0NuLcoy23YhV7cJuZ6IyZ3C8/Ssp9/\nDZV5+0Lr1ok3Rt4ykZlDFVm2u/WyYcOwi3ZhUZb1xDYMNE3WO/Rd/dzt2zOsXhOBQ5YdOHDQqNB1\nSdgyJcuKl1x1VfSydMsL4DEmumULAX/m5EDT4GWulV8yGMYOBKAd5fjIwUueZYKfCMjPG5ETkw5k\nJbv88W0YCj5y0QKSLKv4wq5A8yaJWjoZ/FxaVGSDvWGCH8hnWm9tT5Y1be8iy2VFyfmVwV5ZzsUf\nnakvhGzYMBIpy7l6fZwCQvu6NHlNQ+8jtbghDs9uTnDIsgMHDhoVQsig++26Z+adUEElzNwxU7Ic\npOkqy4b6ZKMKXXPNNdx7770JywgEpKpfTjuCuA3/IYSV5Te51Fh2xMb3jc/xzq2fHLSAj507w9E0\nOrVrvmT5//4PFs4P3QcphvpT1+m/nMkKDtirbBh6SewO8N5Clkv0Cmry2ie9veqkR8JuGdhk8EsR\ngsSeZQ/B2NcphrKs3sOZvIObEhyy7MCBg0aFroeSWWQYW0ztruvZaX9drt1DlrPlWTbIhq4TDEJ5\neXj9pEmTGDduXMJydF1Gw6ilEB2XrbLsJWyULA7ustQhFnzk8tN86VlWnmi3v/lKTHfcEQ6jRUFB\n0vuZh9S95EvFfS+IswwhZTmGDWNvUpZdQkfXUsj6qNlk5SM2Wc5YWU7CswzEHMEyttE0dKHxw3z5\nPgoG4XomcGJgRkb1aypwyLIDBw4aFSqBAVqGaZtDuweDFrEjbTQbZVkXjBwJ7dtDVVXq5bjQCeKW\nyrI5+UBogp+ZLP+161vG50TKcg5+/v1v03GaoQ3jk0/glVdknofObGEH7SAveSXPrBJ6ycdPzl5i\nwxBxleU+ffYOsiwE6EEdkUIsdU2DTSHrk99EkAN4bE+VcR7TraOW2LMMRKTsjNgQ0ELK8ppfBb/8\nAhMmyInBH9cNzqh+TQUOWXbgwEGjQinLqTQodrBTlpsiWVbK8lyOYSCL0i5HkWVN6Lzzjly2c2ec\nHWxgzrQWiyybh3lntDrb+BxvICCAhwt5n7XflxkNbnMky7//vUxSk58P+XipI3lVWcGPjPBSQ5FU\nlveCCX4F1JFDIKay3KrV3kGWGxpABHVaFSb/bsvJCZPlOleRsTyI2zagxoGsMD4/1//ZNGqZ2LMs\nKxCfLKNJz7ILHbcbvvsujao0YThk2YEDB40Kw4aRRWVZIXtkOXsNulKWX+R6FjMw7bqZbRjmjkLK\nZYUyrQVxW/zPyoahhnnncCzb68ONd7zJ/T1YD8DXnGp4lpszSXS75T0aGaUgEcyTtbbSSXq59wLP\nsoqwIkrsybLLtXeQZfVuc+ckf9+0aQO7CiVZdpsy5w063sMHH0Rv35dVAPRiDR/3uCXlOsp019HX\nQNdTJ8s6LjQEbjd06JByVZo0HLLswIGDRoUQ4CaYsbJsnuCXjfa3tFQSnaOYL319c+ZkXihhsizQ\n6Nw5vTJkcgIZaUHzNRhkOVZ7FgtmZVnHhcukLGuhJBx9RpzL0/yZobxtUZPjKctKjf8Nv1AcSnri\nDjRfz3ImZFmdiyqKQxMfWz5ZVh2kWMqyy2W2EbXcpCTpCgHbciRZLhLhGOgHHuxh332jt72C15nA\n9ayjZ3qVTCIpCZA0WXah4/G0nPjKCg5ZduDAQaMibMPIbIKfHVnORFmeO1eS5RwVOm769IzqpxAM\nhGaL40o7u9V++8n/v9Kbgs2rDOIaSCPKnVlZtpvg5/e04naeJtChK19+ad136lT46afoMlWaXAv0\n7NpZ9iTcbkl6UyXLECaFATz4yCXobb4KezIoK5M2DCBmdqC9RVmuqpLvtoCeIll2d4leGCMKy7ec\nxI1MALS0TmUySUnkhgkm+LmsNowM52s3OThk2YEDB40KIZCxjJsYWZ4yJWIGehebBiwNKGX51j+7\nKClJr4wePeT/JRxM4folaSvLEFZMI8myUpbr/fIc/PnPcMAB1n2HDoVDDoku0y42tdaMFcS8vJBC\nmOLohzkaRhA3fnIoXd+yleW1ayGXUIcgxmTIvSUaxsiR8r5ZvjK1+8bnsknVlwT7TOdUCpukJLoO\nFRXJKctCV6HiNMO25na3vMvqkGUHDhw0KnQd8mggmGPTQKQAO99uJmT5uONAMzVQlduzowiqCX59\nD0z/9Wsmy/kbVxkWh0xsGJFxlhVZrmuQ5yCFiGkxlOXmS5YHD5bnqXPXzJVlg0i2UBQUyOcZgFz7\nsGZ7i7I8ZYp8Fnx6irG57ULNJRHfO61TaZOUZMIE+O1vkyPLaptt2zV85JJHQ4tTlcEhyw4cOGhk\nCCEjDQRzMyPLdqHjMoU5a9bW9dnx3IqgJI0uT/qv3+7d5f/V9MFDkLpVm4D0lGVlw4iMs6wKq22Q\njbT0SScHV0TjW0++bfKUpo7OlDKcieTnQ65Hp7AodWVZEUcVOs62I9GC0KqVKSZ1DGV5byHLICc7\nVojUspMqsmwOHbe7yLLdBL+bbpL/U/Esb96s0UCe0RlsaZfVIcsOHDhoVOi6JMt6FpXlbNgwwEqW\ntQZvZoWpMv2Zk2U1MVCFJVOTyDJWlm1sGCcNlo30oEFpV5dqWjdLG8ZLXMdErmXb/5bTV18RPwSI\nDTQNSkLE8RcG7BVk2eWC97lQfslLrCy/9moLY1UmuAlQQhUVpEaW1fyNBpeph5pEbzUtsqxpMXeU\nMdhtwgyZoEajyne5aCAvPKrQwuCQZQcOHDQqFFneHcpyVsmyLztkWSnLqYSTioTLBV98ER7iz8+V\nPzidCX45+AngiZroo+mysIv+4KaqCg47LPkyv+coy3cdV7O0YShlbci6FxmgL4abb067rMX8pkna\nMCor5WTWbMHMu7QYynLr1hi927tHNd+Jn/GwbFl4VKFWT2FYBvAJ2Qn2aabzZzc5AHj0UTjhhPTq\nCDKDnwv7Z9NNMBxnPYGyPHq0ZpDllqYqg0OWHThw0MhQNoxsKssKGZNlk3dQS4eJ2pUZUpY1d2aV\n69o1TJYLW8kyQqohgQAAIABJREFU01GWW1FPPa3kcKweLsclQoW53akKqgzhS/7GY8Z3HVeztGGo\nGNN5FaVywdFHp7S/+f7z0qpJKstDh8Kxx0JtbXbKsxClGJ7l1q3hxMFyXVM7H9lC//7hqCB+kZpn\n+ddqGaS4tV4ZXhjj3hs5EsaMkZ+zrSx7CODX5HU65+wg1dXR25RXb0cD6nOXW5TllkaYHbLswIGD\nRkVDQ3aUZTVKmZe3ezzL2VJGDWU5AxsGyM6BGsruVzkXgUZ+6ZqUy8nHSz2tpGc5RGj9ftlQCk1L\nOQIEQBUlvMmlxvcgblzNOL5wEaF4tzFCocWCpkE/ljGAxYC0zTQ1ZfmXX+T/Xbt2Q+ExyDJAUTu5\nLhvnY8EC2Lw542KyAiHC75/tSNJ7wZmpjUptri1hGf14/vCXwgu7dYu5fWaigH1SEpDKslK3VywL\n2oaa/3n9AgAmfDaNnqyjP8tk2MlmOJIUDw5ZduDAQaPi2qsC5BAg4MmMLA8bBqNHwwMPhJdlqiz7\nMTX2WXr5qzjLmXiWQc73UcryX/k/AIoXz056//nzoVevsLJstmH4/aG4wq7UFDEzzGmyd9GGvPrd\nwcZ2H8xxZksIKXwpkmWAFfTDPXAA1dVSqW5qSqoaNaiqyk55FhtGnNjaujt7ZPnII8MRYhobI0fK\n/mUBYal+0Lx/pViKRn+WMafPsOS2zmCupNBik2UPAYMsuwmSb/OKDr9iBUfxg9xv/a8OWXbgwIGD\nbGLh3FDYswzJck4OPPggFBVlz7NsJnxphZqwQTaiYYCVLCsyFyiyz5hmh8cfl//NZFnZMJSynAlZ\n9hK+njtoT6vaHWmX1RjQ9fBv6M4GuTCV+HmE7z+3WxKopmjDKC6W/+2G2NOBmbCJLl1jbqd7skeW\nIWuPZ8Z44gn5/1LeNJbNfii9hEbJhmBT91k6/FTgimLZh/IjixhIAfUEXPI6uQnaBjdxhbITBkwX\nQLQqiNtRao5wyLIDBw4aFa2oB8g4zrIZWSPL5gk22fLcqnIyTO/t8YRtGGqCjiD5H6wa1lx8HHZ0\nLgKNuhqdW26R1hgPgYyyKvpMqnw57civK0+7rMZAIACbkWSvJ+vR0bCV1uJAnWOPR+4q49D6mpSh\nc3coy0voz9tcjBYjGgaEyXJLjZ7QOpTmvQ07GXTtwWmVkSpZTgcq654ZZ/FfBoasQwEtTJbtbv/C\nXNmBrPbW8U9GyTKDukOWHThw4CCbyEf6+TK1YZiRLS7i9Zsm+GVJujJCqGWBLCtlWQ2jpvK7VTXc\nBBEuNzou1q/Tee45adFwE0S4U1OWZ86UKbAhHNYOQspyMyPLwWA4JB9AHQUpsxJvyKqamysvd/s+\noRBiFRXZqmbG2B3Kcj5eGsiLS/ayrSw3NagslgE8qfaxwmWkOLCTtg1DCISAgw+G11+HQpOFRCnL\nLnRbC7o7NAm6qraWZRwEhOw3TUXqzxIcsuzAgYNGhSLL2VSWVZSl227LrByLUpstpWQ3kGVF6mJ5\nD+NVw0MA3S1Dx6n9fb70lOWTT5bRFSA0vBtCOe0kWW5CimoiBALWtN21pO5XLiuT/zvIeV5sKgjl\nC1+xgmXLmsbpyKay3NAAW7ZItbiBvLi3eEsny+qZDJC6lWngQPm/PMn+ZSae5VrdxwYRRAhYuhSu\nvDIcxQPA7w57lu2gh0JM7qqtNt5HjrLswIEDB1mGIsvbq7NHljt0kA3HuedmVo6ZLLv8WWrUs0SW\n3e6wp9rImJaCmhOpLDeQZzSShg0jRWVZYdUqmDYN/soTnLHvQnbQHpcelEF9mwkiyXIdqfmVAU48\nUZLRv/9dft9UeCAAcyatoH9/+PDDrFQ1I2RTWb74Ypka3EMAPzl7hCzX1IQ/+5uQHVzZS9Ihyw8/\nLP8XFcn/W+gUd/tMbBijKv7DwcFqC9G2U5bdBG3JeCCUvKiqtiYcPUh3yLIDBw4cZBUdkPLb+/9r\n28g1iYaFLGcpKYnhWc7QUO3xhAlcQcj3rbLuJQMLWXZ72EQ39mMjIMlyOjYMhT59oEsXeJK/srzV\nYZTTTq7Y0Xwm+QWDmSvLHTtKxfbII+X3dfTERw4bvlwBwPnnZ6WqGSGbyvLHH8v/KtFNXLKcIzt6\nmZLl448HD35G8U/WfLE6o7KyAQ2d3/ExbamgkmKCaZDl3/1OhvI75hjpeX78xuR+VzrKcrleE7Xv\nDbxofPa74ivLQV32UCpNZLm+ys/9o03bN4UhlAzhkGUHDhw0KvogG4LNBX0buSbR2C1kWQ81HFmw\nYdQTkRkshcQpZhuGcLlZTw+6swEN3VCWk55hZAP18zSNMFlOdly5CSAQsCbMSIcsR+Lb72SnRN+4\nKeOysgXlp82WZxn2rLL888/wNLfxT+7G98LEjMrKBm7k33zMOfyZZ9iZYpprM0pKZN+ykja07hT/\n3lOPaSavlFiRNJSyfA4fodXWRK0PhkazfAG/8XvffbGCXRUmslxfn37FmggcsuzAgYNGRSe2AlBZ\n2KWRaxIN3fSKdPmzQ5a1BNEwfD4f48aNw+uNfzyzsmyUnYKyrBwb0obhYT09yMNHR7bh84UibGTQ\n+ioS5veb6pngNzUlRCrLljCCGSCAp0nGoM1WNAxITlkWOdnzLN/ECwBU1TQ+pVFhBt3oaVl3zDhQ\nunY48cT42x17LPz5z/Dqq+kfyyz+1pg6hkGXnKh7N/+kzTsvRu6GEGFSvIP2ALRu2GFVopuR/SoW\nGv/OcuDAwV6NVtTjI4cnn0k/pu/ugjmslTtrynJ8svz8889z77338oQK2BoDbreNjzZDZRmgB+vx\n+0OTBTOwiqj8HQ3myGDNaDg20rOs7EKZQscVFaorW1i3bh01NdHqXzyoS5INZfk3/MxSDqKA+j2m\nLPcmbFHw+BtfwTRn/TRHhEkHl18Oq1fDqafG387thqefziwxi/nRrCBsidO18Hs5t3R91H66Ht6x\nLhQussDldciyAwcOHGQTPTp4adDyOeWUxq5JNH5Amk295MkQEdlAArJcFZL46hMMXWoaHDIoUllO\nfoZT5AQ/RZbP4SOCAYGGQMuALKv8HQ0NJjtLMybLaiJqptidZLlXr14MGTIkrX2zoSzfz/0cxHKA\nxJ7lEFl+mesyOqaRihxwBxp/5MJM/s2xxtOBpkHv3pnWKDmYBzvM0TB0LUz+9VybSdim+POBUEch\n3xOISZY/++wzypuRHUvBIcsOHDhoNEycCJVlXkRe9iJhZBPvcDHt2MFcBuFvyA7BSWTD0EOtljsJ\nv/B1t0WQ5ZSV5Trc6Ai3xyDL9/AwvX94SyrLrszJ8uGHN0+yHBlnOVs2DB0XHdnGbI6jLyuzUqYZ\nc+fOTWl7dUmyacMAOP0sT1zLu7JhANaQFinC7CtvCsqySrIE0LZDTrMRVYNBnUF8xx08bunMBU1Z\nPG3JsilcpT80mTHP5bcly8FgkLPOOotDDz00u5XfA3DIsgMHDhoNt98eUuzSjdq/26FRQTt0XKxd\nkx2yvL16E6cC1TGUYzVhxpWEXziYa53gl4pnedu2/wCFLAeEy001xca6ki3LM1aWi4pg+nQZQo5m\nSJYDAStZNqfvThd33y3J8sl8w3F8x208nXGZ2UI2yLI5znfJvvEtCEpZBmDt2rSOd/HFVnLaFMiy\nmWjqnhwjNF9Th8/nZwqX8zgjrV59Eb5O9spy+JqrMHl5bntlWQkBmzY1nQmuycIhyw4cOGg0HH+8\nJMuF7ZomWVa2YR0XxYXZIcvTFk9iOvBpDAUwFWVZc0e8wlNQlit3TgZgNUSFiFs8pypjZRnglFNg\nn33ILGtCIyEQsBKfbCjLd95pnTRaRoeMy0wXn34KOTlhkpwpWY6McdytR/w5CBZlOU2y/O67TY8s\nmzsMQXdmNow9iYYGP71YB0AxYQP7spyBxmeRa/MMmGwYiZRlvQlObE0WDll24MBBo+HzzyVZ1lo1\nTbI8YgRs3Ai5eS6KCrLzohcJ4iyrBiUZZTmyiFSUZaHLBqwEojL1CbSMlWVLvVzNjyxH2jC2JkgM\nkQxKSqxkuS3htNdCCOrq6ux2SwoixXN7332yQ7BcWozJ4NAA1NZav7dum2BymzmXc5pkGcL+Wj8e\n3IHGzwZoIcuuzCb47Un4Y8zJqA4UcDPjAdi2LUEZIbKc4wpYvOSKLKd6jzYlOGTZgQMHjYp2lKOp\nzAhNDJoG3boh/cV6kOrqanwZTvQTocZUi0GGU7FhRCIVshwISJJRQLSy3EAeGgKRJbJsSRveTKBs\nGAs5jEe4kxuYkHGZmmad9NWRMPt4+umnKSwspLS0NK2yUyUiahBCDWBkymOiyLYnvrKcm2e6J9as\nSfu4SlmuohhNT/7+310wR8PYuKX5kOWybfaTgxt8GpO5EoD33olOTCJsJvh5dD+tTeq06kk5ZNmB\nAwcOUsS6dZCHl6OYjzbomMauTlwIlxt0neLiYs4444wMCwspxzFsFoosJ2XDiOSgKZBlVQ9NHsyy\nyk0w49BxZhjFNKPGMhiUNoxqWnMXj7AtC8oyQDXhjqGZLE+eLG0xmzdvTqvcYAqpzuX28r/bDfnU\nc1ntixldnyiynBOfKOaZR/QTSZZxoJTlSkpwpxANZnfB3DGs9jUfG8bsWQ22y30Bl9EByHHZjK4J\n8wQ/ec21YIBiqqK2cciyAwcOHKSIdevgfS6gFV44+eTGrk5cCLcbV4iIzpw5M6OydD1EhmMob6nY\nMPx+uILJnMaXQGrKshouFoAeYcPwEMgqWW6unmU3QYttIivlmtIflxAOldAQCkidl5eeNzpVP6hZ\nWX6Kv/B45Q2Qwb2dqrJcXw/XqbTKGXhZm5qybJ4cl2mc5T2Jed9Fk+WfgYAIPwM57ugOmTnO8oCB\n8j2iBf3W+NkOWXbgwIGD9ODzwVl8Jr8c07SV5YAnH0/QXnlJFWrYMhZZTsUnrOvwH67ga07Djyel\n0HGG2Eu0DaMDZVRQy8JsJWJpxmTZPKyeDZg9rebJaYos5+amp0ZmoiwfyAr5JYPrE+lZTkSWZ8yQ\nMZZ/4eCMyLJSlqsobhLKspksm8Pa7Qn4I2dZpoCd5db7pwY4BJhW/2xcZVkPhu+Zm2+T27kCfst9\nru4rZ4KfAwcOHKQIy3uzY8dGq0cyCLjz8QSzQxyDoR8ey7OsyLJdwzJ79mw0TWPlShmf97zzoG9f\nGDNGKpbpKstEKMt/5D88zVyO37Q2Y4+2PFjzI8vKhpFtsmyOqmFOdKLIcrrqWybKsjFknqaqDVJZ\nthCkBDYMFS1Sx5U2WXYR5GLeAaBGa42rCSjL5kmhR7ZZHWfL7GLmzJnk5uby3Xffpbm/lSyrO3ND\ncElYWXZFd8gCAdM116RveePagDXxjqMsO4jErFmwYUNj18JBOli/Hi65BLyNnwRqr0D1VilFPcbf\nsjfcv5vgc+eTq2fpxgi1F7Emzyn7hV3DonytX331FQC5ubByJQwZEibLSTdISu0hrCxfw8vspA0u\nBEvZCYRJXCZojtEwIm0Yf/tb6mXous7TTz9tiXKh4jXXUmBLliMV4pdeeolXX33VtvxXX32Vrl27\n0tDQkLayPHWqnGQLRMd/SwF1dREZ6xKUpdKh67hYv1YndEunhGt5maP4QR6OXFxNTFnuHNxz8YQ/\n+ugjAGbNmpXW/lpEVknz3aSegVw7G0bQ+kz7yWHNSr9Dlh3YQ9dh3jw48UQ46qjGro2DdDBihGw4\nPv20sWvS8lFfD/cOlw3JIg5p5NpIVebFF1+Mud7vyseVJUuCJiRxDMQgN4osp6IUalqILAf8Se9n\nVpaF2w38kUlU8gm/s2yXjaFTl7v5kuVMlOVPPvmE22+/nZEjRxrLlLIcwGOxYagh9Mih9Ouvv56r\nr77atvyrr76a0tJSdu7cmbayDNADqfBUbE1/FKG21prqmV694m4/YoT8r+Ni0U86qWbp3rwZ/sBU\n43tA8+DSY5Plb7+Fyy7b/bdgSUHACKG2J1Eb8sEUFBQk2NIeLqzvI+vdpBHERd8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FesWEH37t0tYdsSwS6EoRlaQT6tW4evbw076aMC5dog1ghGdXU1QqRPdvqG/m+rT6yeJ2MDMN+x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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1273c0da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                   -4.495335\n",
      "p-value                           0.000200\n",
      "#Lags Used                        0.000000\n",
      "Number of Observations Used    3631.000000\n",
      "Critical Value (1%)              -3.432152\n",
      "Critical Value (5%)              -2.862336\n",
      "Critical Value (10%)             -2.567194\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "new_df['season_80'] = new_df.price - new_df.price.shift(100)  \n",
    "test_stationarity(new_df.season_80.dropna(inplace=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SaQzOc9xQpY2egcMjRp2HKm0431mSJGl8Bud5wEobkiRJk2dwniestCFJkjQ5Bud5wkob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\nBEEoAAmcBUEQBEEQBKEA/g83x1pkAb1rDwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b401550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(new_df.season_80.iloc[85:], lags=366, ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(new_df.season_80.iloc[85:], lags=366, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I don't think there is seasonality in this!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:6: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=5,center=False).mean()\n",
      "  \n",
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=5,center=False).std()\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "image/png": 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xDEEQisfvLKtk0kxQqsqtqg0qIpa11i8Bs3O8dGQl1i8IglDveIOSiLMsCEIp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2mblwnLTPNvurHVk/mFlOYhhzHuSPjWF0tzh1FjwCnPqSHrEMsDMkvSAIwpLxmMekWsyOYdjO\nsrMIZzn+smkSn8p2PL7Aw3nvsVcmRVnLIJY3a1qZGLGcl1keF8OwC/w2z02ZWa74FHaZZJxzO7M8\nQSwbZ3n9hD5Y+ru/Ky+zDLrf8kr/rI5cWGixPH9muUOauQ5DqFXsLB89qtuhv+lN8Na3VraZuVAK\nPscj9O8VF6kOjrs2BX5VOstuZ4uzZ7WZ4IpYFgRB2BuMzSxXLZY9RY9aImaSiX41OHNG32YZxHLo\n1th097HPP5VtHTdlNwy7wO+uO8Z3w3g+/4Mf4ddKfwxVMmsMQyktlk1m2XXjGEZJYvkUh1ljM50K\nGFOmswxAu72QGAboJf/0T8814LBSHAc+yhMAqN3x1cq3lVfgN69YznOWTS2F6vc4c0Zvx7S+XHYk\nhiEIgjAnPdUgRG1LDMPz9CnbVbb0BfH2PE/3woXlON3sODqKsd8/jRPN1g3DOMunTuQ7y+YL+n/y\nfAB+vdyHUCmNRioctbN8FpjeWXa6neT/TTpEJU1vPMkRAI5zd3qh7xOGHs2wDa3Ze1nnOcsewfJW\n3i0IpeAveQoAwVO/u9JtDRb4VZlZNtES5ffpdLRYNt18lp2dIekFQRCWGaV0kdVggd8ihpK4VjEQ\nWWd5Y0NfduRIpUuYCqV0kd9RX4uuxGVS08cwkmhC388t8DOxk51Io5H2Ne7UrD7LtfGt40xmWcVT\n8FxXi+WwMZ+z/OY3w8MelorlC7gnvXJzU4/oLtNZNmJ5Ac7ysuM4cA/HdbTqZS+rdFtJgd/guOsK\nMsu2WDbDT6R1nCAIwh5BKV3kN+gsq0UU+NnFQKST8jwPXvEK+OEfhn//7ytdwlQ4jhbLD+59GYB7\nOT+5vKizbAabAOMzyzuIej0t8Itc14phjBfLjgM9lTrLn/hE7CzPGcO48Uadfz7FYSBfLJfVDQNI\nYhhVF/jtBMx+3KWJV5u9fdu024pQeow85WWWB1vHDTrLRpTvlNZxO/ijRRAEYTnIE8uLah03KJb/\n/K/0l893fqcupL/ppsoL6qfCiOUHBF8HsmK5aOs4/PzM8vHj8O53l770hdBogCLu66ec9LFOEI5K\nQddp4vTSArwG3VJGnbvuiBhGp6Od5bBTylASc5/iLGvs9sZVPxV54671sAeIfQAAHw1JREFU51a5\nMYxBZ9n3jbO8M8Ty3t4jBUEQSiLPWV7IUBKVFcsbHb29N76x0s0WRilYd9NKwyqcZYB/82/gz/5M\nT5jbSTQa8HEeC8DXj15DjQ/qKyaIFsfRw0MI2sllTTpEc8YwYLJYboTtUoaSAGmBnzjLmePrOeaC\nTL0t21k28Yhozs+twRjGSGdZYhiCIAh7A9tZtsddO4sYd205y6FyuOEGxf79WlwuE44DHZUKq3u4\nILl82gl+ySCTwHKWc4TVu94Ff/InpSx7YdTr8H6ewQqbfOaSZyQxDCacpjbT3lQc2Hbx8QhK6Yah\n1IjMshHL0fzO8nCBn4jlqgXy4LbszLKJYZSVWR7lLCcO9g6JYeztPVIQBKEE9Jf+cAxjIUNJrGlf\noXJ5z3sq3dzMeB7ccU/a2eE+zgOmn+DnuhAqDyKIfGuC3w45jTuJRpyaaLOCUlbkZBpnWXkQ6oOH\nBrqFXFRCj+mVFb2eNs1hZzmMSnGW7dZxYSiZZVhs9n6wz/KpU9W1jsuNYeyQQoOdsUpBEIQlp80y\nOMvLKxxbrXSdW42D9B0tsqaNYQCJC5VxlneZWIaB0/ATTlMnznKgDx6S4SElZJYB/tW/gtMcGnKW\n3bCPQ1RegV+nIzGMmEWLZTuG8ZznxI7vnO+rvNZxoPBxUUE/7qk9/3YWhYhlQRCEOXEc6EZ16vQW\nPpRkJ4rljZXzqccm87QxDADlTc4s71RssWyfhp+UHXUc8FWOWC5peqHnQZcGR7k/vbDToea3595O\nnrMsYnl7YhiY4lLKKfDLc5ZBfwY4Vuu4nfL+FbEsCIIwJ0pBL3BxCRLtEEXgRIst8NspYrlXX0vE\n4bTdMCDtIW26YURKLVZZVEjdmj2SdZYnZ5aDyE3Espm0F6yUM57OFGY5lpii06FuCgpLziy7IpYX\n3gLRjmFAOQf5ea3jQO9LJobhLaBbUFnsjFUKgiAsMY4DvTArlrWzvJgYhp1ZXlZWVlKxHDlOIg6V\ngquv1r+bnyOxnOUW8+Vll41RzvKkg63EWQ4DLuROvsTDAOi3yhXLGTodasH8DnbuuGsRy9viLCvr\nSNUlmLsbRl7rOIjFchzDcNTOcZb39h4pCIJQAkawXsMneXD4VeDShTnLmRiGs7wf6a1WOrUPx0nE\nYRjCM58JX/oSXHnl+Pswo3FV4HOAc6gDBypc8WKxj6kyx1cFMstP4sPJ5f1GtWK5LGc52Sf6fYlh\nxCy6k82gs1xGZjmvdRxkYxiL6ENfFjtjlYIgCEuM7nWrv1xu+tBlwGKd5Z0Ww4gcN3GW+339c5JQ\nBjLO8j61vhzTVkpiVG/dSd0CTJ9lJ/Sp0U8u75UklgfHqQOlOsvJffu+dpYjEctzHH8UJsksR1ln\nuawCv8E4ho5h9NIYxg5xlkUsC4IgzIktlg1hCGrRzvISi+VazXIoHYcHPKD4fTj1+PEFPvs5B/vK\nEYTLwKCznEzzc8afk9eC08UJfI