Part_4_ Model_outcome_vs_Market.ipynb

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    "### Download and combine all data. Apply various algorithms for predictions\n",
    "This notebook is used for checking how well the six different classifier algorithms would have predicted next day's stock price movemements in the 8 chosen stocks.\n",
    "\n",
    "The follwoing is done for each stock/cashtag separately. Then the outcome shown as six graphs, one for each classifier, is shown and saved as an image file to be presented in an article, or alike.\n",
    "\n",
    "First, a dataframe containing the combined stock and tweet data is downloaded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import os\n",
    "import xlsxwriter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Reading only one excel at a time; this is the data the models are tested against"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "xls = pd.ExcelFile('C:/Users/jangn/OneDrive/CODE/Stock_markets/Prediction_data_Spring_2019/$AAL_prediction.xlsx') # Update cashtag!\n",
    "Spring2019 = pd.read_excel(xls, header = 0,encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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    "editable": true,
    "scrolled": true
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Pct_change</th>\n",
       "      <th>follower count</th>\n",
       "      <th>compound</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>Compound_multiplied</th>\n",
       "      <th>Predicted_change</th>\n",
       "      <th>Buy/Sell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-15</th>\n",
       "      <td>32.349998</td>\n",
       "      <td>31.420000</td>\n",
       "      <td>32.279999</td>\n",
       "      <td>31.430000</td>\n",
       "      <td>-2.633205</td>\n",
       "      <td>4002.475410</td>\n",
       "      <td>0.232066</td>\n",
       "      <td>0.047541</td>\n",
       "      <td>0.846262</td>\n",
       "      <td>0.106180</td>\n",
       "      <td>-571.878784</td>\n",
       "      <td>-1.882455</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-16</th>\n",
       "      <td>32.093332</td>\n",
       "      <td>31.290000</td>\n",
       "      <td>32.019999</td>\n",
       "      <td>31.413333</td>\n",
       "      <td>-1.882455</td>\n",
       "      <td>1587.333333</td>\n",
       "      <td>0.259539</td>\n",
       "      <td>0.042778</td>\n",
       "      <td>0.815000</td>\n",
       "      <td>0.142222</td>\n",
       "      <td>601.459044</td>\n",
       "      <td>-1.131705</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-17</th>\n",
       "      <td>31.836666</td>\n",
       "      <td>31.160000</td>\n",
       "      <td>31.760000</td>\n",
       "      <td>31.396666</td>\n",
       "      <td>-1.131705</td>\n",
       "      <td>824.615385</td>\n",
       "      <td>0.219554</td>\n",
       "      <td>0.061154</td>\n",
       "      <td>0.825846</td>\n",
       "      <td>0.113000</td>\n",
       "      <td>81.045608</td>\n",
       "      <td>-0.380955</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-18</th>\n",
       "      <td>31.580000</td>\n",
       "      <td>31.030001</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>31.379999</td>\n",
       "      <td>-0.380955</td>\n",
       "      <td>11031.893639</td>\n",
       "      <td>0.171607</td>\n",
       "      <td>0.065358</td>\n",
       "      <td>0.821524</td>\n",
       "      <td>0.113131</td>\n",
       "      <td>4030.680298</td>\n",
       "      <td>0.285715</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-19</th>\n",
       "      <td>32.119999</td>\n",
       "      <td>31.379999</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>31.590000</td>\n",
       "      <td>0.285715</td>\n",
       "      <td>1824.300000</td>\n",
       "      <td>0.155815</td>\n",
       "      <td>0.059550</td>\n",
       "      <td>0.804050</td>\n",
       "      <td>0.136400</td>\n",
       "      <td>551.926725</td>\n",
       "      <td>-1.714289</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-20</th>\n",
       "      <td>31.549999</td>\n",
       "      <td>30.770000</td>\n",
       "      <td>31.500000</td>\n",
       "      <td>30.959999</td>\n",
       "      <td>-1.714289</td>\n",
       "      <td>11407.000000</td>\n",
       "      <td>0.270644</td>\n",
       "      <td>0.040125</td>\n",
       "      <td>0.798250</td>\n",
       "      <td>0.161625</td>\n",
       "      <td>6926.773819</td>\n",
       "      <td>1.812889</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-21</th>\n",
       "      <td>31.770000</td>\n",
       "      <td>30.790001</td>\n",
       "      <td>30.889999</td>\n",
       "      <td>31.450001</td>\n",
       "      <td>1.812889</td>\n",
       "      <td>15514.461538</td>\n",
       "      <td>0.412185</td>\n",
       "      <td>0.014231</td>\n",
       "      <td>0.838385</td>\n",
       "      <td>0.147385</td>\n",
       "      <td>9229.666808</td>\n",
       "      <td>-2.394636</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-22</th>\n",
       "      <td>31.440001</td>\n",
       "      <td>30.530001</td>\n",
       "      <td>31.320000</td>\n",
       "      <td>30.570000</td>\n",
       "      <td>-2.394636</td>\n",
       "      <td>13077.428571</td>\n",
       "      <td>0.073005</td>\n",
       "      <td>0.079048</td>\n",
       "      <td>0.829571</td>\n",
       "      <td>0.091429</td>\n",
       "      <td>4761.821548</td>\n",
       "      <td>-1.945809</td>\n",
       "      <td>-1.0</td>\n",
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       "    <tr>\n",
       "      <th>2019-03-23</th>\n",
       "      <td>31.200000</td>\n",
       "      <td>30.406667</td>\n",
       "      <td>31.056667</td>\n",
       "      <td>30.449999</td>\n",
       "      <td>-1.945809</td>\n",
       "      <td>54000.300000</td>\n",
       "      <td>0.293590</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.838300</td>\n",
       "      <td>0.131000</td>\n",
       "      <td>23649.090490</td>\n",
       "      <td>-1.496982</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-24</th>\n",
       "      <td>30.960000</td>\n",
       "      <td>30.283333</td>\n",
       "      <td>30.793334</td>\n",
       "      <td>30.329999</td>\n",
       "      <td>-1.496982</td>\n",
       "      <td>5097.615385</td>\n",
       "      <td>0.048138</td>\n",
       "      <td>0.081231</td>\n",
       "      <td>0.845846</td>\n",
       "      <td>0.072923</td>\n",
       "      <td>-774.460854</td>\n",
       "      <td>-1.048155</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>30.719999</td>\n",
       "      <td>30.160000</td>\n",
       "      <td>30.530001</td>\n",
       "      <td>30.209999</td>\n",
       "      <td>-1.048155</td>\n",
       "      <td>6812.970588</td>\n",
       "      <td>0.202871</td>\n",
       "      <td>0.050912</td>\n",
       "      <td>0.831971</td>\n",
       "      <td>0.117088</td>\n",
       "      <td>395.799526</td>\n",
       "      <td>-0.983288</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>30.620001</td>\n",
       "      <td>29.969999</td>\n",
       "      <td>30.510000</td>\n",
       "      <td>30.209999</td>\n",
       "      <td>-0.983288</td>\n",
       "      <td>13870.171429</td>\n",
       "      <td>0.017854</td>\n",
       "      <td>0.080600</td>\n",
       "      <td>0.839771</td>\n",
       "      <td>0.079629</td>\n",
       "      <td>3391.438680</td>\n",
       "      <td>2.213413</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>31.200001</td>\n",
       "      <td>30.150000</td>\n",
       "      <td>30.270000</td>\n",
       "      <td>30.940001</td>\n",
       "      <td>2.213413</td>\n",
       "      <td>10882.428571</td>\n",
       "      <td>0.286906</td>\n",
       "      <td>0.048400</td>\n",
       "      <td>0.818229</td>\n",
       "      <td>0.133371</td>\n",
       "      <td>4553.928774</td>\n",
       "      <td>-0.354726</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>31.370001</td>\n",
       "      <td>30.440001</td>\n",
       "      <td>31.010000</td>\n",
       "      <td>30.900000</td>\n",
       "      <td>-0.354726</td>\n",
       "      <td>7413.473684</td>\n",
       "      <td>0.298808</td>\n",
       "      <td>0.065605</td>\n",
       "      <td>0.799079</td>\n",
       "      <td>0.135263</td>\n",
       "      <td>3184.396013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                 High        Low       Open  Adj Close  Pct_change  \\\n",
       "2019-03-15  32.349998  31.420000  32.279999  31.430000   -2.633205   \n",
       "2019-03-16  32.093332  31.290000  32.019999  31.413333   -1.882455   \n",
       "2019-03-17  31.836666  31.160000  31.760000  31.396666   -1.131705   \n",
       "2019-03-18  31.580000  31.030001  31.500000  31.379999   -0.380955   \n",
       "2019-03-19  32.119999  31.379999  31.500000  31.590000    0.285715   \n",
       "2019-03-20  31.549999  30.770000  31.500000  30.959999   -1.714289   \n",
       "2019-03-21  31.770000  30.790001  30.889999  31.450001    1.812889   \n",
       "2019-03-22  31.440001  30.530001  31.320000  30.570000   -2.394636   \n",
       "2019-03-23  31.200000  30.406667  31.056667  30.449999   -1.945809   \n",
