Dow 30 Price Directions Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c4be5785",
   "metadata": {},
   "source": [
    "#  FIN:9160 Final Project | Predicting Moving Direction of Dow 30 Stocks Using Sentiment Scores of News"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed6605ce",
   "metadata": {},
   "source": [
    "Spring 2022 | Instructor: Qihang Lin and Tong Yao"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86bac046",
   "metadata": {},
   "source": [
    "## Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "c00f89f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "a2c725d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import ast\n",
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "21a60b30",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\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>timestamp</th>\n",
       "      <th>headline</th>\n",
       "      <th>body</th>\n",
       "      <th>subject</th>\n",
       "      <th>TextBlobTitle</th>\n",
       "      <th>TextBlobBody</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01 05:34:04.421000-05:00</td>\n",
       "      <td>Reuters Insider - North Korean hackers stole s...</td>\n",
       "      <td>\\nClick the following link to watch video: htt...</td>\n",
       "      <td>['R:MSFT.O']</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.069231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-01 05:41:01.678000-05:00</td>\n",
       "      <td>Reuters Insider - North Korean hackers stole s...</td>\n",
       "      <td>\\nClick the following link to watch video: htt...</td>\n",
       "      <td>['R:MSFT.O']</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.069231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-01 05:41:33.917000-05:00</td>\n",
       "      <td>Reuters Insider - North Korean hackers stole s...</td>\n",
       "      <td>\\nClick the following link to watch video: htt...</td>\n",
       "      <td>['R:MSFT.O']</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.069231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-01 05:55:41.532000-05:00</td>\n",
       "      <td>Reuters Insider - U.S. Morning Call: Wall Stre...</td>\n",
       "      <td>\\nClick the following link to watch video: htt...</td>\n",
       "      <td>['R:MSFT.O']</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.135553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-01 06:01:17.652000-05:00</td>\n",
       "      <td>Reuters Insider - U.S. Morning Call: Wall Stre...</td>\n",
       "      <td>\\nClick the following link to watch video: htt...</td>\n",
       "      <td>['R:MSFT.O']</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.135553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         timestamp  \\\n",
       "0 2020-01-01 05:34:04.421000-05:00   \n",
       "1 2020-01-01 05:41:01.678000-05:00   \n",
       "2 2020-01-01 05:41:33.917000-05:00   \n",
       "3 2020-01-01 05:55:41.532000-05:00   \n",
       "4 2020-01-01 06:01:17.652000-05:00   \n",
       "\n",
       "                                            headline  \\\n",
       "0  Reuters Insider - North Korean hackers stole s...   \n",
       "1  Reuters Insider - North Korean hackers stole s...   \n",
       "2  Reuters Insider - North Korean hackers stole s...   \n",
       "3  Reuters Insider - U.S. Morning Call: Wall Stre...   \n",
       "4  Reuters Insider - U.S. Morning Call: Wall Stre...   \n",
       "\n",
       "                                                body       subject  \\\n",
       "0  \\nClick the following link to watch video: htt...  ['R:MSFT.O']   \n",
       "1  \\nClick the following link to watch video: htt...  ['R:MSFT.O']   \n",
       "2  \\nClick the following link to watch video: htt...  ['R:MSFT.O']   \n",
       "3  \\nClick the following link to watch video: htt...  ['R:MSFT.O']   \n",
       "4  \\nClick the following link to watch video: htt...  ['R:MSFT.O']   \n",
       "\n",
       "   TextBlobTitle  TextBlobBody  \n",
       "0            0.1      0.069231  \n",
       "1            0.1      0.069231  \n",
       "2            0.1      0.069231  \n",
       "3            0.0      0.135553  \n",
       "4            0.0      0.135553  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnews=pd.read_csv('News.RTRS_CMPNY_AMER.202001-202012-DowJones30-TextBlob_Research.csv')\n",
    "dfnews[\"timestamp\"]=pd.to_datetime(dfnews[\"timestamp\"],format=\"%Y-%m-%dT%H:%M:%S.%fZ\",utc=True)\n",
    "dfnews[\"timestamp\"]=dfnews[\"timestamp\"].dt.tz_convert('US/Eastern')\n",
    "dfnews.head()\n",
    "#sentiment_df = pd.read_csv(\"News.RTRS_CMPNY_AMER.202001-202012-DowJones30-TextBlob_Research.csv\")\n",
    "#sentiment_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "c76201be",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PERMNO</th>\n",
       "      <th>date</th>\n",
       "      <th>TICKER</th>\n",
       "      <th>PRC</th>\n",
       "      <th>VOL</th>\n",
       "      <th>RET</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10107</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>160.62</td>\n",
       "      <td>22610236</td>\n",
       "      <td>0.018516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10107</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>158.62</td>\n",
       "      <td>21099013</td>\n",
       "      <td>-0.012452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10107</td>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>159.03</td>\n",
       "      <td>21156101</td>\n",
       "      <td>0.002585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10107</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>157.58</td>\n",
       "      <td>21844325</td>\n",
       "      <td>-0.009118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10107</td>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052</td>\n",
       "      <td>0.015928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   PERMNO        date TICKER     PRC       VOL       RET\n",
       "0   10107  2020-01-02   MSFT  160.62  22610236  0.018516\n",
       "1   10107  2020-01-03   MSFT  158.62  21099013 -0.012452\n",
       "2   10107  2020-01-06   MSFT  159.03  21156101  0.002585\n",
       "3   10107  2020-01-07   MSFT  157.58  21844325 -0.009118\n",
       "4   10107  2020-01-08   MSFT  160.09  27722052  0.015928"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.read_csv('Dow30_Daily.csv')\n",
    "df1[\"date\"]=pd.to_datetime(df1[\"date\"],format=\"%Y%m%d\")\n",
    "df1=df1[(df1.date<=\"2020-12-31\") & (df1.date>=\"2020-01-02\")]\n",
    "df1.reset_index(drop=True, inplace=True)\n",
    "df1[\"date\"]=df1[\"date\"].dt.date\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "344b09ff",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "dfnews2 = dfnews.copy()\n",
    "dfnews2['subject'] = dfnews2['subject'].apply(ast.literal_eval)\n",
    "dfnews2 = dfnews2.explode('subject')\n",
    "dfnews2['TICKER'] = dfnews2['subject'].str[2:].str[:-2]\n",
    "dfnews2 = dfnews2.drop_duplicates(subset=['body','headline','TICKER','TextBlobTitle','TextBlobBody'])[['timestamp','TICKER','TextBlobTitle','TextBlobBody']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "2ed04d7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfnews_grouped = dfnews2.groupby([pd.Grouper(key='timestamp',freq='1d'), pd.Grouper('TICKER')])[[\"TextBlobTitle\",\"TextBlobBody\"]].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "3e64e2be",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfnews_grouped.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "012f4e86",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfnews_grouped['date'] = dfnews_grouped.timestamp.dt.date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "346935f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged = df1.merge(dfnews_grouped, how='left', on = ['date','TICKER'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "bfa302db",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged.fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "7dc327f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged.drop(columns=['timestamp','PERMNO'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "10e1dbfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>TICKER</th>\n",
       "      <th>PRC</th>\n",
       "      <th>VOL</th>\n",
       "      <th>RET</th>\n",
       "      <th>TextBlobTitle</th>\n",
       "      <th>TextBlobBody</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>160.62000</td>\n",
       "      <td>22610236</td>\n",
       "      <td>0.018516</td>\n",
       "      <td>0.170833</td>\n",
       "      <td>0.230521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>158.62000</td>\n",
       "      <td>21099013</td>\n",
       "      <td>-0.012452</td>\n",
       "      <td>0.341667</td>\n",
       "      <td>0.310370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>159.03000</td>\n",
       "      <td>21156101</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>157.58000</td>\n",
       "      <td>21844325</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>160.09000</td>\n",
       "      <td>27722052</td>\n",
       "      <td>0.015928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7838</th>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>UNH</td>\n",
       "      <td>340.79001</td>\n",
       "      <td>1360598</td>\n",
       "      <td>0.009479</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7839</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>UNH</td>\n",
       "      <td>345.95001</td>\n",
       "      <td>2308222</td>\n",
       "      <td>0.015141</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7840</th>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>UNH</td>\n",
       "      <td>347.35001</td>\n",
       "      <td>2275855</td>\n",
       "      <td>0.004047</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7841</th>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>UNH</td>\n",
       "      <td>344.98999</td>\n",
       "      <td>1866024</td>\n",
       "      <td>-0.006794</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.017635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7842</th>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>UNH</td>\n",
       "      <td>350.67999</td>\n",
       "      <td>1969227</td>\n",
       "      <td>0.016493</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7843 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date TICKER        PRC       VOL       RET  TextBlobTitle  \\\n",
       "0     2020-01-02   MSFT  160.62000  22610236  0.018516       0.170833   \n",
       "1     2020-01-03   MSFT  158.62000  21099013 -0.012452       0.341667   \n",
       "2     2020-01-06   MSFT  159.03000  21156101  0.002585       0.000000   \n",
       "3     2020-01-07   MSFT  157.58000  21844325 -0.009118       0.133333   \n",
       "4     2020-01-08   MSFT  160.09000  27722052  0.015928       0.000000   \n",
       "...          ...    ...        ...       ...       ...            ...   \n",
       "7838  2020-12-24    UNH  340.79001   1360598  0.009479       0.000000   \n",
       "7839  2020-12-28    UNH  345.95001   2308222  0.015141       0.000000   \n",
       "7840  2020-12-29    UNH  347.35001   2275855  0.004047       0.000000   \n",
       "7841  2020-12-30    UNH  344.98999   1866024 -0.006794       0.068182   \n",
       "7842  2020-12-31    UNH  350.67999   1969227  0.016493       0.000000   \n",
       "\n",
       "      TextBlobBody  \n",
       "0         0.230521  \n",
       "1         0.310370  \n",
       "2         0.000000  \n",
       "3        -0.070000  \n",
       "4         0.012165  \n",
       "...            ...  \n",
       "7838      0.000000  \n",
       "7839      0.048081  \n",
       "7840      0.000000  \n",
       "7841      0.017635  \n",
       "7842      0.000000  \n",
       "\n",
       "[7843 rows x 7 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "bbaf459e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged_old = df_merged.copy()\n",
