1. Data Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stock Portfolio Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Goal: Create a machine learning-driven portfolio optimization system for stocks, dynamically adjusting allocations to maximize returns and minimize risks based on predictive analytics. Set to be used by the Investing Association in Fall 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Stocks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import yfinance as yf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dow_30 = [\n",
    "    \"AAPL\", \"AMGN\", \"AXP\", \"BA\", \"CAT\", \"CRM\", \"CSCO\", \"CVX\", \n",
    "    \"DIS\", \"DOW\", \"GS\", \"HD\", \"HON\", \"IBM\", \"INTC\", \"JNJ\", \n",
    "    \"JPM\", \"MCD\", \"MMM\", \"MRK\", \"MSFT\", \"NKE\", \"PG\", \"TRV\", \n",
    "    \"UNH\", \"V\", \"VZ\", \"WBA\", \"WMT\", \"XOM\"\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### yfinance (historical data from Yahoo Finance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for AAPL...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for AMGN...\n",
      "Fetching data for AXP...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for BA...\n",
      "Fetching data for CAT...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for CRM...\n",
      "Fetching data for CSCO...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for CVX...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for DIS...\n",
      "Fetching data for DOW...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for GS...\n",
      "Fetching data for HD...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for HON...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for IBM...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for INTC...\n",
      "Fetching data for JNJ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for JPM...\n",
      "Fetching data for MCD...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for MMM...\n",
      "Fetching data for MRK...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for MSFT...\n",
      "Fetching data for NKE...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for PG...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for TRV...\n",
      "Fetching data for UNH...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for V...\n",
      "Fetching data for VZ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for WBA...\n",
      "Fetching data for WMT...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed\n",
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching data for XOM...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "# Specify the symbols for the cryptocurrencies you are interested in\n",
    "symbols = [f\"{coin}\" for coin in dow_30]  \n",
    "\n",
    "\n",
    "df = pd.DataFrame()\n",
    "\n",
    "# Specify the date range\n",
    "start_date = '2014-09-1'\n",
    "end_date = '2024-05-17'\n",
    "\n",
    "# Fetch the historical data for each symbol\n",
    "for symbol in symbols:\n",
    "    print(f\"Fetching data for {symbol}...\")\n",
    "    data = yf.download(symbol, start=start_date, end=end_date)\n",
    "    if not data.empty:\n",
    "        data['Symbol'] = symbol.replace('-USD', '')  \n",
    "        df = pd.concat([df, data], axis=0)\n",
    "\n",
    "\n",
    "df.reset_index(inplace=True)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
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       "      <th>Symbol</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-09-02</td>\n",
       "      <td>25.764999</td>\n",
       "      <td>25.934999</td>\n",
       "      <td>25.680000</td>\n",
       "      <td>25.825001</td>\n",
       "      <td>22.956745</td>\n",
       "      <td>214256000</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-09-03</td>\n",
       "      <td>25.775000</td>\n",
       "      <td>25.799999</td>\n",
       "      <td>24.645000</td>\n",
       "      <td>24.735001</td>\n",
       "      <td>21.987804</td>\n",
       "      <td>501684000</td>\n",
       "      <td>AAPL</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-09-04</td>\n",
       "      <td>24.712500</td>\n",
       "      <td>25.022499</td>\n",
       "      <td>24.447500</td>\n",
       "      <td>24.530001</td>\n",
       "      <td>21.805578</td>\n",
       "      <td>342872000</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-05</td>\n",
       "      <td>24.700001</td>\n",
       "      <td>24.847500</td>\n",
       "      <td>24.577499</td>\n",
       "      <td>24.742500</td>\n",
       "      <td>21.994474</td>\n",
       "      <td>233828000</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-08</td>\n",
       "      <td>24.825001</td>\n",
       "      <td>24.827499</td>\n",
       "      <td>24.512501</td>\n",
       "      <td>24.590000</td>\n",
       "      <td>21.858906</td>\n",
       "      <td>185426800</td>\n",
       "      <td>AAPL</td>\n",
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       "      <th>72171</th>\n",
       "      <td>2024-05-10</td>\n",
       "      <td>118.540001</td>\n",
       "      <td>118.660004</td>\n",
       "      <td>117.580002</td>\n",
       "      <td>117.959999</td>\n",
