getting-started-yahoo-finance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8c09e7e6-641d-4e09-b3f3-30547de9cb23",
   "metadata": {},
   "source": [
    "# The `yfinance API` (***Yahoo!*** Finance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "53e48a31-b558-4fee-a66c-40a20c442447",
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from pprint import pprint \n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "37a3c3f0-dec1-4b68-a5bc-cb670cb5dbf0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let's look into Apple Inc: \"AAPL\" (ticker symbol)\n"
     ]
    }
   ],
   "source": [
    "# Create a Ticker object using ticker symbol: Apple Inc \n",
    "print(\"Let's explore Apple Inc: \\\"AAPL\\\" (ticker symbol)\")\n",
    "ticker = yf.Ticker(\"AAPL\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e045e3f8-7785-4815-8f9b-d776c6f2b9df",
   "metadata": {},
   "source": [
    "# 1) Historical Market data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b79b3095-e4de-4d6d-bb13-ea6613aea8e6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Historical Data Columns:\n",
      "['Open', 'High', 'Low', 'Close', 'Volume', 'Dividends', 'Stock Splits']\n"
     ]
    }
   ],
   "source": [
    "data_period = \"1y\"\n",
    "\n",
    "# Fetch historical market data\n",
    "historical_data_df = ticker.history(period=data_period)  # data for the last year\n",
    "print(\"Historical Data Columns:\")\n",
    "pprint(list(historical_data_df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "97cbdb15-0f50-4988-9f14-b2396ab56018",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Historical data date indexes (indexes associated to each row)\n",
      "DatetimeIndex(['2024-02-12 00:00:00-05:00', '2024-02-13 00:00:00-05:00',\n",
      "               '2024-02-14 00:00:00-05:00', '2024-02-15 00:00:00-05:00',\n",
      "               '2024-02-16 00:00:00-05:00', '2024-02-20 00:00:00-05:00',\n",
      "               '2024-02-21 00:00:00-05:00', '2024-02-22 00:00:00-05:00',\n",
      "               '2024-02-23 00:00:00-05:00', '2024-02-26 00:00:00-05:00',\n",
      "               ...\n",
      "               '2025-01-28 00:00:00-05:00', '2025-01-29 00:00:00-05:00',\n",
      "               '2025-01-30 00:00:00-05:00', '2025-01-31 00:00:00-05:00',\n",
      "               '2025-02-03 00:00:00-05:00', '2025-02-04 00:00:00-05:00',\n",
      "               '2025-02-05 00:00:00-05:00', '2025-02-06 00:00:00-05:00',\n",
      "               '2025-02-07 00:00:00-05:00', '2025-02-10 00:00:00-05:00'],\n",
      "              dtype='datetime64[ns, America/New_York]', name='Date', length=250, freq=None)\n"
     ]
    }
   ],
   "source": [
    "# Dates\n",
    "print(\"Historical data date indexes (indexes associated to each row)\")\n",
    "print(historical_data_df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e732a1ef-4209-4edd-9816-15a4de7b5401",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Historical data dataframe\n"
     ]
    },
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-02-12 00:00:00-05:00</th>\n",
       "      <td>187.740676</td>\n",
       "      <td>187.989774</td>\n",
       "      <td>186.116548</td>\n",
       "      <td>186.475250</td>\n",
       "      <td>41781900</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2024-02-13 00:00:00-05:00</th>\n",
       "      <td>185.100243</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-14 00:00:00-05:00</th>\n",
       "      <td>184.651864</td>\n",
       "      <td>184.861098</td>\n",
       "      <td>181.782242</td>\n",
       "      <td>183.486069</td>\n",
       "      <td>54630500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-15 00:00:00-05:00</th>\n",
       "      <td>182.888248</td>\n",
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       "      <td>183.197128</td>\n",
       "      <td>65434500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-02-16 00:00:00-05:00</th>\n",
       "      <td>182.758709</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Open        High         Low       Close  \\\n",
       "Date                                                                        \n",
       "2024-02-12 00:00:00-05:00  187.740676  187.989774  186.116548  186.475250   \n",
