115 lines
61 KiB
Plaintext
115 lines
61 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<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",
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"</h4>\n",
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"\n",
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"<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>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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",
|
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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
|
|
}
|