    "#df_merged['RET'] = (df_merged['RET'] > 0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "9ec45db8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>TICKER</th>\n",
       "      <th>PRC</th>\n",
       "      <th>VOL</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>160.62000</td>\n",
       "      <td>22610236</td>\n",
       "      <td>0.018516</td>\n",
       "      <td>0.170833</td>\n",
       "      <td>0.230521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>158.62000</td>\n",
       "      <td>21099013</td>\n",
       "      <td>-0.012452</td>\n",
       "      <td>0.341667</td>\n",
       "      <td>0.310370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>159.03000</td>\n",
       "      <td>21156101</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>157.58000</td>\n",
       "      <td>21844325</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-08</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>160.09000</td>\n",
       "      <td>27722052</td>\n",
       "      <td>0.015928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7838</th>\n",
       "      <td>2020-12-24</td>\n",
       "      <td>UNH</td>\n",
       "      <td>340.79001</td>\n",
       "      <td>1360598</td>\n",
       "      <td>0.009479</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7839</th>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>UNH</td>\n",
       "      <td>345.95001</td>\n",
       "      <td>2308222</td>\n",
       "      <td>0.015141</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.048081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7840</th>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>UNH</td>\n",
       "      <td>347.35001</td>\n",
       "      <td>2275855</td>\n",
       "      <td>0.004047</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7841</th>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>UNH</td>\n",
       "      <td>344.98999</td>\n",
       "      <td>1866024</td>\n",
       "      <td>-0.006794</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.017635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7842</th>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>UNH</td>\n",
       "      <td>350.67999</td>\n",
       "      <td>1969227</td>\n",
       "      <td>0.016493</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7843 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date TICKER        PRC       VOL       RET  TextBlobTitle  \\\n",
       "0     2020-01-02   MSFT  160.62000  22610236  0.018516       0.170833   \n",
       "1     2020-01-03   MSFT  158.62000  21099013 -0.012452       0.341667   \n",
       "2     2020-01-06   MSFT  159.03000  21156101  0.002585       0.000000   \n",
       "3     2020-01-07   MSFT  157.58000  21844325 -0.009118       0.133333   \n",
       "4     2020-01-08   MSFT  160.09000  27722052  0.015928       0.000000   \n",
       "...          ...    ...        ...       ...       ...            ...   \n",
       "7838  2020-12-24    UNH  340.79001   1360598  0.009479       0.000000   \n",
       "7839  2020-12-28    UNH  345.95001   2308222  0.015141       0.000000   \n",
       "7840  2020-12-29    UNH  347.35001   2275855  0.004047       0.000000   \n",
       "7841  2020-12-30    UNH  344.98999   1866024 -0.006794       0.068182   \n",
       "7842  2020-12-31    UNH  350.67999   1969227  0.016493       0.000000   \n",
       "\n",
       "      TextBlobBody  \n",
       "0         0.230521  \n",
       "1         0.310370  \n",
       "2         0.000000  \n",
       "3        -0.070000  \n",
       "4         0.012165  \n",
       "...            ...  \n",
       "7838      0.000000  \n",
       "7839      0.048081  \n",
       "7840      0.000000  \n",
       "7841      0.017635  \n",
       "7842      0.000000  \n",
       "\n",
       "[7843 rows x 7 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged_old"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b12f3941",
   "metadata": {},
   "source": [
    "Create lagged sequences for [\"PRC\",\"VOL\",\"RET\",\"Sentiment\"] and use them as input features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "11ed9c62",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_23/1498584696.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_UNH['Sentiment'] = df_UNH['TextBlobTitle']+df_UNH['TextBlobBody']\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_UNH= df_merged[df_merged['TICKER']=='UNH']\n",
    "df_UNH['Sentiment'] = df_UNH['TextBlobTitle']+df_UNH['TextBlobBody']\n",
    "plt.figure(figsize=(25, 20))\n",
    "plt.plot(df_UNH['date'], df_UNH['RET'], label = \"Return\")\n",
    "plt.plot(df_UNH['date'], df_UNH['Sentiment'], label = \"Sentiment\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "ff527fa4",
   "metadata": {
    "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>date</th>\n",
       "      <th>TICKER</th>\n",
       "      <th>PRC</th>\n",
       "      <th>VOL</th>\n",
       "      <th>RET</th>\n",
       "      <th>TextBlobTitle</th>\n",
       "      <th>TextBlobBody</th>\n",
       "      <th>PRC1</th>\n",
       "      <th>VOL1</th>\n",
       "      <th>RET1</th>\n",
       "      <th>...</th>\n",
       "      <th>PRC5</th>\n",
       "      <th>VOL5</th>\n",
       "      <th>RET5</th>\n",
       "      <th>TextBlobTitle5</th>\n",
       "      <th>TextBlobBody5</th>\n",
       "      <th>PRC6</th>\n",
       "      <th>VOL6</th>\n",
       "      <th>RET6</th>\n",
       "      <th>TextBlobTitle6</th>\n",
       "      <th>TextBlobBody6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>161.34000</td>\n",
       "      <td>20709064</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.069512</td>\n",
       "      <td>162.09000</td>\n",
       "      <td>21375474.0</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>...</td>\n",
       "      <td>158.62</td>\n",
       "      <td>21099013.0</td>\n",
       "      <td>-0.012452</td>\n",
       "      <td>0.341667</td>\n",
       "      <td>0.310370</td>\n",
       "      <td>160.62</td>\n",
       "      <td>22610236.0</td>\n",
       "      <td>0.018516</td>\n",
       "      <td>0.170833</td>\n",
       "      <td>0.230521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-13</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>163.28000</td>\n",
       "      <td>21611045</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030012</td>\n",
       "      <td>161.34000</td>\n",
       "      <td>20709064.0</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>...</td>\n",
       "      <td>159.03</td>\n",
       "      <td>21156101.0</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>158.62</td>\n",
       "      <td>21099013.0</td>\n",
       "      <td>-0.012452</td>\n",
       "      <td>0.341667</td>\n",
       "      <td>0.310370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-14</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>162.13000</td>\n",
       "      <td>23469298</td>\n",
       "      <td>-0.007043</td>\n",
       "      <td>0.039205</td>\n",
       "      <td>0.062122</td>\n",
       "      <td>163.28000</td>\n",
       "      <td>21611045.0</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>...</td>\n",
       "      <td>157.58</td>\n",
       "      <td>21844325.0</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.070000</td>\n",
       "      <td>159.03</td>\n",
       "      <td>21156101.0</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-15</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>163.17999</td>\n",
       "      <td>21377516</td>\n",
       "      <td>0.006476</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>162.13000</td>\n",
       "      <td>23469298.0</td>\n",
       "      <td>-0.007043</td>\n",
       "      <td>...</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052.0</td>\n",
       "      <td>0.015928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012165</td>\n",
       "      <td>157.58</td>\n",
       "      <td>21844325.0</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>166.17000</td>\n",
       "      <td>23832244</td>\n",
       "      <td>0.018323</td>\n",
       "      <td>-0.009200</td>\n",
       "      <td>0.037589</td>\n",
       "      <td>163.17999</td>\n",
       "      <td>21377516.0</td>\n",
       "      <td>0.006476</td>\n",
       "      <td>...</td>\n",
       "      <td>162.09</td>\n",
       "      <td>21375474.0</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>-0.014394</td>\n",
       "      <td>-0.011663</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052.0</td>\n",
       "      <td>0.015928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date TICKER        PRC       VOL       RET  TextBlobTitle  \\\n",
       "0  2020-01-10   MSFT  161.34000  20709064 -0.004627       0.045455   \n",
       "1  2020-01-13   MSFT  163.28000  21611045  0.012024       0.000000   \n",
       "2  2020-01-14   MSFT  162.13000  23469298 -0.007043       0.039205   \n",
       "3  2020-01-15   MSFT  163.17999  21377516  0.006476       0.000000   \n",
       "4  2020-01-16   MSFT  166.17000  23832244  0.018323      -0.009200   \n",
       "\n",
       "   TextBlobBody       PRC1        VOL1      RET1  ...    PRC5        VOL5  \\\n",
       "0      0.069512  162.09000  21375474.0  0.012493  ...  158.62  21099013.0   \n",
       "1      0.030012  161.34000  20709064.0 -0.004627  ...  159.03  21156101.0   \n",
       "2      0.062122  163.28000  21611045.0  0.012024  ...  157.58  21844325.0   \n",
       "3      0.000000  162.13000  23469298.0 -0.007043  ...  160.09  27722052.0   \n",
       "4      0.037589  163.17999  21377516.0  0.006476  ...  162.09  21375474.0   \n",
       "\n",
       "       RET5  TextBlobTitle5  TextBlobBody5    PRC6        VOL6      RET6  \\\n",
       "0 -0.012452        0.341667       0.310370  160.62  22610236.0  0.018516   \n",
       "1  0.002585        0.000000       0.000000  158.62  21099013.0 -0.012452   \n",
       "2 -0.009118        0.133333      -0.070000  159.03  21156101.0  0.002585   \n",
       "3  0.015928        0.000000       0.012165  157.58  21844325.0 -0.009118   \n",
       "4  0.012493       -0.014394      -0.011663  160.09  27722052.0  0.015928   \n",
       "\n",
       "   TextBlobTitle6  TextBlobBody6  \n",
       "0        0.170833       0.230521  \n",
       "1        0.341667       0.310370  \n",
       "2        0.000000       0.000000  \n",
       "3        0.133333      -0.070000  \n",
       "4        0.000000       0.012165  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_lag=6\n",
    "df_merged_copy = df_merged_old.copy().drop(columns=['TextBlobTitle', 'TextBlobBody'])\n",
    "for i in range(num_lag):\n",
    "    columnID=[s+str(i+1) for s in [\"PRC\",\"VOL\",\"RET\",\"TextBlobTitle\",\"TextBlobBody\"]]\n",
    "    columnID_no_sentiment=[s+str(i+1) for s in [\"PRC\",\"VOL\",\"RET\"]]\n",
    "    \n",
    "    df_merged[columnID]=df_merged_old[[\"PRC\",\"VOL\",\"RET\",\"TextBlobTitle\",\"TextBlobBody\"]].shift(i+1)\n",
    "    df_merged_copy[columnID_no_sentiment] = df_merged_copy[[\"PRC\",\"VOL\",\"RET\"]].shift(i+1)\n",
    "    \n",
    "df_merged_copy.dropna(inplace=True)\n",
    "df_merged_copy.reset_index(drop=True,inplace=True)\n",
    "\n",
    "df_merged.dropna(how=\"any\",inplace=True)\n",
    "df_merged.reset_index(drop=True,inplace=True)\n",
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61d147a8",
   "metadata": {},
   "source": [
    "### Pre-modeling Correlation Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "3145dfcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged_corr = df_merged.copy()\n",
    "df_merged_corr['Sentiment'] = df_merged_corr['TextBlobTitle'] + df_merged_corr['TextBlobBody']\n",
    "df_merged_corr_tick = df_merged_corr[['TICKER', 'PRC', 'VOL', 'RET', 'RET1', 'RET2', 'RET3', 'RET4', 'RET5', 'RET6', 'Sentiment']]\n",
