EMA_sentiment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\swapk\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "sns.set(style='darkgrid', context='talk', palette='Dark2')\n",
    "my_year_month_fmt = mdates.DateFormatter('%m/%y')\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import scorer\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('stock_data.csv')\n",
    "data.rename(columns={'Date':'date'},inplace=True)\n",
    "sentiments_df = pd.read_csv('./preprocessing/sentiments.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>date</th>\n",
       "      <th>headline</th>\n",
       "      <th>body</th>\n",
       "      <th>newsType</th>\n",
       "      <th>text</th>\n",
       "      <th>score</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc</td>\n",
       "      <td>2011-07-06</td>\n",
       "      <td>UPDATE 1-Samsung estimates Q2 profit down 26 pct</td>\n",
       "      <td>* Sees Q2 op profit at 3.7 trln won vs 3.9 tr...</td>\n",
       "      <td>topStory</td>\n",
       "      <td>['update', '1', 'samsung', 'estimates', 'q2', ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc</td>\n",
       "      <td>2011-07-07</td>\n",
       "      <td>UPDATE 1-Canada eyes Nortel patent sale review...</td>\n",
       "      <td>* Gives no detail on timeline for decision  (...</td>\n",
       "      <td>topStory</td>\n",
       "      <td>['update', '1', 'canada', 'eyes', 'nortel', 'p...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc</td>\n",
       "      <td>2011-07-08</td>\n",
       "      <td>Analysis: Young startups demand steeper prices...</td>\n",
       "      <td>SAN FRANCISCO A year ago  Mike Maples's invest...</td>\n",
       "      <td>topStory</td>\n",
       "      <td>['analysis', 'young', 'startups', 'demand', 's...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>18</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc</td>\n",
       "      <td>2011-07-11</td>\n",
       "      <td>RIM annual meeting likely to exacerbate invest...</td>\n",
       "      <td>TORONTO Research In Motion investors desperate...</td>\n",
       "      <td>topStory</td>\n",
       "      <td>['rim', 'annual', 'meeting', 'likely', 'exacer...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc</td>\n",
       "      <td>2011-07-12</td>\n",
       "      <td>Electronic Arts buying PopCap Games for up to ...</td>\n",
       "      <td>NEW YORK Electronic Arts Inc   the video game ...</td>\n",
       "      <td>topStory</td>\n",
       "      <td>['electronic', 'arts', 'buying', 'popcap', 'ga...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0 ticker       name       date  \\\n",
       "0           0   AAPL  Apple Inc 2011-07-06   \n",
       "1           4   AAPL  Apple Inc 2011-07-07   \n",
       "2          14   AAPL  Apple Inc 2011-07-08   \n",
       "3          18   AAPL  Apple Inc 2011-07-11   \n",
       "4          26   AAPL  Apple Inc 2011-07-12   \n",
       "\n",
       "                                            headline  \\\n",
       "0  UPDATE 1-Samsung estimates Q2 profit down 26 pct    \n",
       "1  UPDATE 1-Canada eyes Nortel patent sale review...   \n",
       "2  Analysis: Young startups demand steeper prices...   \n",
       "3  RIM annual meeting likely to exacerbate invest...   \n",
       "4  Electronic Arts buying PopCap Games for up to ...   \n",
       "\n",
       "                                                body  newsType  \\\n",
       "0   * Sees Q2 op profit at 3.7 trln won vs 3.9 tr...  topStory   \n",
       "1   * Gives no detail on timeline for decision  (...  topStory   \n",
       "2  SAN FRANCISCO A year ago  Mike Maples's invest...  topStory   \n",
       "3  TORONTO Research In Motion investors desperate...  topStory   \n",
       "4  NEW YORK Electronic Arts Inc   the video game ...  topStory   \n",
       "\n",
       "                                                text  score  \n",
       "0  ['update', '1', 'samsung', 'estimates', 'q2', ...      0  \n",
       "1  ['update', '1', 'canada', 'eyes', 'nortel', 'p...      0  \n",
       "2  ['analysis', 'young', 'startups', 'demand', 's...      0  \n",
       "3  ['rim', 'annual', 'meeting', 'likely', 'exacer...      2  \n",
       "4  ['electronic', 'arts', 'buying', 'popcap', 'ga...      3  "
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiments_df['date'] = pd.to_datetime(sentiments_df['date'])\n",
    "data.date = pd.to_datetime(data.date)\n",
    "sentiments_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Combine financial and sentiment data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>headline</th>\n",
       "      <th>body</th>\n",
       "      <th>newsType</th>\n",
       "      <th>text</th>\n",
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       "      <th>0</th>\n",
       "      <td>1999-12-31</td>\n",
       "      <td>3.604911</td>\n",
       "      <td>3.674107</td>\n",
       "      <td>3.553571</td>\n",
       "      <td>3.671875</td>\n",