       "2019-03-24  30.960000  30.283333  30.793334  30.329999   -1.496982   \n",
       "2019-03-25  30.719999  30.160000  30.530001  30.209999   -1.048155   \n",
       "2019-03-26  30.620001  29.969999  30.510000  30.209999   -0.983288   \n",
       "2019-03-27  31.200001  30.150000  30.270000  30.940001    2.213413   \n",
       "2019-03-28  31.370001  30.440001  31.010000  30.900000   -0.354726   \n",
       "\n",
       "            follower count  compound       neg       neu       pos  \\\n",
       "2019-03-15     4002.475410  0.232066  0.047541  0.846262  0.106180   \n",
       "2019-03-16     1587.333333  0.259539  0.042778  0.815000  0.142222   \n",
       "2019-03-17      824.615385  0.219554  0.061154  0.825846  0.113000   \n",
       "2019-03-18    11031.893639  0.171607  0.065358  0.821524  0.113131   \n",
       "2019-03-19     1824.300000  0.155815  0.059550  0.804050  0.136400   \n",
       "2019-03-20    11407.000000  0.270644  0.040125  0.798250  0.161625   \n",
       "2019-03-21    15514.461538  0.412185  0.014231  0.838385  0.147385   \n",
       "2019-03-22    13077.428571  0.073005  0.079048  0.829571  0.091429   \n",
       "2019-03-23    54000.300000  0.293590  0.030800  0.838300  0.131000   \n",
       "2019-03-24     5097.615385  0.048138  0.081231  0.845846  0.072923   \n",
       "2019-03-25     6812.970588  0.202871  0.050912  0.831971  0.117088   \n",
       "2019-03-26    13870.171429  0.017854  0.080600  0.839771  0.079629   \n",
       "2019-03-27    10882.428571  0.286906  0.048400  0.818229  0.133371   \n",
       "2019-03-28     7413.473684  0.298808  0.065605  0.799079  0.135263   \n",
       "\n",
       "            Compound_multiplied  Predicted_change  Buy/Sell  \n",
       "2019-03-15          -571.878784         -1.882455      -1.0  \n",
       "2019-03-16           601.459044         -1.131705      -1.0  \n",
       "2019-03-17            81.045608         -0.380955      -1.0  \n",
       "2019-03-18          4030.680298          0.285715       1.0  \n",
       "2019-03-19           551.926725         -1.714289      -1.0  \n",
       "2019-03-20          6926.773819          1.812889       1.0  \n",
       "2019-03-21          9229.666808         -2.394636      -1.0  \n",
       "2019-03-22          4761.821548         -1.945809      -1.0  \n",
       "2019-03-23         23649.090490         -1.496982      -1.0  \n",
       "2019-03-24          -774.460854         -1.048155      -1.0  \n",
       "2019-03-25           395.799526         -0.983288      -1.0  \n",
       "2019-03-26          3391.438680          2.213413       1.0  \n",
       "2019-03-27          4553.928774         -0.354726      -1.0  \n",
       "2019-03-28          3184.396013               NaN       NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spring2019.tail(14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Replace zeros in the Buy/Sell- column with minus ones (-1)\n",
    "The minus ones make the calculations of gain or loss easier (and more logical?), than trying to use zeros from the original version."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Spring2019['Buy/Sell'] = Spring2019['Buy/Sell'].replace(0, -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Checking for NaN in the last row..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Pct_change</th>\n",
       "      <th>follower count</th>\n",
       "      <th>compound</th>\n",
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       "      <th>pos</th>\n",
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       "      <th>Predicted_change</th>\n",
       "      <th>Buy/Sell</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-28</th>\n",
       "      <td>31.370001</td>\n",
       "      <td>30.440001</td>\n",
       "      <td>31.01</td>\n",
       "      <td>30.9</td>\n",
       "      <td>-0.354726</td>\n",
       "      <td>7413.473684</td>\n",
       "      <td>0.298808</td>\n",
       "      <td>0.065605</td>\n",
       "      <td>0.799079</td>\n",
       "      <td>0.135263</td>\n",
       "      <td>3184.396013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "                 High        Low   Open  Adj Close  Pct_change  \\\n",
       "2019-03-28  31.370001  30.440001  31.01       30.9   -0.354726   \n",
       "\n",
       "            follower count  compound       neg       neu       pos  \\\n",
       "2019-03-28     7413.473684  0.298808  0.065605  0.799079  0.135263   \n",
       "\n",
       "            Compound_multiplied  Predicted_change  Buy/Sell  \n",
       "2019-03-28          3184.396013               NaN       NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nan_rows = Spring2019[Spring2019['Predicted_change'].isnull()]\n",
    "nan_rows"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### ...then removing the last row, since its 'Predicted_change' is NaN\n",
    "In other words, since this is the last row/day, there is no data from a following day to use for a prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Remove the last row for each cashtag, since its 'Predicted_change' is NaN\n",
    "Spring2019 = Spring2019[np.isfinite(Spring2019['Predicted_change'])]\n",
    "Spring2019 = Spring2019[np.isfinite(Spring2019['Buy/Sell'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Pct_change</th>\n",
       "      <th>follower count</th>\n",
       "      <th>compound</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>Compound_multiplied</th>\n",
       "      <th>Predicted_change</th>\n",
       "      <th>Buy/Sell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-23</th>\n",
       "      <td>31.200000</td>\n",
       "      <td>30.406667</td>\n",
       "      <td>31.056667</td>\n",
       "      <td>30.449999</td>\n",
       "      <td>-1.945809</td>\n",
       "      <td>54000.300000</td>\n",
       "      <td>0.293590</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.838300</td>\n",
       "      <td>0.131000</td>\n",
       "      <td>23649.090490</td>\n",
       "      <td>-1.496982</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-24</th>\n",
       "      <td>30.960000</td>\n",
       "      <td>30.283333</td>\n",
       "      <td>30.793334</td>\n",
       "      <td>30.329999</td>\n",
       "      <td>-1.496982</td>\n",
       "      <td>5097.615385</td>\n",
       "      <td>0.048138</td>\n",
       "      <td>0.081231</td>\n",
       "      <td>0.845846</td>\n",
       "      <td>0.072923</td>\n",
       "      <td>-774.460854</td>\n",
       "      <td>-1.048155</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>30.719999</td>\n",
       "      <td>30.160000</td>\n",
       "      <td>30.530001</td>\n",
       "      <td>30.209999</td>\n",
       "      <td>-1.048155</td>\n",
       "      <td>6812.970588</td>\n",
       "      <td>0.202871</td>\n",
       "      <td>0.050912</td>\n",
       "      <td>0.831971</td>\n",
       "      <td>0.117088</td>\n",
       "      <td>395.799526</td>\n",
       "      <td>-0.983288</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>30.620001</td>\n",
       "      <td>29.969999</td>\n",
       "      <td>30.510000</td>\n",
       "      <td>30.209999</td>\n",
       "      <td>-0.983288</td>\n",
       "      <td>13870.171429</td>\n",
       "      <td>0.017854</td>\n",
       "      <td>0.080600</td>\n",
       "      <td>0.839771</td>\n",
       "      <td>0.079629</td>\n",
       "      <td>3391.438680</td>\n",
       "      <td>2.213413</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>31.200001</td>\n",
       "      <td>30.150000</td>\n",
       "      <td>30.270000</td>\n",
       "      <td>30.940001</td>\n",
       "      <td>2.213413</td>\n",
       "      <td>10882.428571</td>\n",
       "      <td>0.286906</td>\n",
       "      <td>0.048400</td>\n",
       "      <td>0.818229</td>\n",
       "      <td>0.133371</td>\n",
       "      <td>4553.928774</td>\n",
       "      <td>-0.354726</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open  Adj Close  Pct_change  \\\n",
       "2019-03-23  31.200000  30.406667  31.056667  30.449999   -1.945809   \n",
       "2019-03-24  30.960000  30.283333  30.793334  30.329999   -1.496982   \n",
       "2019-03-25  30.719999  30.160000  30.530001  30.209999   -1.048155   \n",
       "2019-03-26  30.620001  29.969999  30.510000  30.209999   -0.983288   \n",
       "2019-03-27  31.200001  30.150000  30.270000  30.940001    2.213413   \n",
       "\n",
       "            follower count  compound       neg       neu       pos  \\\n",
       "2019-03-23    54000.300000  0.293590  0.030800  0.838300  0.131000   \n",
       "2019-03-24     5097.615385  0.048138  0.081231  0.845846  0.072923   \n",
       "2019-03-25     6812.970588  0.202871  0.050912  0.831971  0.117088   \n",
       "2019-03-26    13870.171429  0.017854  0.080600  0.839771  0.079629   \n",
       "2019-03-27    10882.428571  0.286906  0.048400  0.818229  0.133371   \n",
       "\n",
       "            Compound_multiplied  Predicted_change  Buy/Sell  \n",
       "2019-03-23         23649.090490         -1.496982      -1.0  \n",
       "2019-03-24          -774.460854         -1.048155      -1.0  \n",
       "2019-03-25           395.799526         -0.983288      -1.0  \n",
       "2019-03-26          3391.438680          2.213413       1.0  \n",