    "df_merged_corr = df_merged_corr[['PRC', 'VOL', 'RET', 'RET1', 'RET2', 'RET3', 'RET4', 'RET5', 'RET6', 'Sentiment']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "8cd0673a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.axes()\n",
    "sns.heatmap(df_merged_corr.corr(), annot = True, ax = ax)\n",
    "ax.set_title('Correlation Matrix for Dow30')\n",
    "plt.gcf().set_size_inches(15, 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b5629d1",
   "metadata": {},
   "source": [
    "#### Stocks with Highest Correlations between Returns and Sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "27a7fd4f",
   "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>Ticker</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>0.160867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UNH</td>\n",
       "      <td>0.159514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NKE</td>\n",
       "      <td>0.144597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>V</td>\n",
       "      <td>0.123807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRV</td>\n",
       "      <td>0.116415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>JPM</td>\n",
       "      <td>0.113167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>0.111688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GS</td>\n",
       "      <td>0.091597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>0.089144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>CAT</td>\n",
       "      <td>0.056699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>CSCO</td>\n",
       "      <td>0.054729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CRM</td>\n",
       "      <td>0.054470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>KO</td>\n",
       "      <td>0.052033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>DIS</td>\n",
       "      <td>0.050634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CVX</td>\n",
       "      <td>0.049820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>INTC</td>\n",
       "      <td>0.048343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AXP</td>\n",
       "      <td>0.041831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BA</td>\n",
       "      <td>0.036362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>IBM</td>\n",
       "      <td>0.026477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>PG</td>\n",
       "      <td>0.025441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>DOW</td>\n",
       "      <td>0.014178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>AMGN</td>\n",
       "      <td>0.012588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>WMT</td>\n",
       "      <td>0.008444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>HD</td>\n",
       "      <td>0.006571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>JNJ</td>\n",
       "      <td>0.000891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MCD</td>\n",
       "      <td>-0.003836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>WBA</td>\n",
       "      <td>-0.009487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MRK</td>\n",
       "      <td>-0.013581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>VZ</td>\n",
       "      <td>-0.044216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>HON</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticker  Correlation\n",
       "0     MMM     0.160867\n",
       "1     UNH     0.159514\n",
       "2     NKE     0.144597\n",
       "3       V     0.123807\n",
       "4     TRV     0.116415\n",
       "5     JPM     0.113167\n",
       "6    MSFT     0.111688\n",
       "7      GS     0.091597\n",
       "8    AAPL     0.089144\n",
       "9     CAT     0.056699\n",
       "10   CSCO     0.054729\n",
       "11    CRM     0.054470\n",
       "12     KO     0.052033\n",
       "13    DIS     0.050634\n",
       "14    CVX     0.049820\n",
       "15   INTC     0.048343\n",
       "16    AXP     0.041831\n",
       "17     BA     0.036362\n",
       "18    IBM     0.026477\n",
       "19     PG     0.025441\n",
       "20    DOW     0.014178\n",
       "21   AMGN     0.012588\n",
       "22    WMT     0.008444\n",
       "23     HD     0.006571\n",
       "24    JNJ     0.000891\n",
       "25    MCD    -0.003836\n",
       "26    WBA    -0.009487\n",
       "27    MRK    -0.013581\n",
       "28     VZ    -0.044216\n",
       "29      C          NaN\n",
       "30    HON          NaN"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We want to look at the stocks with the highest correlations to determine whether there is a difference\n",
    "# in our models' abilities to predict depending on which stocks are used\n",
    "stocks = np.sort(df_merged_corr_tick.TICKER.unique()).tolist()\n",
    "corrs = []\n",
    "for stock in stocks:\n",
    "    corrs += [df_merged_corr_tick[(df_merged_corr_tick.TICKER == str(stock))]['RET'].corr(df_merged_corr_tick[df_merged_corr_tick.TICKER == str(stock)].Sentiment)]\n",
    "df_corr = pd.DataFrame({'Ticker':stocks, 'Correlation':corrs})\n",
    "df_corr_sorted = df_corr.sort_values(by = 'Correlation', ascending = False).reset_index(drop = True)\n",
    "df_corr_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "b3ff1b91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05445477050208489"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_corr_sorted.Correlation.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "ac642f55",
   "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>Ticker</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MMM</td>\n",
       "      <td>0.160867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UNH</td>\n",
       "      <td>0.159514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NKE</td>\n",
       "      <td>0.144597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>V</td>\n",
       "      <td>0.123807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRV</td>\n",
       "      <td>0.116415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>WBA</td>\n",
       "      <td>-0.009487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MRK</td>\n",
       "      <td>-0.013581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>VZ</td>\n",
       "      <td>-0.044216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>C</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>HON</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticker  Correlation\n",
       "0     MMM     0.160867\n",
       "1     UNH     0.159514\n",
       "2     NKE     0.144597\n",
       "3       V     0.123807\n",
       "4     TRV     0.116415\n",
       "26    WBA    -0.009487\n",
       "27    MRK    -0.013581\n",
       "28     VZ    -0.044216\n",
       "29      C          NaN\n",
       "30    HON          NaN"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 best and worst stocks (in terms of their correlation)\n",
    "corr_best_worst = df_corr_sorted[:5].append(df_corr_sorted[-5:])\n",
    "corr_best_worst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "25b0ce5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_UNH = df_merged_corr_tick[df_merged_corr_tick.TICKER == 'UNH']\n",
    "ax = plt.axes()\n",
    "sns.heatmap(df_UNH.corr(), annot = True, ax = ax)\n",
    "ax.set_title('Correlation Matrix for UNH')\n",
    "plt.gcf().set_size_inches(15, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "ef0a1e13",
   "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>TextBlobTitle</th>\n",
       "      <th>TextBlobBody</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PRC</th>\n",
       "      <td>0.041899</td>\n",
       "      <td>0.103665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOL</th>\n",
       "      <td>0.086635</td>\n",
       "      <td>0.136489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RET</th>\n",
       "      <td>0.027473</td>\n",
       "      <td>0.056998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobTitle</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.211129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobBody</th>\n",
       "      <td>0.211129</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRC1</th>\n",
       "      <td>0.039705</td>\n",
       "      <td>0.100060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOL1</th>\n",
       "      <td>0.089046</td>\n",
       "      <td>0.138954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RET1</th>\n",
       "      <td>-0.013635</td>\n",
       "      <td>-0.004421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobTitle1</th>\n",
       "      <td>0.065009</td>\n",
       "      <td>0.053649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobBody1</th>\n",
       "      <td>0.055258</td>\n",
       "      <td>0.103954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRC2</th>\n",
       "      <td>0.037155</td>\n",
       "      <td>0.100073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOL2</th>\n",
       "      <td>0.081479</td>\n",
       "      <td>0.132885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RET2</th>\n",
       "      <td>0.006575</td>\n",
       "      <td>0.002083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobTitle2</th>\n",
       "      <td>0.052371</td>\n",
       "      <td>0.061504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobBody2</th>\n",
       "      <td>0.059265</td>\n",
       "      <td>0.092835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRC3</th>\n",
       "      <td>0.036395</td>\n",
       "      <td>0.099671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOL3</th>\n",
       "      <td>0.089588</td>\n",
       "      <td>0.131865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RET3</th>\n",
       "      <td>-0.018887</td>\n",
       "      <td>-0.001336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobTitle3</th>\n",
       "      <td>0.028984</td>\n",
       "      <td>0.039172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobBody3</th>\n",
       "      <td>0.042833</td>\n",
       "      <td>0.067264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRC4</th>\n",
       "      <td>0.036873</td>\n",
       "      <td>0.099433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOL4</th>\n",
       "      <td>0.091016</td>\n",
       "      <td>0.127864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RET4</th>\n",
       "      <td>-0.007868</td>\n",
       "      <td>0.004487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobTitle4</th>\n",
       "      <td>0.037361</td>\n",
       "      <td>0.052918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobBody4</th>\n",
       "      <td>0.047961</td>\n",
       "      <td>0.109468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRC5</th>\n",
       "      <td>0.037643</td>\n",
       "      <td>0.098424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOL5</th>\n",
       "      <td>0.075820</td>\n",
       "      <td>0.122370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RET5</th>\n",
       "      <td>0.011597</td>\n",
       "      <td>0.008382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobTitle5</th>\n",
       "      <td>0.034371</td>\n",
       "      <td>0.044678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobBody5</th>\n",
       "      <td>0.031362</td>\n",
       "      <td>0.109207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PRC6</th>\n",
       "      <td>0.036934</td>\n",
       "      <td>0.097378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VOL6</th>\n",
       "      <td>0.069901</td>\n",
       "      <td>0.124401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RET6</th>\n",
       "      <td>0.004859</td>\n",
       "      <td>-0.007535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobTitle6</th>\n",
       "      <td>0.024053</td>\n",
       "      <td>0.052862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TextBlobBody6</th>\n",
       "      <td>0.049946</td>\n",
       "      <td>0.095577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                TextBlobTitle  TextBlobBody\n",
