python-for-finances/portfolios-simulation/portfolios_simulation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries Installation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.0.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.2.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install yfinance -q\n",
    "!pip install pandas -q\n",
    "!pip install numpy -q\n",
    "!pip install matplotlib -q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup portfolio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "amount_to_invest = 50_000.0\n",
    "assets = [\"PETR3.SA\", \"CMIG3.SA\", \"CPLE6.SA\", \"RANI3.SA\", \"TAEE11.SA\", \"FESA4.SA\", \"UNIP6.SA\"]\n",
    "weights = [32.5183, 18.4078, 12.6776, 11.1030, 10.3605, 9.1889, 5.7439]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "if sum(weights) != 100.0:\n",
    "    sum(weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "buy_orders = {\n",
    "    asset: (weights / 100) * amount_to_invest\n",
    "    for asset, weights in zip(assets, weights)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PETR3.SA': 16259.150000000003,\n",
       " 'CMIG3.SA': 9203.900000000001,\n",
       " 'CPLE6.SA': 6338.8,\n",
       " 'RANI3.SA': 5551.5,\n",
       " 'TAEE11.SA': 5180.25,\n",
       " 'FESA4.SA': 4594.45,\n",
       " 'UNIP6.SA': 2871.95}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "buy_orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "if sum(buy_orders.values()) != amount_to_invest:\n",
    "    sum(buy_orders.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "assets = list(buy_orders.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PETR3.SA',\n",
       " 'CMIG3.SA',\n",
       " 'CPLE6.SA',\n",
       " 'RANI3.SA',\n",
       " 'TAEE11.SA',\n",
       " 'FESA4.SA',\n",
       " 'UNIP6.SA']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_invested = sum(buy_orders.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50000.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_invested"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import financial assets data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = \"2017-02-01\"\n",
    "\n",
    "assets_history_price = yf.download(assets, start=start, progress=False)[\"Adj Close\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dummy portfolio simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CMIG3.SA      4.455539\n",
       "CPLE6.SA      1.778329\n",
       "FESA4.SA      6.051053\n",
       "PETR3.SA      6.434494\n",
       "RANI3.SA      2.162255\n",
       "TAEE11.SA    11.382806\n",
       "UNIP6.SA      2.045254\n",
       "Name: 2017-02-01 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_line = assets_history_price.iloc[0]\n",
    "first_line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "buy_orders_df = pd.Series(data = buy_orders, index=assets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_shares = round(buy_orders_df / first_line, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_worth = assets_history_price * number_of_shares"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "net_worth[\"Total net worth\"] = net_worth.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CMIG3.SA</th>\n",
       "      <th>CPLE6.SA</th>\n",
       "      <th>FESA4.SA</th>\n",
       "      <th>PETR3.SA</th>\n",
       "      <th>RANI3.SA</th>\n",
       "      <th>TAEE11.SA</th>\n",
       "      <th>UNIP6.SA</th>\n",
       "      <th>Total net worth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>9205.144042</td>\n",
       "      <td>6337.965883</td>\n",
       "      <td>4592.748901</td>\n",
       "      <td>16259.967590</td>\n",
       "      <td>5550.509322</td>\n",
       "      <td>5179.176650</td>\n",
       "      <td>2871.536605</td>\n",
       "      <td>49997.048993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-02</th>\n",
       "      <td>9615.273934</td>\n",
       "      <td>6465.322914</td>\n",
       "      <td>4614.160777</td>\n",
       "      <td>16038.055955</td>\n",
       "      <td>5550.509322</td>\n",
       "      <td>5222.037277</td>\n",
       "      <td>2855.359305</td>\n",