       "2024-02-13 00:00:00-05:00  185.100243  185.538659  182.848381  184.372864   \n",
       "2024-02-14 00:00:00-05:00  184.651864  184.861098  181.782242  183.486069   \n",
       "2024-02-15 00:00:00-05:00  182.888248  183.824862  180.696183  183.197128   \n",
       "2024-02-16 00:00:00-05:00  182.758709  184.183561  181.015018  181.652710   \n",
       "\n",
       "                             Volume  Dividends  Stock Splits  \n",
       "Date                                                          \n",
       "2024-02-12 00:00:00-05:00  41781900        0.0           0.0  \n",
       "2024-02-13 00:00:00-05:00  56529500        0.0           0.0  \n",
       "2024-02-14 00:00:00-05:00  54630500        0.0           0.0  \n",
       "2024-02-15 00:00:00-05:00  65434500        0.0           0.0  \n",
       "2024-02-16 00:00:00-05:00  49701400        0.0           0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Historical data dataframe\")\n",
    "display(historical_data_df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9ecd630-7c07-44f4-89b4-aaf6e29b4de7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Historical data line plots\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Historical data line plots\")\n",
    "ax1 = historical_data_df.plot.line(subplots=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53a59157-c0c6-4660-9f57-4be966c50a6e",
   "metadata": {},
   "source": [
    "### 1a) Getting \"Open\" Prices \n",
    "\n",
    "- similarly, this technique can be applied to \"High\", \"Low\", \"Close\", \"Volume\", \"Dividends\", \"Stock Splits\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0c37e7f1-6c6d-41b6-aee7-d0bb9c65ee65",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Historical data: Querying \"Open\" princes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2024-02-12 00:00:00-05:00    187.740676\n",
       "2024-02-13 00:00:00-05:00    185.100243\n",
       "2024-02-14 00:00:00-05:00    184.651864\n",
       "2024-02-15 00:00:00-05:00    182.888248\n",
       "2024-02-16 00:00:00-05:00    182.758709\n",
       "                                ...    \n",
       "2025-02-04 00:00:00-05:00    227.250000\n",
       "2025-02-05 00:00:00-05:00    228.529999\n",
       "2025-02-06 00:00:00-05:00    231.289993\n",
       "2025-02-07 00:00:00-05:00    232.600006\n",
       "2025-02-10 00:00:00-05:00    229.570007\n",
       "Name: Open, Length: 250, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Historical data: Querying \\\"Open\\\" princes\")\n",
    "historical_data_df[\"Open\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63af3aef-abc2-4f88-a339-cb63467e3d23",
   "metadata": {},
   "source": [
    "### 1b) Getting historical data at a specified date using indexing  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4e78b394-1097-4d0f-bda8-0d2559e620f5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indexing into historical data to get all historical features:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Open            2.295700e+02\n",
       "High            2.305850e+02\n",
       "Low             2.272000e+02\n",
       "Close           2.276500e+02\n",
       "Volume          3.162892e+07\n",
       "Dividends       0.000000e+00\n",
       "Stock Splits    0.000000e+00\n",
       "Name: 2025-02-10 00:00:00-05:00, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Indexing into historical data to get all historical features:\")\n",
    "historical_data_df.loc[\"2025-02-10\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "05f6a058-f33c-45b9-8e7a-4c91176cf010",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Today's (2025-02-10) data\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Open            2.295700e+02\n",
       "High            2.305850e+02\n",
       "Low             2.272000e+02\n",
       "Close           2.276500e+02\n",
       "Volume          3.162892e+07\n",
       "Dividends       0.000000e+00\n",
       "Stock Splits    0.000000e+00\n",
       "Name: 2025-02-10 00:00:00-05:00, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(f\"Today's ({date.today()}) data\")\n",
    "historical_data_df.loc[str(date.today())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b6dca02-1e2e-4e5b-aee4-3317beffcd2f",
   "metadata": {},