       "PRC                  0.041899      0.103665\n",
       "VOL                  0.086635      0.136489\n",
       "RET                  0.027473      0.056998\n",
       "TextBlobTitle        1.000000      0.211129\n",
       "TextBlobBody         0.211129      1.000000\n",
       "PRC1                 0.039705      0.100060\n",
       "VOL1                 0.089046      0.138954\n",
       "RET1                -0.013635     -0.004421\n",
       "TextBlobTitle1       0.065009      0.053649\n",
       "TextBlobBody1        0.055258      0.103954\n",
       "PRC2                 0.037155      0.100073\n",
       "VOL2                 0.081479      0.132885\n",
       "RET2                 0.006575      0.002083\n",
       "TextBlobTitle2       0.052371      0.061504\n",
       "TextBlobBody2        0.059265      0.092835\n",
       "PRC3                 0.036395      0.099671\n",
       "VOL3                 0.089588      0.131865\n",
       "RET3                -0.018887     -0.001336\n",
       "TextBlobTitle3       0.028984      0.039172\n",
       "TextBlobBody3        0.042833      0.067264\n",
       "PRC4                 0.036873      0.099433\n",
       "VOL4                 0.091016      0.127864\n",
       "RET4                -0.007868      0.004487\n",
       "TextBlobTitle4       0.037361      0.052918\n",
       "TextBlobBody4        0.047961      0.109468\n",
       "PRC5                 0.037643      0.098424\n",
       "VOL5                 0.075820      0.122370\n",
       "RET5                 0.011597      0.008382\n",
       "TextBlobTitle5       0.034371      0.044678\n",
       "TextBlobBody5        0.031362      0.109207\n",
       "PRC6                 0.036934      0.097378\n",
       "VOL6                 0.069901      0.124401\n",
       "RET6                 0.004859     -0.007535\n",
       "TextBlobTitle6       0.024053      0.052862\n",
       "TextBlobBody6        0.049946      0.095577"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged.corr()[['TextBlobTitle','TextBlobBody']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "13686dd9",
   "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>TICKER</th>\n",
       "      <th>PRC</th>\n",
       "      <th>VOL</th>\n",
       "      <th>RET</th>\n",
       "      <th>PRC1</th>\n",
       "      <th>VOL1</th>\n",
       "      <th>RET1</th>\n",
       "      <th>PRC2</th>\n",
       "      <th>VOL2</th>\n",
       "      <th>...</th>\n",
       "      <th>RET3</th>\n",
       "      <th>PRC4</th>\n",
       "      <th>VOL4</th>\n",
       "      <th>RET4</th>\n",
       "      <th>PRC5</th>\n",
       "      <th>VOL5</th>\n",
       "      <th>RET5</th>\n",
       "      <th>PRC6</th>\n",
       "      <th>VOL6</th>\n",
       "      <th>RET6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>161.34000</td>\n",
       "      <td>20709064</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>162.09000</td>\n",
       "      <td>21375474.0</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>159.03</td>\n",
       "      <td>21156101.0</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>158.62</td>\n",
       "      <td>21099013.0</td>\n",
       "      <td>-0.012452</td>\n",
       "      <td>160.62</td>\n",
       "      <td>22610236.0</td>\n",
       "      <td>0.018516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-13</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>163.28000</td>\n",
       "      <td>21611045</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>161.34000</td>\n",
       "      <td>20709064.0</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>162.09</td>\n",
       "      <td>21375474.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015928</td>\n",
       "      <td>157.58</td>\n",
       "      <td>21844325.0</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>159.03</td>\n",
       "      <td>21156101.0</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>158.62</td>\n",
       "      <td>21099013.0</td>\n",
       "      <td>-0.012452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-14</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>162.13000</td>\n",
       "      <td>23469298</td>\n",
       "      <td>-0.007043</td>\n",
       "      <td>163.28000</td>\n",
       "      <td>21611045.0</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>161.34</td>\n",
       "      <td>20709064.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052.0</td>\n",
       "      <td>0.015928</td>\n",
       "      <td>157.58</td>\n",
       "      <td>21844325.0</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>159.03</td>\n",
       "      <td>21156101.0</td>\n",
       "      <td>0.002585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-15</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>163.17999</td>\n",
       "      <td>21377516</td>\n",
       "      <td>0.006476</td>\n",
       "      <td>162.13000</td>\n",
       "      <td>23469298.0</td>\n",
       "      <td>-0.007043</td>\n",
       "      <td>163.28</td>\n",
       "      <td>21611045.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>162.09</td>\n",
       "      <td>21375474.0</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052.0</td>\n",
       "      <td>0.015928</td>\n",
       "      <td>157.58</td>\n",
       "      <td>21844325.0</td>\n",
       "      <td>-0.009118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>166.17000</td>\n",
       "      <td>23832244</td>\n",
       "      <td>0.018323</td>\n",
       "      <td>163.17999</td>\n",
       "      <td>21377516.0</td>\n",
       "      <td>0.006476</td>\n",
       "      <td>162.13</td>\n",
       "      <td>23469298.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>161.34</td>\n",
       "      <td>20709064.0</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>162.09</td>\n",
       "      <td>21375474.0</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052.0</td>\n",
       "      <td>0.015928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date TICKER        PRC       VOL       RET       PRC1        VOL1  \\\n",
       "0  2020-01-10   MSFT  161.34000  20709064 -0.004627  162.09000  21375474.0   \n",
       "1  2020-01-13   MSFT  163.28000  21611045  0.012024  161.34000  20709064.0   \n",
       "2  2020-01-14   MSFT  162.13000  23469298 -0.007043  163.28000  21611045.0   \n",
       "3  2020-01-15   MSFT  163.17999  21377516  0.006476  162.13000  23469298.0   \n",
       "4  2020-01-16   MSFT  166.17000  23832244  0.018323  163.17999  21377516.0   \n",
       "\n",
       "       RET1    PRC2        VOL2  ...      RET3    PRC4        VOL4      RET4  \\\n",
       "0  0.012493  160.09  27722052.0  ... -0.009118  159.03  21156101.0  0.002585   \n",
       "1 -0.004627  162.09  21375474.0  ...  0.015928  157.58  21844325.0 -0.009118   \n",
       "2  0.012024  161.34  20709064.0  ...  0.012493  160.09  27722052.0  0.015928   \n",
       "3 -0.007043  163.28  21611045.0  ... -0.004627  162.09  21375474.0  0.012493   \n",
       "4  0.006476  162.13  23469298.0  ...  0.012024  161.34  20709064.0 -0.004627   \n",
       "\n",
       "     PRC5        VOL5      RET5    PRC6        VOL6      RET6  \n",
       "0  158.62  21099013.0 -0.012452  160.62  22610236.0  0.018516  \n",
       "1  159.03  21156101.0  0.002585  158.62  21099013.0 -0.012452  \n",
       "2  157.58  21844325.0 -0.009118  159.03  21156101.0  0.002585  \n",
       "3  160.09  27722052.0  0.015928  157.58  21844325.0 -0.009118  \n",
       "4  162.09  21375474.0  0.012493  160.09  27722052.0  0.015928  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "972dbc33",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged.to_csv(\"df_merged.csv\", sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d47c92f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Prediction performances with using sentiment scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "6197e946",
   "metadata": {},
   "outputs": [],
   "source": [
    "featurename_s=list(df_merged.columns)\n",
    "featurename_s.remove(\"date\")\n",
    "featurename_s.remove(\"TICKER\")\n",
    "featurename_s.remove(\"PRC\")\n",
    "featurename_s.remove(\"VOL\")\n",
    "featurename_s.remove(\"RET\")\n",
    "featurename_s.remove(\"TextBlobTitle\")\n",
    "featurename_s.remove(\"TextBlobBody\")\n",
    "targetname_s=\"RET\"\n",
    "#featurename_s\n",
    "\n",
    "featurename_ns=list(df_merged_copy.columns)\n",
    "featurename_ns.remove(\"date\")\n",
    "featurename_ns.remove(\"TICKER\")\n",
    "featurename_ns.remove(\"PRC\")\n",
    "featurename_ns.remove(\"VOL\")\n",
    "featurename_ns.remove(\"RET\")\n",
    "targetname_ns='RET'\n",
    "#featurename_ns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "4ef5ce0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import random as python_random\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "os.environ['PYTHONHASHSEED']=str(2022)\n",
    "np.random.seed(2022)\n",
    "python_random.seed(2022)\n",
    "tf.random.set_seed(2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "e0407746",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AAPL',\n",
       " 'AMGN',\n",
       " 'AXP',\n",
       " 'BA',\n",
       " 'CAT',\n",
       " 'CRM',\n",
       " 'CSCO',\n",
       " 'CVX',\n",
       " 'DIS',\n",
       " 'DOW',\n",
       " 'GS',\n",
       " 'HD',\n",
       " 'IBM',\n",
       " 'INTC',\n",
       " 'JNJ',\n",
       " 'JPM',\n",
       " 'KO',\n",
       " 'MCD',\n",
       " 'MMM',\n",
       " 'MRK',\n",
       " 'MSFT',\n",
       " 'NKE',\n",
       " 'PG',\n",
       " 'TRV',\n",
       " 'UNH',\n",
       " 'V',\n",
       " 'VZ',\n",
       " 'WBA',\n",
       " 'WMT']"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocklist = np.sort(df_merged.TICKER.unique())\n",
    "stocklist_list = stocklist.tolist()\n",
    "stocklist_list.remove('HON')\n",
    "stocklist_list.remove('C')\n",
    "stocklist_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "bbb9bc5a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>TICKER</th>\n",
       "      <th>PRC</th>\n",
       "      <th>VOL</th>\n",
       "      <th>RET</th>\n",
       "      <th>TextBlobTitle</th>\n",
       "      <th>TextBlobBody</th>\n",
       "      <th>PRC1</th>\n",
       "      <th>VOL1</th>\n",
       "      <th>RET1</th>\n",
       "      <th>...</th>\n",
       "      <th>PRC5</th>\n",
       "      <th>VOL5</th>\n",
       "      <th>RET5</th>\n",
       "      <th>TextBlobTitle5</th>\n",
       "      <th>TextBlobBody5</th>\n",
       "      <th>PRC6</th>\n",
       "      <th>VOL6</th>\n",
       "      <th>RET6</th>\n",
       "      <th>TextBlobTitle6</th>\n",
       "      <th>TextBlobBody6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>161.34000</td>\n",
       "      <td>20709064</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.069512</td>\n",
       "      <td>162.09000</td>\n",
       "      <td>21375474.0</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>...</td>\n",
       "      <td>158.62</td>\n",
       "      <td>21099013.0</td>\n",
       "      <td>-0.012452</td>\n",
       "      <td>0.341667</td>\n",
       "      <td>0.310370</td>\n",
       "      <td>160.62</td>\n",
       "      <td>22610236.0</td>\n",
       "      <td>0.018516</td>\n",
       "      <td>0.170833</td>\n",
       "      <td>0.230521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-13</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>163.28000</td>\n",
       "      <td>21611045</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030012</td>\n",
       "      <td>161.34000</td>\n",
       "      <td>20709064.0</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>...</td>\n",
       "      <td>159.03</td>\n",
       "      <td>21156101.0</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>158.62</td>\n",
       "      <td>21099013.0</td>\n",
       "      <td>-0.012452</td>\n",
       "      <td>0.341667</td>\n",