       "      <td>50360.719485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-03</th>\n",
       "      <td>9906.921354</td>\n",
       "      <td>6517.062172</td>\n",
       "      <td>4603.454839</td>\n",
       "      <td>16481.878019</td>\n",
       "      <td>5550.509322</td>\n",
       "      <td>5231.563163</td>\n",
       "      <td>2851.314646</td>\n",
       "      <td>51142.703515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-06</th>\n",
       "      <td>9788.441837</td>\n",
       "      <td>6415.574983</td>\n",
       "      <td>4571.338111</td>\n",
       "      <td>16108.668256</td>\n",
       "      <td>5530.758809</td>\n",
       "      <td>5164.888473</td>\n",
       "      <td>2891.758229</td>\n",
       "      <td>50471.428698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-07</th>\n",
       "      <td>9742.872944</td>\n",
       "      <td>6495.171587</td>\n",
       "      <td>4603.454839</td>\n",
       "      <td>15906.927777</td>\n",
       "      <td>5550.509322</td>\n",
       "      <td>5131.551127</td>\n",
       "      <td>2871.536605</td>\n",
       "      <td>50302.024201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-04</th>\n",
       "      <td>39171.358109</td>\n",
       "      <td>30579.119728</td>\n",
       "      <td>38784.898842</td>\n",
       "      <td>83087.762699</td>\n",
       "      <td>27004.221810</td>\n",
       "      <td>16191.813316</td>\n",
       "      <td>114720.838715</td>\n",
       "      <td>349540.013219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-07</th>\n",
       "      <td>38572.220158</td>\n",
       "      <td>30757.320408</td>\n",
       "      <td>37570.500000</td>\n",
       "      <td>83643.696144</td>\n",
       "      <td>28085.397628</td>\n",
       "      <td>16125.270538</td>\n",
       "      <td>113007.957001</td>\n",
       "      <td>347762.361876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-08</th>\n",
       "      <td>39150.701576</td>\n",
       "      <td>30792.961224</td>\n",
       "      <td>36963.300579</td>\n",
       "      <td>83593.164627</td>\n",
       "      <td>27312.880881</td>\n",
       "      <td>16066.050625</td>\n",
       "      <td>112320.000000</td>\n",
       "      <td>346199.059512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-09</th>\n",
       "      <td>40183.701576</td>\n",
       "      <td>30935.521088</td>\n",
       "      <td>36750.778610</td>\n",
       "      <td>84553.417686</td>\n",
       "      <td>27595.250000</td>\n",
       "      <td>15783.949375</td>\n",
       "      <td>112221.720428</td>\n",
       "      <td>348024.338764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-11</th>\n",
       "      <td>40948.119370</td>\n",
       "      <td>32325.478912</td>\n",
       "      <td>34299.208958</td>\n",
       "      <td>84578.693085</td>\n",
       "      <td>27466.899510</td>\n",
       "      <td>15847.650833</td>\n",
       "      <td>108725.763428</td>\n",
       "      <td>344191.814095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1625 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                CMIG3.SA      CPLE6.SA      FESA4.SA      PETR3.SA  \\\n",
       "Date                                                                 \n",
       "2017-02-01   9205.144042   6337.965883   4592.748901  16259.967590   \n",
       "2017-02-02   9615.273934   6465.322914   4614.160777  16038.055955   \n",
       "2017-02-03   9906.921354   6517.062172   4603.454839  16481.878019   \n",
       "2017-02-06   9788.441837   6415.574983   4571.338111  16108.668256   \n",
       "2017-02-07   9742.872944   6495.171587   4603.454839  15906.927777   \n",
       "...                  ...           ...           ...           ...   \n",
       "2023-08-04  39171.358109  30579.119728  38784.898842  83087.762699   \n",
       "2023-08-07  38572.220158  30757.320408  37570.500000  83643.696144   \n",
       "2023-08-08  39150.701576  30792.961224  36963.300579  83593.164627   \n",
       "2023-08-09  40183.701576  30935.521088  36750.778610  84553.417686   \n",
       "2023-08-11  40948.119370  32325.478912  34299.208958  84578.693085   \n",