       "      <td>2.458919</td>\n",
       "      <td>40952800</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>3.745536</td>\n",
       "      <td>4.017857</td>\n",
       "      <td>3.631696</td>\n",
       "      <td>3.997768</td>\n",
       "      <td>2.677157</td>\n",
       "      <td>133949200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>3.866071</td>\n",
       "      <td>3.950893</td>\n",
       "      <td>3.613839</td>\n",
       "      <td>3.660714</td>\n",
       "      <td>2.451444</td>\n",
       "      <td>128094400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>3.705357</td>\n",
       "      <td>3.948661</td>\n",
       "      <td>3.678571</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>2.487319</td>\n",
       "      <td>194580400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>3.790179</td>\n",
       "      <td>3.821429</td>\n",
       "      <td>3.392857</td>\n",
       "      <td>3.392857</td>\n",
       "      <td>2.272070</td>\n",
       "      <td>191993200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "        date      Open      High       Low     Close  Adj Close     Volume  \\\n",
       "0 1999-12-31  3.604911  3.674107  3.553571  3.671875   2.458919   40952800   \n",
       "1 2000-01-03  3.745536  4.017857  3.631696  3.997768   2.677157  133949200   \n",
       "2 2000-01-04  3.866071  3.950893  3.613839  3.660714   2.451444  128094400   \n",
       "3 2000-01-05  3.705357  3.948661  3.678571  3.714286   2.487319  194580400   \n",
       "4 2000-01-06  3.790179  3.821429  3.392857  3.392857   2.272070  191993200   \n",
       "\n",
       "   Unnamed: 0 ticker name headline body newsType text  score  \n",
       "0         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN  \n",
       "1         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN  \n",
       "2         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN  \n",
       "3         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN  \n",
       "4         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN  "
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.merge(data,sentiments_df,on='date',how='left')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Short Mean Average (20 days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
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       "      <th>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>headline</th>\n",
       "      <th>body</th>\n",
       "      <th>newsType</th>\n",
       "      <th>text</th>\n",
       "      <th>score</th>\n",
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       "      <th>0</th>\n",
       "      <td>1999-12-31</td>\n",
       "      <td>3.604911</td>\n",
       "      <td>3.674107</td>\n",
       "      <td>3.553571</td>\n",
       "      <td>3.671875</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>3.745536</td>\n",
       "      <td>4.017857</td>\n",
       "      <td>3.631696</td>\n",
       "      <td>3.997768</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>3.866071</td>\n",
       "      <td>3.950893</td>\n",
       "      <td>3.613839</td>\n",
       "      <td>3.660714</td>\n",
       "      <td>2.451444</td>\n",
       "      <td>128094400</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>3.705357</td>\n",
       "      <td>3.948661</td>\n",
       "      <td>3.678571</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>2.487319</td>\n",
       "      <td>194580400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>3.790179</td>\n",
       "      <td>3.821429</td>\n",
       "      <td>3.392857</td>\n",
       "      <td>3.392857</td>\n",
       "      <td>2.272070</td>\n",
       "      <td>191993200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2000-01-07</td>\n",
       "      <td>3.446429</td>\n",
       "      <td>3.607143</td>\n",
       "      <td>3.410714</td>\n",
       "      <td>3.553571</td>\n",
       "      <td>2.379695</td>\n",
       "      <td>115183600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2000-01-10</td>\n",
       "      <td>3.642857</td>\n",
       "      <td>3.651786</td>\n",
       "      <td>3.383929</td>\n",
       "      <td>3.491071</td>\n",
       "      <td>2.337840</td>\n",
       "      <td>126266000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2000-01-11</td>\n",
       "      <td>3.426339</td>\n",
       "      <td>3.549107</td>\n",
       "      <td>3.232143</td>\n",
       "      <td>3.312500</td>\n",
       "      <td>2.218258</td>\n",
       "      <td>110387200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2000-01-12</td>\n",
       "      <td>3.392857</td>\n",
       "      <td>3.410714</td>\n",
       "      <td>3.089286</td>\n",
       "      <td>3.113839</td>\n",
       "      <td>2.085222</td>\n",
       "      <td>244017200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2000-01-13</td>\n",
       "      <td>3.374439</td>\n",
       "      <td>3.526786</td>\n",