       "      <td>0.310370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-14</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>162.13000</td>\n",
       "      <td>23469298</td>\n",
       "      <td>-0.007043</td>\n",
       "      <td>0.039205</td>\n",
       "      <td>0.062122</td>\n",
       "      <td>163.28000</td>\n",
       "      <td>21611045.0</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>...</td>\n",
       "      <td>157.58</td>\n",
       "      <td>21844325.0</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.070000</td>\n",
       "      <td>159.03</td>\n",
       "      <td>21156101.0</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-15</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>163.17999</td>\n",
       "      <td>21377516</td>\n",
       "      <td>0.006476</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>162.13000</td>\n",
       "      <td>23469298.0</td>\n",
       "      <td>-0.007043</td>\n",
       "      <td>...</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052.0</td>\n",
       "      <td>0.015928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012165</td>\n",
       "      <td>157.58</td>\n",
       "      <td>21844325.0</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>-0.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>166.17000</td>\n",
       "      <td>23832244</td>\n",
       "      <td>0.018323</td>\n",
       "      <td>-0.009200</td>\n",
       "      <td>0.037589</td>\n",
       "      <td>163.17999</td>\n",
       "      <td>21377516.0</td>\n",
       "      <td>0.006476</td>\n",
       "      <td>...</td>\n",
       "      <td>162.09</td>\n",
       "      <td>21375474.0</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>-0.014394</td>\n",
       "      <td>-0.011663</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052.0</td>\n",
       "      <td>0.015928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012165</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date TICKER        PRC       VOL       RET  TextBlobTitle  \\\n",
       "0  2020-01-10   MSFT  161.34000  20709064 -0.004627       0.045455   \n",
       "1  2020-01-13   MSFT  163.28000  21611045  0.012024       0.000000   \n",
       "2  2020-01-14   MSFT  162.13000  23469298 -0.007043       0.039205   \n",
       "3  2020-01-15   MSFT  163.17999  21377516  0.006476       0.000000   \n",
       "4  2020-01-16   MSFT  166.17000  23832244  0.018323      -0.009200   \n",
       "\n",
       "   TextBlobBody       PRC1        VOL1      RET1  ...    PRC5        VOL5  \\\n",
       "0      0.069512  162.09000  21375474.0  0.012493  ...  158.62  21099013.0   \n",
       "1      0.030012  161.34000  20709064.0 -0.004627  ...  159.03  21156101.0   \n",
       "2      0.062122  163.28000  21611045.0  0.012024  ...  157.58  21844325.0   \n",
       "3      0.000000  162.13000  23469298.0 -0.007043  ...  160.09  27722052.0   \n",
       "4      0.037589  163.17999  21377516.0  0.006476  ...  162.09  21375474.0   \n",
       "\n",
       "       RET5  TextBlobTitle5  TextBlobBody5    PRC6        VOL6      RET6  \\\n",
       "0 -0.012452        0.341667       0.310370  160.62  22610236.0  0.018516   \n",
       "1  0.002585        0.000000       0.000000  158.62  21099013.0 -0.012452   \n",
       "2 -0.009118        0.133333      -0.070000  159.03  21156101.0  0.002585   \n",
       "3  0.015928        0.000000       0.012165  157.58  21844325.0 -0.009118   \n",
       "4  0.012493       -0.014394      -0.011663  160.09  27722052.0  0.015928   \n",
       "\n",
       "   TextBlobTitle6  TextBlobBody6  \n",
       "0        0.170833       0.230521  \n",
       "1        0.341667       0.310370  \n",
       "2        0.000000       0.000000  \n",
       "3        0.133333      -0.070000  \n",
       "4        0.000000       0.012165  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged_s.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "1d6b8a6d",
   "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>TICKER</th>\n",
       "      <th>PRC</th>\n",
       "      <th>VOL</th>\n",
       "      <th>RET</th>\n",
       "      <th>PRC1</th>\n",
       "      <th>VOL1</th>\n",
       "      <th>RET1</th>\n",
       "      <th>PRC2</th>\n",
       "      <th>VOL2</th>\n",
       "      <th>...</th>\n",
       "      <th>RET3</th>\n",
       "      <th>PRC4</th>\n",
       "      <th>VOL4</th>\n",
       "      <th>RET4</th>\n",
       "      <th>PRC5</th>\n",
       "      <th>VOL5</th>\n",
       "      <th>RET5</th>\n",
       "      <th>PRC6</th>\n",
       "      <th>VOL6</th>\n",
       "      <th>RET6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>161.34000</td>\n",
       "      <td>20709064</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>162.09000</td>\n",
       "      <td>21375474.0</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>159.03</td>\n",
       "      <td>21156101.0</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>158.62</td>\n",
       "      <td>21099013.0</td>\n",
       "      <td>-0.012452</td>\n",
       "      <td>160.62</td>\n",
       "      <td>22610236.0</td>\n",
       "      <td>0.018516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-13</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>163.28000</td>\n",
       "      <td>21611045</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>161.34000</td>\n",
       "      <td>20709064.0</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>162.09</td>\n",
       "      <td>21375474.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015928</td>\n",
       "      <td>157.58</td>\n",
       "      <td>21844325.0</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>159.03</td>\n",
       "      <td>21156101.0</td>\n",
       "      <td>0.002585</td>\n",
       "      <td>158.62</td>\n",
       "      <td>21099013.0</td>\n",
       "      <td>-0.012452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-14</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>162.13000</td>\n",
       "      <td>23469298</td>\n",
       "      <td>-0.007043</td>\n",
       "      <td>163.28000</td>\n",
       "      <td>21611045.0</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>161.34</td>\n",
       "      <td>20709064.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052.0</td>\n",
       "      <td>0.015928</td>\n",
       "      <td>157.58</td>\n",
       "      <td>21844325.0</td>\n",
       "      <td>-0.009118</td>\n",
       "      <td>159.03</td>\n",
       "      <td>21156101.0</td>\n",
       "      <td>0.002585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-15</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>163.17999</td>\n",
       "      <td>21377516</td>\n",
       "      <td>0.006476</td>\n",
       "      <td>162.13000</td>\n",
       "      <td>23469298.0</td>\n",
       "      <td>-0.007043</td>\n",
       "      <td>163.28</td>\n",
       "      <td>21611045.0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>162.09</td>\n",
       "      <td>21375474.0</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052.0</td>\n",
       "      <td>0.015928</td>\n",
       "      <td>157.58</td>\n",
       "      <td>21844325.0</td>\n",
       "      <td>-0.009118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>MSFT</td>\n",
       "      <td>166.17000</td>\n",
       "      <td>23832244</td>\n",
       "      <td>0.018323</td>\n",
       "      <td>163.17999</td>\n",
       "      <td>21377516.0</td>\n",
       "      <td>0.006476</td>\n",
       "      <td>162.13</td>\n",
       "      <td>23469298.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012024</td>\n",
       "      <td>161.34</td>\n",
       "      <td>20709064.0</td>\n",
       "      <td>-0.004627</td>\n",
       "      <td>162.09</td>\n",
       "      <td>21375474.0</td>\n",
       "      <td>0.012493</td>\n",
       "      <td>160.09</td>\n",
       "      <td>27722052.0</td>\n",
       "      <td>0.015928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date TICKER        PRC       VOL       RET       PRC1        VOL1  \\\n",
       "0  2020-01-10   MSFT  161.34000  20709064 -0.004627  162.09000  21375474.0   \n",
       "1  2020-01-13   MSFT  163.28000  21611045  0.012024  161.34000  20709064.0   \n",
       "2  2020-01-14   MSFT  162.13000  23469298 -0.007043  163.28000  21611045.0   \n",
       "3  2020-01-15   MSFT  163.17999  21377516  0.006476  162.13000  23469298.0   \n",
       "4  2020-01-16   MSFT  166.17000  23832244  0.018323  163.17999  21377516.0   \n",
       "\n",
       "       RET1    PRC2        VOL2  ...      RET3    PRC4        VOL4      RET4  \\\n",
       "0  0.012493  160.09  27722052.0  ... -0.009118  159.03  21156101.0  0.002585   \n",
       "1 -0.004627  162.09  21375474.0  ...  0.015928  157.58  21844325.0 -0.009118   \n",
       "2  0.012024  161.34  20709064.0  ...  0.012493  160.09  27722052.0  0.015928   \n",
       "3 -0.007043  163.28  21611045.0  ... -0.004627  162.09  21375474.0  0.012493   \n",
       "4  0.006476  162.13  23469298.0  ...  0.012024  161.34  20709064.0 -0.004627   \n",
       "\n",
       "     PRC5        VOL5      RET5    PRC6        VOL6      RET6  \n",
       "0  158.62  21099013.0 -0.012452  160.62  22610236.0  0.018516  \n",
       "1  159.03  21156101.0  0.002585  158.62  21099013.0 -0.012452  \n",
       "2  157.58  21844325.0 -0.009118  159.03  21156101.0  0.002585  \n",
       "3  160.09  27722052.0  0.015928  157.58  21844325.0 -0.009118  \n",
       "4  162.09  21375474.0  0.012493  160.09  27722052.0  0.015928  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merged_ns.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5004f283",
   "metadata": {},
   "source": [
    "# Fixed parameter results all stocks (No CV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "881e703b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'WMT'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy (with sentiment scores):\n",
      "0.5070993914807302\n",
      "XGBoost Accuracy (with sentiment scores):\n",
      "0.5165652467883706\n",
      "AdaBoost Accuracy (with sentiment scores):\n",
      "0.5057471264367815\n",
      "Random Forest Accuracy (without sentiment scores):\n",
      "0.5037187288708587\n",
      "XGBoost Accuracy (without sentiment scores):\n",
      "0.504394861392833\n",
      "AdaBoost Accuracy (without sentiment scores):\n",
      "0.49898580121703856\n"
     ]
    }
   ],
   "source": [
    "# make one with sentiment scores and one without\n",
    "#df_merged = df_merged.loc[df_merged['TICKER'].isin(stocklist)]\n",
    "df_merged_s = df_merged.loc[df_merged['TICKER'].isin(stocklist)]\n",
    "df_merged_ns = df_merged_copy.loc[df_merged_copy['TICKER'].isin(stocklist)]\n",
    "\n",
    "from IPython.display import display, clear_output\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "\n",
    "acc_rf = []\n",
    "acc_xg = []\n",
    "acc_ad = []\n",
    "acc_rf_ns = []\n",
    "acc_xg_ns = []\n",
    "acc_ad_ns = []\n",
    "\n",
    "\n",
    "df_master_s = df_merged_s.copy()\n",
    "df_master_ns = df_merged_ns.copy()\n",
    "\n",
    "date_all=np.sort(df_merged_s.date.unique())\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "param_list = {  \n",
    " 'max_depth':[25],       #Candidate for max_depth\n",
    " 'n_estimators':[1000]  #Candidate for n_estimators\n",
    "}\n",
    "param_list_ada = {  \n",
    "  'n_estimators':[10]  #Candidate for n_estimators\n",
    "}\n",
    "\n",
    "\n",
    "for stock in stocklist_list:\n",
    "    \n",
    "    df_merged_s = df_master_s[(df_master_s.TICKER == stock)].copy()#select for specific stock sentiment\n",
    "    df_merged_ns = df_master_ns[(df_master_ns.TICKER == stock)].copy()#select for specific stock without sentiment\n",
    "    \n",
    "    teststart=datetime.fromisoformat(\"2020-10-20\").date()\n",
    "\n",
    "    # Sentiment\n",
    "    train_x = df_merged_s[df_merged_s.date<teststart].loc[:,featurename_s].copy()\n",