5xAoD/xXM4c/xhpazLdpZPO7rnMp0OXtjTv9th64Jk3je+L5nl\nmO0Qy2ows1ySs2zSHeatKgV+giAIexQ7NwxAvx/HMKp3lu0Cv3l7o1ZJ5nS+cnjve+E3fxMuvXT6\n+zDdMFQQaLG8F5zlKf4uwMOJfB7I1znLfp7H++h75SiuZjN93dZXdG9sOh2cMO5zPYew1Y9TESon\nEcviLC9WLEN+DGPe/sd2v3mAiy7SP+3MssQwBEEQ9hBDn/f//M8LG3e9Uwr8PM/KpyrFsWPwkpcU\nuw87s7zbxXIUy+RJxV76YEkX+F3CHfwzDwLUxDZ805Lpj71ylBAFnQ4qHD1BcVoS99HxIAhiASVi\neRliGGWNuzbOsi2WHTOUZAcV+IlYFgRBmBPHQXdnMHzlKwtzlu0YRrTkYnkdfS7WDXoz3YdTj0VU\nuLudZTuGMUksuy74kYcb6QOIs+iix9XVctaVafnXPKBbvdnO8hxiJyOWY2e5HnbminbsBhbtLGux\nPOAslySWzd2a/THjLC/g87EsdsYqBUEQlhjHIR2SAXDrrWlmeYFiOVjibhieB2c4COjWcbOQOMu+\nz/5od4llWxQXeXo8D/zIxY189rHOOvv45V+Gpz61nHVlxHJrPx2aRO0OKirPWY4ssbwWnIVd1OVk\nFsyxwkMfWv22TJGlE/fphnLOiA3GMAzZzPLOKfBb3k9WQRCEHcKgWO5+8faFxTDs9lvL7Cy7bvx8\nAIE32yhm4yyrwGffLnOWbWxjVU0Qzq4LvVA7y2ts8FUu5SUvKa9Xry2WfUssuyU4y8m0SyOW/ZAV\n/9yeF8tK6VaKhw8vZls96jh+erZHd8OYr3/dYAzD0KOOCuIYhhT4CYIg7B0GxfK7b1pfaAzjFPpb\nNVziAr8ggH/iWwH42JNfN9N9GGe51tukhr9rxbLdZ3eS5vU86EceHgH7WKd1bF+ps1parXRwSLCq\nxXKwVWaBH5xra7Fc767rSYEHD8677B3PlVfCsWOL2VaPOmpILM/3WXKZ7qA51LCmTw1HCvwEQRD2\nHkplxfIKW5w+FelsbsUTBhwH7uECABr+ZqXbmgffh5McRRHx9Su+a6b7MM5yq3NaX7CLWsfZZJzl\nKTLLvUg/L0e8szzr+9ZKNetaLetgbHUfHZqEWx19IGgWMCPmsflosdzqndUX7HFneZEoBV0aQ87y\nvJnlt78d/uiP4JGPTC/73Od2bus4iWEIgiDMieOkEQOAVTbpnuvoaWQVf/HbYnm1c7LSbc2D70++\nzSTcuM9yq3NKX7CLneVNdEWU094Ye1vPg36ov8qb/iasrZW6llYLNtD36dXQMYySneUAV8TyNpIb\nw/DmE7Grq/DsZ2cv27fPbKurxbIU+AmCIOwdBmMYK2xxgPiLv2JBl3GW++OF1XbST4fLzZyn3UvO\nsimG9NbPjL2tFsuWsCn5ObHFcjPY1GK5I87ybiEvs+wQzt1nedS2TnGYxuZpfL+cuMeiELEsCIIw\nJ46DdpFjVtjSrc2g8i9+peBujgOkRVdLSBli2WvoL9Z6P46blBnOXSLq9fQ1neS8mQK/hAqc5fvQ\nw0hUvZa2jovmd5bNQ/Px+KdbAlb6IpYXzagCPzWnszxqW/dzlMbG/amzvEN6au+MVQqCICwxdgzj\nFIe2zVleZuwYxqzmp3GW6/0tfUFjtq4ay06tBu/m+7iQu3j5q3+EQ2Nu63nQjaoTy80mvIsf4ALu\n4eHPfA0Hbv4EqrOBU+JQEh+PW7/ks3JMxPJ20KOO63eT/1fl+BqxXO9u4PW2dDHnDhHLpTjLSqnX\nKqUipdTR+P9KKfXflFK3KaU+o5R6dBnbEQRBWEYcBz6NrmS5m+MLdZYdB06PlVPLge0sz/qUmMxy\nzd/dYrlehxCXX+CnJopf14Wu7SwfKndfaLXAp8bP8tNEq2u6jZzv40blDSXx8fDwWfPjyImI5YUx\nssCvImf5JEcAONC+R19YcQF0WcwtlpVSDwC+C7jDuvh64PL430uB/z7vdgRBEJYVx4HX8os8if/L\nR3kCl/FV/oqn6CsX4CxHOPwkv8Ctv/23lW5rHmz9c/z4bPfhNrQobJgYxi4Vy7Z+mBQdNUNJEo4e\nLXUt9jQ5103zxU4JQ0kMfWo06LLqi7O8HWhnuZdMEHEI5y7wy8M4ywAHO7FY3iHOchmr/K/Avwf+\n2Lrs2cDvRlEUAf+glDqolDoeRdHdJWxPEARhqXAc2GKVv+FJfC//J3vlApxlgF/kJ3nNv6x0U3Px\n4z+uv4vPPx++4ztmu49ULMeFjLtULBdtHefbX+VHjpS6llyxHPiljLs2dGhqsRycw3dqeLs0i76M\n2EONdFaqFreOq6bAb0+KZaXUs4A7oyj6tMq+oy8CvmH9/5vxZUNiWSn1UrT7zCWXXDLPcgRBELYF\n2/3bYiV75QIK/AyLGmIwC40GvOEN891HvaHoUqfZP5fe6S7EFstTOcv2V3nFznIvbvNWprPcoUmT\nDo1gi563glfW+EFhIrZYfve7eiRiuWpnuXuvvnC3iGWl1F9CbvXIG4E3AHleRt6eHuVcRhRFNwE3\nAVx77bW5txEEQVhmbEHTpJO9suL2Zva2d0j8b2ZqNf3FXu+u6wt2qVjOTPCboBuHxHLJ0+/yneWg\ndGf5AGeh28FfEVd50Rix/KqXdoFVavRRFXyY2GL5cG+XOctRFD0l73Kl1LcADwaMq3wx8Eml1GPR\nTvIDrJtfDNw192oFQRCWEFuw/iPXJL+HKJwFTPDbK9RquhjpcBAPJdmlYnmuGEbJXQy8gbv28VCB\nj0P5znKTDr4nYnmRmAI/gDq6yK9JB9Us/72lVNo//FD/Pn3hDhHLM3/MRlH02SiKzoui6EFRFD0I\nLZAfHUXRPcDNwAvjrhiPB85KXlkQhN1KZJ0Tezffz6/zSgD6XmvEX5THXhLL9XpcuW9OVO5SsWwf\nX3U6o28HWmsELGawg13gV0Y3DIOI5e3FOMtGLDfoViaW++iduxbEO/ZuF8sT+ABwO3Ab8JsQf3MI\ngiDsQgYFzRd5GAC+iOVSMc5ywi4Vy7azPKler9lMnWW/vjL+xnOScZZLzCzX6HMZX6VFm0DE8kKx\nM8u2WK7qvRXFsrMWxn2dd0h2rDRJH7vL5vcI+JGy7lsQBGGZMWLZcSAM0y+f/gK++PeSWDbOcsIe\nEMuT6kMPHUrFcm/1YKWTxlxXu9gq8HG98pzlFm0AruUWgpoU+i+SQbGsCKnTr+S9ZSJFoeOmYnmP\nO8uCIAh7BiOWjbAxXz6LcJZ3iDFTCraz7Ctv1x4pFHlNDx1Kn5OguVrRijSJs2x3wyjhNfg5Xg/A\nhdxNUBNnedGYz6sG3cRdrlQsKxHLgiAIe462NsYSsWxyef4Cvvh3qbmaS60Gm2hB2FO794HbzvIk\nDh+GezkfIO1QURFJZjkMcCKfQLmTKxCnwMSWAFa37pv7/oTpGXSWG8QitkKxHImzLAiCsPcwzrIZ\n1pc4y271YnkvOcv1OtzGZcDuFstFneX7OA+ArQdcWdGKNKZNncksB6ocoXOGQzyOfwBg39lvlnKf\nwvTY3TASsVzBYJg0huFRj6TATxAEYU8xKJbPEf+ygOEKe2l+Q60GX+Kh+nfjTO1CijjLhw7BvVzA\nM7mZL7zhD6pbFNkCPzfyCdX8eeWbbtI/v8nFAHj99tz3KUzPoLN88dHFOMutcEv/p8jOvo2IWBYE\nQZiTwRiGEcuKcJtWtDup11OxvBptbvNqqqOIfjAHaH/KM1FHDlezoBhT4OeEgRbLzvyu4LXX6p8m\nSvLNK66b+z6F6RkUy8lBaJViWbk0onZl26kCEcuCIAhzYs4kGuFiuhOo/MGlwozUatl8626lSAzD\nrq+rWnfYA1DqYbcUZ9mc7Q/weDif4y9e+r6571Mohl3gtwixHDourUicZUEQhD3FStze1ky2/gpX\nAPC33/66bVrR7qRWg69y6XYvo3Jm1Q/HjpW7jkFssVyLuqVklm1N9gUeTrRW7Xh4Icugs+wF1Yvl\nsxsuTXGWBUEQ9hZXX61/GtF8jgMoIr74iBu2b1G7kHodfGp8lG/jTfz0di+nMmYt2jz//HLXMUjG\nWY66RCU4y4NaaYfUe+0q7AK/RYjlAJcVdpazLLulIAjCnPz+78OHPgR33bXdK9ndGBH57XyU9753\ne9dSBR/4gG4FV1Qsv+pV8JnPwGq1bZYzYrkRdghKyCwPNl0oYcaJUIDtcJZ9vFQs7xBnWcSyIAjC\nnBw4AN/7vfAbv7E923/ta+Hyy7dn24vEmFAPfCA873nbu5YquP762f7uV3+13HWMYjCGETrlZZYN\n3d3b5GQp2Q6xHODimuJncZYFQRD2Ftt1Cvltb9ue7S4a47iG0mRkWzDdMEDHMMrohrG6Cn/4h/C/\n/ze85z1pZxlhcdgFft4CCvzMPlTVdqpAMsuCIAglIXnLajFiOQi2dx17lcHMcljSUJJ//a/hQQ/S\nv4tYXixKpRNHa/QX0g0jI5bFWRYEQdhbiFiuFvO9KmJ5sbz5zfDFLw5nlsuIYRhaLf1za6u0uxSm\nxIhXh3Ah464zYnnfzuh+Ih/tgiAIJSFiuVrEWd4ebrxR/7zvvmqcZUjFsjjLi0UpCOOQgUuwULHc\n95rUZm39smAkhiEIglASIparZa85y9dcs90ryOJ5tlgu11l+8YvhsY+FH/3R0u5SmAKlUvG6KLFs\n9qFec3/p26gK+WgXBEEoiR1ikuxY9lKB3+bm8h182QV+XtClvnKgtPs+7zz42MdKuzuhAIuOYZiC\nwn5r54hlcZYFQRBK4qKLtnsFu5u9FMNYWVm+2qdMZpkuq/ukKfJORymI0Cr2TfwMD+F2fcUCxPJO\ncpZFLAuCIJTENdfAhz8Mr4unXJsvB6Ec9pJYXkaGxPKBJbO+hRlJP6h+lF/TvyxALPvNnVHcByKW\nBUEQSuWJT0xFXRRt71p2G+Z5feQjt3cdexVbLDfp4NbFWd7p5B3QB44HTvnycCiGsbJznGU5LBQE\nQRB2BPW6du4f8YjtXsneZNBZXrpQtVCYPLHshn6l2+yiXWt/B2WWZU8XBEGoCIlhlM8Tn7jdK9i7\nOI7dOSHU6lnYdfhOrVJxmMQwdpCzLDEMQRCEipAYhrCbUCoVOoA4y7uAvAP6fm2l0m2mYlkyy4Ig\nCHsWU4Amxpuw22jTSv8jO/iuxPeald6/6b4hzrIgCMIeRsSysFvpYAkpcZZ3PHnOcqSqlYZNOgAE\nIpYFQRD2LmZohohlYbchzvLuIk8sq6jaqT+f5VsA6B7dOY3pRSwLgiCUjHGWK+i+JAjbijjLux8n\nqraR+S/yWp7Bn3DPt31vpdspE/koFwRBKBmJYQi7FXGWdxe5znJYrVju0eD9PAPX2zntgkQsC4Ig\nlIyIZWG3khHL4izvSqp2lg076fNRxLIgCELJiFgWdisSw9hdbEdm2bCTYmo7aKmCIAg7AynwE3Yr\nkS0bLr54+xYilEKeWF6Us7yTELEsCIJQMuIsC7uVjLi68sptW4dQHYtylncSIpYFQRBKRsSysFsR\nsby7yHOWtw4cX8i2d9KEUxHLgiAIJWNiGDspkycI05ARV1dcsW3rEKrhxbyTP3/9/13ItkQsC4Ig\n7GHEWRZ2K5kDwLW1bVuHUA6DzvK7+AH8ix64kG2LWBYEQdjDNJvZn4KwW8g7bS/sXIZfT0WjsZht\n7ySxLH1fBEEQSuZtb4Pjx+E5z9nulQhCuSgFj+KfePPbWjxzuxcjVIKI5WFELAuCIJTMwYPwpjdt\n9yoEoXwcBz7No9i4aLtXIpRB3pmCen3x61h2JIYhCIIgCMJUGHElxau7i9u4NPm9ylqLW26Bxz1O\n/y7OsiAIgiAIuw4Ry7sL83o+mk9ykDNAtWL5mmvg/PP17yKWBUEQBEHYdZhOL1Lot7tYZz/r7Aeq\n7+KzE/cdOTYUBEEQBGEqOh39M5Qhb7uC3HHXFSvDH/xB/fPRj652O2UizrIgCIIgCIXw/e1egVAG\neWK5amf5Wc/aWREMEGdZEARBEISCiFjevcgwpWFELAuCIAiCUAiTXRZ2NtsRw9iJyFMiCIIgCEIh\nRCzvXsRZHkbEsiAIgiAIU3HDDfqniOXdwXZklncic4tlpdSPKqW+rJT6vFLqF6zLX6+Uui2+7qnz\nbkcQBEEQhO3lyBH9U8Ty7kDE8nTM1Q1DKfWdwLOBq6Mo6iqlzosvvwp4PvBw4ELgL5VSV0RRJG8v\nQRAEQdiheLFqELG8e5HM8jDzPiWvAH4+iqIuQBRF98WXPxv4wyiKulEUfQ24DXjsnNsSBEEQBGEb\nMa6jdMPYHYizPB3ziuUrgO9QSn1MKfVhpdRj4ssvAr5h3e6b8WWCIAiCIOxQjJASZ3n3ImJ5mIkx\nDKXUXwIX5Fz1xvjvDwGPBx4DvEcp9RAgb5hhbgtqpdRLgZcCXHLJJdOtWhAEQRCEhSMxjN2FtI6b\njoliOYqip4y6Tin1CuB/R1EUAR9XSoXAUbST/ADrphcDd424/5uAmwCuvfbaHTbTRRAEQRD2DhLD\n2F1IDGM65j1++CPgOgCl1BVAHbgfuBl4vlKqoZR6MHA58PE5tyUIgiAIwjZy3XX657d/+/auQ6gO\nEcvDzNUNA3gn8E6l1OeAHvCi2GX+vFLqPcAXAB/4EemEIQiCIAg7m6c8BdbXYW1tu1ciVIXEMIaZ\nSyxHUdQD/u2I694CvGWe+xcEQRAEYbkQoby7EWd5GDl+EARBEARBEABxlvOQp0QQBEEQBEEQRiBi\nWRAEQRAEQQAgkr5kQ8xb4CcIgiAIgiDscD70IfjHf4TDh7d7JcuHiGVBEARBEIQ9zsMfDt/5ndu9\niuVEYhiCIAiCIAh7nEZju1ewvIhYFgRBEARB2OM0m9u9guVFxLIgCIIgCMIep17f7hUsLyKWBUEQ\nBEEQ9jhKbfcKlhcRy4IgCIIgCIIwAhHLgiAIgiAIgjACEcuCIAiCIAiCMAIRy4IgCIIgCIIwAhHL\ngiAIgiAIgjACEcuCIAiCIAiCMAIZdy0IgiAIgrBH+e//HYJgu1ex3IhYFgRBEARB2KO8/OXbvYLl\nR2IYgiAIgiAIgjACEcuCIAiCIAiCMAIRy4IgCIIgCIIwAhHLgiAIgiAIgjACEcuCIAiCIAiCMAIR\ny4IgCIIgCIIwAhHLgiAIgiAIgjACEcuCIAiCIAiCMAIRy4IgCIIgCIIwAhHLgiAIgiAIgjACEcuC\nIAiCIAiCMAIRy4IgCP9/e/cWKld5hnH8/2A8JRqNVdtoROuNRUpRCbbWEooR8YT2soWCgtCbXqhF\nWkUQvOuFlN4VSmxND0Q8K9ILxQMqeGgSYxuNIKXWU3THWo0pRUP79GK+SBpcCs2755vJen6wmNkz\nw8f7PXvNWu+e+WZ2RETEgDTLERERERED0ixHRERERAxIsxwRERERMSDNckRERETEgDTLERERERED\n0ixHRERERAyQ7d41fELSDuBvveuYIccC7/Yu4gCRLOskyzrJsk6yrJEcayXPGouV48m2j/u8B81U\nsxz/S9JG26t713EgSJZ1kmWdZFknWdZIjrWSZ43eOWYZRkRERETEgDTLERERERED0izPtl/2LuAA\nkizrJMs6ybJOsqyRHGslzxpdc8ya5YiIiIiIAXllOSIiIiJiQJrlKZJ0kqTHJG2T9KKkq9vtx0h6\nWNIr7XJFu/0rkp6W9JGk6/YZ61eSFiRt7TGX3qqyHBpnTAqzPEzSc5JeaOPc3GtOvVQ+x9v9B0l6\nXtKD055Lb8XHy1cl/VnSFkkbe8ynl+Icj5Z0l6SX23jn9JhTT4XHy9Pa/rhn2ynpml7z6qF437y2\njbFV0gZJh5XWmmUY0yNpJbDS9mZJRwKbgO8AVwLv2f6ppOuBFbZ/Iul44OT2mH/YvmWvsdYAu4Df\n2P7qtOfSW1WWQ+PYfqnDtLoozFLAMtu7JB0MPAVcbfuZDtPqovI53sb7EbAaWG770mnOpbfi4+Wr\nwGrbo/u+2+Ic1wNP2l4n6RBgqe33pz2nnqqf423Mg4A3ga/bHs3/mig895zI5Hxzuu1/SboD+IPt\n26pqzSvLU2R7u+3N7fqHwDbgROByYH172HomOwK2F2z/Edj9KWM9Abw3jbpnUVWWnzHOaBRmadu7\n2o8Ht21Uf41XPsclrQIuAdZNofSZU5nlmFXlKGk5sAa4tT3u47E1yrBo++Va4C9japShPMslwOGS\nlgBLgbcqa02z3ImkU4AzgWeBL9reDpOdBzi+X2XzpyrLfcYZpf3NUpNlA1uABeBh28ny/98vfw78\nGPjPIpU4NwqyNPCQpE2SfrBYdc66/czxVGAH8GtNlgatk7RsEcudeYXn8e8CG6rrmyf7k6XtN4Fb\ngNeA7cAHth+qrC/NcgeSjgDuBq6xvbN3PfOsKsv8TmoysP1v22cAq4CzJY1uiRDsf5aSLgUWbG8q\nL27OFD03z7V9FnAR8MO2jG1UCnJcApwF/ML2mcA/gesLS5wrheeeQ4DLgDuraps3BcfLFUxejf4y\ncAKwTNL3K2tMszxlbS3n3cDvbd/Tbn6nrd3Zs4ZnoVd986Qqy4FxRqV6v2xvzz4OXFhc6swryvJc\n4LK21vZ24DxJv1ukkmdW1X5p+612uQDcC5y9OBXPpqIc3wDe2OvdoruYNM+jU3y8vAjYbPud+kpn\nX1GW5wN/tb3D9m7gHuCblXWmWZ6i9gGoW4Fttn+2110PAFe061cA90+7tnlTleVnjDMahVkeJ+no\ndv1wJgewl+srnl1VWdq+wfYq26cweYv2Udulr5TMusL9cln78BBt2cAFwGi+Rahwn3wbeF3Sae2m\ntcBoPgi9xyKcx7/HSJdgFGb5GvANSUvbmGuZrH+uYzvblDbgW0zWzv0J2NK2i4EvAI8Ar7TLY9rj\nv8Tkr/mdwPvt+vJ23wYma3N2t9uv6j2/ecxyaJze85vTLL8GPN/G2Qrc1Htu85rlPmN+G3iw99zm\nNUsma21faNuLwI295zaPObb7zgA2trHuY/ItBd3nOMd5LgX+DhzVe14HQJY3M3lxZivwW+DQylrz\n1XEREREREQOyDCMiIiIiYkCa5YiIiIiIAWmWIyIiIiIGpFmOiIiIiBiQZjkiIiIiYkCa5YiIiIiI\nAWmWIyIiIiIGpFmOiIiIiBjwX/e23y+h92FUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12438e588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                -7.457554e+00\n",
      "p-value                        5.461482e-11\n",
      "#Lags Used                     2.400000e+01\n",
      "Number of Observations Used    1.825000e+03\n",
      "Critical Value (1%)           -3.433938e+00\n",
      "Critical Value (5%)           -2.863125e+00\n",
      "Critical Value (10%)          -2.567614e+00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stock['season_16'] = stock.close - stock.close.shift(16)  \n",
    "test_stationarity(stock.season_16.dropna(inplace=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>price</td>      <th>  No. Observations:  </th>   <td>3732</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 0)</td> <th>  Log Likelihood     </th> <td>-9988.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 20 Dec 2017</td> <th>  AIC                </th> <td>19980.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>14:59:47</td>     <th>  BIC                </th> <td>19992.452</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>06-29-2010</td>    <th>  HQIC               </th> <td>19984.431</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 11-22-2017</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>   -0.0108</td> <td>    0.011</td> <td>   -1.024</td> <td> 0.306</td> <td>   -0.031</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>   12.3815</td> <td>    0.128</td> <td>   96.873</td> <td> 0.000</td> <td>   12.131</td> <td>   12.632</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>44.77</td> <th>  Jarque-Bera (JB):  </th> <td>10230.27</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.28</td>  <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>42.66</td> <th>  Skew:              </th>   <td>-0.26</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>   <td>11.10</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Statespace Model Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   No. Observations:                 3732\n",
       "Model:               SARIMAX(1, 1, 0)   Log Likelihood               -9988.001\n",
       "Date:                Wed, 20 Dec 2017   AIC                          19980.002\n",
       "Time:                        14:59:47   BIC                          19992.452\n",
       "Sample:                    06-29-2010   HQIC                         19984.431\n",
       "                         - 11-22-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.0108      0.011     -1.024      0.306      -0.031       0.010\n",
       "sigma2        12.3815      0.128     96.873      0.000      12.131      12.632\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       44.77   Jarque-Bera (JB):             10230.27\n",
       "Prob(Q):                              0.28   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):              42.66   Skew:                            -0.26\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        11.10\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = sm.tsa.statespace.SARIMAX(new_df.price, trend='n', order=(1,1,0))\n",
