yf_altman_z-score.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9fa96f-dfcf-4ef6-8e76-0ef72892c618",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70abee0f-e0e8-4e34-a00c-0b5f1f93f415",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read stocks\n",
    "import yfinance as yf\n",
    "\n",
    "# For DataFrame\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad36b6ef-7014-48f6-9a44-ced8767da3e4",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3db86b5f-3432-4b52-930d-de2cfcb4df0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random companies in the Software-Infrastructure Industry under Technology Sector\n",
    "SYMBOLS = 'HCP,QLYS,EEFT,ALTR,S,BOX,DOX,PATH,TDC,SPLK,INFA,IOT,CFLT,GTLB,ACIW,MDB,CRWD,GEN'.split(',')\n",
    "# A dictionary to hold scores for each symbol\n",
    "symbol_to_score = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57a2059e-35d2-49d1-9fd6-a4d70fe0a1b4",
   "metadata": {},
   "source": [
    "## Methods to calculate the Z-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "849e4e6a-63d8-47c9-9b1d-160d64be96e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ratio_x_1: working capital / total assets\n",
    "def ratio_x_1(ticker:yf.ticker.Ticker) -> float:\n",
    "    df = ticker.balance_sheet\n",
    "    working_capital = df.loc['Current Assets'].iloc[0] - df.loc['Current Liabilities'].iloc[0]\n",
    "    total_assets = df.loc['Total Assets'].iloc[0]\n",
    "    return working_capital/total_assets\n",
    "\n",
    "# retained earnings / total assets\n",
    "def ratio_x_2(ticker:yf.ticker.Ticker) -> float:\n",
    "    df = ticker.balance_sheet\n",
    "    retained_earnings = df.loc['Retained Earnings'].iloc[0]\n",
    "    total_assets = df.loc['Total Assets'].iloc[0]\n",
    "    return retained_earnings/total_assets\n",
    "\n",
    "# earnings before interest and tax / total assets\n",
    "def ratio_x_3(ticker:yf.ticker.Ticker) -> float:\n",
    "    df = ticker.income_stmt\n",
    "    ebit = df.loc['EBIT'].iloc[0]\n",
    "    total_assets = ticker.balance_sheet.loc['Total Assets'].iloc[0]\n",
    "    return ebit/total_assets\n",
    "\n",
    "# market value of equity / total liabilities\n",
    "def ratio_x_4(ticker:yf.ticker.Ticker) -> float:\n",
    "    equity_market_value = ticker.info['sharesOutstanding'] * ticker.info['currentPrice']\n",
    "    total_liabilities = ticker.balance_sheet.loc['Total Liabilities Net Minority Interest'].iloc[0]\n",
    "    return equity_market_value/total_liabilities\n",
    "\n",
    "# sales / total assets\n",
    "def ratio_x_5(ticker:yf.ticker.Ticker) -> float:\n",
    "    df = ticker.income_stmt\n",
    "    sales = df.loc['Total Revenue'].iloc[0]\n",
    "    total_assets = ticker.balance_sheet.loc['Total Assets'].iloc[0]\n",
    "    return sales/total_assets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9553fba9-1545-4f9c-a649-8cbb53fbf38c",
   "metadata": {},
   "source": [
    "## Calculate Z-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "26640a20-f078-4204-a96e-ee00148edc2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def z_score(ticker:yf.ticker.Ticker) -> float:\n",
    "    ratio_1 = ratio_x_1(ticker)\n",
    "    ratio_2 = ratio_x_2(ticker)\n",
    "    ratio_3 = ratio_x_3(ticker)\n",
    "    ratio_4 = ratio_x_4(ticker)\n",
    "    ratio_5 = ratio_x_5(ticker)\n",
    "    # Z = 1.2X1 + 1.4X2 + 3.3X3 + 0.6X4 + 1.0X5.\n",
    "    zscore = 1.2*ratio_1 + 1.4*ratio_2 + 3.3*ratio_3 + 0.6*ratio_4 + 1.0*ratio_5\n",
    "    return zscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "21035529-92af-46b2-9688-6f7890c20855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'HCP': 3.966377993725474,\n",
       " 'QLYS': 9.440156406216962,\n",
       " 'EEFT': 2.2088606360107548,\n",
       " 'ALTR': 4.889793303042627,\n",
       " 'S': 5.550169753018658,\n",
       " 'BOX': 0.8240135124924809,\n",
       " 'DOX': 4.883301178383062,\n",
       " 'PATH': 7.845345253857153,\n",
       " 'TDC': 1.0667425910691386,\n",
       " 'SPLK': 2.2338104274185366,\n",
       " 'INFA': 1.7443872763487027,\n",
       " 'IOT': 4.755161281413089,\n",
       " 'CFLT': 2.287857500338857,\n",
       " 'GTLB': 11.211532361994733,\n",
       " 'ACIW': 2.2943724120376174,\n",
       " 'MDB': 9.53030235538038,\n",
       " 'CRWD': 11.829298257627572,\n",
       " 'GEN': 0.8740704803810675}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Loop through symbol list\n",
    "for symbol in SYMBOLS:\n",
    "    ticker = yf.Ticker(symbol)\n",
    "    symbol_to_score[symbol]=z_score(ticker)\n",
    "symbol_to_score"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e689b1a8-12a0-46d5-b5cd-db89121aa58f",