       "      <td>117.009598</td>\n",
       "      <td>13648100</td>\n",
       "      <td>XOM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72172</th>\n",
       "      <td>2024-05-13</td>\n",
       "      <td>118.419998</td>\n",
       "      <td>119.040001</td>\n",
       "      <td>117.120003</td>\n",
       "      <td>117.910004</td>\n",
       "      <td>116.960007</td>\n",
       "      <td>15060600</td>\n",
       "      <td>XOM</td>\n",
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       "    <tr>\n",
       "      <th>72173</th>\n",
       "      <td>2024-05-14</td>\n",
       "      <td>116.790001</td>\n",
       "      <td>117.739998</td>\n",
       "      <td>116.480003</td>\n",
       "      <td>117.669998</td>\n",
       "      <td>117.669998</td>\n",
       "      <td>15079100</td>\n",
       "      <td>XOM</td>\n",
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       "    <tr>\n",
       "      <th>72174</th>\n",
       "      <td>2024-05-15</td>\n",
       "      <td>117.610001</td>\n",
       "      <td>118.839996</td>\n",
       "      <td>116.080002</td>\n",
       "      <td>118.580002</td>\n",
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       "      <td>18244300</td>\n",
       "      <td>XOM</td>\n",
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       "    <tr>\n",
       "      <th>72175</th>\n",
       "      <td>2024-05-16</td>\n",
       "      <td>118.540001</td>\n",
       "      <td>119.300003</td>\n",
       "      <td>117.540001</td>\n",
       "      <td>117.870003</td>\n",
       "      <td>117.870003</td>\n",
       "      <td>15745200</td>\n",
       "      <td>XOM</td>\n",
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       "<p>72176 rows × 8 columns</p>\n",
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       "            Date        Open        High         Low       Close   Adj Close  \\\n",
       "0     2014-09-02   25.764999   25.934999   25.680000   25.825001   22.956745   \n",
       "1     2014-09-03   25.775000   25.799999   24.645000   24.735001   21.987804   \n",
       "2     2014-09-04   24.712500   25.022499   24.447500   24.530001   21.805578   \n",
       "3     2014-09-05   24.700001   24.847500   24.577499   24.742500   21.994474   \n",
       "4     2014-09-08   24.825001   24.827499   24.512501   24.590000   21.858906   \n",
       "...          ...         ...         ...         ...         ...         ...   \n",
       "72171 2024-05-10  118.540001  118.660004  117.580002  117.959999  117.009598   \n",
       "72172 2024-05-13  118.419998  119.040001  117.120003  117.910004  116.960007   \n",
       "72173 2024-05-14  116.790001  117.739998  116.480003  117.669998  117.669998   \n",
       "72174 2024-05-15  117.610001  118.839996  116.080002  118.580002  118.580002   \n",
       "72175 2024-05-16  118.540001  119.300003  117.540001  117.870003  117.870003   \n",
       "\n",
       "          Volume Symbol  \n",
       "0      214256000   AAPL  \n",
       "1      501684000   AAPL  \n",
       "2      342872000   AAPL  \n",
       "3      233828000   AAPL  \n",
       "4      185426800   AAPL  \n",
       "...          ...    ...  \n",
       "72171   13648100    XOM  \n",
       "72172   15060600    XOM  \n",
       "72173   15079100    XOM  \n",
       "72174   18244300    XOM  \n",
       "72175   15745200    XOM  \n",
       "\n",
       "[72176 rows x 8 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
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       "      <th>Symbol</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-09-02</td>\n",
       "      <td>25.764999</td>\n",
       "      <td>25.934999</td>\n",
       "      <td>25.680000</td>\n",
       "      <td>25.825001</td>\n",
       "      <td>22.956745</td>\n",
       "      <td>214256000</td>\n",
       "      <td>AAPL</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-09-03</td>\n",
       "      <td>25.775000</td>\n",
       "      <td>25.799999</td>\n",
       "      <td>24.645000</td>\n",
       "      <td>24.735001</td>\n",
       "      <td>21.987804</td>\n",
       "      <td>501684000</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-09-04</td>\n",
       "      <td>24.712500</td>\n",
       "      <td>25.022499</td>\n",
       "      <td>24.447500</td>\n",
       "      <td>24.530001</td>\n",
       "      <td>21.805578</td>\n",
       "      <td>342872000</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-05</td>\n",
       "      <td>24.700001</td>\n",
       "      <td>24.847500</td>\n",
       "      <td>24.577499</td>\n",
       "      <td>24.742500</td>\n",
       "      <td>21.994474</td>\n",
       "      <td>233828000</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-08</td>\n",
       "      <td>24.825001</td>\n",
       "      <td>24.827499</td>\n",
       "      <td>24.512501</td>\n",
       "      <td>24.590000</td>\n",
       "      <td>21.858906</td>\n",
       "      <td>185426800</td>\n",
       "      <td>AAPL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "        Date       Open       High        Low      Close  Adj Close  \\\n",
       "0 2014-09-02  25.764999  25.934999  25.680000  25.825001  22.956745   \n",