    "results = mod.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>price</td>       <th>  No. Observations:  </th>   <td>3732</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(14, 1, 0)</td> <th>  Log Likelihood     </th> <td>-9984.312</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 20 Dec 2017</td>  <th>  AIC                </th> <td>19998.625</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:00:04</td>      <th>  BIC                </th> <td>20091.995</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>06-29-2010</td>     <th>  HQIC               </th> <td>20031.839</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 11-22-2017</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>   -0.0103</td> <td>    0.011</td> <td>   -0.959</td> <td> 0.337</td> <td>   -0.031</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>    0.0138</td> <td>    0.011</td> <td>    1.314</td> <td> 0.189</td> <td>   -0.007</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>  <td>   -0.0116</td> <td>    0.012</td> <td>   -0.970</td> <td> 0.332</td> <td>   -0.035</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4</th>  <td>   -0.0009</td> <td>    0.012</td> <td>   -0.078</td> <td> 0.938</td> <td>   -0.024</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5</th>  <td> 9.271e-05</td> <td>    0.014</td> <td>    0.007</td> <td> 0.995</td> <td>   -0.027</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L6</th>  <td>   -0.0192</td> <td>    0.012</td> <td>   -1.608</td> <td> 0.108</td> <td>   -0.043</td> <td>    0.004</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L7</th>  <td>   -0.0049</td> <td>    0.012</td> <td>   -0.415</td> <td> 0.678</td> <td>   -0.028</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L8</th>  <td>   -0.0128</td> <td>    0.011</td> <td>   -1.141</td> <td> 0.254</td> <td>   -0.035</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L9</th>  <td>   -0.0096</td> <td>    0.014</td> <td>   -0.688</td> <td> 0.491</td> <td>   -0.037</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L10</th> <td>    0.0093</td> <td>    0.012</td> <td>    0.796</td> <td> 0.426</td> <td>   -0.014</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L11</th> <td>    0.0022</td> <td>    0.014</td> <td>    0.153</td> <td> 0.878</td> <td>   -0.025</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L12</th> <td>    0.0256</td> <td>    0.012</td> <td>    2.084</td> <td> 0.037</td> <td>    0.002</td> <td>    0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L13</th> <td>   -0.0148</td> <td>    0.015</td> <td>   -1.013</td> <td> 0.311</td> <td>   -0.043</td> <td>    0.014</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L14</th> <td>    0.0002</td> <td>    0.012</td> <td>    0.019</td> <td> 0.984</td> <td>   -0.023</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>   12.3569</td> <td>    0.130</td> <td>   94.899</td> <td> 0.000</td> <td>   12.102</td> <td>   12.612</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>36.85</td> <th>  Jarque-Bera (JB):  </th> <td>10237.81</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.61</td>  <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>42.67</td> <th>  Skew:              </th>   <td>-0.26</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>   <td>11.10</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Statespace Model Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   No. Observations:                 3732\n",
       "Model:              SARIMAX(14, 1, 0)   Log Likelihood               -9984.312\n",
       "Date:                Wed, 20 Dec 2017   AIC                          19998.625\n",
       "Time:                        15:00:04   BIC                          20091.995\n",
       "Sample:                    06-29-2010   HQIC                         20031.839\n",
       "                         - 11-22-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1         -0.0103      0.011     -0.959      0.337      -0.031       0.011\n",
       "ar.L2          0.0138      0.011      1.314      0.189      -0.007       0.034\n",
       "ar.L3         -0.0116      0.012     -0.970      0.332      -0.035       0.012\n",
       "ar.L4         -0.0009      0.012     -0.078      0.938      -0.024       0.022\n",
       "ar.L5       9.271e-05      0.014      0.007      0.995      -0.027       0.027\n",
       "ar.L6         -0.0192      0.012     -1.608      0.108      -0.043       0.004\n",
       "ar.L7         -0.0049      0.012     -0.415      0.678      -0.028       0.018\n",
       "ar.L8         -0.0128      0.011     -1.141      0.254      -0.035       0.009\n",
       "ar.L9         -0.0096      0.014     -0.688      0.491      -0.037       0.018\n",
       "ar.L10         0.0093      0.012      0.796      0.426      -0.014       0.032\n",
       "ar.L11         0.0022      0.014      0.153      0.878      -0.025       0.030\n",
       "ar.L12         0.0256      0.012      2.084      0.037       0.002       0.050\n",
       "ar.L13        -0.0148      0.015     -1.013      0.311      -0.043       0.014\n",
       "ar.L14         0.0002      0.012      0.019      0.984      -0.023       0.023\n",
       "sigma2        12.3569      0.130     94.899      0.000      12.102      12.612\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       36.85   Jarque-Bera (JB):             10237.81\n",
       "Prob(Q):                              0.61   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):              42.67   Skew:                            -0.26\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        11.10\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = sm.tsa.statespace.SARIMAX(new_df.price, trend='n', order=(14,1,0), seasonal_order=(0,0,0,0))\n",
    "results = mod.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>price</td>       <th>  No. Observations:  </th>   <td>3732</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(3, 1, 30)</td> <th>  Log Likelihood     </th> <td>-9972.530</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 20 Dec 2017</td>  <th>  AIC                </th> <td>20013.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:01:32</td>      <th>  BIC                </th> <td>20224.700</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>06-29-2010</td>     <th>  HQIC               </th> <td>20088.346</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 11-22-2017</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>        <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.1076</td> <td>    0.180</td> <td>    0.598</td> <td> 0.550</td> <td>   -0.245</td> <td>    0.460</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>   -0.0810</td> <td>    0.164</td> <td>   -0.495</td> <td> 0.621</td> <td>   -0.402</td> <td>    0.240</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3</th>  <td>   -0.7125</td> <td>    0.167</td> <td>   -4.277</td> <td> 0.000</td> <td>   -1.039</td> <td>   -0.386</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.1143</td> <td>    0.180</td> <td>   -0.634</td> <td> 0.526</td> <td>   -0.468</td> <td>    0.239</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>    0.1013</td> <td>    0.165</td> <td>    0.612</td> <td> 0.541</td> <td>   -0.223</td> <td>    0.426</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3</th>  <td>    0.6977</td> <td>    0.171</td> <td>    4.088</td> <td> 0.000</td> <td>    0.363</td> <td>    1.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L4</th>  <td>    0.0014</td> <td>    0.015</td> <td>    0.093</td> <td> 0.926</td> <td>   -0.028</td> <td>    0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L5</th>  <td>    0.0133</td> <td>    0.016</td> <td>    0.810</td> <td> 0.418</td> <td>   -0.019</td> <td>    0.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L6</th>  <td>   -0.0330</td> <td>    0.015</td> <td>   -2.162</td> <td> 0.031</td> <td>   -0.063</td> <td>   -0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L7</th>  <td>    0.0040</td> <td>    0.015</td> <td>    0.260</td> <td> 0.795</td> <td>   -0.026</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L8</th>  <td>   -0.0184</td> <td>    0.016</td> <td>   -1.188</td> <td> 0.235</td> <td>   -0.049</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L9</th>  <td>   -0.0231</td> <td>    0.018</td> <td>   -1.312</td> <td> 0.189</td> <td>   -0.058</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L10</th> <td>    0.0126</td> <td>    0.014</td> <td>    0.888</td> <td> 0.375</td> <td>   -0.015</td> <td>    0.041</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L11</th> <td>   -0.0077</td> <td>    0.017</td> <td>   -0.464</td> <td> 0.642</td> <td>   -0.040</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L12</th> <td>    0.0190</td> <td>    0.016</td> <td>    1.188</td> <td> 0.235</td> <td>   -0.012</td> <td>    0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L13</th> <td>   -0.0017</td> <td>    0.019</td> <td>   -0.089</td> <td> 0.929</td> <td>   -0.038</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L14</th> <td>    0.0046</td> <td>    0.017</td> <td>    0.268</td> <td> 0.789</td> <td>   -0.029</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L15</th> <td>    0.0456</td> <td>    0.016</td> <td>    2.799</td> <td> 0.005</td> <td>    0.014</td> <td>    0.078</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L16</th> <td>   -0.0206</td> <td>    0.017</td> <td>   -1.225</td> <td> 0.220</td> <td>   -0.053</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L17</th> <td>    0.0032</td> <td>    0.018</td> <td>    0.178</td> <td> 0.858</td> <td>   -0.032</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L18</th> <td>    0.0143</td> <td>    0.017</td> <td>    0.826</td> <td> 0.409</td> <td>   -0.020</td> <td>    0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L19</th> <td>   -0.0027</td> <td>    0.019</td> <td>   -0.146</td> <td> 0.884</td> <td>   -0.039</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L20</th> <td>-8.989e-05</td> <td>    0.017</td> <td>   -0.005</td> <td> 0.996</td> <td>   -0.033</td> <td>    0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L21</th> <td>    0.0033</td> <td>    0.016</td> <td>    0.206</td> <td> 0.837</td> <td>   -0.028</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L22</th> <td>    0.0097</td> <td>    0.017</td> <td>    0.579</td> <td> 0.563</td> <td>   -0.023</td> <td>    0.042</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L23</th> <td>    0.0106</td> <td>    0.017</td> <td>    0.639</td> <td> 0.523</td> <td>   -0.022</td> <td>    0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L24</th> <td>    0.0181</td> <td>    0.016</td> <td>    1.149</td> <td> 0.251</td> <td>   -0.013</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L25</th> <td>   -0.0312</td> <td>    0.017</td> <td>   -1.808</td> <td> 0.071</td> <td>   -0.065</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L26</th> <td>    0.0203</td> <td>    0.017</td> <td>    1.161</td> <td> 0.246</td> <td>   -0.014</td> <td>    0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L27</th> <td>    0.0049</td> <td>    0.020</td> <td>    0.245</td> <td> 0.807</td> <td>   -0.035</td> <td>    0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L28</th> <td>   -0.0426</td> <td>    0.018</td> <td>   -2.314</td> <td> 0.021</td> <td>   -0.079</td> <td>   -0.007</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L29</th> <td>   -0.0207</td> <td>    0.016</td> <td>   -1.315</td> <td> 0.189</td> <td>   -0.051</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L30</th> <td>    0.0225</td> <td>    0.017</td> <td>    1.334</td> <td> 0.182</td> <td>   -0.011</td> <td>    0.055</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>   12.2909</td> <td>    0.137</td> <td>   89.389</td> <td> 0.000</td> <td>   12.021</td> <td>   12.560</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>16.41</td> <th>  Jarque-Bera (JB):  </th> <td>9740.23</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>1.00</td>  <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>42.22</td> <th>  Skew:              </th>  <td>-0.25</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>  <td>10.90</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Statespace Model Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   No. Observations:                 3732\n",
       "Model:              SARIMAX(3, 1, 30)   Log Likelihood               -9972.530\n",
       "Date:                Wed, 20 Dec 2017   AIC                          20013.060\n",
       "Time:                        15:01:32   BIC                          20224.700\n",
       "Sample:                    06-29-2010   HQIC                         20088.346\n",
       "                         - 11-22-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.1076      0.180      0.598      0.550      -0.245       0.460\n",
       "ar.L2         -0.0810      0.164     -0.495      0.621      -0.402       0.240\n",
       "ar.L3         -0.7125      0.167     -4.277      0.000      -1.039      -0.386\n",
       "ma.L1         -0.1143      0.180     -0.634      0.526      -0.468       0.239\n",
       "ma.L2          0.1013      0.165      0.612      0.541      -0.223       0.426\n",
       "ma.L3          0.6977      0.171      4.088      0.000       0.363       1.032\n",
       "ma.L4          0.0014      0.015      0.093      0.926      -0.028       0.031\n",
       "ma.L5          0.0133      0.016      0.810      0.418      -0.019       0.046\n",
       "ma.L6         -0.0330      0.015     -2.162      0.031      -0.063      -0.003\n",
       "ma.L7          0.0040      0.015      0.260      0.795      -0.026       0.034\n",
       "ma.L8         -0.0184      0.016     -1.188      0.235      -0.049       0.012\n",
       "ma.L9         -0.0231      0.018     -1.312      0.189      -0.058       0.011\n",
       "ma.L10         0.0126      0.014      0.888      0.375      -0.015       0.041\n",
       "ma.L11        -0.0077      0.017     -0.464      0.642      -0.040       0.025\n",
       "ma.L12         0.0190      0.016      1.188      0.235      -0.012       0.050\n",
       "ma.L13        -0.0017      0.019     -0.089      0.929      -0.038       0.035\n",
       "ma.L14         0.0046      0.017      0.268      0.789      -0.029       0.038\n",
       "ma.L15         0.0456      0.016      2.799      0.005       0.014       0.078\n",
       "ma.L16        -0.0206      0.017     -1.225      0.220      -0.053       0.012\n",
       "ma.L17         0.0032      0.018      0.178      0.858      -0.032       0.038\n",
       "ma.L18         0.0143      0.017      0.826      0.409      -0.020       0.048\n",
       "ma.L19        -0.0027      0.019     -0.146      0.884      -0.039       0.034\n",
       "ma.L20     -8.989e-05      0.017     -0.005      0.996      -0.033       0.033\n",
       "ma.L21         0.0033      0.016      0.206      0.837      -0.028       0.035\n",