   "metadata": {},
   "source": [
    "## Adds styles to DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0c50e234-f9f5-4b9a-b03e-2cb363895245",
   "metadata": {},
   "outputs": [],
   "source": [
    "def highlight_distress(val):\n",
    "    return 'background-color: indianred' if val != '' else \"\"\n",
    "    \n",
    "def highlight_grey(val):\n",
    "    return 'background-color: grey' if val != '' else \"\"\n",
    "\n",
    "def highlight_safe(val):\n",
    "    return 'background-color: green' if val != '' else \"\"\n",
    "\n",
    "def format_score(val):\n",
    "    try:\n",
    "        return '{:.2f}'.format(float(val))\n",
    "    except:\n",
    "        return ''\n",
    "    \n",
    "def make_pretty(styler):\n",
    "    # No index\n",
    "    styler.hide(axis='index')\n",
    "    \n",
    "    # Column formatting\n",
    "    styler.format(format_score, subset=['Distress Zone', 'Grey Zone', 'Safe Zone'])\n",
    "\n",
    "    # Left text alignment for some columns\n",
    "    styler.set_properties(subset=['Symbol', 'Distress Zone', 'Grey Zone', 'Safe Zone'], **{'text-align': 'center', 'width': '100px'})\n",
    "\n",
    "    styler.map(highlight_grey, subset=['Grey Zone'])\n",
    "    styler.map(highlight_safe, subset=['Safe Zone'])\n",
    "    styler.map(highlight_distress, subset=['Distress Zone'])\n",
    "    return styler"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63755a4e-ef19-42d0-a3f8-a2a8562be745",
   "metadata": {},
   "source": [
    "## Display DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d741d0a5-c547-4e82-87a0-f8beabfd3f55",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  background-color: indianred;\n",
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       "</style>\n",
       "<table id=\"T_878aa\">\n",
       "  <caption>Altman Z Score</caption>\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th id=\"T_878aa_level0_col0\" class=\"col_heading level0 col0\" >Symbol</th>\n",
       "      <th id=\"T_878aa_level0_col1\" class=\"col_heading level0 col1\" >Distress Zone</th>\n",
       "      <th id=\"T_878aa_level0_col2\" class=\"col_heading level0 col2\" >Grey Zone</th>\n",
       "      <th id=\"T_878aa_level0_col3\" class=\"col_heading level0 col3\" >Safe Zone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row0_col0\" class=\"data row0 col0\" >HCP</td>\n",
       "      <td id=\"T_878aa_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_878aa_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_878aa_row0_col3\" class=\"data row0 col3\" >3.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row1_col0\" class=\"data row1 col0\" >QLYS</td>\n",
       "      <td id=\"T_878aa_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_878aa_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_878aa_row1_col3\" class=\"data row1 col3\" >9.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row2_col0\" class=\"data row2 col0\" >EEFT</td>\n",
       "      <td id=\"T_878aa_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_878aa_row2_col2\" class=\"data row2 col2\" >2.21</td>\n",
       "      <td id=\"T_878aa_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row3_col0\" class=\"data row3 col0\" >ALTR</td>\n",
       "      <td id=\"T_878aa_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_878aa_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_878aa_row3_col3\" class=\"data row3 col3\" >4.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row4_col0\" class=\"data row4 col0\" >S</td>\n",
       "      <td id=\"T_878aa_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_878aa_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_878aa_row4_col3\" class=\"data row4 col3\" >5.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row5_col0\" class=\"data row5 col0\" >BOX</td>\n",
       "      <td id=\"T_878aa_row5_col1\" class=\"data row5 col1\" >0.82</td>\n",
       "      <td id=\"T_878aa_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_878aa_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row6_col0\" class=\"data row6 col0\" >DOX</td>\n",
       "      <td id=\"T_878aa_row6_col1\" class=\"data row6 col1\" ></td>\n",
       "      <td id=\"T_878aa_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_878aa_row6_col3\" class=\"data row6 col3\" >4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row7_col0\" class=\"data row7 col0\" >PATH</td>\n",
       "      <td id=\"T_878aa_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_878aa_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "      <td id=\"T_878aa_row7_col3\" class=\"data row7 col3\" >7.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row8_col0\" class=\"data row8 col0\" >TDC</td>\n",