       "      <td>3.303571</td>\n",
       "      <td>3.455357</td>\n",
       "      <td>2.313924</td>\n",
       "      <td>258171200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2000-01-14</td>\n",
       "      <td>3.571429</td>\n",
       "      <td>3.651786</td>\n",
       "      <td>3.549107</td>\n",
       "      <td>3.587054</td>\n",
       "      <td>2.402116</td>\n",
       "      <td>97594000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2000-01-18</td>\n",
       "      <td>3.607143</td>\n",
       "      <td>3.785714</td>\n",
       "      <td>3.587054</td>\n",
       "      <td>3.712054</td>\n",
       "      <td>2.485824</td>\n",
       "      <td>114794400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2000-01-19</td>\n",
       "      <td>3.772321</td>\n",
       "      <td>3.883929</td>\n",
       "      <td>3.691964</td>\n",
       "      <td>3.805804</td>\n",
       "      <td>2.548605</td>\n",
       "      <td>149410800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2000-01-20</td>\n",
       "      <td>4.125000</td>\n",
       "      <td>4.339286</td>\n",
       "      <td>4.053571</td>\n",
       "      <td>4.053571</td>\n",
       "      <td>2.714526</td>\n",
       "      <td>457783200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2000-01-21</td>\n",
       "      <td>4.080357</td>\n",
       "      <td>4.080357</td>\n",
       "      <td>3.935268</td>\n",
       "      <td>3.975446</td>\n",
       "      <td>2.662209</td>\n",
       "      <td>123981200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2000-01-24</td>\n",
       "      <td>3.872768</td>\n",
       "      <td>4.026786</td>\n",
       "      <td>3.754464</td>\n",
       "      <td>3.794643</td>\n",
       "      <td>2.541132</td>\n",
       "      <td>110219200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2000-01-25</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>4.040179</td>\n",
       "      <td>3.656250</td>\n",
       "      <td>4.008929</td>\n",
       "      <td>2.684630</td>\n",
       "      <td>124286400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2000-01-26</td>\n",
       "      <td>3.928571</td>\n",
       "      <td>4.078125</td>\n",
       "      <td>3.919643</td>\n",
       "      <td>3.935268</td>\n",
       "      <td>2.635303</td>\n",
       "      <td>91789600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2000-01-27</td>\n",
       "      <td>3.886161</td>\n",
       "      <td>4.035714</td>\n",
       "      <td>3.821429</td>\n",
       "      <td>3.928571</td>\n",
       "      <td>2.630817</td>\n",
       "      <td>85036000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2000-01-28</td>\n",
       "      <td>3.863839</td>\n",
       "      <td>3.959821</td>\n",
       "      <td>3.593750</td>\n",
       "      <td>3.629464</td>\n",
       "      <td>2.430518</td>\n",
       "      <td>105837200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.470876</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date      Open      High       Low     Close  Adj Close     Volume  \\\n",
       "0  1999-12-31  3.604911  3.674107  3.553571  3.671875   2.458919   40952800   \n",
       "1  2000-01-03  3.745536  4.017857  3.631696  3.997768   2.677157  133949200   \n",
       "2  2000-01-04  3.866071  3.950893  3.613839  3.660714   2.451444  128094400   \n",
       "3  2000-01-05  3.705357  3.948661  3.678571  3.714286   2.487319  194580400   \n",
       "4  2000-01-06  3.790179  3.821429  3.392857  3.392857   2.272070  191993200   \n",
       "5  2000-01-07  3.446429  3.607143  3.410714  3.553571   2.379695  115183600   \n",
       "6  2000-01-10  3.642857  3.651786  3.383929  3.491071   2.337840  126266000   \n",
       "7  2000-01-11  3.426339  3.549107  3.232143  3.312500   2.218258  110387200   \n",
       "8  2000-01-12  3.392857  3.410714  3.089286  3.113839   2.085222  244017200   \n",
       "9  2000-01-13  3.374439  3.526786  3.303571  3.455357   2.313924  258171200   \n",
       "10 2000-01-14  3.571429  3.651786  3.549107  3.587054   2.402116   97594000   \n",
       "11 2000-01-18  3.607143  3.785714  3.587054  3.712054   2.485824  114794400   \n",
       "12 2000-01-19  3.772321  3.883929  3.691964  3.805804   2.548605  149410800   \n",
       "13 2000-01-20  4.125000  4.339286  4.053571  4.053571   2.714526  457783200   \n",
       "14 2000-01-21  4.080357  4.080357  3.935268  3.975446   2.662209  123981200   \n",
       "15 2000-01-24  3.872768  4.026786  3.754464  3.794643   2.541132  110219200   \n",
       "16 2000-01-25  3.750000  4.040179  3.656250  4.008929   2.684630  124286400   \n",
       "17 2000-01-26  3.928571  4.078125  3.919643  3.935268   2.635303   91789600   \n",
       "18 2000-01-27  3.886161  4.035714  3.821429  3.928571   2.630817   85036000   \n",
       "19 2000-01-28  3.863839  3.959821  3.593750  3.629464   2.430518  105837200   \n",
       "\n",
       "    Unnamed: 0 ticker name headline body newsType text  score  short_rolling  \n",
