{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Equations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EPS\n",
    "$\n",
    "\\begin{align}\n",
    "\\textit{Earnings Per Share} & = \\frac{\\textit{Earnings}}{\\textit{Market Value Per Share}} \\\\\n",
    "\\textit{EPS} & = \\frac{\\textit{Earnings}}{\\textit{Share Price}}\n",
    "\\end{align}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### P/E Ratio\n",
    "$\n",
    "\\begin{align}\n",
    "\\textit{Price/Earnings Ratio} & = \\frac{\\textit{Market Value Per Share}}{\\textit{Earnings Per Share}} \\\\\n",
    "\\textit{P/E} & = \\frac{\\textit{Share Price}}{\\textit{EPS}}\n",
    "\\end{align}\n",
    "$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IPO parameters:\n",
    "- `n_shares` $ \\text{number of shares} $\n",
    "- `price` $ \\text{price per share} $"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>mktcap</th>\n",
       "      <th>n_shares</th>\n",
       "      <th>price</th>\n",
       "      <th>earnings</th>\n",
       "      <th>eps</th>\n",
       "      <th>pe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>10000000.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>266666.0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>3.750009</td>\n",
       "      <td>9.999975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>100000.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10000000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000000.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mktcap    n_shares   price   earnings         eps         pe\n",
       "0  10000000.0  10000000.0     1.0  1000000.0    0.100000  10.000000\n",
       "1  10000000.0    266666.0    37.5  1000000.0    3.750009   9.999975\n",
       "2  10000000.0    100000.0   100.0  1000000.0   10.000000  10.000000\n",
       "3  10000000.0     10000.0  1000.0  1000000.0  100.000000  10.000000"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mktcap = 10_000_000\n",
    "earnings = 1_000_000\n",
    "\n",
    "import pandas as pd\n",
    "df = pd.DataFrame.from_records([], columns=(\"mktcap\",\"n_shares\",\"price\",\"earnings\"))\n",
    "constdict = dict(mktcap=mktcap, earnings=earnings)\n",
    "prices = [1, 37.50, 100, 1000]\n",
    "for price in prices:\n",
    "    data = {**constdict, 'price': price, 'n_shares': mktcap // price}\n",
    "    data['eps'] = data['earnings'] / data['n_shares']\n",
    "    data['pe'] = data['price'] / data['eps']\n",
    "    df = df.append(data, ignore_index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation 1\n",
    "EPS is not useful for comparing the performance of different securities because it is sensitive to ``n_shares`` and ``price``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '10,000,000'),\n",
       " Text(0, 0, '266,666'),\n",
       " Text(0, 0, '100,000'),\n",
       " Text(0, 0, '10,000')]"
      ]
     },
     "execution_count": 97,
     "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": [
    "%matplotlib inline\n",
    "xcol='n_shares'\n",
    "ax = df[[xcol, 'eps', 'pe']].plot(\n",
    "    x=xcol, y=['eps','pe'], logx='sym', xticks=df[xcol], use_index=True) #, table=True)\n",
    "ax.set_xticklabels(df[xcol].apply(lambda x: \"{:,.0f}\".format(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '1.0'),\n",
       " Text(0, 0, '37.5'),\n",
       " Text(0, 0, '100.0'),\n",
       " Text(0, 0, '1,000.0')]"
      ]
     },
     "execution_count": 100,
     "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": [
    "xcol='price'\n",
    "ax = df[[xcol, 'eps', 'pe']].plot(\n",
    "    x=xcol, y=['eps','pe'], logx='sym', xticks=df[xcol], use_index=True) #, table=True)\n",
    "ax.set_xticklabels(df[xcol].apply(lambda x: \"{:,.1f}\".format(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0.1'), Text(0, 0, '3.8'), Text(0, 0, '10.0'), Text(0, 0, '100.0')]"
      ]
     },
     "execution_count": 107,
     "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": [
    "xcol='eps'\n",
    "ax = df[[xcol, 'price', 'pe']].plot(\n",
    "    x=xcol, y=['price','pe'], logx='sym', xticks=df[xcol], use_index=True) #, table=True)\n",
    "ax.set_xticklabels(df[xcol].apply(lambda x: \"{:,.1f}\".format(x)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation 2\n",
    "- For a higher EPS, set a higher initial price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## P/E Ratio further study\n",
    "$\n",
    "\\begin{align}\n",
    "\\textit{P/E} & = \\frac{\\textit{Share Price}}{\\frac{\\textit{Earnings}}{\\textit{Share Price}}} \\\\\n",
    "\\textit{P/E} & = \\frac{\\textit{Share Price}}{1} * \\frac{\\textit{Share Price}}{\\textit{Earnings}} \\\\\n",
    "\\textit{P/E} & = \\frac{\\textit{Share Price}^2}{\\textit{Earnings}}\n",
    "\\end{align}\n",
    "$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sympy as sp\n",
    "eps = sp.var(\"EPS\")\n",
    "earnings = sp.var(\"earnings\")\n",
    "mktcap = sp.var(\"mktcap\")\n",
    "price = sp.var(\"price\")\n",
    "pe = sp.var(\"PE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle EPS = \\frac{earnings}{price}$"
      ],
      "text/plain": [
       "Eq(EPS, earnings/price)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eps_eq = sp.Eq(eps, earnings / price)\n",
    "eps_eq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle PE = \\frac{price}{EPS}$"
      ],
      "text/plain": [
       "Eq(PE, price/EPS)"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pe_eq = sp.Eq(pe, price / eps)\n",
    "pe_eq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle PE = \\frac{price^{2}}{earnings}$"
      ],
      "text/plain": [
       "Eq(PE, price**2/earnings)"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pe_eq_full = pe_eq.subs({eps: eps_eq.rhs})\n",
    "pe_eq_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle PE = \\frac{price}{earnings \\frac{1}{price}}$"
      ],
      "text/plain": [
       "Eq(PE, price/((earnings/price)))"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sympy.core.evaluate import evaluate\n",
    "with evaluate(False):\n",
    "    pe_eq_full_unsimplified = pe_eq.subs({eps: eps_eq.rhs})\n",
    "pe_eq_full_unsimplified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(PE, price**2/earnings)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pe_eq_full.lhs, pe_eq_full.rhs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Eq(PE, price**2/earnings)'"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(pe_eq_full)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{price^{2}}{earnings}$"
      ],
      "text/plain": [
       "price**2/earnings"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pe_eq_full.rhs"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}