   "source": [
    "# 2) Financial data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "690c10fa-42bd-4c18-b151-1242ecaf1802",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Basic Financial Metrics:\n",
      "\n",
      "['Tax Effect Of Unusual Items',\n",
      " 'Tax Rate For Calcs',\n",
      " 'Normalized EBITDA',\n",
      " 'Net Income From Continuing Operation Net Minority Interest',\n",
      " 'Reconciled Depreciation',\n",
      " 'Reconciled Cost Of Revenue',\n",
      " 'EBITDA',\n",
      " 'EBIT',\n",
      " 'Net Interest Income',\n",
      " 'Interest Expense',\n",
      " 'Interest Income',\n",
      " 'Normalized Income',\n",
      " 'Net Income From Continuing And Discontinued Operation',\n",
      " 'Total Expenses',\n",
      " 'Total Operating Income As Reported',\n",
      " 'Diluted Average Shares',\n",
      " 'Basic Average Shares',\n",
      " 'Diluted EPS',\n",
      " 'Basic EPS',\n",
      " 'Diluted NI Availto Com Stockholders',\n",
      " 'Net Income Common Stockholders',\n",
      " 'Net Income',\n",
      " 'Net Income Including Noncontrolling Interests',\n",
      " 'Net Income Continuous Operations',\n",
      " 'Tax Provision',\n",
      " 'Pretax Income',\n",
      " 'Other Income Expense',\n",
      " 'Other Non Operating Income Expenses',\n",
      " 'Net Non Operating Interest Income Expense',\n",
      " 'Interest Expense Non Operating',\n",
      " 'Interest Income Non Operating',\n",
      " 'Operating Income',\n",
      " 'Operating Expense',\n",
      " 'Research And Development',\n",
      " 'Selling General And Administration',\n",
      " 'Gross Profit',\n",
      " 'Cost Of Revenue',\n",
      " 'Total Revenue',\n",
      " 'Operating Revenue']\n"
     ]
    }
   ],
   "source": [
    "# Fetch basic financials\n",
    "financials_df = ticker.financials\n",
    "print(\"Basic Financial Metrics:\\n\")\n",
    "pprint(list(financials_df.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "16682d63-09d6-437f-a956-f843f68fafa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Financials data frame:\n"
     ]
    },
    {
     "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>2024-09-30</th>\n",
       "      <th>2023-09-30</th>\n",
       "      <th>2022-09-30</th>\n",
       "      <th>2021-09-30</th>\n",
       "      <th>2020-09-30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Tax Effect Of Unusual Items</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tax Rate For Calcs</th>\n",
       "      <td>0.241</td>\n",
       "      <td>0.147</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.133</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normalized EBITDA</th>\n",
       "      <td>134661000000.0</td>\n",
       "      <td>125820000000.0</td>\n",
       "      <td>130541000000.0</td>\n",
       "      <td>123136000000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Net Income From Continuing Operation Net Minority Interest</th>\n",
       "      <td>93736000000.0</td>\n",
       "      <td>96995000000.0</td>\n",
       "      <td>99803000000.0</td>\n",
       "      <td>94680000000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Reconciled Depreciation</th>\n",
       "      <td>11445000000.0</td>\n",
       "      <td>11519000000.0</td>\n",
       "      <td>11104000000.0</td>\n",
       "      <td>11284000000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        2024-09-30  \\\n",
       "Tax Effect Of Unusual Items                                    0.0   \n",
       "Tax Rate For Calcs                                           0.241   \n",
       "Normalized EBITDA                                   134661000000.0   \n",
       "Net Income From Continuing Operation Net Minori...   93736000000.0   \n",
       "Reconciled Depreciation                              11445000000.0   \n",
       "\n",
       "                                                        2023-09-30  \\\n",
       "Tax Effect Of Unusual Items                                    0.0   \n",
       "Tax Rate For Calcs                                           0.147   \n",
       "Normalized EBITDA                                   125820000000.0   \n",
       "Net Income From Continuing Operation Net Minori...   96995000000.0   \n",
