Small_Recipes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SimFin - Small Recipes Cookbook\n",
    "\n",
    "[Original repository on GitHub](https://github.com/simfin/simfin-tutorials)\n",
    "\n",
    "This is a collection of small recipes and examples for the [simfin](https://github.com/SimFin/simfin) Python package and [database](https://simfin.com/). You are assumed to be familiar with the basic [tutorials](https://github.com/simfin/simfin-tutorials) before reading this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import the main functionality from the SimFin Python API.\n",
    "import simfin as sf\n",
    "\n",
    "# Import names used for easy access to SimFin's data-columns.\n",
    "from simfin.names import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SimFin data-directory.\n",
    "sf.set_data_dir('~/simfin_data/')\n",
    "\n",
    "# SimFin load API key or use free data.\n",
    "sf.load_api_key(path='~/simfin_api_key.txt', default_key='free')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Datasets\n",
    "\n",
    "In these examples, we will use the following datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset \"us-income-ttm\" on disk (11 days old), loading.\n",
      "Dataset \"us-cashflow-ttm\" on disk (11 days old), loading.\n",
      "CPU times: user 544 ms, sys: 20 ms, total: 564 ms\n",
      "Wall time: 562 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Data for USA.\n",
    "market = 'us'\n",
    "\n",
    "# Load TTM Income Statements.\n",
    "df_income_ttm = sf.load_income(variant='ttm', market=market)\n",
    "\n",
    "# Load TTM Cash-Flow Statements.\n",
    "df_cashflow_ttm = sf.load_cashflow(variant='ttm', market=market)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use TTM datasets by default.\n",
    "df_income = df_income_ttm\n",
    "df_cashflow = df_cashflow_ttm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EBITDA\n",
    "\n",
    "EBITDA (Earnings Before Interest, Taxes, Depreciation and Amortization) commonly has two different definitions, this is the first:\n",
    "\n",
    "$$\n",
    "EBITDA_1 = Net\\ Income + Interest + Taxes + Depreciation + Amortization\n",
    "$$\n",
    "\n",
    "The following shows how to calculate this EBITDA formula using simfin data from two datasets: Income Statements and Cash-Flow Statements.\n",
    "\n",
    "Note that Interest Expense and Income Tax are negated in order to add them back to the Net Income, because those data-columns have negative values in the Income Statement.\n",
    "\n",
    "Also note that we use `fillna(0)` in case some values are NaN (Not-a-Number), which would give an overall result of NaN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ebitda1 = df_income[NET_INCOME].fillna(0) \\\n",
    "           - df_income[INTEREST_EXP_NET].fillna(0) \\\n",
    "           - df_income[INCOME_TAX].fillna(0) \\\n",
    "           + df_cashflow[DEPR_AMOR].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the other definition of EBITDA:\n",
    "\n",
    "$$\n",
    "EBITDA_2 = Operating\\ Income + Depreciation + Amortization\n",
    "$$\n",
    "\n",
    "The results of using formula 1 and 2 are not always identical, because the Operating Income is not always identical to Net Income + Interest + Taxes, as explained [here](https://www.investopedia.com/ask/answers/012015/what-difference-between-ebit-and-operating-income.asp) and shown in the statistics below.\n",
    "\n",
    "This is how to calculate EBITDA using the second formula with simfin data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ebitda2 = df_income[OPERATING_INCOME].fillna(0) \\\n",
    "           + df_cashflow[DEPR_AMOR].fillna(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can print the results of the two EBITDA calculations for ticker MSFT (Microsoft), which are clearly not identical:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Report Date\n",
       "2010-03-31    2.576700e+10\n",
       "2010-06-30    2.768600e+10\n",
       "2010-09-30    3.019900e+10\n",
       "2010-12-31    2.986100e+10\n",
       "2011-03-31    3.057100e+10\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ebitda1.loc['MSFT'].dropna().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Report Date\n",
       "2010-03-31    2.450100e+10\n",
       "2010-06-30    2.677100e+10\n",
       "2010-09-30    2.945300e+10\n",
       "2010-12-31    2.915300e+10\n",
       "2011-03-31    2.971500e+10\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ebitda2.loc['MSFT'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is only for a small fraction of the financial reports that these two EBITDA formulas give identical results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06296906461651107"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_ebitda1 == df_ebitda2).sum() / len(df_ebitda1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot a histogram showing the ratio between the two EBITDA formulas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4fe9584ba8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate ratio between the two EBITDA formulas.\n",
    "df_ratio = (df_ebitda1 / df_ebitda2)\n",
    "\n",
    "# There are some extreme outliers, so this boolean mask tells\n",
    "# us which rows have an EBITDA ratio between 0 and 2.\n",
    "mask = df_ratio.between(left=0, right=2)\n",
    "\n",
    "# Plot histogram with the ratios between the two EBITDA formulas.\n",
    "df_ratio[mask].plot(kind='hist', bins=100, grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EBITDA - SimFin Function\n",
    "\n",
    "The simfin package provides a function `sf.ebitda` for calculating the EBITDA where you can switch betwen the two EBITDA formulas. If you pass the argument `formula=NET_INCOME` then it uses the first formula for the Net Income. If you pass the argument `formula=OPERATING_INCOME` or `formula=OP_INCOME` then it uses the second formula for the Operating Income."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ebitda1 = sf.ebitda(df_income=df_income,\n",
    "                       df_cashflow=df_cashflow,\n",
    "                       formula=NET_INCOME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_ebitda2 = sf.ebitda(df_income=df_income,\n",
    "                       df_cashflow=df_cashflow,\n",
    "                       formula=OP_INCOME)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can show the results for ticker MSFT and once again see that they are clearly different for the two formulas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Report Date\n",
       "2010-03-31    2.576700e+10\n",
       "2010-06-30    2.768600e+10\n",
       "2010-09-30    3.019900e+10\n",
       "2010-12-31    2.986100e+10\n",
       "2011-03-31    3.057100e+10\n",
       "Name: EBITDA, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ebitda1.loc['MSFT'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Report Date\n",
       "2010-03-31    2.450100e+10\n",
       "2010-06-30    2.677100e+10\n",
       "2010-09-30    2.945300e+10\n",
       "2010-12-31    2.915300e+10\n",
       "2011-03-31    2.971500e+10\n",
       "Name: EBITDA, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ebitda2.loc['MSFT'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## License (MIT)\n",
    "\n",
    "This is published under the\n",
    "[MIT License](https://github.com/simfin/simfin-tutorials/blob/master/LICENSE.txt)\n",
    "which allows very broad use for both academic and commercial purposes.\n",
    "\n",
    "You are very welcome to modify and use this source-code in your own project. Please keep a link to the [original repository](https://github.com/simfin/simfin-tutorials).\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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