       "      <td id=\"T_878aa_row8_col1\" class=\"data row8 col1\" >1.07</td>\n",
       "      <td id=\"T_878aa_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "      <td id=\"T_878aa_row8_col3\" class=\"data row8 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row9_col0\" class=\"data row9 col0\" >SPLK</td>\n",
       "      <td id=\"T_878aa_row9_col1\" class=\"data row9 col1\" ></td>\n",
       "      <td id=\"T_878aa_row9_col2\" class=\"data row9 col2\" >2.23</td>\n",
       "      <td id=\"T_878aa_row9_col3\" class=\"data row9 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row10_col0\" class=\"data row10 col0\" >INFA</td>\n",
       "      <td id=\"T_878aa_row10_col1\" class=\"data row10 col1\" >1.74</td>\n",
       "      <td id=\"T_878aa_row10_col2\" class=\"data row10 col2\" ></td>\n",
       "      <td id=\"T_878aa_row10_col3\" class=\"data row10 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row11_col0\" class=\"data row11 col0\" >IOT</td>\n",
       "      <td id=\"T_878aa_row11_col1\" class=\"data row11 col1\" ></td>\n",
       "      <td id=\"T_878aa_row11_col2\" class=\"data row11 col2\" ></td>\n",
       "      <td id=\"T_878aa_row11_col3\" class=\"data row11 col3\" >4.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row12_col0\" class=\"data row12 col0\" >CFLT</td>\n",
       "      <td id=\"T_878aa_row12_col1\" class=\"data row12 col1\" ></td>\n",
       "      <td id=\"T_878aa_row12_col2\" class=\"data row12 col2\" >2.29</td>\n",
       "      <td id=\"T_878aa_row12_col3\" class=\"data row12 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row13_col0\" class=\"data row13 col0\" >GTLB</td>\n",
       "      <td id=\"T_878aa_row13_col1\" class=\"data row13 col1\" ></td>\n",
       "      <td id=\"T_878aa_row13_col2\" class=\"data row13 col2\" ></td>\n",
       "      <td id=\"T_878aa_row13_col3\" class=\"data row13 col3\" >11.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row14_col0\" class=\"data row14 col0\" >ACIW</td>\n",
       "      <td id=\"T_878aa_row14_col1\" class=\"data row14 col1\" ></td>\n",
       "      <td id=\"T_878aa_row14_col2\" class=\"data row14 col2\" >2.29</td>\n",
       "      <td id=\"T_878aa_row14_col3\" class=\"data row14 col3\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row15_col0\" class=\"data row15 col0\" >MDB</td>\n",
       "      <td id=\"T_878aa_row15_col1\" class=\"data row15 col1\" ></td>\n",
       "      <td id=\"T_878aa_row15_col2\" class=\"data row15 col2\" ></td>\n",
       "      <td id=\"T_878aa_row15_col3\" class=\"data row15 col3\" >9.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row16_col0\" class=\"data row16 col0\" >CRWD</td>\n",
       "      <td id=\"T_878aa_row16_col1\" class=\"data row16 col1\" ></td>\n",
       "      <td id=\"T_878aa_row16_col2\" class=\"data row16 col2\" ></td>\n",
       "      <td id=\"T_878aa_row16_col3\" class=\"data row16 col3\" >11.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_878aa_row17_col0\" class=\"data row17 col0\" >GEN</td>\n",
       "      <td id=\"T_878aa_row17_col1\" class=\"data row17 col1\" >0.87</td>\n",
       "      <td id=\"T_878aa_row17_col2\" class=\"data row17 col2\" ></td>\n",
       "      <td id=\"T_878aa_row17_col3\" class=\"data row17 col3\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "distress = [''] * len(SYMBOLS)\n",
    "grey = [''] * len(SYMBOLS)\n",
    "safe = [''] * len(SYMBOLS)\n",
    "\n",
    "for idx, zscore in enumerate(symbol_to_score.values()):\n",
    "    if zscore <= 1.8:\n",
    "        distress[idx] = zscore\n",
    "    elif zscore > 1.8 and zscore <= 2.99:\n",
    "        grey[idx] = zscore\n",
    "    else:\n",
    "        safe[idx] = zscore\n",
    "\n",
    "# Create a dictionary for the DF\n",
    "data_dict = {'Symbol': SYMBOLS, 'Distress Zone': distress, 'Grey Zone': grey, 'Safe Zone': safe} \n",
    "df = pd.DataFrame.from_dict(data_dict)\n",
    "# Drop any rows with NaN values\n",
    "df.dropna(inplace=True)\n",
    "\n",
    "styles = [\n",
    "    dict(selector='td', props=[('font-size', '10pt'),('border-style','solid'),('border-width','1px')]),\n",
    "    dict(selector='th.col_heading', props=[('font-size', '11pt'),('text-align', 'center')]),\n",
    "    dict(selector='caption', props=[('text-align', 'center'),\n",
    "                                     ('font-size', '14pt'), ('font-weight', 'bold')])\n",
    "]\n",
    "# Apply styles\n",
    "df_styled = df.style.set_table_styles(styles)\n",
    "df.style.pipe(make_pretty).set_caption('Altman Z Score').set_table_styles(styles)"
   ]
  }
 ],
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