       "1 2014-09-03  25.775000  25.799999  24.645000  24.735001  21.987804   \n",
       "2 2014-09-04  24.712500  25.022499  24.447500  24.530001  21.805578   \n",
       "3 2014-09-05  24.700001  24.847500  24.577499  24.742500  21.994474   \n",
       "4 2014-09-08  24.825001  24.827499  24.512501  24.590000  21.858906   \n",
       "\n",
       "      Volume Symbol  \n",
       "0  214256000   AAPL  \n",
       "1  501684000   AAPL  \n",
       "2  342872000   AAPL  \n",
       "3  233828000   AAPL  \n",
       "4  185426800   AAPL  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
    "df = df.rename(columns={'Volume': 'Volume($)'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date         0\n",
       "Open         0\n",
       "High         0\n",
       "Low          0\n",
       "Close        0\n",
       "Adj Close    0\n",
       "Volume($)    0\n",
       "Symbol       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "\n",
    "# Calculate daily returns for each stock\n",
    "df['Daily Return'] = df.groupby('Symbol')['Close'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date             0\n",
       "Open             0\n",
       "High             0\n",
       "Low              0\n",
       "Close            0\n",
       "Adj Close        0\n",
       "Volume($)        0\n",
       "Symbol           0\n",
       "Daily Return    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Symbol</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Symbol, Close]\n",
       "Index: []"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered_date = df[df['Date'] == '2024-01-01']\n",
    "\n",
    "df_sorted_by_close = df_filtered_date.sort_values(by='Close', ascending=False)\n",
    "\n",
    "df_sorted_by_close[['Symbol', 'Close']]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['AAPL', 'AMGN', 'AXP', 'BA', 'CAT', 'CRM', 'CSCO', 'CVX', 'DIS',\n",
       "       'DOW', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'JPM', 'MCD',\n",
       "       'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'V', 'VZ', 'WBA',\n",
       "       'WMT', 'XOM'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Symbol'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume($)</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Daily Return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-09-02</td>\n",
       "      <td>25.764999</td>\n",
       "      <td>25.934999</td>\n",
       "      <td>25.680000</td>\n",
       "      <td>25.825001</td>\n",
       "      <td>22.956745</td>\n",
       "      <td>214256000</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-09-03</td>\n",
       "      <td>25.775000</td>\n",
       "      <td>25.799999</td>\n",
       "      <td>24.645000</td>\n",
       "      <td>24.735001</td>\n",
       "      <td>21.987804</td>\n",
       "      <td>501684000</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>-0.042207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-09-04</td>\n",
       "      <td>24.712500</td>\n",
       "      <td>25.022499</td>\n",
       "      <td>24.447500</td>\n",
       "      <td>24.530001</td>\n",
       "      <td>21.805578</td>\n",
       "      <td>342872000</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>-0.008288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-09-05</td>\n",
       "      <td>24.700001</td>\n",
       "      <td>24.847500</td>\n",
       "      <td>24.577499</td>\n",
       "      <td>24.742500</td>\n",
       "      <td>21.994474</td>\n",
       "      <td>233828000</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>0.008663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-09-08</td>\n",
       "      <td>24.825001</td>\n",
       "      <td>24.827499</td>\n",
       "      <td>24.512501</td>\n",
       "      <td>24.590000</td>\n",
       "      <td>21.858906</td>\n",
       "      <td>185426800</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>-0.006163</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date       Open       High        Low      Close  Adj Close  \\\n",
       "0 2014-09-02  25.764999  25.934999  25.680000  25.825001  22.956745   \n",
       "1 2014-09-03  25.775000  25.799999  24.645000  24.735001  21.987804   \n",
       "2 2014-09-04  24.712500  25.022499  24.447500  24.530001  21.805578   \n",
       "3 2014-09-05  24.700001  24.847500  24.577499  24.742500  21.994474   \n",
       "4 2014-09-08  24.825001  24.827499  24.512501  24.590000  21.858906   \n",
       "\n",
       "   Volume($) Symbol  Daily Return  \n",
       "0  214256000   AAPL           NaN  \n",
       "1  501684000   AAPL     -0.042207  \n",
       "2  342872000   AAPL     -0.008288  \n",
       "3  233828000   AAPL      0.008663  \n",
       "4  185426800   AAPL     -0.006163  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('data/dow_30.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<details>\n",
       "<summary>Click to view session information</summary>\n",
       "<pre>\n",
       "-----\n",
       "pandas              2.2.2\n",
       "requests            2.31.0\n",
       "session_info        1.0.0\n",
       "yfinance            0.2.38\n",
       "-----\n",
       "</pre>\n",