       "2019-03-27          4553.928774         -0.354726      -1.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spring2019.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Pct_change</th>\n",
       "      <th>follower count</th>\n",
       "      <th>compound</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>Compound_multiplied</th>\n",
       "      <th>Predicted_change</th>\n",
       "      <th>Buy/Sell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>29.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>32.732069</td>\n",
       "      <td>31.809310</td>\n",
       "      <td>32.439655</td>\n",
       "      <td>32.116896</td>\n",
       "      <td>-0.955412</td>\n",
       "      <td>11156.666741</td>\n",
       "      <td>0.167221</td>\n",
       "      <td>0.065350</td>\n",
       "      <td>0.822298</td>\n",
       "      <td>0.112368</td>\n",
       "      <td>4059.862514</td>\n",
       "      <td>-0.927536</td>\n",
       "      <td>-0.310345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.704166</td>\n",
       "      <td>1.520185</td>\n",
       "      <td>1.735428</td>\n",
       "      <td>1.546593</td>\n",
       "      <td>1.643896</td>\n",
       "      <td>12053.204792</td>\n",
       "      <td>0.113932</td>\n",
       "      <td>0.026239</td>\n",
       "      <td>0.028760</td>\n",
       "      <td>0.027381</td>\n",
       "      <td>7019.385685</td>\n",
       "      <td>1.647099</td>\n",
       "      <td>0.967451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>30.620001</td>\n",
       "      <td>29.969999</td>\n",
       "      <td>30.270000</td>\n",
       "      <td>30.209999</td>\n",
       "      <td>-3.945320</td>\n",
       "      <td>824.615385</td>\n",
       "      <td>-0.015900</td>\n",
       "      <td>0.014231</td>\n",
       "      <td>0.714500</td>\n",
       "      <td>0.069097</td>\n",
       "      <td>-10022.823087</td>\n",
       "      <td>-3.945320</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>31.580000</td>\n",
       "      <td>30.790001</td>\n",
       "      <td>31.320000</td>\n",
       "      <td>30.959999</td>\n",
       "      <td>-2.350865</td>\n",
       "      <td>4048.149533</td>\n",
       "      <td>0.073005</td>\n",
       "      <td>0.048400</td>\n",
       "      <td>0.815000</td>\n",
       "      <td>0.091429</td>\n",
       "      <td>196.036746</td>\n",
       "      <td>-2.350865</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>32.169999</td>\n",
       "      <td>31.379999</td>\n",
       "      <td>31.813333</td>\n",
       "      <td>31.840000</td>\n",
       "      <td>-1.163118</td>\n",
       "      <td>9859.388889</td>\n",
       "      <td>0.171607</td>\n",
       "      <td>0.062150</td>\n",
       "      <td>0.831971</td>\n",
       "      <td>0.113000</td>\n",
       "      <td>3624.624743</td>\n",
       "      <td>-1.131705</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>33.419998</td>\n",
       "      <td>32.520000</td>\n",
       "      <td>33.220001</td>\n",
       "      <td>32.759998</td>\n",
       "      <td>0.285715</td>\n",
       "      <td>11407.000000</td>\n",
       "      <td>0.232066</td>\n",
       "      <td>0.079048</td>\n",
       "      <td>0.841444</td>\n",
       "      <td>0.133371</td>\n",
       "      <td>5137.035271</td>\n",
       "      <td>0.285715</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>36.330002</td>\n",
       "      <td>35.270000</td>\n",
       "      <td>36.110001</td>\n",
       "      <td>35.689999</td>\n",
       "      <td>2.213413</td>\n",
       "      <td>54000.300000</td>\n",
       "      <td>0.412185</td>\n",
       "      <td>0.131048</td>\n",
       "      <td>0.849533</td>\n",
       "      <td>0.163444</td>\n",
       "      <td>27806.342575</td>\n",
       "      <td>2.213413</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            High        Low       Open  Adj Close  Pct_change  follower count  \\\n",
       "count  29.000000  29.000000  29.000000  29.000000   29.000000       29.000000   \n",
       "mean   32.732069  31.809310  32.439655  32.116896   -0.955412    11156.666741   \n",
       "std     1.704166   1.520185   1.735428   1.546593    1.643896    12053.204792   \n",
       "min    30.620001  29.969999  30.270000  30.209999   -3.945320      824.615385   \n",
       "25%    31.580000  30.790001  31.320000  30.959999   -2.350865     4048.149533   \n",
       "50%    32.169999  31.379999  31.813333  31.840000   -1.163118     9859.388889   \n",
       "75%    33.419998  32.520000  33.220001  32.759998    0.285715    11407.000000   \n",
       "max    36.330002  35.270000  36.110001  35.689999    2.213413    54000.300000   \n",
       "\n",
       "        compound        neg        neu        pos  Compound_multiplied  \\\n",
       "count  29.000000  29.000000  29.000000  29.000000            29.000000   \n",
       "mean    0.167221   0.065350   0.822298   0.112368          4059.862514   \n",
       "std     0.113932   0.026239   0.028760   0.027381          7019.385685   \n",
       "min    -0.015900   0.014231   0.714500   0.069097        -10022.823087   \n",
       "25%     0.073005   0.048400   0.815000   0.091429           196.036746   \n",
       "50%     0.171607   0.062150   0.831971   0.113000          3624.624743   \n",
       "75%     0.232066   0.079048   0.841444   0.133371          5137.035271   \n",
       "max     0.412185   0.131048   0.849533   0.163444         27806.342575   \n",
       "\n",
       "       Predicted_change   Buy/Sell  \n",
       "count         29.000000  29.000000  \n",
       "mean          -0.927536  -0.310345  \n",
       "std            1.647099   0.967451  \n",
       "min           -3.945320  -1.000000  \n",
       "25%           -2.350865  -1.000000  \n",
       "50%           -1.131705  -1.000000  \n",
       "75%            0.285715   1.000000  \n",
       "max            2.213413   1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spring2019.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Train - Test split -  not needed\n",
    "The x_train and y_train is taken from the original data from 03/2016-06/2016 <br>\n",
    "The x_test and y_test is the data from 28 days during March 2019\n",
    "\n",
    "#### Remove the last row for each cashtag in all_data dataframe (=training data), since its 'Predicted_change_stock' is NaN\n",
    "#### Make sure to change the file name here also!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "xls = pd.ExcelFile('C:/Users/jangn/OneDrive/CODE/Stock_markets/Cashtags/$AAL_minusones.xlsx')\n",
    "\n",
    "all_data = pd.read_excel(xls, header = 0,encoding='latin-1')\n",
    "\n",
    "# Remove the last row for each cashtag, since its 'Predicted_change_stock' is NaN\n",
    "all_data = all_data[np.isfinite(all_data['Predicted_change_stock'])]\n",
    "all_data = all_data[np.isfinite(all_data['Buy/Sell'])]\n",
    "\n",
    "x_train = np.array(all_data[['Compound_multiplied']]) #\n",
    "y_train = np.array(all_data[['Buy/Sell']])\n",
    "\n",
    "x_test = np.array(Spring2019[['Compound_multiplied']])\n",
    "y_test = np.array(Spring2019[['Buy/Sell']])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "### KNN - K-Nearest-Neighbors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=7, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "neigh = KNeighborsClassifier(n_neighbors=7)\n",
    "neigh.fit(x_train, y_train.ravel()) \n",
    "\n",
    "#The addition -- .ravel() -- was needed to y_train to get rid of this warning: DataConversionWarning: A column-vector y \n",
    "#was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Accuracy after cross validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.628571428571\n"
     ]
    }
   ],
   "source": [
    "neigh_cv = cross_val_score(neigh, x_train, y_train.ravel(), cv=10) #Also here, and below, -- .ravel()-- was needed\n",
    "print(neigh_cv.mean()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=42, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logreg = LogisticRegression(random_state=42)\n",
    "\n",
    "logreg.fit(x_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Accuracy after cross validation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.605952380952\n"
     ]
    }
   ],
   "source": [
    "logreg_cv = cross_val_score(logreg, x_train, y_train.ravel(), cv=10)\n",
    "print(logreg_cv.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Support Vector Machines (SVM) with different kernels \n",
    "#### kernel = 'linear'\n",
    "To be noted! SVM normally works better with scaled data, i.e. features e.g. between [0,1]. This is because it tries to maximize the distance between the separating plane and the support vectors. If one feature (i.e. one dimension in this space) has very large values, it will dominate the other features when calculationg the distance. However, since this analysis only uses one feature, the \"Compound_multiplied\", scaling will not be needed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "svm_linear = SVC( kernel = 'linear')\n",
    "svm_linear.fit(x_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Accuracy for 'linear' after cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.618452380952\n"
     ]
    }
   ],
   "source": [
    "svm_linear_cv = cross_val_score(svm_linear, x_train, y_train.ravel(), cv=10)\n",