    "    train_y = df_merged_s[df_merged_s.date<teststart].loc[:,targetname_s].copy()\n",
    "    train_y=le.fit_transform(train_y>0)\n",
    "    test_x = df_merged_s[df_merged_s.date>=teststart].loc[:,featurename_s].copy()\n",
    "    test_y = df_merged_s[df_merged_s.date>=teststart].loc[:,targetname_s].copy()\n",
    "    test_y=le.transform(test_y>0) \n",
    "   \n",
    "\n",
    "    # No Sentiment\n",
    "    train_x2 = df_merged_ns[df_merged_ns.date<teststart].loc[:,featurename_ns].copy()\n",
    "    train_y2 = df_merged_ns[df_merged_ns.date<teststart].loc[:,targetname_ns].copy()\n",
    "    train_y2 = le.fit_transform(train_y2>0)\n",
    "    test_x2 = df_merged_ns[df_merged_ns.date>=teststart].loc[:,featurename_ns].copy()\n",
    "    test_y2 = df_merged_ns[df_merged_ns.date>=teststart].loc[:,targetname_ns].copy()\n",
    "    test_y2=le.transform(test_y2>0) \n",
    "\n",
    "    \n",
    "    min_max_scaler = preprocessing.MinMaxScaler()\n",
    "    # Sentiment\n",
    "    train_x = min_max_scaler.fit_transform(train_x)\n",
    "    test_x = min_max_scaler.transform(test_x)\n",
    "    # No sentiment\n",
    "    train_x2 = min_max_scaler.fit_transform(train_x2)\n",
    "    test_x2 = min_max_scaler.transform(test_x2)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # Random Forest with sentiment\n",
    "    rf = RandomForestClassifier(n_estimators=1000,   #How many trees in total\n",
    "                            max_depth=25,        #The depth of each tree\n",
    "                            random_state=2022)\n",
    "    rf.fit(train_x, train_y)\n",
    "    df_merged_s.loc[df_merged_s.date>=teststart,\"SignalRF_s\"]=rf.predict_proba(test_x)[:,1]\n",
    "    acc_rf.append(accuracy_score(test_y, rf.predict(test_x)))\n",
    "    \n",
    "    \n",
    "    #Random forest without sentiment\n",
    "    rf2 = RandomForestClassifier(n_estimators=1000,   #How many trees in total\n",
    "                        max_depth=25,        #The depth of each tree\n",
    "                        random_state=2022)\n",
    "    rf2.fit(train_x2, train_y2)\n",
    "    df_merged_ns.loc[df_merged_ns.date>=teststart,\"SignalRF_ns\"]=rf2.predict_proba(test_x2)[:,1]\n",
    "    acc_rf_ns.append(accuracy_score(test_y2, rf2.predict(test_x2)))\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # XGBoost with sentiment\n",
    "    xgb=XGBClassifier(n_estimators=1000,    #How many trees in total\n",
    "          max_depth=25,         #The depth of each tree\n",
    "          nthread=4,           #Number of threads used in parallel computing\n",
    "          random_state=2022)\n",
    "    xgb.fit(train_x, train_y)\n",
    "    df_merged_s.loc[df_merged_s.date>=teststart,\"SignalXG_s\"]=xgb.predict_proba(test_x)[:,1]\n",
    "    acc_xg.append(accuracy_score(test_y,xgb.predict(test_x)))\n",
    "    \n",
    "    \n",
    "    #XGboost without sentiment\n",
    "    xgb2 = XGBClassifier(n_estimators=1000,    #How many trees in total\n",
    "      max_depth=25,         #The depth of each tree\n",
    "      nthread=4,           #Number of threads used in parallel computing\n",
    "      random_state=2022)\n",
    "    xgb2.fit(train_x2, train_y2)\n",
    "    df_merged_ns.loc[df_merged_ns.date>=teststart,\"SignalXG_ns\"]=xgb2.predict_proba(test_x2)[:,1]\n",
    "    acc_xg_ns.append(accuracy_score(test_y2,xgb2.predict(test_x2)))\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #AdaBoost with sentiment\n",
    "    ad = AdaBoostClassifier(n_estimators=10,  #How many trees in total\n",
    "                        random_state=2022)\n",
    "    ad.fit(train_x, train_y)\n",
    "    df_merged_s.loc[df_merged_s.date>=teststart,\"SignalAD_s\"]=ad.predict_proba(test_x)[:,1]\n",
    "    acc_ad.append(accuracy_score(test_y,ad.predict(test_x)))\n",
    "    \n",
    "    \n",
    "    #Adaboost without sentiment\n",
    "    ad2 = AdaBoostClassifier(n_estimators=10,  #How many trees in total\n",
    "                    random_state=2022)\n",
    "    ad2.fit(train_x2, train_y2)\n",
    "    df_merged_ns.loc[df_merged_ns.date>=teststart,\"SignalAD_ns\"]=ad2.predict_proba(test_x2)[:,1]\n",
    "    acc_ad_ns.append(accuracy_score(test_y2,ad2.predict(test_x2)))\n",
    "    \n",
    "    clear_output()\n",
    "    display(stock)\n",
    "\n",
    "    \n",
    "    \n",
    "print(\"Random Forest Accuracy (with sentiment scores):\")\n",
    "print(np.mean(acc_rf))\n",
    "print(\"XGBoost Accuracy (with sentiment scores):\")\n",
    "print(np.mean(acc_xg))\n",
    "print(\"AdaBoost Accuracy (with sentiment scores):\")\n",
    "print(np.mean(acc_ad))\n",
    "\n",
    "print(\"Random Forest Accuracy (without sentiment scores):\")\n",
    "print(np.mean(acc_rf_ns))\n",
    "print(\"XGBoost Accuracy (without sentiment scores):\")\n",
    "print(np.mean(acc_xg_ns))\n",
    "print(\"AdaBoost Accuracy (without sentiment scores):\")\n",
    "print(np.mean(acc_ad_ns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "a4646868",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_scores_s = pd.DataFrame({'stocks':stocklist_list, 'Random Forest':acc_rf,'XGBoost':acc_xg,'AdaBoost':acc_ad})\n",
    "df_all_scores_ns = pd.DataFrame({'stocks':stocklist_list, 'Random Forest':acc_rf_ns,'XGBoost':acc_xg_ns,'AdaBoost':acc_ad_ns})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "id": "dd7166eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "best_fixed = ['AXP','CRM','CVX','INTC','MRK','PG','TRV','VZ']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "02fb8c2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>Random Forest</th>\n",
       "      <th>XGBoost</th>\n",
       "      <th>AdaBoost</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.507099</td>\n",
       "      <td>0.516565</td>\n",
       "      <td>0.505747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.066849</td>\n",
       "      <td>0.068382</td>\n",
       "      <td>0.059837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.450980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.509804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.549020</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.529412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Random Forest    XGBoost   AdaBoost\n",
       "count      29.000000  29.000000  29.000000\n",
       "mean        0.507099   0.516565   0.505747\n",
       "std         0.066849   0.068382   0.059837\n",
       "min         0.352941   0.352941   0.411765\n",
       "25%         0.470588   0.509804   0.450980\n",
       "50%         0.509804   0.529412   0.509804\n",
       "75%         0.549020   0.568627   0.529412\n",
       "max         0.647059   0.647059   0.666667"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_scores_s.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "584d6814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Random Forest</th>\n",
       "      <th>XGBoost</th>\n",
       "      <th>AdaBoost</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.503719</td>\n",
       "      <td>0.504395</td>\n",
       "      <td>0.498986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.070328</td>\n",
       "      <td>0.082008</td>\n",
       "      <td>0.079057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.352941</td>\n",
       "      <td>0.274510</td>\n",
       "      <td>0.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0.470588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.529412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.549020</td>\n",
       "      <td>0.549020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.627451</td>\n",
       "      <td>0.627451</td>\n",
       "      <td>0.607843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Random Forest    XGBoost   AdaBoost\n",
       "count      29.000000  29.000000  29.000000\n",
       "mean        0.503719   0.504395   0.498986\n",
       "std         0.070328   0.082008   0.079057\n",
       "min         0.352941   0.274510   0.294118\n",
       "25%         0.470588   0.490196   0.470588\n",
       "50%         0.509804   0.509804   0.529412\n",
       "75%         0.529412   0.549020   0.549020\n",
       "max         0.627451   0.627451   0.607843"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_scores_ns.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "dba984f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>stocks</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>XGBoost</th>\n",
       "      <th>AdaBoost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AXP</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CRM</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CVX</td>\n",
       "      <td>0.549020</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.549020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>INTC</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MRK</td>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.490196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PG</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.490196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>TRV</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.431373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>VZ</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0.509804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stocks  Random Forest   XGBoost  AdaBoost\n",
       "2     AXP       0.647059  0.568627  0.666667\n",
       "5     CRM       0.588235  0.509804  0.588235\n",
       "7     CVX       0.549020  0.647059  0.549020\n",
       "13   INTC       0.470588  0.568627  0.588235\n",
       "19    MRK       0.509804  0.588235  0.490196\n",
       "22     PG       0.588235  0.588235  0.490196\n",
       "23    TRV       0.588235  0.588235  0.431373\n",
       "26     VZ       0.588235  0.490196  0.509804"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_scores_s[df_all_scores_s.stocks.isin(best_fixed)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "f77029f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\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>stocks</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>XGBoost</th>\n",
       "      <th>AdaBoost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AXP</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.568627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CRM</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0.588235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CVX</td>\n",
       "      <td>0.627451</td>\n",
       "      <td>0.607843</td>\n",
       "      <td>0.549020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>INTC</td>\n",
       "      <td>0.470588</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.490196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MRK</td>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.509804</td>\n",
       "      <td>0.607843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>PG</td>\n",
       "      <td>0.607843</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.529412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>TRV</td>\n",
       "      <td>0.549020</td>\n",
       "      <td>0.627451</td>\n",
       "      <td>0.450980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>VZ</td>\n",
       "      <td>0.627451</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.509804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   stocks  Random Forest   XGBoost  AdaBoost\n",
       "2     AXP       0.588235  0.568627  0.568627\n",
       "5     CRM       0.470588  0.490196  0.588235\n",
       "7     CVX       0.627451  0.607843  0.549020\n",
       "13   INTC       0.470588  0.529412  0.490196\n",
       "19    MRK       0.509804  0.509804  0.607843\n",
       "22     PG       0.607843  0.588235  0.529412\n",
       "23    TRV       0.549020  0.627451  0.450980\n",
       "26     VZ       0.627451  0.568627  0.509804"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all_scores_ns[df_all_scores_ns.stocks.isin(best_fixed)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baf4a9b1",