       "\n",
       "                RANI3.SA     TAEE11.SA       UNIP6.SA  Total net worth  \n",
       "Date                                                                    \n",
       "2017-02-01   5550.509322   5179.176650    2871.536605     49997.048993  \n",
       "2017-02-02   5550.509322   5222.037277    2855.359305     50360.719485  \n",
       "2017-02-03   5550.509322   5231.563163    2851.314646     51142.703515  \n",
       "2017-02-06   5530.758809   5164.888473    2891.758229     50471.428698  \n",
       "2017-02-07   5550.509322   5131.551127    2871.536605     50302.024201  \n",
       "...                  ...           ...            ...              ...  \n",
       "2023-08-04  27004.221810  16191.813316  114720.838715    349540.013219  \n",
       "2023-08-07  28085.397628  16125.270538  113007.957001    347762.361876  \n",
       "2023-08-08  27312.880881  16066.050625  112320.000000    346199.059512  \n",
       "2023-08-09  27595.250000  15783.949375  112221.720428    348024.338764  \n",
       "2023-08-11  27466.899510  15847.650833  108725.763428    344191.814095  \n",
       "\n",
       "[1625 rows x 8 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net_worth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparison with IBOV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "ibov = yf.download(\"^BVSP\", start=start, progress=False)[\"Adj Close\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "ibov_df = pd.DataFrame(ibov)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "ibov_df.rename(columns={\"Adj Close\": \"IBOV\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparison = pd.merge(ibov_df, net_worth, how = \"inner\", on = \"Date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IBOV</th>\n",
       "      <th>CMIG3.SA</th>\n",
       "      <th>CPLE6.SA</th>\n",
       "      <th>FESA4.SA</th>\n",
       "      <th>PETR3.SA</th>\n",
       "      <th>RANI3.SA</th>\n",
       "      <th>TAEE11.SA</th>\n",
       "      <th>UNIP6.SA</th>\n",
       "      <th>Total net worth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>64836.000000</td>\n",
       "      <td>9205.144042</td>\n",
       "      <td>6337.965883</td>\n",
       "      <td>4592.748901</td>\n",
       "      <td>16259.967590</td>\n",
       "      <td>5550.509322</td>\n",
       "      <td>5179.176650</td>\n",
       "      <td>2871.536605</td>\n",
       "      <td>49997.048993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-02</th>\n",
       "      <td>64578.000000</td>\n",
       "      <td>9615.273934</td>\n",
       "      <td>6465.322914</td>\n",
       "      <td>4614.160777</td>\n",
       "      <td>16038.055955</td>\n",
       "      <td>5550.509322</td>\n",
       "      <td>5222.037277</td>\n",
       "      <td>2855.359305</td>\n",
       "      <td>50360.719485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-03</th>\n",
       "      <td>64954.000000</td>\n",
       "      <td>9906.921354</td>\n",
       "      <td>6517.062172</td>\n",
       "      <td>4603.454839</td>\n",
       "      <td>16481.878019</td>\n",
       "      <td>5550.509322</td>\n",
       "      <td>5231.563163</td>\n",
       "      <td>2851.314646</td>\n",
       "      <td>51142.703515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-06</th>\n",
       "      <td>63993.000000</td>\n",
       "      <td>9788.441837</td>\n",
       "      <td>6415.574983</td>\n",
       "      <td>4571.338111</td>\n",
       "      <td>16108.668256</td>\n",
       "      <td>5530.758809</td>\n",
       "      <td>5164.888473</td>\n",
       "      <td>2891.758229</td>\n",
       "      <td>50471.428698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-07</th>\n",
       "      <td>64199.000000</td>\n",
       "      <td>9742.872944</td>\n",
       "      <td>6495.171587</td>\n",
       "      <td>4603.454839</td>\n",
       "      <td>15906.927777</td>\n",
       "      <td>5550.509322</td>\n",
       "      <td>5131.551127</td>\n",
       "      <td>2871.536605</td>\n",
       "      <td>50302.024201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-04</th>\n",
       "      <td>119508.000000</td>\n",