       "0          NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "1          NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "2          NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "3          NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "4          NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "5          NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "6          NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "7          NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "8          NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "9          NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "10         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "11         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "12         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "13         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "14         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "15         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "16         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "17         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "18         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN            NaN  \n",
       "19         NaN    NaN  NaN      NaN  NaN      NaN  NaN    NaN       2.470876  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['short_rolling'] = data['Adj Close'].rolling(window=20).mean()\n",
    "data.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Long Mean Average (100 days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>headline</th>\n",
       "      <th>body</th>\n",
       "      <th>newsType</th>\n",
       "      <th>text</th>\n",
       "      <th>score</th>\n",
       "      <th>short_rolling</th>\n",
       "      <th>long_rolling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4712</th>\n",
       "      <td>2018-09-24</td>\n",
       "      <td>216.820007</td>\n",
       "      <td>221.259995</td>\n",
       "      <td>216.630005</td>\n",
       "      <td>220.789993</td>\n",
       "      <td>220.022293</td>\n",
       "      <td>27693400</td>\n",
       "      <td>8365.0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc</td>\n",
       "      <td>Deals of the day-Mergers and acquisitions</td>\n",
       "      <td>Sept 24 The following bids  mergers  acquisiti...</td>\n",
       "      <td>topStory</td>\n",
       "      <td>['deals', 'day', 'mergers', 'acquisitions', 's...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>221.197207</td>\n",
       "      <td>198.074993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4713</th>\n",
       "      <td>2018-09-25</td>\n",
       "      <td>219.750000</td>\n",
       "      <td>222.820007</td>\n",
       "      <td>219.699997</td>\n",
       "      <td>222.190002</td>\n",
       "      <td>221.417435</td>\n",
       "      <td>24554400</td>\n",
       "      <td>8369.0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc</td>\n",
       "      <td>Qualcomm accuses Apple of stealing its secrets...</td>\n",
       "      <td>Qualcomm Inc on Tuesday accused Apple Inc of s...</td>\n",
       "      <td>topStory</td>\n",
       "      <td>['qualcomm', 'accuses', 'apple', 'stealing', '...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>221.408968</td>\n",
       "      <td>198.539326</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4714</th>\n",
       "      <td>2018-09-26</td>\n",
       "      <td>221.000000</td>\n",
       "      <td>223.750000</td>\n",
       "      <td>219.759995</td>\n",
       "      <td>220.419998</td>\n",
       "      <td>219.653595</td>\n",
       "      <td>23984700</td>\n",
       "      <td>8373.0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc</td>\n",
       "      <td>Apple  Salesforce team up to bring Siri to mor...</td>\n",
       "      <td>SAN FRANCISCO Apple Inc  and Salesforce.com In...</td>\n",
       "      <td>topStory</td>\n",
       "      <td>['apple', 'salesforce', 'team', 'bring', 'siri...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>221.444843</td>\n",
       "      <td>198.917368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4715</th>\n",
       "      <td>2018-09-27</td>\n",
       "      <td>223.820007</td>\n",
       "      <td>226.440002</td>\n",
       "      <td>223.539993</td>\n",
       "      <td>224.949997</td>\n",
       "      <td>224.167831</td>\n",
       "      <td>30181200</td>\n",
       "      <td>8374.0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc</td>\n",
       "      <td>US STOCKS-Apple  Amazon lead Wall St higher</td>\n",
       "      <td>* Indexes up: 0.26 pct  S&amp;P 0.44 pct  Nasdaq 0...</td>\n",
       "      <td>topStory</td>\n",
       "      <td>['us', 'stocks', 'apple', 'amazon', 'lead', 'w...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>221.543000</td>\n",
       "      <td>199.327396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4716</th>\n",