       "ma.L22         0.0097      0.017      0.579      0.563      -0.023       0.042\n",
       "ma.L23         0.0106      0.017      0.639      0.523      -0.022       0.043\n",
       "ma.L24         0.0181      0.016      1.149      0.251      -0.013       0.049\n",
       "ma.L25        -0.0312      0.017     -1.808      0.071      -0.065       0.003\n",
       "ma.L26         0.0203      0.017      1.161      0.246      -0.014       0.055\n",
       "ma.L27         0.0049      0.020      0.245      0.807      -0.035       0.044\n",
       "ma.L28        -0.0426      0.018     -2.314      0.021      -0.079      -0.007\n",
       "ma.L29        -0.0207      0.016     -1.315      0.189      -0.051       0.010\n",
       "ma.L30         0.0225      0.017      1.334      0.182      -0.011       0.055\n",
       "sigma2        12.2909      0.137     89.389      0.000      12.021      12.560\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       16.41   Jarque-Bera (JB):              9740.23\n",
       "Prob(Q):                              1.00   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):              42.22   Skew:                            -0.25\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        10.90\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = sm.tsa.statespace.SARIMAX(new_df.price, trend='n', order=(3,1,30), seasonal_order=(0,0,0,0))\n",
    "results = mod.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>price</td>      <th>  No. Observations:  </th>   <td>3732</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(2, 1, 3)</td> <th>  Log Likelihood     </th> <td>-9987.180</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 20 Dec 2017</td> <th>  AIC                </th> <td>19986.360</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>15:06:34</td>     <th>  BIC                </th> <td>20023.708</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>06-29-2010</td>    <th>  HQIC               </th> <td>19999.646</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 11-22-2017</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.4195</td> <td>    0.530</td> <td>    0.791</td> <td> 0.429</td> <td>   -0.619</td> <td>    1.458</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2</th>  <td>    0.1986</td> <td>    0.544</td> <td>    0.365</td> <td> 0.715</td> <td>   -0.867</td> <td>    1.265</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1</th>  <td>   -0.4301</td> <td>    0.530</td> <td>   -0.811</td> <td> 0.417</td> <td>   -1.469</td> <td>    0.609</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2</th>  <td>   -0.1805</td> <td>    0.547</td> <td>   -0.330</td> <td> 0.742</td> <td>   -1.254</td> <td>    0.892</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3</th>  <td>   -0.0184</td> <td>    0.014</td> <td>   -1.339</td> <td> 0.181</td> <td>   -0.045</td> <td>    0.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>   12.3758</td> <td>    0.129</td> <td>   96.281</td> <td> 0.000</td> <td>   12.124</td> <td>   12.628</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>          <td>42.61</td> <th>  Jarque-Bera (JB):  </th> <td>10176.92</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                <td>0.36</td>  <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>42.81</td> <th>  Skew:              </th>   <td>-0.25</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>   <td>11.08</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Statespace Model Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   No. Observations:                 3732\n",
       "Model:               SARIMAX(2, 1, 3)   Log Likelihood               -9987.180\n",
       "Date:                Wed, 20 Dec 2017   AIC                          19986.360\n",
       "Time:                        15:06:34   BIC                          20023.708\n",
       "Sample:                    06-29-2010   HQIC                         19999.646\n",
       "                         - 11-22-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.4195      0.530      0.791      0.429      -0.619       1.458\n",
       "ar.L2          0.1986      0.544      0.365      0.715      -0.867       1.265\n",
       "ma.L1         -0.4301      0.530     -0.811      0.417      -1.469       0.609\n",
       "ma.L2         -0.1805      0.547     -0.330      0.742      -1.254       0.892\n",
       "ma.L3         -0.0184      0.014     -1.339      0.181      -0.045       0.009\n",
       "sigma2        12.3758      0.129     96.281      0.000      12.124      12.628\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                       42.61   Jarque-Bera (JB):             10176.92\n",
       "Prob(Q):                              0.36   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):              42.81   Skew:                            -0.25\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                        11.08\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = sm.tsa.statespace.SARIMAX(new_df.price, trend='n', order=(2,1,3))\n",
    "results = mod.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1640"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# cut out 2017 data\n",
    "1866-226"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YqeUWlgVlBNgx5jhatR1cl6vUPsgWBgrFV1xvUh7awUijNWXMuB1v9Pcl5FU25RZB4ASt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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1271356d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df['forecast'] = results.predict(dynamic=False)\n",
    "new_df[['price', 'forecast']].plot(figsize=(12, 8)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZQbKIiIjIiPOzwahL78D10VN7ZH0HWZOcm0m2ueUcqX6CXS+cV/OxHwfCnbDDTLJqkhUk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lvXu7vtEuST3/wZpsf3XJz/N79YlM+oYYyLZK3ghTaGEktCSOhYvfgADgZaQiRjcaP2m+Rx4+JH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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124dd4940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df[['price', 'forecast']].iloc[1640:].plot(figsize=(12, 8)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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57FygnyIUkkVEREQqFAY+rrEV37hHtrc49PnTDR9nzl2Xs+OebwBw/wP3Ej7x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x4YViRLKXdKKV90/u4DXgOmAu8EkplMNwPvcv5+J3CLtHkWaBBCjMsr8vrd/ejK6JQ2sswEihNJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11e576160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df[['price', 'forecast']].iloc[1800:].plot(figsize=(12, 8)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TJlNXkqPncCmF67kZLLWIBwbJREQUU6WVTRjYMxMWi0C/vHQcr+ve5RYu3Q49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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1272a2e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df['forecast'] = [np.NaN for i in range(3280)] + list(results.predict(start=3280, end=3731, dynamic=True))\n",
    "new_df[['price', 'forecast']].plot(figsize=(12, 8)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RJFmIOuzNtFsQ0tc/AXUhFs+CxkaVZFB9yWf2Qml2J0clhBBC+IYkyULU0Wjh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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1272ac400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df['forecast'] = [np.NaN for i in range(3280)] + list(results.predict(start=3280, end=3731, dynamic=False))\n",
    "new_df[['price', 'forecast']].iloc[3600:].plot(figsize=(12, 8)) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Basically I'm a day behind; always."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def profit_calculator(dataframe, predict_col, early_stop=None):  \n",
    "    money_counter = 0\n",
    "    transactions = 0\n",
    "    own = False\n",
    "    last_buy = 0\n",
    "    current_price = 0\n",
    "    for num, i in enumerate(dataframe.iloc[:early_stop].iterrows()):\n",
    "        if i[1][predict_col] == 1 and own == False:\n",
    "            money_counter -= i[1]['price']\n",
    "            own = True\n",
    "            transactions += 1\n",
    "            last_buy = i[1]['price']\n",
    "        elif i[1][predict_col] == 0 and own == True:\n",
    "            money_counter += i[1]['price']\n",
    "            own = False\n",
    "            transactions += 1\n",
    "        else:\n",
    "            pass\n",
    "        current_price=i[1]['price']\n",
    "    print('Own?', own)\n",
    "    print('Last Buying price: $', last_buy)\n",
    "    print('Current price: $', current_price)\n",
    "    print('Cash? $', money_counter)\n",
    "    if own == True:\n",
    "        print('Profit: $', current_price + money_counter)\n",
    "    else:\n",
    "        print('Profit: $', money_counter)\n",
    "    print('Number of Transactins:', transactions)\n",
    "    print('Cost of transactions: $', transactions*5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['target'] = new_df.price.shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>first_difference</th>\n",
       "      <th>season_80</th>\n",
       "      <th>forecast</th>\n",
       "      <th>target</th>\n",
       "      <th>predict_grow</th>\n",
       "      <th>true_grow</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-20 16:00:00</th>\n",
       "      <td>308.74</td>\n",
       "      <td>-5.05</td>\n",
       "      <td>-54.95</td>\n",
       "      <td>313.454153</td>\n",
       "      <td>310.86</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-21 09:30:00</th>\n",
       "      <td>310.86</td>\n",
       "      <td>2.12</td>\n",
       "      <td>-53.63</td>\n",
       "      <td>308.870018</td>\n",
       "      <td>317.81</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-21 16:00:00</th>\n",
       "      <td>317.81</td>\n",
       "      <td>6.95</td>\n",
       "      <td>-44.94</td>\n",
       "      <td>310.770208</td>\n",
       "      <td>316.77</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-22 09:30:00</th>\n",
       "      <td>316.77</td>\n",
       "      <td>-1.04</td>\n",
       "      <td>-47.05</td>\n",
       "      <td>317.846330</td>\n",
       "      <td>312.60</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-22 16:00:00</th>\n",
       "      <td>312.60</td>\n",
       "      <td>-4.17</td>\n",
       "      <td>-53.63</td>\n",
       "      <td>316.869165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      price  first_difference  season_80    forecast  target  \\\n",
       "date                                                                           \n",
       "2017-11-20 16:00:00  308.74             -5.05     -54.95  313.454153  310.86   \n",
       "2017-11-21 09:30:00  310.86              2.12     -53.63  308.870018  317.81   \n",
       "2017-11-21 16:00:00  317.81              6.95     -44.94  310.770208  316.77   \n",
       "2017-11-22 09:30:00  316.77             -1.04     -47.05  317.846330  312.60   \n",
       "2017-11-22 16:00:00  312.60             -4.17     -53.63  316.869165     NaN   \n",
       "\n",
       "                     predict_grow  true_grow  \n",
       "date                                          \n",
       "2017-11-20 16:00:00             1          1  \n",
       "2017-11-21 09:30:00             0          1  \n",
       "2017-11-21 16:00:00             0          0  \n",
       "2017-11-22 09:30:00             1          0  \n",
       "2017-11-22 16:00:00             1          0  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['forecast'] = results.predict(dynamic=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['predict_grow'] = new_df[['forecast', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 0 else 0, axis=1)\n",
    "new_df['true_grow'] = new_df[['target', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 0 else 0, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? True\n",
      "Last Buying price: $ 316.77\n",
      "Current price: $ 316.77\n",
      "Cash? $ -264.04\n",
      "Profit: $ 52.73\n",
      "Number of Transactins: 231\n",
      "Cost of transactions: $ 1155\n"
     ]
    }
   ],
   "source": [
    "profit_calculator(new_df.iloc[3280:], 'predict_grow', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 858.16\n",
      "Profit: $ 858.16\n",
      "Number of Transactins: 236\n",
      "Cost of transactions: $ 1180\n"
     ]
    }
   ],
   "source": [
    "profit_calculator(new_df.iloc[3280:], 'true_grow', -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Damn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29 09:30:00</th>\n",
       "      <td>19.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-29 16:00:00</th>\n",
       "      <td>23.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30 09:30:00</th>\n",
       "      <td>25.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30 16:00:00</th>\n",
       "      <td>23.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01 09:30:00</th>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price\n",
       "date                      \n",
       "2010-06-29 09:30:00  19.00\n",
       "2010-06-29 16:00:00  23.89\n",
       "2010-06-30 09:30:00  25.79\n",
       "2010-06-30 16:00:00  23.83\n",
       "2010-07-01 09:30:00  25.00"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa.arima_model import ARMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py:473: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n",
