{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h4>Claudio, questo notebook genera la chart relativa alla salute finanziaria. Essenzialmente ci sono quattro colonne che sono stackate a coppie di due: Total Assets/Current Assets e Total Debt/Current Debt. Sostanzialmente asset e debiti a breve termine e lungo termine (quelli a lungo termine saranno sempre superiori in quanto incorporano quelli a breve termine in entrambi i casi). Come vedi il colore degli asset e debiti a breve è molto più marcato perché questi sono nettamente più rilevanti. Se hai qualche idea su come mostrarlo diversamente sono bene accette, io penso che questa soluzione faccia il suo, penso che le stacked bar siano molto efficaci in questo contesto. \n",
    "</h4>\n",
    "\n",
    "<h4>*Nota: ho scelto proprio APPL e MSFT per mostrare che secondo me anche questo garfico ha bisogno di una versione \"relativa\". Quello che vedi sotto compara i valori assoluti, però anche averne uno in cui le altezze delle colonne sono determinate rispetto alle dimensioni dell'azienda non sarebbe male. Domani vediamo comunque.</h4>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sys\n",
    "sys.path.append('../group-1')\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import matplotlib.font_manager as font_manager\n",
    "\n",
    "def compare_balance_sheets(ticker_list: list):\n",
    "    num_charts = len(ticker_list)\n",
    "\n",
    "    fig, axes = plt.subplots(1, num_charts, figsize=(12*num_charts, 12), sharey=True)\n",
    "\n",
    " \n",
    "    font = font_manager.FontProperties(weight='bold', size=12)\n",
    "\n",
    "    for i, ticker in enumerate(ticker_list):\n",
    "        assets_debt = pd.read_csv(r'../Companies_Data/'+ticker+'_Data/'+ticker+'_balance_sheet_4Y+4Q.csv')\n",
    "        selected_data = assets_debt[['TotalAssets', 'TotalDebt', 'CurrentAssets', 'CurrentDebt']]\n",
    "\n",
    "        sns.set(style=\"whitegrid\")  \n",
    "\n",
    "        ax = axes[i]\n",
    "        asset_labels = ['Total Assets & Current Assets', ' Total Debt & Current Debt']\n",
    "        asset_values = [selected_data.iloc[0]['TotalAssets'], selected_data.iloc[0]['CurrentAssets']]\n",
    "        current_asset_values = [0, selected_data.iloc[0]['CurrentAssets']]\n",
    "        ax.bar(range(len(asset_labels)), asset_values, color=['green', 'red'], label='Assets', alpha=0.5)\n",
    "        ax.bar(range(len(asset_labels)), current_asset_values, color=['white', 'red'], alpha=0.5)\n",
    "\n",
    "    \n",
    "        debt_labels = ['Total Debt/ Current Debt', '']\n",
    "        debt_values = [selected_data.iloc[0]['TotalDebt'], selected_data.iloc[0]['CurrentDebt']]\n",
    "        current_debt_values = [0, selected_data.iloc[0]['CurrentDebt']]\n",
    "        ax.bar(range(len(debt_labels)), debt_values, color=['darkgreen', 'darkred'], label='Debts')\n",
    "        ax.bar(range(len(debt_labels)), current_debt_values, color=['white', 'darkred'])\n",
    "\n",
    "        ax.set_title(f'{ticker} - Balance Sheet', fontproperties=font, fontsize=14, weight='bold')\n",
    "        ax.set_xlabel(ticker, fontproperties=font)\n",
    "        ax.set_ylabel('Amount in $', fontproperties=font)\n",
    "        ax.set_xticks(range(len(asset_labels)))  \n",
    "        ax.set_xticklabels(asset_labels, fontproperties=font)\n",
    "\n",
    "\n",
    "        ax.xaxis.grid(False)\n",
    "        ax.yaxis.grid(False)\n",
    "\n",
    "     \n",
    "        green_patch = plt.Line2D([0], [0], color='green', alpha=0.5, linewidth=0, marker='s')\n",
    "        red_patch = plt.Line2D([0], [0], color='red', alpha=0.5, linewidth=0, marker='s')\n",
    "        ax.legend([green_patch, red_patch], ['Assets', 'Debts'], loc='best')\n",
    "\n",
    "\n",
    "    plt.tight_layout() \n",
    "    plt.show()\n",
    "\n",
    "ticker_list = ['AAPL', 'MSFT']\n",
    "compare_balance_sheets(ticker_list)\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".env",
   "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": "3.11.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}