       "      <td>2018-09-28</td>\n",
       "      <td>224.789993</td>\n",
       "      <td>225.839996</td>\n",
       "      <td>224.020004</td>\n",
       "      <td>225.740005</td>\n",
       "      <td>224.955093</td>\n",
       "      <td>22929400</td>\n",
       "      <td>8379.0</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>Apple Inc</td>\n",
       "      <td>U.S. trade judge declines to block iPhone impo...</td>\n",
       "      <td>A U.S. trade judge on Friday declined to block...</td>\n",
       "      <td>topStory</td>\n",
       "      <td>['u', 'trade', 'judge', 'declines', 'block', '...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>221.578377</td>\n",
       "      <td>199.736493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           date        Open        High         Low       Close   Adj Close  \\\n",
       "4712 2018-09-24  216.820007  221.259995  216.630005  220.789993  220.022293   \n",
       "4713 2018-09-25  219.750000  222.820007  219.699997  222.190002  221.417435   \n",
       "4714 2018-09-26  221.000000  223.750000  219.759995  220.419998  219.653595   \n",
       "4715 2018-09-27  223.820007  226.440002  223.539993  224.949997  224.167831   \n",
       "4716 2018-09-28  224.789993  225.839996  224.020004  225.740005  224.955093   \n",
       "\n",
       "        Volume  Unnamed: 0 ticker       name  \\\n",
       "4712  27693400      8365.0   AAPL  Apple Inc   \n",
       "4713  24554400      8369.0   AAPL  Apple Inc   \n",
       "4714  23984700      8373.0   AAPL  Apple Inc   \n",
       "4715  30181200      8374.0   AAPL  Apple Inc   \n",
       "4716  22929400      8379.0   AAPL  Apple Inc   \n",
       "\n",
       "                                               headline  \\\n",
       "4712         Deals of the day-Mergers and acquisitions    \n",
       "4713  Qualcomm accuses Apple of stealing its secrets...   \n",
       "4714  Apple  Salesforce team up to bring Siri to mor...   \n",
       "4715       US STOCKS-Apple  Amazon lead Wall St higher    \n",
       "4716  U.S. trade judge declines to block iPhone impo...   \n",
       "\n",
       "                                                   body  newsType  \\\n",
       "4712  Sept 24 The following bids  mergers  acquisiti...  topStory   \n",
       "4713  Qualcomm Inc on Tuesday accused Apple Inc of s...  topStory   \n",
       "4714  SAN FRANCISCO Apple Inc  and Salesforce.com In...  topStory   \n",
       "4715  * Indexes up: 0.26 pct  S&P 0.44 pct  Nasdaq 0...  topStory   \n",
       "4716  A U.S. trade judge on Friday declined to block...  topStory   \n",
       "\n",
       "                                                   text  score  short_rolling  \\\n",
       "4712  ['deals', 'day', 'mergers', 'acquisitions', 's...    0.0     221.197207   \n",
       "4713  ['qualcomm', 'accuses', 'apple', 'stealing', '...    2.0     221.408968   \n",
       "4714  ['apple', 'salesforce', 'team', 'bring', 'siri...    0.0     221.444843   \n",
       "4715  ['us', 'stocks', 'apple', 'amazon', 'lead', 'w...    0.0     221.543000   \n",
       "4716  ['u', 'trade', 'judge', 'declines', 'block', '...    1.0     221.578377   \n",
       "\n",
       "      long_rolling  \n",
       "4712    198.074993  \n",
       "4713    198.539326  \n",
       "4714    198.917368  \n",
       "4715    199.327396  \n",
       "4716    199.736493  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['long_rolling'] = data['Adj Close'].rolling(window=100).mean()\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Exponential Mean Average (20 days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['ema_short'] = data['Adj Close'].ewm(span=20, adjust=False).mean()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "\n",
    "ax.plot(data.date, data['Adj Close'], label='Adj Close')\n",
    "ax.plot(data.date, data.ema_short, label = 'Span 20-days EMA')\n",
    "ax.plot(data.date, data.short_rolling, label = '20-days SMA')\n",
    "\n",
    "ax.legend(loc='best')\n",
    "ax.set_ylabel('Price in $')\n",
    "ax.xaxis.set_major_formatter(my_year_month_fmt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate trading positions\n",
    "\n",
    "Difference between `Adjusted Closing Price` and `EMA` will give the raw position of the stock. If the difference is positive then we are at a profitable position, so we can consider selling.\n",
    "\n",
    "## Sentiment Categories\n",
    "\n",
    " - 0: neutral\n",
    " - 1: negative\n",
    " - 2: very negative\n",
    " - 3: positive\n",
    " - 4: very positive\n",
    " - 5: no sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['trading_positions_raw'] = data['Adj Close'] - data.ema_short"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4717.000000\n",
       "mean        0.438144\n",
       "std         2.123461\n",
       "min       -13.060000\n",
       "25%        -0.114107\n",
       "50%         0.077323\n",
       "75%         0.861002\n",
       "max        14.163577\n",