      "/anaconda/lib/python3.6/site-packages/statsmodels/tsa/arima_model.py:1441: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return np.sqrt(np.diag(-inv(hess)))\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>ARMA Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>price</td>      <th>  No. Observations:  </th>   <td>3732</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>            <td>ARMA(5, 21)</td>   <th>  Log Likelihood     </th> <td>-9985.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>3.509</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>          <td>Wed, 20 Dec 2017</td> <th>  AIC                </th> <td>20026.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>              <td>16:52:06</td>     <th>  BIC                </th> <td>20200.324</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>           <td>06-29-2010</td>    <th>  HQIC               </th> <td>20088.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                 <td>- 11-22-2017</td>   <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "        <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>        <td>  335.7079</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1.price</th>  <td>    0.7689</td> <td>    0.004</td> <td>  185.376</td> <td> 0.000</td> <td>    0.761</td> <td>    0.777</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L2.price</th>  <td>    0.6074</td> <td>    0.005</td> <td>  115.249</td> <td> 0.000</td> <td>    0.597</td> <td>    0.618</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L3.price</th>  <td>    0.0842</td> <td>    0.003</td> <td>   33.293</td> <td> 0.000</td> <td>    0.079</td> <td>    0.089</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L4.price</th>  <td>   -0.9482</td> <td>    0.001</td> <td> -777.670</td> <td> 0.000</td> <td>   -0.951</td> <td>   -0.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L5.price</th>  <td>    0.4876</td> <td>    0.002</td> <td>  201.837</td> <td> 0.000</td> <td>    0.483</td> <td>    0.492</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L1.price</th>  <td>    0.2218</td> <td>    0.017</td> <td>   13.125</td> <td> 0.000</td> <td>    0.189</td> <td>    0.255</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L2.price</th>  <td>   -0.3640</td> <td>    0.017</td> <td>  -21.294</td> <td> 0.000</td> <td>   -0.397</td> <td>   -0.330</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L3.price</th>  <td>   -0.4676</td> <td>    0.018</td> <td>  -26.454</td> <td> 0.000</td> <td>   -0.502</td> <td>   -0.433</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L4.price</th>  <td>    0.4857</td> <td>    0.020</td> <td>   24.863</td> <td> 0.000</td> <td>    0.447</td> <td>    0.524</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L5.price</th>  <td>   -0.0073</td> <td>    0.021</td> <td>   -0.351</td> <td> 0.726</td> <td>   -0.048</td> <td>    0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L6.price</th>  <td>   -0.0062</td> <td>    0.021</td> <td>   -0.297</td> <td> 0.766</td> <td>   -0.047</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L7.price</th>  <td>   -0.0149</td> <td>    0.021</td> <td>   -0.712</td> <td> 0.477</td> <td>   -0.056</td> <td>    0.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L8.price</th>  <td>   -0.0054</td> <td>    0.021</td> <td>   -0.257</td> <td> 0.797</td> <td>   -0.047</td> <td>    0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L9.price</th>  <td>   -0.0031</td> <td>    0.021</td> <td>   -0.149</td> <td> 0.882</td> <td>   -0.044</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L10.price</th> <td>    0.0064</td> <td>    0.021</td> <td>    0.304</td> <td> 0.761</td> <td>   -0.035</td> <td>    0.048</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L11.price</th> <td>    0.0115</td> <td>    0.021</td> <td>    0.553</td> <td> 0.581</td> <td>   -0.029</td> <td>    0.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L12.price</th> <td>    0.0194</td> <td>    0.021</td> <td>    0.925</td> <td> 0.355</td> <td>   -0.022</td> <td>    0.061</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L13.price</th> <td>   -0.0179</td> <td>    0.021</td> <td>   -0.843</td> <td> 0.399</td> <td>   -0.059</td> <td>    0.024</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L14.price</th> <td>   -0.0054</td> <td>    0.021</td> <td>   -0.260</td> <td> 0.795</td> <td>   -0.046</td> <td>    0.035</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L15.price</th> <td>    0.0261</td> <td>    0.021</td> <td>    1.215</td> <td> 0.224</td> <td>   -0.016</td> <td>    0.068</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L16.price</th> <td>    0.0135</td> <td>    0.021</td> <td>    0.651</td> <td> 0.515</td> <td>   -0.027</td> <td>    0.054</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L17.price</th> <td>   -0.0213</td> <td>    0.022</td> <td>   -0.964</td> <td> 0.335</td> <td>   -0.065</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L18.price</th> <td>   -0.0133</td> <td>    0.018</td> <td>   -0.728</td> <td> 0.467</td> <td>   -0.049</td> <td>    0.023</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L19.price</th> <td>    0.0250</td> <td>    0.019</td> <td>    1.326</td> <td> 0.185</td> <td>   -0.012</td> <td>    0.062</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L20.price</th> <td>   -0.0086</td> <td>    0.017</td> <td>   -0.502</td> <td> 0.615</td> <td>   -0.042</td> <td>    0.025</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ma.L21.price</th> <td>    0.0151</td> <td>    0.016</td> <td>    0.933</td> <td> 0.351</td> <td>   -0.017</td> <td>    0.047</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<caption>Roots</caption>\n",
       "<tr>\n",
       "    <td></td>    <th>           Real</th> <th>         Imaginary</th> <th>         Modulus</th> <th>        Frequency</th>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.1</th>               -0.7653                    -0.6699j                    1.0171                   -0.3856     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.2</th>               -0.7653                    +0.6699j                    1.0171                    0.3856     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.3</th>                1.0001                    -0.0000j                    1.0001                   -0.0000     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.4</th>                1.2376                    -0.6712j                    1.4080                   -0.0791     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AR.5</th>                1.2376                    +0.6712j                    1.4080                    0.0791     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.1</th>               -0.7725                    -0.6655j                    1.0196                   -0.3868     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.2</th>               -0.7725                    +0.6655j                    1.0196                    0.3868     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.3</th>               -1.2167                    -0.0000j                    1.2167                   -0.5000     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.4</th>               -1.1090                    -0.4183j                    1.1853                   -0.4426     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.5</th>               -1.1090                    +0.4183j                    1.1853                    0.4426     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.6</th>               -0.7975                    -0.8280j                    1.1496                   -0.3720     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.7</th>               -0.7975                    +0.8280j                    1.1496                    0.3720     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.8</th>               -0.4266                    -1.1378j                    1.2151                   -0.3071     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.9</th>               -0.4266                    +1.1378j                    1.2151                    0.3071     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.10</th>               1.1823                    -0.2007j                    1.1992                   -0.0268     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.11</th>               1.1823                    +0.2007j                    1.1992                    0.0268     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.12</th>               1.0979                    -0.5090j                    1.2102                   -0.0691     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.13</th>               1.0979                    +0.5090j                    1.2102                    0.0691     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.14</th>               0.9125                    -0.7780j                    1.1992                   -0.1124     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.15</th>               0.9125                    +0.7780j                    1.1992                    0.1124     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.16</th>               0.6431                    -1.0601j                    1.2399                   -0.1632     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.17</th>               0.6431                    +1.0601j                    1.2399                    0.1632     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.18</th>               0.1603                    -1.2595j                    1.2697                   -0.2299     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.19</th>               0.1603                    +1.2595j                    1.2697                    0.2299     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.20</th>               0.0021                    -1.5930j                    1.5930                   -0.2498     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MA.21</th>               0.0021                    +1.5930j                    1.5930                    0.2498     \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                              ARMA Model Results                              \n",
       "==============================================================================\n",
       "Dep. Variable:                  price   No. Observations:                 3732\n",
       "Model:                    ARMA(5, 21)   Log Likelihood               -9985.016\n",
       "Method:                       css-mle   S.D. of innovations              3.509\n",
       "Date:                Wed, 20 Dec 2017   AIC                          20026.032\n",
       "Time:                        16:52:06   BIC                          20200.324\n",
       "Sample:                    06-29-2010   HQIC                         20088.032\n",
       "                         - 11-22-2017                                         \n",
       "================================================================================\n",
       "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------\n",
       "const          335.7079        nan        nan        nan         nan         nan\n",
       "ar.L1.price      0.7689      0.004    185.376      0.000       0.761       0.777\n",
       "ar.L2.price      0.6074      0.005    115.249      0.000       0.597       0.618\n",
       "ar.L3.price      0.0842      0.003     33.293      0.000       0.079       0.089\n",
       "ar.L4.price     -0.9482      0.001   -777.670      0.000      -0.951      -0.946\n",
       "ar.L5.price      0.4876      0.002    201.837      0.000       0.483       0.492\n",
       "ma.L1.price      0.2218      0.017     13.125      0.000       0.189       0.255\n",
       "ma.L2.price     -0.3640      0.017    -21.294      0.000      -0.397      -0.330\n",
       "ma.L3.price     -0.4676      0.018    -26.454      0.000      -0.502      -0.433\n",
       "ma.L4.price      0.4857      0.020     24.863      0.000       0.447       0.524\n",
       "ma.L5.price     -0.0073      0.021     -0.351      0.726      -0.048       0.034\n",
       "ma.L6.price     -0.0062      0.021     -0.297      0.766      -0.047       0.035\n",
       "ma.L7.price     -0.0149      0.021     -0.712      0.477      -0.056       0.026\n",
       "ma.L8.price     -0.0054      0.021     -0.257      0.797      -0.047       0.036\n",
       "ma.L9.price     -0.0031      0.021     -0.149      0.882      -0.044       0.038\n",
       "ma.L10.price     0.0064      0.021      0.304      0.761      -0.035       0.048\n",
       "ma.L11.price     0.0115      0.021      0.553      0.581      -0.029       0.052\n",
       "ma.L12.price     0.0194      0.021      0.925      0.355      -0.022       0.061\n",
       "ma.L13.price    -0.0179      0.021     -0.843      0.399      -0.059       0.024\n",
       "ma.L14.price    -0.0054      0.021     -0.260      0.795      -0.046       0.035\n",
       "ma.L15.price     0.0261      0.021      1.215      0.224      -0.016       0.068\n",
       "ma.L16.price     0.0135      0.021      0.651      0.515      -0.027       0.054\n",
       "ma.L17.price    -0.0213      0.022     -0.964      0.335      -0.065       0.022\n",
       "ma.L18.price    -0.0133      0.018     -0.728      0.467      -0.049       0.023\n",
       "ma.L19.price     0.0250      0.019      1.326      0.185      -0.012       0.062\n",
       "ma.L20.price    -0.0086      0.017     -0.502      0.615      -0.042       0.025\n",
       "ma.L21.price     0.0151      0.016      0.933      0.351      -0.017       0.047\n",
       "                                    Roots                                     \n",
       "==============================================================================\n",
       "                  Real           Imaginary           Modulus         Frequency\n",
       "------------------------------------------------------------------------------\n",