   "metadata": {},
   "source": [
    "## Models (RF, XGB, ADA) Trained and tested with both Sentiment and Non-sentiment data, cross validation for best parameters testing (ALL STOCK TICKERS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8256df10",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'AXP'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make one with sentiment scores and one without\n",
    "#df_merged = df_merged.loc[df_merged['TICKER'].isin(stocklist)]\n",
    "df_merged_s = df_merged.loc[df_merged['TICKER'].isin(stocklist)]\n",
    "df_merged_ns = df_merged_copy.loc[df_merged_copy['TICKER'].isin(stocklist)]\n",
    "\n",
    "from IPython.display import display, clear_output\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "\n",
    "acc_rf = []\n",
    "acc_xg = []\n",
    "acc_ad = []\n",
    "acc_rf_ns = []\n",
    "acc_xg_ns = []\n",
    "acc_ad_ns = []\n",
    "\n",
    "best_params_rf_s = []\n",
    "best_params_rf_ns = []\n",
    "best_params_xgb_s = []\n",
    "best_params_xgb_ns = []\n",
    "best_params_ada_s = []\n",
    "best_params_ada_ns = []\n",
    "\n",
    "df_master_s = df_merged_s.copy()\n",
    "df_master_ns = df_merged_ns.copy()\n",
    "\n",
    "date_all=np.sort(df_merged_s.date.unique())\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "param_list = {  \n",
    " 'max_depth':[10,25,50,100, 250],       #Candidate for max_depth\n",
    " 'n_estimators':[100,250,500,1000]  #Candidate for n_estimators\n",
    "}\n",
    "param_list_ada = {  \n",
    "  'n_estimators':[10,50, 100, 250, 500]  #Candidate for n_estimators\n",
    "}\n",
    "\n",
    "\n",
    "for stock in stocklist_list:\n",
    "    \n",
    "    df_merged_s = df_master_s[(df_master_s.TICKER == stock)].copy()#select for specific stock sentiment\n",
    "    df_merged_ns = df_master_ns[(df_master_ns.TICKER == stock)].copy()#select for specific stock without sentiment\n",
    "    \n",
    "    teststart=datetime.fromisoformat(\"2020-10-20\").date()\n",
    "\n",
    "    # Sentiment\n",
    "    train_x = df_merged_s[df_merged_s.date<teststart].loc[:,featurename_s].copy()\n",
    "    train_y = df_merged_s[df_merged_s.date<teststart].loc[:,targetname_s].copy()\n",
    "    train_y=le.fit_transform(train_y>0)\n",
    "    test_x = df_merged_s[df_merged_s.date>=teststart].loc[:,featurename_s].copy()\n",
    "    test_y = df_merged_s[df_merged_s.date>=teststart].loc[:,targetname_s].copy()\n",
    "    test_y=le.transform(test_y>0) \n",
    "   \n",
    "\n",
    "    # No Sentiment\n",
    "    train_x2 = df_merged_ns[df_merged_ns.date<teststart].loc[:,featurename_ns].copy()\n",
    "    train_y2 = df_merged_ns[df_merged_ns.date<teststart].loc[:,targetname_ns].copy()\n",
    "    train_y2 = le.fit_transform(train_y2>0)\n",
    "    test_x2 = df_merged_ns[df_merged_ns.date>=teststart].loc[:,featurename_ns].copy()\n",
    "    test_y2 = df_merged_ns[df_merged_ns.date>=teststart].loc[:,targetname_ns].copy()\n",
    "    test_y2=le.transform(test_y2>0) \n",
    "\n",
    "    \n",
    "    min_max_scaler = preprocessing.MinMaxScaler()\n",
    "    # Sentiment\n",
    "    train_x = min_max_scaler.fit_transform(train_x)\n",
    "    test_x = min_max_scaler.transform(test_x)\n",
    "    # No sentiment\n",
    "    train_x2 = min_max_scaler.fit_transform(train_x2)\n",
    "    test_x2 = min_max_scaler.transform(test_x2)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # Random Forest with sentiment\n",
    "    rf = RandomForestClassifier(n_estimators=100,   #How many trees in total\n",
    "                            max_depth=5,        #The depth of each tree\n",
    "                            random_state=2022)\n",
    "    rf = GridSearchCV(estimator = rf, \n",
    "                   param_grid = param_list,\n",
    "                   scoring = 'accuracy',  #The performance metric to select the best parameters.\n",
    "                   cv=3                   #Number of folds, i.e., K\n",
    "                  ) \n",
    "    rf.fit(train_x, train_y)\n",
    "    best_params_rf_s+=[rf.best_params_]\n",
    "    df_merged_s.loc[df_merged_s.date>=teststart,\"SignalRF_s\"]=rf.predict_proba(test_x)[:,1]\n",
    "    acc_rf.append(accuracy_score(test_y, rf.predict(test_x)))\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #Random forest without sentiment\n",
    "    rf2 = RandomForestClassifier(n_estimators=100,   #How many trees in total\n",
    "                        max_depth=5,        #The depth of each tree\n",
    "                        random_state=2022)\n",
    "#     #rf2 = GridSearchCV(estimator = rf2, \n",
    "#                        param_grid = param_list,\n",
    "#                        scoring = 'accuracy',  #The performance metric to select the best parameters.\n",
    "#                        cv=10                   #Number of folds, i.e., K\n",
    "#                       ) \n",
    "    rf2.fit(train_x2, train_y2)\n",
    "    #best_params_rf_ns+=[rf2.best_params_]\n",
    "    df_merged_ns.loc[df_merged_ns.date>=teststart,\"SignalRF_ns\"]=rf2.predict_proba(test_x2)[:,1]\n",
    "    acc_rf_ns.append(accuracy_score(test_y2, rf2.predict(test_x2)))\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    # XGBoost with sentiment\n",
    "    xgb=XGBClassifier(n_estimators=100,    #How many trees in total\n",
    "          max_depth=2,         #The depth of each tree\n",
    "          nthread=4,           #Number of threads used in parallel computing\n",
    "          random_state=2022)\n",
    "    xgb = GridSearchCV(estimator = xgb, \n",
    "                   param_grid = param_list,\n",
    "                   scoring = 'accuracy',  #The performance metric to select the best parameters.\n",
    "                   cv=3                   #Number of folds, i.e., K\n",
    "                  )\n",
    "    xgb.fit(train_x, train_y)\n",
    "    best_params_xgb_s+=[xgb.best_params_]\n",
    "    df_merged_s.loc[df_merged_s.date>=teststart,\"SignalXG_s\"]=xgb.predict_proba(test_x)[:,1]\n",
    "    acc_xg.append(accuracy_score(test_y,xgb.predict(test_x)))\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #XGboost without sentiment\n",
    "    xgb2 = XGBClassifier(n_estimators=100,    #How many trees in total\n",
    "      max_depth=2,         #The depth of each tree\n",
    "      nthread=4,           #Number of threads used in parallel computing\n",
    "      random_state=2022)\n",
    "#     #xgb2 = GridSearchCV(estimator = xgb2, \n",
    "#                    param_grid = param_list,\n",
    "#                    scoring = 'accuracy',  #The performance metric to select the best parameters.\n",
    "#                    cv=10                   #Number of folds, i.e., K\n",
    "#                   ) \n",
    "    xgb2.fit(train_x2, train_y2)\n",
    "    #best_params_xgb_ns+=[xgb2.best_params_]\n",
    "    df_merged_ns.loc[df_merged_ns.date>=teststart,\"SignalXG_ns\"]=xgb2.predict_proba(test_x2)[:,1]\n",
    "    acc_xg_ns.append(accuracy_score(test_y2,xgb2.predict(test_x2)))\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #AdaBoost with sentiment\n",
    "    ad = AdaBoostClassifier(n_estimators=10,  #How many trees in total\n",
    "                        random_state=2022)\n",
    "    ad = GridSearchCV(estimator = ad, \n",
    "                   param_grid = param_list_ada,\n",
    "                   scoring = 'accuracy',  #The performance metric to select the best parameters.\n",
    "                   cv=3                   #Number of folds, i.e., K\n",
    "                  ) \n",
    "    ad.fit(train_x, train_y)\n",
    "    best_params_ada_s+=[ad.best_params_]\n",
    "    df_merged_s.loc[df_merged_s.date>=teststart,\"SignalAD_s\"]=ad.predict_proba(test_x)[:,1]\n",
    "    acc_ad.append(accuracy_score(test_y,ad.predict(test_x)))\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #Adaboost without sentiment\n",
    "    ad2 = AdaBoostClassifier(n_estimators=10,  #How many trees in total\n",
    "                    random_state=2022)\n",
    "#     #ad2 = GridSearchCV(estimator = ad2, \n",
    "#                param_grid = param_list_ada,\n",
    "#                scoring = 'accuracy',  #The performance metric to select the best parameters.\n",
    "#                cv=10                   #Number of folds, i.e., K\n",
    "#               ) \n",
    "    ad2.fit(train_x2, train_y2)\n",
    "    #best_params_ada_ns+=[ad2.best_params_]\n",
    "    df_merged_ns.loc[df_merged_ns.date>=teststart,\"SignalAD_ns\"]=ad2.predict_proba(test_x2)[:,1]\n",
    "    acc_ad_ns.append(accuracy_score(test_y2,ad2.predict(test_x2)))\n",
    "    \n",
    "    clear_output()\n",
    "    display(stock)\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "print(\"Random Forest Accuracy (with sentiment scores):\")\n",
    "print(np.mean(acc_rf))\n",
    "print(\"XGBoost Accuracy (with sentiment scores):\")\n",
    "print(np.mean(acc_xg))\n",
    "print(\"AdaBoost Accuracy (with sentiment scores):\")\n",
    "print(np.mean(acc_ad))\n",
    "\n",
    "print(\"Random Forest Accuracy (without sentiment scores):\")\n",
    "print(np.mean(acc_rf_ns))\n",
    "print(\"XGBoost Accuracy (without sentiment scores):\")\n",
    "print(np.mean(acc_xg_ns))\n",
    "print(\"AdaBoost Accuracy (without sentiment scores):\")\n",
    "print(np.mean(acc_ad_ns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31ad81e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_scores_s = pd.DataFrame({'stocks':stocklist_list, 'Random Forest':acc_rf,'XGBoost':acc_xg,'AdaBoost':acc_ad})\n",
    "df_all_scores_ns = pd.DataFrame({'stocks':stocklist_list, 'Random Forest':acc_rf_ns,'XGBoost':acc_xg_ns,'AdaBoost':acc_ad_ns})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9a34ec3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_scores_s.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c395e44",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all_scores_ns.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a985fc1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_scores_s_cv = ['PG','AXP','CVX','BA','CRM','TRV','VZ','JPM']\n",
    "top_scores_s_cv_df = df_all_scores_s[df_all_scores_s.stocks.isin(top_scores_s_cv)]\n",
    "top_scores_s_cv_ns_df = df_all_scores_ns[df_all_scores_ns.stocks.isin(top_scores_s_cv)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bddb269a",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_scores_s_cv_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b917fdf0",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_scores_s_cv_ns_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e08fd47",
   "metadata": {},
   "source": [
    "### Further correlation analysis: correlations of top performing stocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b49c33fe",
   "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>Ticker</th>\n",
       "      <th>Correlation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRV</td>\n",
       "      <td>0.116415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>CRM</td>\n",
       "      <td>0.054470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>CVX</td>\n",
       "      <td>0.049820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>INTC</td>\n",
       "      <td>0.048343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>AXP</td>\n",
       "      <td>0.041831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>PG</td>\n",
       "      <td>0.025441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>MRK</td>\n",
       "      <td>-0.013581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>VZ</td>\n",
       "      <td>-0.044216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ticker  Correlation\n",
       "4     TRV     0.116415\n",
       "11    CRM     0.054470\n",
       "14    CVX     0.049820\n",
       "15   INTC     0.048343\n",
       "16    AXP     0.041831\n",
       "19     PG     0.025441\n",
       "27    MRK    -0.013581\n",