    "print(svm_linear_cv.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Naive Bayes\n",
    "\n",
    "Please note! A MinMaxScaler is needed to get the features in the range MultinomialNB requires."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler() \n",
    "X_minmax = scaler.fit_transform(x_train)\n",
    "\n",
    "mnb = MultinomialNB()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Accuracy after cross validation (Same as linear SVM??!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.622619047619\n"
     ]
    }
   ],
   "source": [
    "mnb_cv = cross_val_score(mnb, X_minmax, y_train.ravel(), cv=10) \n",
    "print(mnb_cv.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=42,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "dtc = tree.DecisionTreeClassifier(random_state=42)\n",
    "dtc.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Decision Tree as an image\n",
    "If you wish to print a picture of the tree, untag the rows in the cell below!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# features = ['Buy/Sell']\n",
    "\n",
    "# import pydotplus\n",
    "# from sklearn.externals.six import StringIO  \n",
    "# from IPython.display import Image\n",
    "# dot_data = StringIO()  \n",
    "# tree.export_graphviz(dtc, out_file=dot_data,  \n",
    "#                          feature_names=features)  \n",
    "# graph = pydotplus.graph_from_dot_data(dot_data.getvalue())  \n",
    "# Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Accuracy after cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.711904761905\n"
     ]
    }
   ],
   "source": [
    "dtc_cv = cross_val_score(dtc, x_train, y_train, cv=10) \n",
    "print(dtc_cv.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
       "            oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier \n",
    "\n",
    "forest_reg = RandomForestClassifier(random_state=42)\n",
    "forest_reg.fit(x_train, y_train.ravel())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#### Accuracy after cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.66369047619\n"
     ]
    }
   ],
   "source": [
    "forest_reg_cv = cross_val_score(forest_reg, x_train, y_train.ravel(), cv=10) \n",
    "print(forest_reg_cv.mean())  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Results Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KNN: \t\t\t 0.628571428571\n",
      "Logistic Regression: \t 0.605952380952\n",
      "SVM linear: \t\t 0.618452380952\n",
      "Naive Bayes: \t\t 0.622619047619\n",
      "Decision Tree: \t\t 0.711904761905\n",
      "Random Forest: \t\t 0.66369047619\n"
     ]
    }
   ],
   "source": [
    "print(\"KNN: \\t\\t\\t\", neigh_cv.mean())\n",
    "print(\"Logistic Regression: \\t\", logreg_cv.mean())\n",
    "print(\"SVM linear: \\t\\t\", svm_linear_cv.mean())\n",
    "#print(\"SVM rbf: \\t\\t\", svm_rbf_cv.mean())\n",
    "print(\"Naive Bayes: \\t\\t\", mnb_cv.mean())\n",
    "print(\"Decision Tree: \\t\\t\", dtc_cv.mean())\n",
    "print(\"Random Forest: \\t\\t\", forest_reg_cv.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "results = []\n",
    "cv = [neigh_cv.mean(), logreg_cv.mean(), svm_linear_cv.mean(),mnb_cv.mean(),dtc_cv.mean(), forest_reg_cv.mean()]\n",
    "results.append(cv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "results = {'0': ['KNN', 'LogReg', 'SVM linear', 'Naive Bayes', 'Decision Tree', 'Random Forest'],\n",
    "          '1':[neigh_cv.mean(), logreg_cv.mean(), svm_linear_cv.mean(),mnb_cv.mean(),dtc_cv.mean(),forest_reg_cv.mean()]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "summary = pd.DataFrame.from_dict(results)\n",
    "summary = summary.transpose()\n",
    "summary = summary.rename(index = {'0':'Model', '1':'AAL'}) # Update cashtag!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Model</th>\n",
       "      <td>KNN</td>\n",
       "      <td>LogReg</td>\n",
       "      <td>SVM linear</td>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>Random Forest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAL</th>\n",
       "      <td>0.628571</td>\n",
       "      <td>0.605952</td>\n",
       "      <td>0.618452</td>\n",
       "      <td>0.622619</td>\n",
       "      <td>0.711905</td>\n",
       "      <td>0.66369</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1           2            3              4  \\\n",
       "Model       KNN    LogReg  SVM linear  Naive Bayes  Decision Tree   \n",
       "AAL    0.628571  0.605952    0.618452     0.622619       0.711905   \n",
       "\n",
       "                   5  \n",
       "Model  Random Forest  \n",
       "AAL          0.66369  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# import xlsxwriter\n",
    "# import os\n",
    "\n",
    "writer_df = pd.ExcelWriter('summary.xlsx', engine='xlsxwriter')\n",
    "summary.to_excel(writer_df)\n",
    "writer_df.save()\n",
    "\n",
    "os.rename('summary.xlsx', 'AAL_summary.xlsx') # Update cashtag!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Given the input 'Compound_multiplied', should the stock be bought or sold?\n",
    "\n",
    "Now we finally have a model using which we can actually predict whether price expected to go up or down.<br>\n",
    "By typing in the 'Compound_multiplied_scaled' value the model gives an outcome.\n",
    "\n",
    "First, turning the features into numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Pct_change</th>\n",
       "      <th>follower count</th>\n",
       "      <th>compound</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>Compound_multiplied</th>\n",
       "      <th>Predicted_change</th>\n",
       "      <th>Buy/Sell</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-03-23</th>\n",
       "      <td>31.200000</td>\n",
       "      <td>30.406667</td>\n",
       "      <td>31.056667</td>\n",
       "      <td>30.449999</td>\n",
       "      <td>-1.945809</td>\n",
       "      <td>54000.300000</td>\n",
       "      <td>0.293590</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.838300</td>\n",
       "      <td>0.131000</td>\n",
       "      <td>23649.090490</td>\n",
       "      <td>-1.496982</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-24</th>\n",
       "      <td>30.960000</td>\n",
       "      <td>30.283333</td>\n",
       "      <td>30.793334</td>\n",
       "      <td>30.329999</td>\n",
       "      <td>-1.496982</td>\n",
       "      <td>5097.615385</td>\n",
       "      <td>0.048138</td>\n",
       "      <td>0.081231</td>\n",
       "      <td>0.845846</td>\n",
       "      <td>0.072923</td>\n",
       "      <td>-774.460854</td>\n",
       "      <td>-1.048155</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-25</th>\n",
       "      <td>30.719999</td>\n",
       "      <td>30.160000</td>\n",
       "      <td>30.530001</td>\n",
       "      <td>30.209999</td>\n",
       "      <td>-1.048155</td>\n",
       "      <td>6812.970588</td>\n",
       "      <td>0.202871</td>\n",
       "      <td>0.050912</td>\n",
       "      <td>0.831971</td>\n",
       "      <td>0.117088</td>\n",
       "      <td>395.799526</td>\n",
       "      <td>-0.983288</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-26</th>\n",
       "      <td>30.620001</td>\n",
       "      <td>29.969999</td>\n",
       "      <td>30.510000</td>\n",
       "      <td>30.209999</td>\n",
       "      <td>-0.983288</td>\n",
       "      <td>13870.171429</td>\n",
       "      <td>0.017854</td>\n",
       "      <td>0.080600</td>\n",
       "      <td>0.839771</td>\n",
       "      <td>0.079629</td>\n",
       "      <td>3391.438680</td>\n",
       "      <td>2.213413</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-27</th>\n",
       "      <td>31.200001</td>\n",
       "      <td>30.150000</td>\n",
       "      <td>30.270000</td>\n",
       "      <td>30.940001</td>\n",
       "      <td>2.213413</td>\n",
       "      <td>10882.428571</td>\n",
       "      <td>0.286906</td>\n",
       "      <td>0.048400</td>\n",
       "      <td>0.818229</td>\n",
       "      <td>0.133371</td>\n",
       "      <td>4553.928774</td>\n",
       "      <td>-0.354726</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open  Adj Close  Pct_change  \\\n",
       "2019-03-23  31.200000  30.406667  31.056667  30.449999   -1.945809   \n",
       "2019-03-24  30.960000  30.283333  30.793334  30.329999   -1.496982   \n",
       "2019-03-25  30.719999  30.160000  30.530001  30.209999   -1.048155   \n",
       "2019-03-26  30.620001  29.969999  30.510000  30.209999   -0.983288   \n",
       "2019-03-27  31.200001  30.150000  30.270000  30.940001    2.213413   \n",
       "\n",
       "            follower count  compound       neg       neu       pos  \\\n",
       "2019-03-23    54000.300000  0.293590  0.030800  0.838300  0.131000   \n",
       "2019-03-24     5097.615385  0.048138  0.081231  0.845846  0.072923   \n",
       "2019-03-25     6812.970588  0.202871  0.050912  0.831971  0.117088   \n",
       "2019-03-26    13870.171429  0.017854  0.080600  0.839771  0.079629   \n",
       "2019-03-27    10882.428571  0.286906  0.048400  0.818229  0.133371   \n",
       "\n",
       "            Compound_multiplied  Predicted_change  Buy/Sell  \n",
       "2019-03-23         23649.090490         -1.496982      -1.0  \n",