       "AR.1            -0.7653           -0.6699j            1.0171           -0.3856\n",
       "AR.2            -0.7653           +0.6699j            1.0171            0.3856\n",
       "AR.3             1.0001           -0.0000j            1.0001           -0.0000\n",
       "AR.4             1.2376           -0.6712j            1.4080           -0.0791\n",
       "AR.5             1.2376           +0.6712j            1.4080            0.0791\n",
       "MA.1            -0.7725           -0.6655j            1.0196           -0.3868\n",
       "MA.2            -0.7725           +0.6655j            1.0196            0.3868\n",
       "MA.3            -1.2167           -0.0000j            1.2167           -0.5000\n",
       "MA.4            -1.1090           -0.4183j            1.1853           -0.4426\n",
       "MA.5            -1.1090           +0.4183j            1.1853            0.4426\n",
       "MA.6            -0.7975           -0.8280j            1.1496           -0.3720\n",
       "MA.7            -0.7975           +0.8280j            1.1496            0.3720\n",
       "MA.8            -0.4266           -1.1378j            1.2151           -0.3071\n",
       "MA.9            -0.4266           +1.1378j            1.2151            0.3071\n",
       "MA.10            1.1823           -0.2007j            1.1992           -0.0268\n",
       "MA.11            1.1823           +0.2007j            1.1992            0.0268\n",
       "MA.12            1.0979           -0.5090j            1.2102           -0.0691\n",
       "MA.13            1.0979           +0.5090j            1.2102            0.0691\n",
       "MA.14            0.9125           -0.7780j            1.1992           -0.1124\n",
       "MA.15            0.9125           +0.7780j            1.1992            0.1124\n",
       "MA.16            0.6431           -1.0601j            1.2399           -0.1632\n",
       "MA.17            0.6431           +1.0601j            1.2399            0.1632\n",
       "MA.18            0.1603           -1.2595j            1.2697           -0.2299\n",
       "MA.19            0.1603           +1.2595j            1.2697            0.2299\n",
       "MA.20            0.0021           -1.5930j            1.5930           -0.2498\n",
       "MA.21            0.0021           +1.5930j            1.5930            0.2498\n",
       "------------------------------------------------------------------------------\n",
       "\"\"\""
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = ARMA(new_df.price, (5, 21)).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['forecast'] = model.predict()\n",
    "new_df['target'] = new_df.price.shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>forecast</th>\n",
       "      <th>target</th>\n",
       "      <th>predict_grow</th>\n",
       "      <th>true_grow</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-06-29 09:30:00</th>\n",
       "      <td>19.00</td>\n",
       "      <td>335.707875</td>\n",
       "      <td>23.89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-29 16:00:00</th>\n",
       "      <td>23.89</td>\n",
       "      <td>19.038717</td>\n",
       "      <td>25.79</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30 09:30:00</th>\n",
       "      <td>25.79</td>\n",
       "      <td>23.872388</td>\n",
       "      <td>23.83</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-30 16:00:00</th>\n",
       "      <td>23.83</td>\n",
       "      <td>25.880261</td>\n",
       "      <td>25.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01 09:30:00</th>\n",
       "      <td>25.00</td>\n",
       "      <td>23.859566</td>\n",
       "      <td>21.96</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price    forecast  target  predict_grow  true_grow\n",
       "date                                                                   \n",
       "2010-06-29 09:30:00  19.00  335.707875   23.89             1          1\n",
       "2010-06-29 16:00:00  23.89   19.038717   25.79             0          1\n",
       "2010-06-30 09:30:00  25.79   23.872388   23.83             0          0\n",
       "2010-06-30 16:00:00  23.83   25.880261   25.00             1          1\n",
       "2010-07-01 09:30:00  25.00   23.859566   21.96             0          0"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' 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-1-a47dfbcfb356>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforecast\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"True Values\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Predictions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Score:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mr2_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforecast\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"MSE:\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean_squared_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnew_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforecast\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 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "plt.scatter(new_df.iloc[1:-1].target, new_df.iloc[1:-1].forecast)\n",
    "plt.xlabel(\"True Values\")\n",
    "plt.ylabel(\"Predictions\")\n",
    "print(\"Score:\", metrics.r2_score(new_df.iloc[1:-1].target, new_df.iloc[1:-1].forecast))\n",
    "print(\"MSE:\", metrics.mean_squared_error(new_df.iloc[1:-1].target, new_df.iloc[1:-1].forecast))\n",
    "plt.savefig('pred_true.png', bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['predict_grow'] = new_df[['forecast', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 0 else 0, axis=1)\n",
    "new_df['true_grow'] = new_df[['target', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 0 else 0, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? True\n",
      "Last Buying price: $ 316.77\n",
      "Current price: $ 316.77\n",
      "Cash? $ -262.83\n",
      "Profit: $ 53.94\n",
      "Number of Transactins: 235\n",
      "Cost of transactions: $ 1175\n"
     ]
    }
   ],
   "source": [
    "profit_calculator(new_df.iloc[3280:], 'predict_grow', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 858.16\n",
      "Profit: $ 858.16\n",
      "Number of Transactins: 236\n",
      "Cost of transactions: $ 1180\n"
     ]
    }
   ],
   "source": [
    "profit_calculator(new_df.iloc[3280:], 'true_grow', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Move the target a bit and see\n",
    "new_df['predict_grow'] = new_df[['forecast', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 5 else 0 if x[0] - x[1] <= -5 else 2, axis=1)\n",
    "new_df['true_grow'] = new_df[['target', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 5 else 0 if x[0] - x[1] <= -5 else 2, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Resuls:\n",
      "Own? False\n",
      "Last Buying price: $ 315.05\n",
      "Current price: $ 316.77\n",
      "Cash? $ -58.45\n",
      "Profit: $ -58.45\n",
      "Number of Transactins: 50\n",
      "Cost of transactions: $ 250\n",
      "\n",
      "Best Case Results:\n",
      "Own? True\n",
      "Last Buying price: $ 310.86\n",
      "Current price: $ 316.77\n",
      "Cash? $ 242.42\n",
      "Profit: $ 559.19\n",
      "Number of Transactins: 51\n",
      "Cost of transactions: $ 255\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Resuls:\")\n",
    "profit_calculator(new_df.iloc[3280:], 'predict_grow', -1)\n",
    "print(\"\\nBest Case Results:\")\n",
    "profit_calculator(new_df.iloc[3280:], 'true_grow', -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OH WELL."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MORE EXPERIMENTING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=21,center=False).mean()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "rolmean = pd.rolling_mean(new_df.price, window=21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['rolmean'] = rolmean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>rolmean</th>\n",
       "      <th>first_difference</th>\n",
       "      <th>forecast</th>\n",
       "      <th>target</th>\n",
       "      <th>predict_grow</th>\n",
       "      <th>true_grow</th>\n",
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       "      <th>date</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>2010-06-29 09:30:00</th>\n",
       "      <td>19.00</td>\n",
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       "      <th>2010-06-29 16:00:00</th>\n",
       "      <td>23.89</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2010-06-30 09:30:00</th>\n",
       "      <td>25.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2010-06-30 16:00:00</th>\n",
       "      <td>23.83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.502</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01 09:30:00</th>\n",
       "      <td>25.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.094</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price  rolmean  first_difference  forecast  target  \\\n",
       "date                                                                      \n",
       "2010-06-29 09:30:00  19.00      NaN               NaN       0.0     NaN   \n",
       "2010-06-29 16:00:00  23.89      NaN               NaN       0.0     NaN   \n",
       "2010-06-30 09:30:00  25.79      NaN               NaN       0.0     NaN   \n",
       "2010-06-30 16:00:00  23.83      NaN               NaN       0.0  23.502   \n",
       "2010-07-01 09:30:00  25.00      NaN               NaN       0.0  24.094   \n",
       "\n",
       "                     predict_grow  true_grow  \n",
       "date                                          \n",
       "2010-06-29 09:30:00             0          2  \n",
       "2010-06-29 16:00:00             0          2  \n",
       "2010-06-30 09:30:00             0          2  \n",
       "2010-06-30 16:00:00             0          2  \n",
       "2010-07-01 09:30:00             0          2  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:6: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=5,center=False).mean()\n",
      "  \n",
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:7: FutureWarning: pd.rolling_std is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=5,center=False).std()\n",
      "  import sys\n"
     ]
    },
    {
     "data": {
      "image/png": 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eIuRckYP4cODVV83/QWzb9oZtww+YWdSyUZ9TfPCzvy5Xk/oMIooFQRAChD8+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Vkl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\nMlyjPNNxFboAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aecce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results of Dickey-Fuller Test:\n",
      "Test Statistic                -8.231582e+00\n",
      "p-value                        6.042789e-13\n",
      "#Lags Used                     2.400000e+01\n",
      "Number of Observations Used    3.686000e+03\n",
      "Critical Value (1%)           -3.432125e+00\n",
      "Critical Value (5%)           -2.862324e+00\n",
      "Critical Value (10%)          -2.567188e+00\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "new_df['first_difference'] = new_df.rolmean - new_df.rolmean.shift(1)  \n",
    "test_stationarity(new_df.first_difference.dropna(inplace=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3+MHP57m7zXnK2HN3m8OSg58/Sx1J0s5ptkP1KuD07lNAXgE85HpqSZo+R7/k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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b2c9ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(new_df.rolmean.iloc[14:], lags=50, ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(new_df.rolmean.iloc[14:], lags=20, ax=ax2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Statespace Model Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>rolmean</td>     <th>  No. Observations:  </th>   <td>3732</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>           <td>SARIMAX(1, 1, 0)</td> <th>  Log Likelihood     </th>  <td>139.963</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>            <td>Wed, 20 Dec 2017</td> <th>  AIC                </th> <td>-275.926</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                <td>20:47:24</td>     <th>  BIC                </th> <td>-263.477</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Sample:</th>             <td>06-29-2010</td>    <th>  HQIC               </th> <td>-271.497</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th></th>                   <td>- 11-22-2017</td>   <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ar.L1</th>  <td>    0.9513</td> <td>    0.004</td> <td>  251.938</td> <td> 0.000</td> <td>    0.944</td> <td>    0.959</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>sigma2</th> <td>    0.0540</td> <td>    0.001</td> <td>   83.417</td> <td> 0.000</td> <td>    0.053</td> <td>    0.055</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Ljung-Box (Q):</th>           <td>nan</td>  <th>  Jarque-Bera (JB):  </th> <td>4731.57</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Q):</th>                 <td>nan</td>  <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Heteroskedasticity (H):</th> <td>47.23</td> <th>  Skew:              </th>  <td>0.02</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td>  <th>  Kurtosis:          </th>  <td>8.53</td>  \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                           Statespace Model Results                           \n",
       "==============================================================================\n",
       "Dep. Variable:                rolmean   No. Observations:                 3732\n",