       "Name: trading_positions_raw, dtype: float64"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['trading_positions_raw'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Impute missing sentiment scores as category 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['score'] = data['score'].fillna(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Trading Positions\n",
    "\n",
    " - -1: Sell\n",
    " - +1: Buy\n",
    " - 0: Hold\n",
    "\n",
    "### When to sell?\n",
    "\n",
    " - if `trading_positions_raw > standard_deviation` \n",
    " - if sentiment score is in category 1, 2 or 5\n",
    " \n",
    "### When to buy?\n",
    "\n",
    " - if `trading_positions_raw < neg(standard_deviation)`\n",
    " - if sentiment score is in category 3, 4 or 5\n",
    " \n",
    "## Why standard deviation?\n",
    "\n",
    "The `trading_positions_raw` is likely to flip at standard deviation, so that is the best point to make decisions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>...</th>\n",
       "      <th>newsType</th>\n",
       "      <th>text</th>\n",
       "      <th>score</th>\n",
       "      <th>short_rolling</th>\n",
       "      <th>long_rolling</th>\n",
       "      <th>ema_short</th>\n",
       "      <th>trading_positions_raw</th>\n",
       "      <th>trading_positions</th>\n",
       "      <th>Open-Close</th>\n",
       "      <th>High-Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999-12-31</td>\n",
       "      <td>3.604911</td>\n",
       "      <td>3.674107</td>\n",
       "      <td>3.553571</td>\n",
       "      <td>3.671875</td>\n",
       "      <td>2.458919</td>\n",
       "      <td>40952800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.458919</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.066964</td>\n",
       "      <td>0.120536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>3.745536</td>\n",
       "      <td>4.017857</td>\n",
       "      <td>3.631696</td>\n",
       "      <td>3.997768</td>\n",
       "      <td>2.677157</td>\n",
       "      <td>133949200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.479704</td>\n",
       "      <td>0.197453</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.252232</td>\n",
       "      <td>0.386161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>3.866071</td>\n",
       "      <td>3.950893</td>\n",
       "      <td>3.613839</td>\n",
       "      <td>3.660714</td>\n",
       "      <td>2.451444</td>\n",
       "      <td>128094400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.477012</td>\n",
       "      <td>-0.025568</td>\n",
       "      <td>0</td>\n",
       "      <td>0.205357</td>\n",
       "      <td>0.337054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>3.705357</td>\n",
       "      <td>3.948661</td>\n",
       "      <td>3.678571</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>2.487319</td>\n",
       "      <td>194580400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.477994</td>\n",
       "      <td>0.009325</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.008929</td>\n",
       "      <td>0.270090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>3.790179</td>\n",
       "      <td>3.821429</td>\n",
       "      <td>3.392857</td>\n",
       "      <td>3.392857</td>\n",
       "      <td>2.272070</td>\n",
       "      <td>191993200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.458382</td>\n",
       "      <td>-0.186312</td>\n",
       "      <td>0</td>\n",
       "      <td>0.397322</td>\n",
       "      <td>0.428572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date      Open      High       Low     Close  Adj Close     Volume  \\\n",
       "0 1999-12-31  3.604911  3.674107  3.553571  3.671875   2.458919   40952800   \n",
       "1 2000-01-03  3.745536  4.017857  3.631696  3.997768   2.677157  133949200   \n",
       "2 2000-01-04  3.866071  3.950893  3.613839  3.660714   2.451444  128094400   \n",
       "3 2000-01-05  3.705357  3.948661  3.678571  3.714286   2.487319  194580400   \n",
       "4 2000-01-06  3.790179  3.821429  3.392857  3.392857   2.272070  191993200   \n",
       "\n",
       "   Unnamed: 0 ticker name    ...    newsType text score short_rolling  \\\n",
       "0         NaN    NaN  NaN    ...         NaN  NaN   5.0           NaN   \n",
       "1         NaN    NaN  NaN    ...         NaN  NaN   5.0           NaN   \n",
       "2         NaN    NaN  NaN    ...         NaN  NaN   5.0           NaN   \n",
       "3         NaN    NaN  NaN    ...         NaN  NaN   5.0           NaN   \n",
       "4         NaN    NaN  NaN    ...         NaN  NaN   5.0           NaN   \n",
       "\n",
       "   long_rolling  ema_short  trading_positions_raw  trading_positions  \\\n",
       "0           NaN   2.458919               0.000000                  0   \n",
       "1           NaN   2.479704               0.197453                  0   \n",
       "2           NaN   2.477012              -0.025568                  0   \n",