       "2019-03-24          -774.460854         -1.048155      -1.0  \n",
       "2019-03-25           395.799526         -0.983288      -1.0  \n",
       "2019-03-26          3391.438680          2.213413       1.0  \n",
       "2019-03-27          4553.928774         -0.354726      -1.0  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spring2019.tail() # length = 11"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Add new columns to Spring2019 for each of the six ML models' prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X = all_data.iloc[:, 14:15].values # creates the 'Compound_multiplied' values from all_data dataframe as a numpy.ndarray\n",
    "y = all_data['Buy/Sell']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Buy_or_Sell_KNN = []\n",
    "Buy_or_Sell = neigh.fit(X, y) \n",
    "outcome_KNN = (Buy_or_Sell.predict(Spring2019[['Compound_multiplied']]))\n",
    "Spring2019['KNN_prediction'] = outcome_KNN\n",
    "\n",
    "Buy_or_Sell_LogReg = []\n",
    "Buy_or_Sell = logreg.fit(X, y) \n",
    "outcome_logreg = (Buy_or_Sell.predict(Spring2019[['Compound_multiplied']]))\n",
    "Spring2019['LogReg_prediction'] = outcome_logreg\n",
    "\n",
    "Buy_or_Sell_SVM = []\n",
    "Buy_or_Sell = neigh.fit(X, y) \n",
    "outcome_svm = (Buy_or_Sell.predict(Spring2019[['Compound_multiplied']]))\n",
    "Spring2019['SVM_prediction'] = outcome_svm\n",
    "\n",
    "Buy_or_Sell_NB = []\n",
    "X_minmax = scaler.fit_transform(X) #Is thi sneeded??\n",
    "Buy_or_Sell = mnb.fit(X_minmax, y)    #mnb.fit(X_minmax, y)\n",
    "outcome_nb = (Buy_or_Sell.predict(Spring2019[['Compound_multiplied']]))\n",
    "Spring2019['Naive_Bayes_prediction'] = outcome_nb\n",
    "\n",
    "Buy_or_Sell_DecTree = []\n",
    "Buy_or_Sell = dtc.fit(X, y) \n",
    "outcome_DT = (Buy_or_Sell.predict(Spring2019[['Compound_multiplied']]))\n",
    "Spring2019['Decision_Tree_prediction'] = outcome_DT\n",
    "\n",
    "Buy_or_Sell_Forest = []\n",
    "Buy_or_Sell = forest_reg.fit(X, y) \n",
    "outcome_RF = (Buy_or_Sell.predict(Spring2019[['Compound_multiplied']]))\n",
    "Spring2019['Random_Forest_prediction'] = outcome_RF"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Daily prediction from each model in one dataframe; Spring2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Pct_change</th>\n",
       "      <th>follower count</th>\n",
       "      <th>compound</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>Compound_multiplied</th>\n",
       "      <th>Predicted_change</th>\n",
       "      <th>Buy/Sell</th>\n",
       "      <th>KNN_prediction</th>\n",
       "      <th>LogReg_prediction</th>\n",
       "      <th>SVM_prediction</th>\n",
       "      <th>Naive_Bayes_prediction</th>\n",
       "      <th>Decision_Tree_prediction</th>\n",
       "      <th>Random_Forest_prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-27</th>\n",
       "      <td>36.330002</td>\n",
       "      <td>35.270000</td>\n",
       "      <td>36.110001</td>\n",
       "      <td>35.689999</td>\n",
       "      <td>-1.163118</td>\n",
       "      <td>11031.893639</td>\n",
       "      <td>0.171607</td>\n",
       "      <td>0.065358</td>\n",
       "      <td>0.821524</td>\n",
       "      <td>0.113131</td>\n",
       "      <td>4030.680298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>35.889999</td>\n",
       "      <td>35.169998</td>\n",
       "      <td>35.630001</td>\n",
       "      <td>35.630001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1851.000000</td>\n",
       "      <td>-0.015900</td>\n",
       "      <td>0.109667</td>\n",
       "      <td>0.774667</td>\n",
       "      <td>0.116000</td>\n",
       "      <td>-354.625300</td>\n",
       "      <td>-2.641940</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>35.959999</td>\n",
       "      <td>34.369999</td>\n",
       "      <td>35.580002</td>\n",
       "      <td>34.639999</td>\n",
       "      <td>-2.641940</td>\n",
       "      <td>11016.083333</td>\n",
       "      <td>0.046036</td>\n",
       "      <td>0.075833</td>\n",
       "      <td>0.839222</td>\n",
       "      <td>0.084972</td>\n",
       "      <td>3885.012439</td>\n",
       "      <td>-2.758606</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-02</th>\n",
       "      <td>35.603333</td>\n",
       "      <td>34.079999</td>\n",
       "      <td>35.306667</td>\n",
       "      <td>34.333333</td>\n",
       "      <td>-2.758606</td>\n",
       "      <td>40384.000000</td>\n",
       "      <td>0.202125</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>0.714500</td>\n",
       "      <td>0.157500</td>\n",
       "      <td>27806.342575</td>\n",
       "      <td>-2.875271</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-03</th>\n",
       "      <td>35.246666</td>\n",
       "      <td>33.790000</td>\n",
       "      <td>35.033333</td>\n",
       "      <td>34.026667</td>\n",
       "      <td>-2.875271</td>\n",
       "      <td>8222.190476</td>\n",
       "      <td>-0.009190</td>\n",
       "      <td>0.131048</td>\n",
       "      <td>0.788524</td>\n",
       "      <td>0.080381</td>\n",
       "      <td>5137.035271</td>\n",
       "      <td>-2.991937</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-04</th>\n",
       "      <td>34.889999</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>34.759998</td>\n",
       "      <td>33.720001</td>\n",
       "      <td>-2.991937</td>\n",
       "      <td>11879.176471</td>\n",
       "      <td>0.158529</td>\n",
       "      <td>0.066529</td>\n",
       "      <td>0.834824</td>\n",
       "      <td>0.098647</td>\n",
       "      <td>7312.270988</td>\n",
       "      <td>-2.350865</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-05</th>\n",
       "      <td>34.380001</td>\n",
       "      <td>33.169998</td>\n",
       "      <td>34.029999</td>\n",
       "      <td>33.230000</td>\n",
       "      <td>-2.350865</td>\n",
       "      <td>6355.945946</td>\n",
       "      <td>0.259919</td>\n",
       "      <td>0.046405</td>\n",
       "      <td>0.832892</td>\n",
       "      <td>0.120784</td>\n",
       "      <td>3624.624743</td>\n",
       "      <td>-1.384717</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-06</th>\n",
       "      <td>33.419998</td>\n",
       "      <td>32.520000</td>\n",
       "      <td>33.220001</td>\n",
       "      <td>32.759998</td>\n",
       "      <td>-1.384717</td>\n",
       "      <td>34260.903226</td>\n",
       "      <td>0.226706</td>\n",
       "      <td>0.064097</td>\n",
       "      <td>0.794032</td>\n",
       "      <td>0.141774</td>\n",
       "      <td>-10022.823087</td>\n",
       "      <td>-1.467888</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-07</th>\n",
       "      <td>32.700001</td>\n",
       "      <td>31.620001</td>\n",
       "      <td>32.700001</td>\n",
       "      <td>32.220001</td>\n",
       "      <td>-1.467888</td>\n",
       "      <td>6684.250000</td>\n",
       "      <td>0.387569</td>\n",
       "      <td>0.036750</td>\n",
       "      <td>0.799806</td>\n",
       "      <td>0.163444</td>\n",
       "      <td>4093.940403</td>\n",
       "      <td>0.031349</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-08</th>\n",
       "      <td>32.119999</td>\n",
       "      <td>31.370001</td>\n",
       "      <td>31.900000</td>\n",
       "      <td>31.910000</td>\n",
       "      <td>0.031349</td>\n",
       "      <td>1840.743590</td>\n",
       "      <td>0.124172</td>\n",
       "      <td>0.086923</td>\n",
       "      <td>0.823026</td>\n",
       "      <td>0.090051</td>\n",
       "      <td>13.952828</td>\n",
       "      <td>0.452842</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-09</th>\n",
       "      <td>32.169999</td>\n",
       "      <td>31.436667</td>\n",
       "      <td>31.813333</td>\n",
       "      <td>31.956666</td>\n",
       "      <td>0.452842</td>\n",
       "      <td>9859.388889</td>\n",
       "      <td>0.093711</td>\n",
       "      <td>0.059778</td>\n",
       "      <td>0.841444</td>\n",
       "      <td>0.098833</td>\n",
       "      <td>6087.410011</td>\n",
       "      <td>0.874335</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open  Adj Close  Pct_change  \\\n",
       "2019-02-27  36.330002  35.270000  36.110001  35.689999   -1.163118   \n",
       "2019-02-28  35.889999  35.169998  35.630001  35.630001    0.000000   \n",
       "2019-03-01  35.959999  34.369999  35.580002  34.639999   -2.641940   \n",
       "2019-03-02  35.603333  34.079999  35.306667  34.333333   -2.758606   \n",
       "2019-03-03  35.246666  33.790000  35.033333  34.026667   -2.875271   \n",
       "2019-03-04  34.889999  33.500000  34.759998  33.720001   -2.991937   \n",
       "2019-03-05  34.380001  33.169998  34.029999  33.230000   -2.350865   \n",
       "2019-03-06  33.419998  32.520000  33.220001  32.759998   -1.384717   \n",
       "2019-03-07  32.700001  31.620001  32.700001  32.220001   -1.467888   \n",
       "2019-03-08  32.119999  31.370001  31.900000  31.910000    0.031349   \n",
       "2019-03-09  32.169999  31.436667  31.813333  31.956666    0.452842   \n",
       "\n",
       "            follower count  compound       neg       neu       pos  \\\n",