       "Model:               SARIMAX(1, 1, 0)   Log Likelihood                 139.963\n",
       "Date:                Wed, 20 Dec 2017   AIC                           -275.926\n",
       "Time:                        20:47:24   BIC                           -263.477\n",
       "Sample:                    06-29-2010   HQIC                          -271.497\n",
       "                         - 11-22-2017                                         \n",
       "Covariance Type:                  opg                                         \n",
       "==============================================================================\n",
       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "ar.L1          0.9513      0.004    251.938      0.000       0.944       0.959\n",
       "sigma2         0.0540      0.001     83.417      0.000       0.053       0.055\n",
       "===================================================================================\n",
       "Ljung-Box (Q):                         nan   Jarque-Bera (JB):              4731.57\n",
       "Prob(Q):                               nan   Prob(JB):                         0.00\n",
       "Heteroskedasticity (H):              47.23   Skew:                             0.02\n",
       "Prob(H) (two-sided):                  0.00   Kurtosis:                         8.53\n",
       "===================================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
       "\"\"\""
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = sm.tsa.statespace.SARIMAX(new_df.rolmean, trend='n', order=(1,1,0))\n",
    "results = mod.fit()\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['forecast'] = results.predict()\n",
    "new_df['target'] = new_df.rolmean.shift(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score: 0.999936351892\n",
      "MSE: 0.72662850242\n"
     ]
    },
    {
     "data": {
      "image/png": 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61g9/sY17VSWIiDgg6UngLEleItxsFJWak6ZT39DV55jKhq6b3KRkVavql72km4E/Ipuj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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b2c9240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(new_df.iloc[21:-1].target, new_df.iloc[21:-1].forecast)\n",
    "plt.xlabel(\"True Values\")\n",
    "plt.ylabel(\"Predictions\")\n",
    "print(\"Score:\", metrics.r2_score(new_df.iloc[21:-1].target, new_df.iloc[21:-1].forecast))\n",
    "print(\"MSE:\", metrics.mean_squared_error(new_df.iloc[21:-1].target, new_df.iloc[21:-1].forecast))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df['predict_grow'] = new_df[['forecast', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 0 else 0, axis=1)\n",
    "new_df['true_grow'] = new_df[['target', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 0 else 0, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Resuls:\n",
      "Own? False\n",
      "Last Buying price: $ 306.01\n",
      "Current price: $ 316.77\n",
      "Cash? $ -4.19\n",
      "Profit: $ -4.19\n",
      "Number of Transactins: 44\n",
      "Cost of transactions: $ 220\n",
      "\n",
      "Best Case Results:\n",
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 8.31\n",
      "Profit: $ 8.31\n",
      "Number of Transactins: 44\n",
      "Cost of transactions: $ 220\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Resuls:\")\n",
    "profit_calculator(new_df.iloc[3280:], 'predict_grow', -1)\n",
    "print(\"\\nBest Case Results:\")\n",
    "profit_calculator(new_df.iloc[3280:], 'true_grow', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, roc_auc_score\n",
    "def eval_sklearn_model(y_true, predictions, model=None, X=None):\n",
    "    \"\"\"This function takes the true values for y and the predictions made by the model and prints out the confusion matrix along with Accuracy, Precision, and, if model and X provided, Roc_Auc Scores.\"\"\"\n",
    "    cnf_matrix = confusion_matrix(y_true, predictions)\n",
    "\n",
    "    print('True Negative: ', cnf_matrix[0, 0], '| False Positive: ', cnf_matrix[0, 1])\n",
    "    print('False Negative: ', cnf_matrix[1, 0], '| True Positive: ', cnf_matrix[1, 1], '\\n')\n",
    "\n",
    "    sensitivity = cnf_matrix[1, 1]/ (cnf_matrix[1, 0] + cnf_matrix[1, 1])\n",
    "    specificity = cnf_matrix[0, 0]/ (cnf_matrix[0, 1] + cnf_matrix[0, 0])\n",
    "\n",
    "    print('Sensitivity (TP/ TP + FN): ', sensitivity)\n",
    "    print('Specificity (TN/ TN + FP): ', specificity, '\\n')\n",
    "\n",
    "    print('Accuracy: ', accuracy_score(y_true, predictions, normalize=True))\n",
    "    print('Precision: ', precision_score(y_true, predictions))\n",
    "    if model != None:\n",
    "        print('Roc-Auc: ', roc_auc_score(y_true, [x[1] for x in model.predict_proba(X)]))\n",
    "    else:\n",
    "        pass\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Negative:  2092 | False Positive:  25\n",
      "False Negative:  33 | True Positive:  1582 \n",
      "\n",
      "Sensitivity (TP/ TP + FN):  0.979566563467\n",
      "Specificity (TN/ TN + FP):  0.988190836089 \n",
      "\n",
      "Accuracy:  0.984458735263\n",
      "Precision:  0.984443061605\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "eval_sklearn_model(new_df.true_grow, new_df.predict_grow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Move the target a bit and see\n",
    "new_df['predict_grow'] = new_df[['forecast', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 5 else 0 if x[0] - x[1] <= -5 else 2, axis=1)\n",
    "new_df['true_grow'] = new_df[['target', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 5 else 0 if x[0] - x[1] <= -5 else 2, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model Resuls:\n",
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 21.56\n",
      "Profit: $ 21.56\n",
      "Number of Transactins: 18\n",
      "Cost of transactions: $ 90\n",
      "\n",
      "Best Case Results:\n",
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 21.57\n",
      "Profit: $ 21.57\n",
      "Number of Transactins: 18\n",
      "Cost of transactions: $ 90\n"
     ]
    }
   ],
   "source": [
    "print(\"Model Resuls:\")\n",
    "profit_calculator(new_df.iloc[3280:], 'predict_grow', -1)\n",
    "print(\"\\nBest Case Results:\")\n",
    "profit_calculator(new_df.iloc[3280:], 'true_grow', -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is weird!  Increasing the rolling mean, I can get SUPER accurate predictions from the model.  But at the same time the change in price is reduced so the potential for profit is decreased!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "def profit_calculator(dataframe, predict_col, early_stop=None):  \n",
    "    money_counter = 0\n",
    "    transactions = 0\n",
    "    own = False\n",
    "    last_buy = 0\n",
    "    current_price = 0\n",
    "    for num, i in enumerate(dataframe.iloc[:early_stop].iterrows()):\n",
    "        if i[1][predict_col] == 1 and own == False:\n",
    "            money_counter -= i[1]['price']\n",
    "            own = True\n",
    "            transactions += 1\n",
    "            last_buy = i[1]['price']\n",
    "        elif i[1][predict_col] == 0 and own == True:\n",
    "            money_counter += i[1]['price']\n",
    "            own = False\n",
    "            transactions += 1\n",
    "        else:\n",
    "            pass\n",
    "        current_price=i[1]['price']\n",
    "    print('Own?', own)\n",
    "    print('Last Buying price: $', last_buy)\n",
    "    print('Current price: $', current_price)\n",
    "    print('Cash? $', money_counter)\n",
    "    if own == True:\n",
    "        print('Profit: $', current_price + money_counter)\n",
    "        profits.append(current_price + money_counter)\n",
    "    else:\n",
    "        print('Profit: $', money_counter)\n",
    "        profits.append(money_counter)\n",
    "    print('Number of Transactins:', transactions)\n",
    "    print('Cost of transactions: $', transactions*5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=1,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 315.05\n",
      "Current price: $ 316.77\n",
      "Cash? $ -35.59\n",
      "Profit: $ -35.59\n",
      "Number of Transactins: 50\n",
      "Cost of transactions: $ 250\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=2,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 315.05\n",
      "Current price: $ 316.77\n",
      "Cash? $ -33.595\n",
      "Profit: $ -33.595\n",
      "Number of Transactins: 62\n",
      "Cost of transactions: $ 310\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=3,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 315.05\n",
      "Current price: $ 316.77\n",
      "Cash? $ -20.3\n",
      "Profit: $ -20.3\n",
      "Number of Transactins: 52\n",
      "Cost of transactions: $ 260\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=4,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ -13.17\n",
      "Profit: $ -13.17\n",
      "Number of Transactins: 48\n",
      "Cost of transactions: $ 240\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=5,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? True\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ -310.09\n",
      "Profit: $ 6.68\n",
      "Number of Transactins: 43\n",
      "Cost of transactions: $ 215\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=6,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? True\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ -291.46\n",
      "Profit: $ 25.31\n",
      "Number of Transactins: 39\n",
      "Cost of transactions: $ 195\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=7,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? True\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ -262.85\n",
      "Profit: $ 53.92\n",
      "Number of Transactins: 39\n",
      "Cost of transactions: $ 195\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=8,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 300.13\n",
      "Current price: $ 316.77\n",
      "Cash? $ 79.21\n",
      "Profit: $ 79.21\n",
      "Number of Transactins: 38\n",
      "Cost of transactions: $ 190\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=9,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 300.13\n",
      "Current price: $ 316.77\n",
      "Cash? $ 86.68\n",
      "Profit: $ 86.68\n",
      "Number of Transactins: 34\n",
      "Cost of transactions: $ 170\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=10,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 80.285\n",
      "Profit: $ 80.285\n",
      "Number of Transactins: 34\n",
      "Cost of transactions: $ 170\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=11,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 300.13\n",
      "Current price: $ 316.77\n",
      "Cash? $ 62.855\n",
      "Profit: $ 62.855\n",
      "Number of Transactins: 30\n",
      "Cost of transactions: $ 150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=12,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 300.13\n",
      "Current price: $ 316.77\n",
      "Cash? $ 76.275\n",
      "Profit: $ 76.275\n",
      "Number of Transactins: 30\n",
      "Cost of transactions: $ 150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=13,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 321.08\n",
      "Current price: $ 316.77\n",
      "Cash? $ 65.965\n",
      "Profit: $ 65.965\n",
      "Number of Transactins: 30\n",
      "Cost of transactions: $ 150\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=14,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 24.645\n",
      "Profit: $ 24.645\n",
      "Number of Transactins: 26\n",
      "Cost of transactions: $ 130\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=15,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 20.675\n",
      "Profit: $ 20.675\n",
      "Number of Transactins: 24\n",
      "Cost of transactions: $ 120\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=16,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 27.735\n",
      "Profit: $ 27.735\n",
      "Number of Transactins: 24\n",
      "Cost of transactions: $ 120\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=17,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ -18.235\n",
      "Profit: $ -18.235\n",
      "Number of Transactins: 18\n",
      "Cost of transactions: $ 90\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=18,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ -14.485\n",
      "Profit: $ -14.485\n",
      "Number of Transactins: 18\n",
      "Cost of transactions: $ 90\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=19,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 21.2\n",
      "Profit: $ 21.2\n",
      "Number of Transactins: 18\n",
      "Cost of transactions: $ 90\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=20,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 21.2\n",
      "Profit: $ 21.2\n",
      "Number of Transactins: 18\n",
      "Cost of transactions: $ 90\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=21,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 21.56\n",
      "Profit: $ 21.56\n",
      "Number of Transactins: 18\n",
      "Cost of transactions: $ 90\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=22,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 27.72\n",
      "Profit: $ 27.72\n",
      "Number of Transactins: 18\n",
      "Cost of transactions: $ 90\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=23,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 28.6\n",
      "Profit: $ 28.6\n",
      "Number of Transactins: 18\n",
      "Cost of transactions: $ 90\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=24,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 45.71\n",