       "3           NaN   2.477994               0.009325                  0   \n",
       "4           NaN   2.458382              -0.186312                  0   \n",
       "\n",
       "   Open-Close  High-Low  \n",
       "0   -0.066964  0.120536  \n",
       "1   -0.252232  0.386161  \n",
       "2    0.205357  0.337054  \n",
       "3   -0.008929  0.270090  \n",
       "4    0.397322  0.428572  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['trading_positions'] = 0\n",
    "data.loc[(data['trading_positions_raw'] > 2) & ((data['score'] == 1) | (data['score'] == 2) | (data['score'] == 5)), 'trading_positions'] = -1\n",
    "\n",
    "data.loc[(data['trading_positions_raw'] < -2) & ((data['score'] == 3) | (data['score'] == 4) | (data['score'] == 5)), 'trading_positions'] = 1\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Buy on the first day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>ticker</th>\n",
       "      <th>name</th>\n",
       "      <th>...</th>\n",
       "      <th>newsType</th>\n",
       "      <th>text</th>\n",
       "      <th>score</th>\n",
       "      <th>short_rolling</th>\n",
       "      <th>long_rolling</th>\n",
       "      <th>ema_short</th>\n",
       "      <th>trading_positions_raw</th>\n",
       "      <th>trading_positions</th>\n",
       "      <th>Open-Close</th>\n",
       "      <th>High-Low</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1999-12-31</td>\n",
       "      <td>3.604911</td>\n",
       "      <td>3.674107</td>\n",
       "      <td>3.553571</td>\n",
       "      <td>3.671875</td>\n",
       "      <td>2.458919</td>\n",
       "      <td>40952800</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.458919</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.066964</td>\n",
       "      <td>0.120536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2000-01-03</td>\n",
       "      <td>3.745536</td>\n",
       "      <td>4.017857</td>\n",
       "      <td>3.631696</td>\n",
       "      <td>3.997768</td>\n",
       "      <td>2.677157</td>\n",
       "      <td>133949200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.479704</td>\n",
       "      <td>0.197453</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.252232</td>\n",
       "      <td>0.386161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2000-01-04</td>\n",
       "      <td>3.866071</td>\n",
       "      <td>3.950893</td>\n",
       "      <td>3.613839</td>\n",
       "      <td>3.660714</td>\n",
       "      <td>2.451444</td>\n",
       "      <td>128094400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.477012</td>\n",
       "      <td>-0.025568</td>\n",
       "      <td>0</td>\n",
       "      <td>0.205357</td>\n",
       "      <td>0.337054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000-01-05</td>\n",
       "      <td>3.705357</td>\n",
       "      <td>3.948661</td>\n",
       "      <td>3.678571</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>2.487319</td>\n",
       "      <td>194580400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.477994</td>\n",
       "      <td>0.009325</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.008929</td>\n",
       "      <td>0.270090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2000-01-06</td>\n",
       "      <td>3.790179</td>\n",
       "      <td>3.821429</td>\n",
       "      <td>3.392857</td>\n",
       "      <td>3.392857</td>\n",
       "      <td>2.272070</td>\n",
       "      <td>191993200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.458382</td>\n",
       "      <td>-0.186312</td>\n",
       "      <td>0</td>\n",
       "      <td>0.397322</td>\n",
       "      <td>0.428572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        date      Open      High       Low     Close  Adj Close     Volume  \\\n",
       "0 1999-12-31  3.604911  3.674107  3.553571  3.671875   2.458919   40952800   \n",
       "1 2000-01-03  3.745536  4.017857  3.631696  3.997768   2.677157  133949200   \n",
       "2 2000-01-04  3.866071  3.950893  3.613839  3.660714   2.451444  128094400   \n",
       "3 2000-01-05  3.705357  3.948661  3.678571  3.714286   2.487319  194580400   \n",
       "4 2000-01-06  3.790179  3.821429  3.392857  3.392857   2.272070  191993200   \n",
       "\n",
       "   Unnamed: 0 ticker name    ...    newsType text score short_rolling  \\\n",
       "0         NaN    NaN  NaN    ...         NaN  NaN   5.0           NaN   \n",
       "1         NaN    NaN  NaN    ...         NaN  NaN   5.0           NaN   \n",
       "2         NaN    NaN  NaN    ...         NaN  NaN   5.0           NaN   \n",
       "3         NaN    NaN  NaN    ...         NaN  NaN   5.0           NaN   \n",
       "4         NaN    NaN  NaN    ...         NaN  NaN   5.0           NaN   \n",
       "\n",
       "   long_rolling  ema_short  trading_positions_raw  trading_positions  \\\n",
       "0           NaN   2.458919               0.000000                  1   \n",