       "2019-02-27    11031.893639  0.171607  0.065358  0.821524  0.113131   \n",
       "2019-02-28     1851.000000 -0.015900  0.109667  0.774667  0.116000   \n",
       "2019-03-01    11016.083333  0.046036  0.075833  0.839222  0.084972   \n",
       "2019-03-02    40384.000000  0.202125  0.128000  0.714500  0.157500   \n",
       "2019-03-03     8222.190476 -0.009190  0.131048  0.788524  0.080381   \n",
       "2019-03-04    11879.176471  0.158529  0.066529  0.834824  0.098647   \n",
       "2019-03-05     6355.945946  0.259919  0.046405  0.832892  0.120784   \n",
       "2019-03-06    34260.903226  0.226706  0.064097  0.794032  0.141774   \n",
       "2019-03-07     6684.250000  0.387569  0.036750  0.799806  0.163444   \n",
       "2019-03-08     1840.743590  0.124172  0.086923  0.823026  0.090051   \n",
       "2019-03-09     9859.388889  0.093711  0.059778  0.841444  0.098833   \n",
       "\n",
       "            Compound_multiplied  Predicted_change  Buy/Sell  KNN_prediction  \\\n",
       "2019-02-27          4030.680298          0.000000       1.0            -1.0   \n",
       "2019-02-28          -354.625300         -2.641940      -1.0             1.0   \n",
       "2019-03-01          3885.012439         -2.758606      -1.0            -1.0   \n",
       "2019-03-02         27806.342575         -2.875271      -1.0            -1.0   \n",
       "2019-03-03          5137.035271         -2.991937      -1.0            -1.0   \n",
       "2019-03-04          7312.270988         -2.350865      -1.0            -1.0   \n",
       "2019-03-05          3624.624743         -1.384717      -1.0             1.0   \n",
       "2019-03-06        -10022.823087         -1.467888      -1.0            -1.0   \n",
       "2019-03-07          4093.940403          0.031349       1.0            -1.0   \n",
       "2019-03-08            13.952828          0.452842       1.0             1.0   \n",
       "2019-03-09          6087.410011          0.874335       1.0            -1.0   \n",
       "\n",
       "            LogReg_prediction  SVM_prediction  Naive_Bayes_prediction  \\\n",
       "2019-02-27               -1.0            -1.0                    -1.0   \n",
       "2019-02-28               -1.0             1.0                    -1.0   \n",
       "2019-03-01               -1.0            -1.0                    -1.0   \n",
       "2019-03-02                1.0            -1.0                    -1.0   \n",
       "2019-03-03               -1.0            -1.0                    -1.0   \n",
       "2019-03-04               -1.0            -1.0                    -1.0   \n",
       "2019-03-05               -1.0             1.0                    -1.0   \n",
       "2019-03-06               -1.0            -1.0                    -1.0   \n",
       "2019-03-07               -1.0            -1.0                    -1.0   \n",
       "2019-03-08               -1.0             1.0                    -1.0   \n",
       "2019-03-09               -1.0            -1.0                    -1.0   \n",
       "\n",
       "            Decision_Tree_prediction  Random_Forest_prediction  \n",
       "2019-02-27                       1.0                       1.0  \n",
       "2019-02-28                       1.0                       1.0  \n",
       "2019-03-01                       1.0                       1.0  \n",
       "2019-03-02                      -1.0                      -1.0  \n",
       "2019-03-03                      -1.0                      -1.0  \n",
       "2019-03-04                      -1.0                      -1.0  \n",
       "2019-03-05                       1.0                       1.0  \n",
       "2019-03-06                      -1.0                      -1.0  \n",
       "2019-03-07                       1.0                       1.0  \n",
       "2019-03-08                      -1.0                      -1.0  \n",
       "2019-03-09                      -1.0                      -1.0  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spring2019.head(11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Comparisons: Buy-and-hold vs. six alternative strategies based on the binary classifier algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "    .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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Pct_change</th>\n",
       "      <th>follower count</th>\n",
       "      <th>compound</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>pos</th>\n",
       "      <th>...</th>\n",
       "      <th>SVM_prediction</th>\n",
       "      <th>Naive_Bayes_prediction</th>\n",
       "      <th>Decision_Tree_prediction</th>\n",
       "      <th>Random_Forest_prediction</th>\n",
       "      <th>Gain_or_Loss_KNN</th>\n",
       "      <th>Gain_or_Loss_LogReg</th>\n",
       "      <th>Gain_or_Loss_SVM</th>\n",
       "      <th>Gain_or_Loss_NaiveBayes</th>\n",
       "      <th>Gain_or_Loss_DecisionTree</th>\n",
       "      <th>Gain_or_Loss_RandomForest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-02-27</th>\n",
       "      <td>36.330002</td>\n",
       "      <td>35.270000</td>\n",
       "      <td>36.110001</td>\n",
       "      <td>35.689999</td>\n",
       "      <td>-1.163118</td>\n",
       "      <td>11031.893639</td>\n",
       "      <td>0.171607</td>\n",
       "      <td>0.065358</td>\n",
       "      <td>0.821524</td>\n",
       "      <td>0.113131</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.420002</td>\n",
       "      <td>0.420002</td>\n",
       "      <td>0.420002</td>\n",
       "      <td>0.420002</td>\n",
       "      <td>-0.420002</td>\n",
       "      <td>-0.420002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-02-28</th>\n",
       "      <td>35.889999</td>\n",
       "      <td>35.169998</td>\n",
       "      <td>35.630001</td>\n",
       "      <td>35.630001</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1851.000000</td>\n",
       "      <td>-0.015900</td>\n",
       "      <td>0.109667</td>\n",
       "      <td>0.774667</td>\n",
       "      <td>0.116000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-01</th>\n",
       "      <td>35.959999</td>\n",
       "      <td>34.369999</td>\n",
       "      <td>35.580002</td>\n",
       "      <td>34.639999</td>\n",
       "      <td>-2.641940</td>\n",
       "      <td>11016.083333</td>\n",
       "      <td>0.046036</td>\n",
       "      <td>0.075833</td>\n",
       "      <td>0.839222</td>\n",
       "      <td>0.084972</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.940002</td>\n",
       "      <td>0.940002</td>\n",
       "      <td>0.940002</td>\n",
       "      <td>0.940002</td>\n",
       "      <td>-0.940002</td>\n",
       "      <td>-0.940002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-02</th>\n",
       "      <td>35.603333</td>\n",
       "      <td>34.079999</td>\n",
       "      <td>35.306667</td>\n",
       "      <td>34.333333</td>\n",
       "      <td>-2.758606</td>\n",
       "      <td>40384.000000</td>\n",
       "      <td>0.202125</td>\n",
       "      <td>0.128000</td>\n",
       "      <td>0.714500</td>\n",
       "      <td>0.157500</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.973334</td>\n",
       "      <td>-0.973334</td>\n",
       "      <td>0.973334</td>\n",
       "      <td>0.973334</td>\n",
       "      <td>0.973334</td>\n",
       "      <td>0.973334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-03-03</th>\n",
       "      <td>35.246666</td>\n",
       "      <td>33.790000</td>\n",
       "      <td>35.033333</td>\n",
       "      <td>34.026667</td>\n",
       "      <td>-2.875271</td>\n",
       "      <td>8222.190476</td>\n",
       "      <td>-0.009190</td>\n",
       "      <td>0.131048</td>\n",
       "      <td>0.788524</td>\n",
       "      <td>0.080381</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.006666</td>\n",
       "      <td>1.006666</td>\n",
       "      <td>1.006666</td>\n",
       "      <td>1.006666</td>\n",
       "      <td>1.006666</td>\n",
       "      <td>1.006666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open  Adj Close  Pct_change  \\\n",
       "2019-02-27  36.330002  35.270000  36.110001  35.689999   -1.163118   \n",
       "2019-02-28  35.889999  35.169998  35.630001  35.630001    0.000000   \n",
       "2019-03-01  35.959999  34.369999  35.580002  34.639999   -2.641940   \n",
       "2019-03-02  35.603333  34.079999  35.306667  34.333333   -2.758606   \n",
       "2019-03-03  35.246666  33.790000  35.033333  34.026667   -2.875271   \n",
       "\n",
       "            follower count  compound       neg       neu       pos  \\\n",
       "2019-02-27    11031.893639  0.171607  0.065358  0.821524  0.113131   \n",
       "2019-02-28     1851.000000 -0.015900  0.109667  0.774667  0.116000   \n",
       "2019-03-01    11016.083333  0.046036  0.075833  0.839222  0.084972   \n",
       "2019-03-02    40384.000000  0.202125  0.128000  0.714500  0.157500   \n",
       "2019-03-03     8222.190476 -0.009190  0.131048  0.788524  0.080381   \n",
       "\n",
       "                      ...              SVM_prediction  Naive_Bayes_prediction  \\\n",
       "2019-02-27            ...                        -1.0                    -1.0   \n",
       "2019-02-28            ...                         1.0                    -1.0   \n",