      "Profit: $ 45.71\n",
      "Number of Transactins: 20\n",
      "Cost of transactions: $ 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=25,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 45.25\n",
      "Profit: $ 45.25\n",
      "Number of Transactins: 20\n",
      "Cost of transactions: $ 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=26,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 54.46\n",
      "Profit: $ 54.46\n",
      "Number of Transactins: 20\n",
      "Cost of transactions: $ 100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=27,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 50.59\n",
      "Profit: $ 50.59\n",
      "Number of Transactins: 18\n",
      "Cost of transactions: $ 90\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=28,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 51.79\n",
      "Profit: $ 51.79\n",
      "Number of Transactins: 16\n",
      "Cost of transactions: $ 80\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=29,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 345.1\n",
      "Current price: $ 316.77\n",
      "Cash? $ 51.79\n",
      "Profit: $ 51.79\n",
      "Number of Transactins: 16\n",
      "Cost of transactions: $ 80\n"
     ]
    }
   ],
   "source": [
    "profits = []\n",
    "for i in range(1, 30):\n",
    "    rolmean = pd.rolling_mean(new_df.price, window=i)\n",
    "    new_df['rolmean'] = rolmean\n",
    "    mod = sm.tsa.statespace.SARIMAX(new_df.rolmean, trend='n', order=(1,1,0))\n",
    "    results = mod.fit()\n",
    "    results.summary()\n",
    "    new_df['forecast'] = results.predict()\n",
    "    new_df['target'] = new_df.rolmean.shift(-1)\n",
    "    new_df['predict_grow'] = new_df[['forecast', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 5 else 0 if x[0] - x[1] <= -5 else 2, axis=1)\n",
    "    profit_calculator(new_df.iloc[3280:], 'predict_grow', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-35.589999999999918,\n",
       " -33.594999999999857,\n",
       " -20.300000000000011,\n",
       " -13.169999999999959,\n",
       " 6.6800000000001205,\n",
       " 25.310000000000002,\n",
       " 53.92000000000013,\n",
       " 79.210000000000036,\n",
       " 86.679999999999893,\n",
       " 80.284999999999854,\n",
       " 62.854999999999848,\n",
       " 76.274999999999864,\n",
       " 65.964999999999918,\n",
       " 24.644999999999868,\n",
       " 20.674999999999898,\n",
       " 27.7349999999999,\n",
       " -18.235000000000127,\n",
       " -14.485000000000127,\n",
       " 21.199999999999932,\n",
       " 21.199999999999932,\n",
       " 21.559999999999945,\n",
       " 27.719999999999914,\n",
       " 28.599999999999909,\n",
       " 45.709999999999923,\n",
       " 45.249999999999886,\n",
       " 54.459999999999923,\n",
       " 50.589999999999975,\n",
       " 51.789999999999907,\n",
       " 51.789999999999907,\n",
       " 86.679999999999893]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=9,center=False).mean()\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 300.13\n",
      "Current price: $ 316.77\n",
      "Cash? $ 86.68\n",
      "Profit: $ 86.68\n",
      "Number of Transactins: 34\n",
      "Cost of transactions: $ 170\n"
     ]
    }
   ],
   "source": [
    "rolmean = pd.rolling_mean(new_df.price, window=9)\n",
    "new_df['rolmean'] = rolmean\n",
    "mod = sm.tsa.statespace.SARIMAX(new_df.rolmean, trend='n', order=(1,1,0))\n",
    "results = mod.fit()\n",
    "results.summary()\n",
    "new_df['forecast'] = results.predict()\n",
    "new_df['target'] = new_df.rolmean.shift(-1)\n",
    "new_df['predict_grow'] = new_df[['forecast', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 5 else 0 if x[0] - x[1] <= -5 else 2, axis=1)\n",
    "profit_calculator(new_df.iloc[3280:], 'predict_grow', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=1,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? True\n",
      "Last Buying price: $ 316.77\n",
      "Current price: $ 316.77\n",
      "Cash? $ -263.43\n",
      "Profit: $ 53.34\n",
      "Number of Transactins: 233\n",
      "Cost of transactions: $ 1165\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=2,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 315.05\n",
      "Current price: $ 316.77\n",
      "Cash? $ 17.77\n",
      "Profit: $ 17.77\n",
      "Number of Transactins: 196\n",
      "Cost of transactions: $ 980\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=3,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in greater\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:879: RuntimeWarning: invalid value encountered in less\n",
      "  return (self.a < x) & (x < self.b)\n",
      "/anaconda/lib/python3.6/site-packages/scipy/stats/_distn_infrastructure.py:1821: RuntimeWarning: invalid value encountered in less_equal\n",
      "  cond2 = cond0 & (x <= self.a)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 315.05\n",
      "Current price: $ 316.77\n",
      "Cash? $ 69.095\n",
      "Profit: $ 69.095\n",
      "Number of Transactins: 168\n",
      "Cost of transactions: $ 840\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=4,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 315.05\n",
      "Current price: $ 316.77\n",
      "Cash? $ 7.095\n",
      "Profit: $ 7.095\n",
      "Number of Transactins: 132\n",
      "Cost of transactions: $ 660\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=5,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 315.05\n",
      "Current price: $ 316.77\n",
      "Cash? $ -15.5\n",
      "Profit: $ -15.5\n",
      "Number of Transactins: 122\n",
      "Cost of transactions: $ 610\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=6,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 313.79\n",
      "Current price: $ 316.77\n",
      "Cash? $ -32.6\n",
      "Profit: $ -32.6\n",
      "Number of Transactins: 102\n",
      "Cost of transactions: $ 510\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=7,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ -17.96\n",
      "Profit: $ -17.96\n",
      "Number of Transactins: 94\n",
      "Cost of transactions: $ 470\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=8,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 4.21\n",
      "Profit: $ 4.21\n",
      "Number of Transactins: 86\n",
      "Cost of transactions: $ 430\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=9,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 313.79\n",
      "Current price: $ 316.77\n",
      "Cash? $ 33.13\n",
      "Profit: $ 33.13\n",
      "Number of Transactins: 82\n",
      "Cost of transactions: $ 410\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=10,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 24.34\n",
      "Profit: $ 24.34\n",
      "Number of Transactins: 72\n",
      "Cost of transactions: $ 360\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=11,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 29.77\n",
      "Profit: $ 29.77\n",
      "Number of Transactins: 72\n",
      "Cost of transactions: $ 360\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=12,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 22.32\n",
      "Profit: $ 22.32\n",
      "Number of Transactins: 72\n",
      "Cost of transactions: $ 360\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=13,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 27.47\n",
      "Profit: $ 27.47\n",
      "Number of Transactins: 72\n",
      "Cost of transactions: $ 360\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=14,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 13.31\n",
      "Profit: $ 13.31\n",
      "Number of Transactins: 62\n",
      "Cost of transactions: $ 310\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=15,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 2.36\n",
      "Profit: $ 2.36\n",
      "Number of Transactins: 58\n",
      "Cost of transactions: $ 290\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=16,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ -3.61\n",
      "Profit: $ -3.61\n",
      "Number of Transactins: 54\n",
      "Cost of transactions: $ 270\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=17,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ -26.74\n",
      "Profit: $ -26.74\n",
      "Number of Transactins: 50\n",
      "Cost of transactions: $ 250\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=18,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ -17.25\n",
      "Profit: $ -17.25\n",
      "Number of Transactins: 52\n",
      "Cost of transactions: $ 260\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=19,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ -21.19\n",
      "Profit: $ -21.19\n",
      "Number of Transactins: 44\n",
      "Cost of transactions: $ 220\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=20,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 351.81\n",
      "Current price: $ 316.77\n",
      "Cash? $ -23.05\n",
      "Profit: $ -23.05\n",
      "Number of Transactins: 42\n",
      "Cost of transactions: $ 210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=21,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 306.01\n",
      "Current price: $ 316.77\n",
      "Cash? $ -4.19\n",
      "Profit: $ -4.19\n",
      "Number of Transactins: 44\n",
      "Cost of transactions: $ 220\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=22,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 11.93\n",
      "Profit: $ 11.93\n",
      "Number of Transactins: 48\n",
      "Cost of transactions: $ 240\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=23,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.7\n",
      "Current price: $ 316.77\n",
      "Cash? $ 14.225\n",
      "Profit: $ 14.225\n",
      "Number of Transactins: 40\n",
      "Cost of transactions: $ 200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=24,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.7\n",
      "Current price: $ 316.77\n",
      "Cash? $ 17.865\n",
      "Profit: $ 17.865\n",
      "Number of Transactins: 38\n",
      "Cost of transactions: $ 190\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=25,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.7\n",
      "Current price: $ 316.77\n",
      "Cash? $ 7.005\n",
      "Profit: $ 7.005\n",
      "Number of Transactins: 34\n",
      "Cost of transactions: $ 170\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=26,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.7\n",
      "Current price: $ 316.77\n",
      "Cash? $ 8.225\n",
      "Profit: $ 8.225\n",
      "Number of Transactins: 34\n",
      "Cost of transactions: $ 170\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=27,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.7\n",
      "Current price: $ 316.77\n",
      "Cash? $ 21.445\n",
      "Profit: $ 21.445\n",
      "Number of Transactins: 38\n",
      "Cost of transactions: $ 190\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=28,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "/anaconda/lib/python3.6/site-packages/statsmodels/base/model.py:496: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  \"Check mle_retvals\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 14.705\n",
      "Profit: $ 14.705\n",
      "Number of Transactins: 34\n",
      "Cost of transactions: $ 170\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:3: FutureWarning: pd.rolling_mean is deprecated for Series and will be removed in a future version, replace with \n",
      "\tSeries.rolling(window=29,center=False).mean()\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Own? False\n",
      "Last Buying price: $ 308.74\n",
      "Current price: $ 316.77\n",
      "Cash? $ 10.815\n",
      "Profit: $ 10.815\n",
      "Number of Transactins: 32\n",
      "Cost of transactions: $ 160\n"
     ]
    }
   ],
   "source": [
    "profits = []\n",
    "for i in range(1, 30):\n",
    "    rolmean = pd.rolling_mean(new_df.price, window=i)\n",
    "    new_df['rolmean'] = rolmean\n",
    "    mod = sm.tsa.statespace.SARIMAX(new_df.rolmean, trend='n', order=(1,1,0))\n",
    "    results = mod.fit()\n",
    "    results.summary()\n",
    "    new_df['forecast'] = results.predict()\n",
    "    new_df['target'] = new_df.rolmean.shift(-1)\n",
    "    new_df['predict_grow'] = new_df[['forecast', 'price']].apply(lambda x: 1 if x[0] - x[1] >= 0 else 0, axis=1)\n",
    "    profit_calculator(new_df.iloc[3280:], 'predict_grow', -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[53.340000000000202,\n",
       " 17.770000000000209,\n",
       " 69.095000000000255,\n",
       " 7.0950000000003115,\n",
       " -15.5,\n",
       " -32.600000000000023,\n",
       " -17.959999999999923,\n",
       " 4.2100000000000932,\n",
       " 33.130000000000166,\n",
       " 24.340000000000146,\n",
       " 29.770000000000152,\n",
       " 22.320000000000107,\n",
       " 27.46999999999997,\n",
       " 13.310000000000002,\n",
       " 2.3600000000000705,\n",
       " -3.6100000000000136,\n",
       " -26.739999999999895,\n",
       " -17.25,\n",
       " -21.190000000000055,\n",
       " -23.050000000000125,\n",
       " -4.1899999999999977,\n",
       " 11.92999999999995,\n",
       " 14.225000000000023,\n",
       " 17.864999999999952,\n",
       " 7.0050000000000523,\n",
       " 8.2250000000000796,\n",
       " 21.44500000000005,\n",
       " 14.705000000000041,\n",
       " 10.815000000000055]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "profits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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