       "1           NaN   2.479704               0.197453                  0   \n",
       "2           NaN   2.477012              -0.025568                  0   \n",
       "3           NaN   2.477994               0.009325                  0   \n",
       "4           NaN   2.458382              -0.186312                  0   \n",
       "\n",
       "   Open-Close  High-Low  \n",
       "0   -0.066964  0.120536  \n",
       "1   -0.252232  0.386161  \n",
       "2    0.205357  0.337054  \n",
       "3   -0.008929  0.270090  \n",
       "4    0.397322  0.428572  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[0,'trading_positions'] = 1\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16,9))\n",
    "\n",
    "ax1.plot(data.date, data['Adj Close'], label='Adj Close')\n",
    "ax1.plot(data.date, data.ema_short, label = 'Span 20-days EMA')\n",
    "\n",
    "ax1.set_ylabel('$')\n",
    "ax1.legend(loc='best')\n",
    "ax1.xaxis.set_major_formatter(my_year_month_fmt)\n",
    "\n",
    "ax2.plot(data.date, data.trading_positions, \n",
    "        label='Trading position')\n",
    "\n",
    "ax2.set_ylabel('Trading position')\n",
    "ax2.xaxis.set_major_formatter(my_year_month_fmt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Open-Close'] = data.Open - data.Close\n",
    "data['High-Low'] = data.High - data.Low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data[['Open-Close','High-Low','ema_short','score']]\n",
    "y = data.trading_positions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train/Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Check feature importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.20284481, 0.24816526, 0.36080194, 0.188188  ])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = GradientBoostingClassifier().fit(X_train, y_train)\n",
    "\n",
    "importances = model.feature_importances_\n",
    "\n",
    "# Plot\n",
    "importances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls = SVC().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_train = accuracy_score(y_train, cls.predict(X_train))\n",
    "\n",
    "accuracy_test = accuracy_score(y_test, cls.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train Accuracy: 94.46%\n",
      "Test Accuracy: 90.89%\n"
     ]
    }
   ],
   "source": [
    "print('\\nTrain Accuracy:{: .2f}%'.format(accuracy_train*100))\n",
    "\n",
    "print('Test Accuracy:{: .2f}%'.format(accuracy_test*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "myList = list(range(1,50))\n",
    "\n",
    "# subsetting just the odd ones\n",
    "neighbors = list(filter(lambda x: x % 2 != 0, myList))\n",
    "\n",
    "# empty list that will hold cv scores\n",
    "cv_scores = []\n",
    "\n",
    "# perform 10-fold cross validation\n",
    "for k in neighbors:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    scores = cross_val_score(knn, X_train, y_train, cv=10, scoring='accuracy')\n",
    "    cv_scores.append(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal number of neighbors is 7\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MSE = [1 - x for x in cv_scores]\n",
    "\n",
    "# determining best k\n",
    "optimal_k = neighbors[MSE.index(min(MSE))]\n",
    "print(\"The optimal number of neighbors is %d\" % optimal_k)\n",
    "\n",
    "# plot misclassification error vs k\n",
    "plt.plot(neighbors, MSE)\n",
    "plt.xlabel('Number of Neighbors K')\n",
    "plt.ylabel('Misclassification Error')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "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": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors=7)\n",
    "knn.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "accuracy_train = accuracy_score(y_train, knn.predict(X_train))\n",
    "\n",
    "accuracy_test = accuracy_score(y_test, knn.predict(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Train Accuracy: 92.98%\n",
      "Test Accuracy: 91.21%\n"
     ]
    }
   ],
   "source": [
    "print('\\nTrain Accuracy:{: .2f}%'.format(accuracy_train*100))\n",
    "\n",
    "print('Test Accuracy:{: .2f}%'.format(accuracy_test*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating buy/sell plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "buys = data[data['trading_positions'] == 1]\n",
    "sells = data[data['trading_positions'] == -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3600x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(50,20))\n",
    "ax1.plot(data.date, data['Adj Close'], label='Adj Close', color='b')\n",
    "# Plot the buy and sell signals on the same plot\n",
    "ax1.plot(sells.date, data.iloc[sells.index]['Adj Close'], 'v', markersize=5, color='r')\n",
    "ax1.plot(buys.date, data.iloc[buys.index]['Adj Close'], '^', markersize=5, color='g')\n",
    "ax1.set_ylabel('Price')\n",
    "ax1.set_xlabel('Date')\n",
    "# Display everything\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"formatted_aapl_stock_data_sentiment.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}