       "2019-03-01            ...                        -1.0                    -1.0   \n",
       "2019-03-02            ...                        -1.0                    -1.0   \n",
       "2019-03-03            ...                        -1.0                    -1.0   \n",
       "\n",
       "            Decision_Tree_prediction  Random_Forest_prediction  \\\n",
       "2019-02-27                       1.0                       1.0   \n",
       "2019-02-28                       1.0                       1.0   \n",
       "2019-03-01                       1.0                       1.0   \n",
       "2019-03-02                      -1.0                      -1.0   \n",
       "2019-03-03                      -1.0                      -1.0   \n",
       "\n",
       "            Gain_or_Loss_KNN  Gain_or_Loss_LogReg  Gain_or_Loss_SVM  \\\n",
       "2019-02-27          0.420002             0.420002          0.420002   \n",
       "2019-02-28          0.000000            -0.000000          0.000000   \n",
       "2019-03-01          0.940002             0.940002          0.940002   \n",
       "2019-03-02          0.973334            -0.973334          0.973334   \n",
       "2019-03-03          1.006666             1.006666          1.006666   \n",
       "\n",
       "            Gain_or_Loss_NaiveBayes  Gain_or_Loss_DecisionTree  \\\n",
       "2019-02-27                 0.420002                  -0.420002   \n",
       "2019-02-28                -0.000000                   0.000000   \n",
       "2019-03-01                 0.940002                  -0.940002   \n",
       "2019-03-02                 0.973334                   0.973334   \n",
       "2019-03-03                 1.006666                   1.006666   \n",
       "\n",
       "            Gain_or_Loss_RandomForest  \n",
       "2019-02-27                  -0.420002  \n",
       "2019-02-28                   0.000000  \n",
       "2019-03-01                  -0.940002  \n",
       "2019-03-02                   0.973334  \n",
       "2019-03-03                   1.006666  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spring2019[\"Gain_or_Loss_KNN\"] = (Spring2019['Adj Close'] - Spring2019['Open'])*Spring2019['KNN_prediction']\n",
    "Spring2019[\"Gain_or_Loss_LogReg\"] = (Spring2019['Adj Close'] - Spring2019['Open'])*Spring2019['LogReg_prediction']\n",
    "Spring2019[\"Gain_or_Loss_SVM\"] = (Spring2019['Adj Close'] - Spring2019['Open'])*Spring2019['SVM_prediction']\n",
    "Spring2019[\"Gain_or_Loss_NaiveBayes\"] = (Spring2019['Adj Close'] - Spring2019['Open'])*Spring2019['Naive_Bayes_prediction']\n",
    "Spring2019[\"Gain_or_Loss_DecisionTree\"] = (Spring2019['Adj Close'] - Spring2019['Open'])*Spring2019['Decision_Tree_prediction']\n",
    "Spring2019[\"Gain_or_Loss_RandomForest\"] = (Spring2019['Adj Close'] - Spring2019['Open'])*Spring2019['Random_Forest_prediction']\n",
    "\n",
    "Spring2019.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Resetting index to replace datetime; needed to make comparison between Buy-and-hold and Algorithm-based below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "Spring2019 = Spring2019.reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Add a column where the daily result is added to last day's cumulative result. But first, take the 'Close' of the first day and set it as result for the first day = starting point on the first row "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jangn\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\Default_Python3\\lib\\site-packages\\ipykernel_launcher.py:3: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\jangn\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\Default_Python3\\lib\\site-packages\\ipykernel_launcher.py:4: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\jangn\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\Default_Python3\\lib\\site-packages\\ipykernel_launcher.py:5: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  \"\"\"\n",
      "C:\\Users\\jangn\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\Default_Python3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  \n",
      "C:\\Users\\jangn\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\Default_Python3\\lib\\site-packages\\ipykernel_launcher.py:7: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  import sys\n",
      "C:\\Users\\jangn\\AppData\\Local\\Enthought\\Canopy\\edm\\envs\\Default_Python3\\lib\\site-packages\\ipykernel_launcher.py:8: FutureWarning: set_value is deprecated and will be removed in a future release. Please use .at[] or .iat[] accessors instead\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#Takes the 'Adj Close' of the first day and sets it as result for the first day=starting point\n",
    "first_day_result = Spring2019.iloc[0]['Adj Close']\n",
    "Spring2019.set_value( 0, 'KNN_Result', first_day_result) \n",
    "Spring2019.set_value( 0, 'LogReg_Result', first_day_result)\n",
    "Spring2019.set_value( 0, 'SVM_Result', first_day_result)\n",
    "Spring2019.set_value( 0, 'Naive_Bayes_Result', first_day_result)\n",
    "Spring2019.set_value( 0, 'Decision_Tree_Result', first_day_result)\n",
    "Spring2019.set_value( 0, 'Random_Forest_Result', first_day_result);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#The cumulative daily result of trading according to the model; B/S or S/B at Open and Close. \n",
    "\n",
    "for i in range(1, len(Spring2019)):\n",
    "    Spring2019.loc[i, 'KNN_Result'] = Spring2019.loc[i-1, 'KNN_Result'] + Spring2019.loc[i, 'Gain_or_Loss_KNN']\n",
    "    Spring2019.loc[i, 'LogReg_Result'] = Spring2019.loc[i-1, 'LogReg_Result'] + Spring2019.loc[i, 'Gain_or_Loss_LogReg']\n",
    "    Spring2019.loc[i, 'SVM_Result'] = Spring2019.loc[i-1, 'SVM_Result'] + Spring2019.loc[i, 'Gain_or_Loss_SVM']\n",
    "    Spring2019.loc[i, 'Naive_Bayes_Result'] = Spring2019.loc[i-1, 'Naive_Bayes_Result'] + Spring2019.loc[i, 'Gain_or_Loss_NaiveBayes']\n",
    "    Spring2019.loc[i, 'Decision_Tree_Result'] = Spring2019.loc[i-1, 'Decision_Tree_Result'] + Spring2019.loc[i, 'Gain_or_Loss_DecisionTree']\n",
    "    Spring2019.loc[i, 'Random_Forest_Result'] = Spring2019.loc[i-1, 'Random_Forest_Result'] + Spring2019.loc[i, 'Gain_or_Loss_RandomForest']\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Pct_change</th>\n",
       "      <th>follower count</th>\n",
       "      <th>compound</th>\n",
       "      <th>neg</th>\n",
       "      <th>neu</th>\n",
       "      <th>...</th>\n",
       "      <th>Gain_or_Loss_SVM</th>\n",
       "      <th>Gain_or_Loss_NaiveBayes</th>\n",
       "      <th>Gain_or_Loss_DecisionTree</th>\n",
       "      <th>Gain_or_Loss_RandomForest</th>\n",
       "      <th>KNN_Result</th>\n",
       "      <th>LogReg_Result</th>\n",
       "      <th>SVM_Result</th>\n",
       "      <th>Naive_Bayes_Result</th>\n",
       "      <th>Decision_Tree_Result</th>\n",
       "      <th>Random_Forest_Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2019-03-23</td>\n",
       "      <td>31.200000</td>\n",
       "      <td>30.406667</td>\n",
       "      <td>31.056667</td>\n",
       "      <td>30.449999</td>\n",
       "      <td>-1.945809</td>\n",
       "      <td>54000.300000</td>\n",
       "      <td>0.293590</td>\n",
       "      <td>0.030800</td>\n",
       "      <td>0.838300</td>\n",
       "      <td>...</td>\n",
       "      <td>0.606667</td>\n",
       "      <td>0.606667</td>\n",
       "      <td>0.606667</td>\n",
       "      <td>0.606667</td>\n",
       "      <td>42.636662</td>\n",
       "      <td>41.056658</td>\n",
       "      <td>42.636662</td>\n",
       "      <td>44.216660</td>\n",
       "      <td>36.623327</td>\n",
       "      <td>36.623327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2019-03-24</td>\n",
       "      <td>30.960000</td>\n",
       "      <td>30.283333</td>\n",
       "      <td>30.793334</td>\n",
       "      <td>30.329999</td>\n",
       "      <td>-1.496982</td>\n",
       "      <td>5097.615385</td>\n",
       "      <td>0.048138</td>\n",
       "      <td>0.081231</td>\n",
       "      <td>0.845846</td>\n",
       "      <td>...</td>\n",
       "      <td>0.463334</td>\n",
       "      <td>0.463334</td>\n",
       "      <td>0.463334</td>\n",
       "      <td>0.463334</td>\n",
       "      <td>43.099997</td>\n",
       "      <td>41.519992</td>\n",
       "      <td>43.099997</td>\n",
       "      <td>44.679995</td>\n",
       "      <td>37.086662</td>\n",
       "      <td>37.086662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2019-03-25</td>\n",
       "      <td>30.719999</td>\n",
       "      <td>30.160000</td>\n",
       "      <td>30.530001</td>\n",
       "      <td>30.209999</td>\n",
       "      <td>-1.048155</td>\n",
       "      <td>6812.970588</td>\n",
       "      <td>0.202871</td>\n",
       "      <td>0.050912</td>\n",
       "      <td>0.831971</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.320002</td>\n",
       "      <td>0.320002</td>\n",
       "      <td>-0.320002</td>\n",
       "      <td>-0.320002</td>\n",
       "      <td>42.779995</td>\n",
       "      <td>41.839994</td>\n",
       "      <td>42.779995</td>\n",
       "      <td>44.999996</td>\n",
       "      <td>36.766660</td>\n",