       "<details>\n",
       "<summary>Click to view modules imported as dependencies</summary>\n",
       "<pre>\n",
       "anyio                       NA\n",
       "appdirs                     1.4.4\n",
       "asttokens                   NA\n",
       "attr                        23.1.0\n",
       "attrs                       23.1.0\n",
       "babel                       2.11.0\n",
       "bottleneck                  1.3.7\n",
       "brotli                      1.0.9\n",
       "bs4                         4.12.2\n",
       "certifi                     2024.02.02\n",
       "chardet                     4.0.0\n",
       "charset_normalizer          2.0.4\n",
       "cloudpickle                 2.2.1\n",
       "colorama                    0.4.6\n",
       "comm                        0.2.1\n",
       "cython_runtime              NA\n",
       "dateutil                    2.8.2\n",
       "debugpy                     1.6.7\n",
       "decorator                   5.1.1\n",
       "executing                   0.8.3\n",
       "fastjsonschema              NA\n",
       "frozendict                  2.4.4\n",
       "google                      NA\n",
       "html5lib                    1.1\n",
       "idna                        3.4\n",
       "ipykernel                   6.28.0\n",
       "ipython_genutils            0.2.0\n",
       "ipywidgets                  7.8.1\n",
       "jedi                        0.18.1\n",
       "jinja2                      3.1.4\n",
       "json5                       NA\n",
       "jsonpointer                 2.4\n",
       "jsonschema                  4.19.2\n",
       "jsonschema_specifications   NA\n",
       "jupyter_events              0.8.0\n",
       "jupyter_server              2.10.0\n",
       "jupyterlab_server           2.25.1\n",
       "lxml                        4.9.3\n",
       "markupsafe                  2.1.3\n",
       "multitasking                0.0.11\n",
       "nbformat                    5.9.2\n",
       "numexpr                     2.8.7\n",
       "numpy                       1.26.2\n",
       "orjson                      3.10.3\n",
       "overrides                   NA\n",
       "packaging                   23.2\n",
       "parso                       0.8.3\n",
       "peewee                      3.17.5\n",
       "pickleshare                 0.7.5\n",
       "pkg_resources               NA\n",
       "platformdirs                3.10.0\n",
       "prometheus_client           NA\n",
       "prompt_toolkit              3.0.43\n",
       "psutil                      5.9.0\n",
       "pure_eval                   0.2.2\n",
       "pyarrow                     13.0.0\n",
       "pydev_ipython               NA\n",
       "pydevconsole                NA\n",
       "pydevd                      2.9.5\n",
       "pydevd_file_utils           NA\n",
       "pydevd_plugins              NA\n",
       "pydevd_tracing              NA\n",
       "pygments                    2.15.1\n",
       "pythoncom                   NA\n",
       "pythonjsonlogger            NA\n",
       "pytz                        2023.3.post1\n",
       "pywintypes                  NA\n",
       "referencing                 NA\n",
       "rfc3339_validator           0.1.4\n",
       "rfc3986_validator           0.1.1\n",
       "rpds                        NA\n",
       "send2trash                  NA\n",
       "simplejson                  3.19.2\n",
       "six                         1.16.0\n",
       "sniffio                     1.3.0\n",
       "socks                       1.7.1\n",
       "soupsieve                   2.5\n",
       "sphinxcontrib               NA\n",
       "stack_data                  0.2.0\n",
       "tornado                     6.3.3\n",
       "traitlets                   5.7.1\n",
       "typing_extensions           NA\n",
       "tzdata                      2023.3\n",
       "urllib3                     2.0.3\n",
       "vscode                      NA\n",
       "wcwidth                     0.2.5\n",
       "webencodings                0.5.1\n",
       "websocket                   0.57.0\n",
       "win32api                    NA\n",
       "win32com                    NA\n",
       "win32con                    NA\n",
       "win32trace                  NA\n",
       "winerror                    NA\n",
       "yaml                        6.0.1\n",
       "zmq                         25.1.2\n",
       "zope                        NA\n",
       "</pre>\n",
       "</details> <!-- seems like this ends pre, so might as well be explicit -->\n",
       "<pre>\n",
       "-----\n",
       "IPython             8.20.0\n",
       "jupyter_client      8.6.0\n",
       "jupyter_core        5.5.0\n",
       "jupyterlab          4.0.11\n",
       "notebook            7.0.8\n",
       "-----\n",
       "Python 3.12.3 | packaged by conda-forge | (main, Apr 15 2024, 18:20:11) [MSC v.1938 64 bit (AMD64)]\n",
       "Windows-10-10.0.19045-SP0\n",
       "-----\n",
       "Session information updated at 2024-07-19 19:14\n",
       "</pre>\n",
       "</details>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import session_info\n",
    "session_info.show()"
   ]
  }
 ],
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