st.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Average Final Portfolio Value': 995.9581818181819,\n",
       " 'Total PnL': -0.910000000000001,\n",
       " 'Average Annualized Volatility': 0.8145454545454546,\n",
       " 'Average Sharpe Ratio': 0.32636363636363636,\n",
       " 'Total Trades': 240,\n",
       " 'Total Turnover': 404542090960.0,\n",
       " 'Highest Sharpe Ratio Date': Timestamp('2023-04-30 00:00:00'),\n",
       " 'Lowest Sharpe Ratio Date': Timestamp('2023-03-31 00:00:00')}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Parsing the provided data and calculating statistics\n",
    "import pandas as pd\n",
    "\n",
    "# Data as provided\n",
    "data = {\n",
    "    \"Date\": [\n",
    "        \"2023-10-31\", \"2023-09-30\", \"2023-08-31\", \"2023-07-31\", \"2023-06-30\",\n",
    "        \"2023-04-30\", \"2023-03-31\", \"2023-02-28\", \"2023-01-31\", \"2022-12-31\",\n",
    "        \"2022-11-30\"\n",
    "    ],\n",
    "    \"Final Portfolio Value\": [\n",
    "        1005.82, 999.7, 989.63, 1009.93, 995.56,\n",
    "        1001.95, 995.93, 992.14, 1024.76, 969.35,\n",
    "        970.77\n",
    "    ],\n",
    "    \"Total PnL\": [\n",
    "        0.44, 1.52, 0.77, -2.29, 0.78,\n",
    "        6.32, -7.11, 3.74, 0.51, -0.56,\n",
    "        -5.03\n",
    "    ],\n",
    "    \"Annualized Volatility\": [\n",
    "        0.55, 0.40, 0.61, 0.78, 0.71,\n",
    "        1.02, 0.80, 0.71, 0.86, 0.77,\n",
    "        1.75\n",
    "    ],\n",
    "    \"Sharpe Ratio\": [\n",
    "        0.80, 3.83, 1.25, -2.94, 1.09,\n",
    "        6.21, -8.91, 5.26, 0.59, -0.72,\n",
    "        -2.87\n",
    "    ],\n",
    "    \"Total Trades\": [\n",
    "        32, 14, 21, 28, 30,\n",
    "        19, 14, 14, 15, 17,\n",
    "        36\n",
    "    ],\n",
    "    \"Total Turnover\": [\n",
    "        20585750037.0, 12126296287.0, 21519550483.0, 36459926160.0, 20412788930.0,\n",
    "        62701979620.0, 31390076138.0, 32150070041.0, 40965904947.0, 36816850421.0,\n",
    "        89412897896.0\n",
    "    ]\n",
    "}\n",
    "\n",
    "\n",
    "\n",
    "# Converting to DataFrame\n",
    "df = pd.DataFrame(data)\n",
    "df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
    "\n",
    "# Calculating summary statistics\n",
    "summary_stats = {\n",
    "    \"Average Final Portfolio Value\": df[\"Final Portfolio Value\"].mean(),\n",
    "    \"Total PnL\": df[\"Total PnL\"].sum(),\n",
    "    \"Average Annualized Volatility\": df[\"Annualized Volatility\"].mean(),\n",
    "    \"Average Sharpe Ratio\": df[\"Sharpe Ratio\"].mean(),\n",
    "    \"Total Trades\": df[\"Total Trades\"].sum(),\n",
    "    \"Total Turnover\": df[\"Total Turnover\"].sum(),\n",
    "    \"Highest Sharpe Ratio Date\": df.loc[df[\"Sharpe Ratio\"].idxmax()][\"Date\"],\n",
    "    \"Lowest Sharpe Ratio Date\": df.loc[df[\"Sharpe Ratio\"].idxmin()][\"Date\"]\n",
    "}\n",
    "\n",
    "summary_stats\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "# Preparing data for plotting\n",
    "dates = df['Date']\n",
    "portfolio_values = df['Final Portfolio Value']\n",
    "pnl = df['Total PnL']\n",
    "volatility = df['Annualized Volatility']\n",
    "sharpe_ratio = df['Sharpe Ratio']\n",
    "trades = df['Total Trades']\n",
    "turnover = df['Total Turnover']\n",
    "\n",
    "# Creating the plot\n",
    "plt.figure(figsize=(15, 8))\n",
    "\n",
    "# Portfolio Value\n",
    "plt.plot(dates, portfolio_values, label='Final Portfolio Value', color='blue', marker='o')\n",
    "\n",
    "# Total PnL\n",
    "plt.plot(dates, pnl, label='Total PnL', color='green', marker='x')\n",
    "\n",
    "# Annualized Volatility\n",
    "plt.plot(dates, volatility, label='Annualized Volatility', color='red', marker='s')\n",
    "\n",
    "# Sharpe Ratio\n",
    "plt.plot(dates, sharpe_ratio, label='Sharpe Ratio', color='purple', marker='^')\n",
    "\n",
    "# Formatting and labels\n",
    "plt.title('Portfolio Statistics Over Time')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Values')\n",
    "plt.xticks(rotation=45)\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))\n",
    "plt.gca().xaxis.set_major_locator(mdates.MonthLocator())\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()\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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}