       "      <td>36.766660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>30.620001</td>\n",
       "      <td>29.969999</td>\n",
       "      <td>30.510000</td>\n",
       "      <td>30.209999</td>\n",
       "      <td>-0.983288</td>\n",
       "      <td>13870.171429</td>\n",
       "      <td>0.017854</td>\n",
       "      <td>0.080600</td>\n",
       "      <td>0.839771</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.300001</td>\n",
       "      <td>0.300001</td>\n",
       "      <td>0.300001</td>\n",
       "      <td>0.300001</td>\n",
       "      <td>42.479994</td>\n",
       "      <td>42.139995</td>\n",
       "      <td>42.479994</td>\n",
       "      <td>45.299997</td>\n",
       "      <td>37.066661</td>\n",
       "      <td>37.066661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2019-03-27</td>\n",
       "      <td>31.200001</td>\n",
       "      <td>30.150000</td>\n",
       "      <td>30.270000</td>\n",
       "      <td>30.940001</td>\n",
       "      <td>2.213413</td>\n",
       "      <td>10882.428571</td>\n",
       "      <td>0.286906</td>\n",
       "      <td>0.048400</td>\n",
       "      <td>0.818229</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.670000</td>\n",
       "      <td>-0.670000</td>\n",
       "      <td>-0.670000</td>\n",
       "      <td>-0.670000</td>\n",
       "      <td>41.809994</td>\n",
       "      <td>41.469995</td>\n",
       "      <td>41.809994</td>\n",
       "      <td>44.629997</td>\n",
       "      <td>36.396661</td>\n",
       "      <td>36.396661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        index       High        Low       Open  Adj Close  Pct_change  \\\n",
       "24 2019-03-23  31.200000  30.406667  31.056667  30.449999   -1.945809   \n",
       "25 2019-03-24  30.960000  30.283333  30.793334  30.329999   -1.496982   \n",
       "26 2019-03-25  30.719999  30.160000  30.530001  30.209999   -1.048155   \n",
       "27 2019-03-26  30.620001  29.969999  30.510000  30.209999   -0.983288   \n",
       "28 2019-03-27  31.200001  30.150000  30.270000  30.940001    2.213413   \n",
       "\n",
       "    follower count  compound       neg       neu          ...           \\\n",
       "24    54000.300000  0.293590  0.030800  0.838300          ...            \n",
       "25     5097.615385  0.048138  0.081231  0.845846          ...            \n",
       "26     6812.970588  0.202871  0.050912  0.831971          ...            \n",
       "27    13870.171429  0.017854  0.080600  0.839771          ...            \n",
       "28    10882.428571  0.286906  0.048400  0.818229          ...            \n",
       "\n",
       "    Gain_or_Loss_SVM  Gain_or_Loss_NaiveBayes  Gain_or_Loss_DecisionTree  \\\n",
       "24          0.606667                 0.606667                   0.606667   \n",
       "25          0.463334                 0.463334                   0.463334   \n",
       "26         -0.320002                 0.320002                  -0.320002   \n",
       "27         -0.300001                 0.300001                   0.300001   \n",
       "28         -0.670000                -0.670000                  -0.670000   \n",
       "\n",
       "    Gain_or_Loss_RandomForest  KNN_Result  LogReg_Result  SVM_Result  \\\n",
       "24                   0.606667   42.636662      41.056658   42.636662   \n",
       "25                   0.463334   43.099997      41.519992   43.099997   \n",
       "26                  -0.320002   42.779995      41.839994   42.779995   \n",
       "27                   0.300001   42.479994      42.139995   42.479994   \n",
       "28                  -0.670000   41.809994      41.469995   41.809994   \n",
       "\n",
       "    Naive_Bayes_Result  Decision_Tree_Result  Random_Forest_Result  \n",
       "24           44.216660             36.623327             36.623327  \n",
       "25           44.679995             37.086662             37.086662  \n",
       "26           44.999996             36.766660             36.766660  \n",
       "27           45.299997             37.066661             37.066661  \n",
       "28           44.629997             36.396661             36.396661  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Spring2019.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Subplots\n",
    "When attempting to use Matplotlib Style, the selected style is not rendered.\n",
    "*ggplot* and *seaborn* styles are working, but variations of seaborn are not. Using here *fivethirtyeight* instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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Nmwo/S9o+4Nm0aRPy8vLg4eEhPFYiIqLqhOMY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K9mvgoAmNN3DpY8vsSq12+Oj9QHeVPyWrRv3q2W7fdbEhieJWbJoSXNrvdCRESmEXQEi5eXF7p3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\n9bCePw9Pixa+zynS0uo0zTOFD8PvfqeMrGqATo6BxAILEREBUOY7dTzzj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rnp6eeu211yx6Z5QsWVJt27bVwoULVa5cuXTPV2q//vqrEhMT9fjjj2vW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jvNFQAAAFzUFc1gcblcCgwMbPzDAQENmzvC40xCglRT43VsZ0KCKsrKLvozH5eXSwcOKMDl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\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l8RozMzPFbs9vKgstDC9z8eJFtcsYGxtj6tSpKj9zc3MDUJgxl3fz5k3cvXsXQGFXdlW9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\n1LhxYy1atEjXX+DiS9u3b/dGuADg09q0a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hm+/vpr7for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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x1584 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pylab\n",
    "from pylab import rcParams\n",
    "plt.style.use('fivethirtyeight') #ggplot or seaborn\n",
    "plt.rcParams['figure.figsize'] = 25, 22 \n",
    "plt.suptitle('American Airlines (AAL) 28.2.-28.3.2019', fontsize=30) #, verticalalignment='bottom'\n",
    "\n",
    "ax1 = Spring2019['Adj Close']\n",
    "ax2 = Spring2019['KNN_Result']\n",
    "ax3 = Spring2019['LogReg_Result']\n",
    "ax4 = Spring2019['SVM_Result']\n",
    "ax5 = Spring2019['Naive_Bayes_Result']\n",
    "ax6 = Spring2019['Decision_Tree_Result']\n",
    "ax7 = Spring2019['Random_Forest_Result']\n",
    "\n",
    "for i in range (1, 7):\n",
    "    plt.subplots_adjust(hspace=0.6, wspace=0.15)\n",
    "        \n",
    "    plt.subplot(3,2,1)\n",
    "    plt.plot(ax1, 'r',  linewidth=2)\n",
    "    plt.plot(ax2,  'b',  linestyle=':', linewidth=5)\n",
    "    plt.xlabel('Days', fontsize=20)\n",
    "    plt.ylabel('Price', fontsize=20)\n",
    "    plt.title('Buy-and-hold vs. KNN strategy', fontsize=25)\n",
    "    a='Buy-and-hold'\n",
    "    b='KNN'\n",
    "    plt.legend((a,b), fontsize=20, loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2, frameon=True)\n",
    "    \n",
    "    plt.subplot(3,2,2)\n",
    "    plt.plot(ax1, 'r',  linewidth=2 )\n",
    "    plt.plot(ax3, 'g',  linestyle=':', linewidth=5)\n",
    "    plt.xlabel('Days', fontsize=20)\n",
    "    plt.ylabel('Price', fontsize=20)\n",
    "    plt.title('Buy-and-hold vs. LogReg strategy', fontsize=25)\n",
    "    a='Buy-and-hold'\n",
    "    b='LogReg'\n",
    "    plt.legend((a,b), fontsize=20, loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2, frameon=True)\n",
    "    \n",
    "    plt.subplot(3,2,3)\n",
    "    plt.plot(ax1, 'r',  linewidth=2 )\n",
    "    plt.plot(ax4, 'c',  linestyle=':', linewidth=5)\n",
    "    plt.xlabel('Days', fontsize=20)\n",
    "    plt.ylabel('Price', fontsize=20)\n",
    "    plt.title('Buy-and-hold vs SVM strategy', fontsize=25)\n",
    "    a='Buy-and-hold'\n",
    "    b='SVM'\n",
    "    plt.legend((a,b), fontsize=20, loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2, frameon=True)\n",
    "    \n",
    "    plt.subplot(3,2,4)\n",
    "    plt.plot(ax1, 'r',  linewidth=2 )\n",
    "    plt.plot(ax5, 'm',  linestyle=':', linewidth=5)\n",
    "    plt.xlabel('Days', fontsize=20)\n",
    "    plt.ylabel('Price', fontsize=20)\n",
    "    plt.title('Buy-and-hold vs NaiveBayes strategy', fontsize=25)\n",
    "    a='Buy-and-hold'\n",
    "    b='NaiveBayes'\n",
    "    plt.legend((a,b), fontsize=20, loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2, frameon=True)\n",
    "    \n",
    "    plt.subplot(3,2,5)\n",
    "    plt.plot(ax1, 'r',  linewidth=2 )\n",
    "    plt.plot(ax6, 'royalblue',  linestyle=':', linewidth=5)\n",
    "    plt.xlabel('Days', fontsize=20)\n",
    "    plt.ylabel('Price', fontsize=20)\n",
    "    plt.title('Buy-and-hold vs Decision Tree strategy', fontsize=25)\n",
    "    a='Buy-and-hold'\n",
    "    b='Decision Tree'\n",
    "    plt.legend((a,b), fontsize=20, loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2, frameon=True)\n",
    "    \n",
    "    plt.subplot(3,2,6)\n",
    "    plt.plot(ax1, 'r',  linewidth=2 )\n",
    "    plt.plot(ax7, 'saddlebrown',  linestyle=':', linewidth=5)\n",
    "    plt.xlabel('Days', fontsize=20)\n",
    "    plt.ylabel('Price', fontsize=20)\n",
    "    plt.title('Buy-and-hold vs Random Forest strategy', fontsize=25)\n",
    "    a='Buy-and-hold'\n",
    "    b='Random Forest'\n",
    "    plt.legend((a,b), fontsize=20, loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2, frameon=True)\n",
    "\n",
    "#pylab.savefig('AAL.jpg') # Saves figure as .jpg-file\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}