       "Reconciled Depreciation                              11519000000.0   \n",
       "\n",
       "                                                        2022-09-30  \\\n",
       "Tax Effect Of Unusual Items                                    0.0   \n",
       "Tax Rate For Calcs                                           0.162   \n",
       "Normalized EBITDA                                   130541000000.0   \n",
       "Net Income From Continuing Operation Net Minori...   99803000000.0   \n",
       "Reconciled Depreciation                              11104000000.0   \n",
       "\n",
       "                                                        2021-09-30 2020-09-30  \n",
       "Tax Effect Of Unusual Items                                    0.0        NaN  \n",
       "Tax Rate For Calcs                                           0.133        NaN  \n",
       "Normalized EBITDA                                   123136000000.0        NaN  \n",
       "Net Income From Continuing Operation Net Minori...   94680000000.0        NaN  \n",
       "Reconciled Depreciation                              11284000000.0        NaN  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"\\nFinancials data frame:\")\n",
    "display(financials_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0706cf-7f5f-43c6-857a-fd6360b1d80c",
   "metadata": {},
   "source": [
    "# 3) Stock Actions data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "64999f4a-6539-4020-a81c-35afe9fe1e24",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Stock Actions dataframe:\n"
     ]
    },
    {
     "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>Dividends</th>\n",
       "      <th>Stock Splits</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-05-10 00:00:00-04:00</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-08-12 00:00:00-04:00</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-08 00:00:00-05:00</th>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           Dividends  Stock Splits\n",
       "Date                                              \n",
       "2024-05-10 00:00:00-04:00       0.25           0.0\n",
       "2024-08-12 00:00:00-04:00       0.25           0.0\n",
       "2024-11-08 00:00:00-05:00       0.25           0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Fetch stock actions like dividends and splits\n",
    "actions_df = ticker.actions\n",
    "print(\"\\nStock Actions dataframe:\")\n",
    "display(actions_df.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6991e90-4f1b-493f-833b-c4d91ce46a17",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Glossary "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4589fe05-d7f1-4e65-93aa-29753f59e73c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### ⭐ Glossary of Historical data metrics:\n",
    "    \n",
    "1️⃣ Open → The stock price at the start of the trading day.\n",
    "\n",
    "2️⃣ High → The highest price the stock reached during the trading day.\n",
    "\n",
    "3️⃣ Low → The lowest price the stock traded at during the day.\n",
    "\n",
    "4️⃣ Close → The stock price at the end of the trading day (most commonly used for analysis).\n",
    "\n",
    "5️⃣ Volume → The total number of shares traded during the day (indicates liquidity and market activity).\n",
    "\n",
    "6️⃣ Dividends → The amount paid per share to shareholders, usually quarterly (if applicable).\n",
    "\n",
    "7️⃣ Stock Splits → The ratio of a stock split (e.g., 2-for-1 means each share becomes two, reducing price per share)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1912f7a9-2c3c-4cf7-829d-df4766baa7b0",
   "metadata": {},
   "source": [
    "### ⭐ Glossary of Financial metrics:\n",
    "    \n",
    "1️⃣ Tax Effect Of Unusual Items → Tax impact of non-recurring or unusual expenses/income.\n",
    "\n",
    "2️⃣ Tax Rate For Calcs → Effective tax rate used for financial calculations.\n",
    "\n",
    "3️⃣ Normalized EBITDA → Adjusted Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), removing irregular items.\n",
    "\n",
    "4️⃣ Net Income From Continuing Operation Net Minority Interest → Profit from ongoing operations after subtracting minority interest.\n",
    "\n",
    "5️⃣ Reconciled Depreciation → Adjusted depreciation expense to align financial statements.\n",