       "28     VZ    -0.044216"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_best_perf = df_corr_sorted[df_corr_sorted.Ticker.isin(top_scores_s_fixed)]\n",
    "corr_best_perf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c406f38",
   "metadata": {},
   "source": [
    "# Cross Validation accuracy scores for top 5 correlated stocks with sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e263a26d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# make one with sentiment scores and one without\n",
    "#df_merged = df_merged.loc[df_merged['TICKER'].isin(stocklist)]\n",
    "df_merged_s = df_merged.loc[df_merged['TICKER'].isin(stocklist)]\n",
    "df_merged_ns = df_merged_copy.loc[df_merged_copy['TICKER'].isin(stocklist)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "68869312",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TRV'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest Accuracy (with sentiment scores):\n",
      "0.4862745098039216\n",
      "XGBoost Accuracy (with sentiment scores):\n",
      "0.5254901960784314\n",
      "AdaBoost Accuracy (with sentiment scores):\n",
      "0.4980392156862745\n",
      "Random Forest Accuracy (without sentiment scores):\n",
      "0.4666666666666667\n",
      "XGBoost Accuracy (without sentiment scores):\n",
      "0.5058823529411766\n",
      "AdaBoost Accuracy (without sentiment scores):\n",
      "0.5215686274509803\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import display, clear_output\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "min_max_scaler = preprocessing.MinMaxScaler()\n",
    "imputer = SimpleImputer(strategy='mean')\n",
    "\n",
    "acc_rf = []\n",
    "acc_xg = []\n",
    "acc_ad = []\n",
    "acc_rf_ns = []\n",
    "acc_xg_ns = []\n",
    "acc_ad_ns = []\n",
    "\n",
    "best_params_rf_s = []\n",
    "best_params_rf_ns = []\n",
    "best_params_xgb_s = []\n",
    "best_params_xgb_ns = []\n",
    "best_params_ada_s = []\n",
    "best_params_ada_ns = []\n",
    "\n",
    "\n",
    "date_all=np.sort(df_merged_s.date.unique())\n",
    "le = preprocessing.LabelEncoder()\n",
    "\n",
    "param_list = {  \n",
    " 'max_depth':[10,25,50,100],       #Candidate for max_depth\n",
    " 'n_estimators':[100,250,500,1000]  #Candidate for n_estimators\n",
    "}\n",
    "param_list_ada = {  \n",
    "  'n_estimators':[10,50,100,500,1000]  #Candidate for n_estimators\n",
    "}\n",
    "\n",
    "stocklist2 = ['UNH','MMM','NKE','V','TRV']\n",
    "for stock in stocklist2:\n",
    "        \n",
    "    teststart=datetime.fromisoformat(\"2020-10-20\").date()\n",
    "    df_merged_s = df_master_s[(df_master_s.TICKER == stock)].copy()#select for specific stock sentiment\n",
    "    df_merged_ns = df_master_ns[(df_master_ns.TICKER == stock)].copy()#select for specific stock without sentiment\n",
    "\n",
    "    # Sentiment\n",
    "    train_x = df_merged_s[df_merged_s.date<teststart].loc[:,featurename_s].copy()\n",
    "    train_y = df_merged_s[df_merged_s.date<teststart].loc[:,targetname_s].copy()\n",
    "    train_y=le.fit_transform(train_y>0)\n",
    "    test_x = df_merged_s[df_merged_s.date>=teststart].loc[:,featurename_s].copy()\n",
    "    test_y = df_merged_s[df_merged_s.date>=teststart].loc[:,targetname_s].copy()\n",
    "    test_y=le.transform(test_y>0) \n",
    "   \n",
    "\n",
    "    # No Sentiment\n",
    "    train_x2 = df_merged_ns[df_merged_ns.date<teststart].loc[:,featurename_ns].copy()\n",
    "    train_y2 = df_merged_ns[df_merged_ns.date<teststart].loc[:,targetname_ns].copy()\n",
    "    train_y2 = le.fit_transform(train_y2>0)\n",
    "    test_x2 = df_merged_ns[df_merged_ns.date>=teststart].loc[:,featurename_ns].copy()\n",
    "    test_y2 = df_merged_ns[df_merged_ns.date>=teststart].loc[:,targetname_ns].copy()\n",
    "    test_y2=le.transform(test_y2>0) \n",
    "\n",
    "    \n",
    "    min_max_scaler = preprocessing.MinMaxScaler()\n",
    "    # Sentiment\n",
    "    train_x = min_max_scaler.fit_transform(train_x)\n",
    "    test_x = min_max_scaler.transform(test_x)\n",
    "    # No sentiment\n",
    "    train_x2 = min_max_scaler.fit_transform(train_x2)\n",
    "    test_x2 = min_max_scaler.transform(test_x2)\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # Random Forest with sentiment\n",
    "    rf = RandomForestClassifier(n_estimators=100,   #How many trees in total\n",
    "                            max_depth=5,        #The depth of each tree\n",
    "                            random_state=2022)\n",
    "    rf = GridSearchCV(estimator = rf, \n",
    "                   param_grid = param_list,\n",
    "                   scoring = 'accuracy',  #The performance metric to select the best parameters.\n",
    "                   cv=3                   #Number of folds, i.e., K\n",
    "                  ) \n",
    "    rf.fit(train_x, train_y)\n",
    "    best_params_rf_s+=[rf.best_params_]\n",
    "    df_merged_s.loc[df_merged_s.date>=teststart,\"SignalRF_s\"]=rf.predict_proba(test_x)[:,1]\n",
    "    acc_rf.append(accuracy_score(test_y, rf.predict(test_x)))\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #Random forest without sentiment\n",
    "    rf2 = RandomForestClassifier(n_estimators=100,   #How many trees in total\n",
    "                        max_depth=5,        #The depth of each tree\n",
    "                        random_state=2022)\n",
    "#     #rf2 = GridSearchCV(estimator = rf2, \n",
    "#                        param_grid = param_list,\n",
    "#                        scoring = 'accuracy',  #The performance metric to select the best parameters.\n",
    "#                        cv=10                   #Number of folds, i.e., K\n",
    "#                       ) \n",
    "    rf2.fit(train_x2, train_y2)\n",
    "    #best_params_rf_ns+=[rf2.best_params_]\n",
    "    df_merged_ns.loc[df_merged_ns.date>=teststart,\"SignalRF_ns\"]=rf2.predict_proba(test_x2)[:,1]\n",
    "    acc_rf_ns.append(accuracy_score(test_y2, rf2.predict(test_x2)))\n",
    "    \n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    # XGBoost with sentiment\n",
    "    xgb=XGBClassifier(n_estimators=100,    #How many trees in total\n",
    "          max_depth=2,         #The depth of each tree\n",
    "          nthread=4,           #Number of threads used in parallel computing\n",
    "          random_state=2022)\n",
    "    xgb = GridSearchCV(estimator = xgb, \n",
    "                   param_grid = param_list,\n",
    "                   scoring = 'accuracy',  #The performance metric to select the best parameters.\n",
    "                   cv=3                   #Number of folds, i.e., K\n",
    "                  )\n",
    "    xgb.fit(train_x, train_y)\n",
    "    best_params_xgb_s+=[xgb.best_params_]\n",
    "    df_merged_s.loc[df_merged_s.date>=teststart,\"SignalXG_s\"]=xgb.predict_proba(test_x)[:,1]\n",
    "    acc_xg.append(accuracy_score(test_y,xgb.predict(test_x)))\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #XGboost without sentiment\n",
    "    xgb2 = XGBClassifier(n_estimators=100,    #How many trees in total\n",
    "      max_depth=2,         #The depth of each tree\n",
    "      nthread=4,           #Number of threads used in parallel computing\n",
    "      random_state=2022)\n",
    "#     #xgb2 = GridSearchCV(estimator = xgb2, \n",
    "#                    param_grid = param_list,\n",
    "#                    scoring = 'accuracy',  #The performance metric to select the best parameters.\n",
    "#                    cv=10                   #Number of folds, i.e., K\n",
    "#                   ) \n",
    "    xgb2.fit(train_x2, train_y2)\n",
    "    #best_params_xgb_ns+=[xgb2.best_params_]\n",
    "    df_merged_ns.loc[df_merged_ns.date>=teststart,\"SignalXG_ns\"]=xgb2.predict_proba(test_x2)[:,1]\n",
    "    acc_xg_ns.append(accuracy_score(test_y2,xgb2.predict(test_x2)))\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #AdaBoost with sentiment\n",
    "    ad = AdaBoostClassifier(n_estimators=10,  #How many trees in total\n",
    "                        random_state=2022)\n",
    "    ad = GridSearchCV(estimator = ad, \n",
    "                   param_grid = param_list_ada,\n",
    "                   scoring = 'accuracy',  #The performance metric to select the best parameters.\n",
    "                   cv=3                   #Number of folds, i.e., K\n",
    "                  ) \n",
    "    ad.fit(train_x, train_y)\n",
    "    best_params_ada_s+=[ad.best_params_]\n",
    "    df_merged_s.loc[df_merged_s.date>=teststart,\"SignalAD_s\"]=ad.predict_proba(test_x)[:,1]\n",
    "    acc_ad.append(accuracy_score(test_y,ad.predict(test_x)))\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    #Adaboost without sentiment\n",
    "    ad2 = AdaBoostClassifier(n_estimators=100,  #How many trees in total\n",
    "                    random_state=2022)\n",
    "#     #ad2 = GridSearchCV(estimator = ad2, \n",
    "#                param_grid = param_list_ada,\n",
    "#                scoring = 'accuracy',  #The performance metric to select the best parameters.\n",
    "#                cv=10                   #Number of folds, i.e., K\n",
    "#               ) \n",
    "    ad2.fit(train_x2, train_y2)\n",
    "    #best_params_ada_ns+=[ad2.best_params_]\n",
    "    df_merged_ns.loc[df_merged_ns.date>=teststart,\"SignalAD_ns\"]=ad2.predict_proba(test_x2)[:,1]\n",
    "    acc_ad_ns.append(accuracy_score(test_y2,ad2.predict(test_x2)))\n",
    "    \n",
    "    clear_output()\n",
    "    display(stock)\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "print(\"Random Forest Accuracy (with sentiment scores):\")\n",
    "print(np.mean(acc_rf))\n",
    "print(\"XGBoost Accuracy (with sentiment scores):\")\n",
    "print(np.mean(acc_xg))\n",
    "print(\"AdaBoost Accuracy (with sentiment scores):\")\n",
    "print(np.mean(acc_ad))\n",
    "\n",
    "print(\"Random Forest Accuracy (without sentiment scores):\")\n",
    "print(np.mean(acc_rf_ns))\n",
    "print(\"XGBoost Accuracy (without sentiment scores):\")\n",
    "print(np.mean(acc_xg_ns))\n",
    "print(\"AdaBoost Accuracy (without sentiment scores):\")\n",
    "print(np.mean(acc_ad_ns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "5b721285",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_most_correlated_scores = pd.DataFrame({'stocks':stocklist2, 'Random Forest':acc_rf,'XGBoost':acc_xg,'AdaBoost':acc_ad})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "6fbc0063",
   "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>stocks</th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>XGBoost</th>\n",
       "      <th>AdaBoost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UNH</td>\n",
       "      <td>0.431373</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0.470588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MMM</td>\n",
       "      <td>0.529412</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0.470588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NKE</td>\n",
       "      <td>0.450980</td>\n",
       "      <td>0.490196</td>\n",
       "      <td>0.470588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>V</td>\n",
       "      <td>0.450980</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.549020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TRV</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.588235</td>\n",
       "      <td>0.529412</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stocks  Random Forest   XGBoost  AdaBoost\n",
       "0    UNH       0.431373  0.490196  0.470588\n",
       "1    MMM       0.529412  0.490196  0.470588\n",
       "2    NKE       0.450980  0.490196  0.470588\n",
       "3      V       0.450980  0.568627  0.549020\n",
       "4    TRV       0.568627  0.588235  0.529412"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_most_correlated_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2940ee7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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