    "\n",
    "6️⃣ Reconciled Cost Of Revenue → Adjusted cost of goods sold (COGS) to reflect reconciliations.\n",
    "\n",
    "7️⃣ EBITDA → Earnings Before Interest, Taxes, Depreciation, and Amortization, a measure of operating performance.\n",
    "\n",
    "8️⃣ EBIT → Earnings Before Interest and Taxes, a profitability metric before interest expenses.\n",
    "\n",
    "9️⃣ Net Interest Income → Interest earned from assets (like loans) minus interest paid on liabilities.\n",
    "\n",
    "🔟 Interest Expense → Total cost of borrowing (interest paid on loans and bonds).\n",
    "\n",
    "1️⃣1️⃣ Interest Income → Revenue earned from interest-bearing investments.\n",
    "\n",
    "1️⃣2️⃣ Normalized Income → Adjusted net income excluding one-time or unusual items.\n",
    "\n",
    "1️⃣3️⃣ Net Income From Continuing And Discontinued Operation → Total profit, including both ongoing and sold/discontinued businesses.\n",
    "\n",
    "1️⃣4️⃣ Total Expenses → Sum of all costs, including operating and non-operating expenses.\n",
    "\n",
    "1️⃣5️⃣ Total Operating Income As Reported → Revenue from core business activities before adjustments.\n",
    "\n",
    "1️⃣6️⃣ Diluted Average Shares → Weighted average number of shares outstanding, including convertible securities.\n",
    "\n",
    "1️⃣7️⃣ Basic Average Shares → Weighted average number of shares outstanding without dilution adjustments.\n",
    "\n",
    "1️⃣8️⃣ Diluted EPS → Earnings Per Share (EPS) considering potential dilution from options and convertible securities.\n",
    "\n",
    "1️⃣9️⃣ Basic EPS → Earnings Per Share (EPS) calculated using only basic outstanding shares.\n",
    "\n",
    "2️⃣0️⃣ Diluted NI Avail to Com Stockholders → Net income available to common shareholders, adjusted for dilution.\n",
    "\n",
    "2️⃣1️⃣ Net Income Common Stockholders → Profit attributable to common stockholders after preferred dividends.\n",
    "\n",
    "2️⃣2️⃣ Net Income → Bottom-line profit after all expenses, taxes, and deductions.\n",
    "\n",
    "2️⃣3️⃣ Net Income Including Noncontrolling Interests → Net income, including profits attributed to minority shareholders.\n",
    "\n",
    "2️⃣4️⃣ Net Income Continuous Operations → Profit from only ongoing business activities (excludes discontinued operations).\n",
    "\n",
    "2️⃣5️⃣ Tax Provision → Total income tax expenses recorded in financial statements.\n",
    "\n",
    "2️⃣6️⃣ Pretax Income → Profit before income tax expenses are deducted.\n",
    "\n",
    "2️⃣7️⃣ Other Income Expense → Non-operating income or expenses, such as gains/losses from investments.\n",
    "\n",
    "2️⃣8️⃣ Other Non-Operating Income Expenses → Income or losses not related to core business operations.\n",
    "\n",
    "2️⃣9️⃣ Net Non-Operating Interest Income Expense → Difference between non-operating interest income and interest expense.\n",
    "\n",
    "3️⃣0️⃣ Interest Expense Non-Operating → Interest costs not directly tied to business operations.\n",
    "\n",
    "3️⃣1️⃣ Interest Income Non-Operating → Interest earnings from investments, not part of core operations.\n",
    "\n",
    "3️⃣2️⃣ Operating Income → Revenue minus operating expenses, a measure of core profitability.\n",
    "\n",
    "3️⃣3️⃣ Operating Expense → Costs related to running the business (e.g., salaries, rent, utilities).\n",
    "\n",
    "3️⃣4️⃣ Research And Development (R&D) → Spending on innovation and new product development.\n",
    "\n",
    "3️⃣5️⃣ Selling General And Administration (SG&A) → Overhead costs, including marketing, admin, and corporate expenses.\n",
    "\n",
    "3️⃣6️⃣ Gross Profit → Revenue minus cost of revenue, indicating profitability before operating costs.\n",
    "\n",
    "3️⃣7️⃣ Cost Of Revenue → Direct costs incurred to produce goods/services (similar to COGS).\n",
    "\n",
    "3️⃣8️⃣ Total Revenue → Total money earned from sales, including operating and non-operating income.\n",
    "\n",
    "3️⃣9️⃣ Operating Revenue → Revenue generated only from core business operations.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "295bb898-f0d6-4401-97ca-b08b8cf49650",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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