       "      <td>39171.358109</td>\n",
       "      <td>30579.119728</td>\n",
       "      <td>38784.898842</td>\n",
       "      <td>83087.762699</td>\n",
       "      <td>27004.221810</td>\n",
       "      <td>16191.813316</td>\n",
       "      <td>114720.838715</td>\n",
       "      <td>349540.013219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-07</th>\n",
       "      <td>119380.000000</td>\n",
       "      <td>38572.220158</td>\n",
       "      <td>30757.320408</td>\n",
       "      <td>37570.500000</td>\n",
       "      <td>83643.696144</td>\n",
       "      <td>28085.397628</td>\n",
       "      <td>16125.270538</td>\n",
       "      <td>113007.957001</td>\n",
       "      <td>347762.361876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-08</th>\n",
       "      <td>119090.000000</td>\n",
       "      <td>39150.701576</td>\n",
       "      <td>30792.961224</td>\n",
       "      <td>36963.300579</td>\n",
       "      <td>83593.164627</td>\n",
       "      <td>27312.880881</td>\n",
       "      <td>16066.050625</td>\n",
       "      <td>112320.000000</td>\n",
       "      <td>346199.059512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-09</th>\n",
       "      <td>118409.000000</td>\n",
       "      <td>40183.701576</td>\n",
       "      <td>30935.521088</td>\n",
       "      <td>36750.778610</td>\n",
       "      <td>84553.417686</td>\n",
       "      <td>27595.250000</td>\n",
       "      <td>15783.949375</td>\n",
       "      <td>112221.720428</td>\n",
       "      <td>348024.338764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-11</th>\n",
       "      <td>118065.140625</td>\n",
       "      <td>40948.119370</td>\n",
       "      <td>32325.478912</td>\n",
       "      <td>34299.208958</td>\n",
       "      <td>84578.693085</td>\n",
       "      <td>27466.899510</td>\n",
       "      <td>15847.650833</td>\n",
       "      <td>108725.763428</td>\n",
       "      <td>344191.814095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1617 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     IBOV      CMIG3.SA      CPLE6.SA      FESA4.SA  \\\n",
       "Date                                                                  \n",
       "2017-02-01   64836.000000   9205.144042   6337.965883   4592.748901   \n",
       "2017-02-02   64578.000000   9615.273934   6465.322914   4614.160777   \n",
       "2017-02-03   64954.000000   9906.921354   6517.062172   4603.454839   \n",
       "2017-02-06   63993.000000   9788.441837   6415.574983   4571.338111   \n",
       "2017-02-07   64199.000000   9742.872944   6495.171587   4603.454839   \n",
       "...                   ...           ...           ...           ...   \n",
       "2023-08-04  119508.000000  39171.358109  30579.119728  38784.898842   \n",
       "2023-08-07  119380.000000  38572.220158  30757.320408  37570.500000   \n",
       "2023-08-08  119090.000000  39150.701576  30792.961224  36963.300579   \n",
       "2023-08-09  118409.000000  40183.701576  30935.521088  36750.778610   \n",
       "2023-08-11  118065.140625  40948.119370  32325.478912  34299.208958   \n",
       "\n",
       "                PETR3.SA      RANI3.SA     TAEE11.SA       UNIP6.SA  \\\n",
       "Date                                                                  \n",
       "2017-02-01  16259.967590   5550.509322   5179.176650    2871.536605   \n",
       "2017-02-02  16038.055955   5550.509322   5222.037277    2855.359305   \n",
       "2017-02-03  16481.878019   5550.509322   5231.563163    2851.314646   \n",
       "2017-02-06  16108.668256   5530.758809   5164.888473    2891.758229   \n",
       "2017-02-07  15906.927777   5550.509322   5131.551127    2871.536605   \n",
       "...                  ...           ...           ...            ...   \n",
       "2023-08-04  83087.762699  27004.221810  16191.813316  114720.838715   \n",
       "2023-08-07  83643.696144  28085.397628  16125.270538  113007.957001   \n",
       "2023-08-08  83593.164627  27312.880881  16066.050625  112320.000000   \n",
       "2023-08-09  84553.417686  27595.250000  15783.949375  112221.720428   \n",
       "2023-08-11  84578.693085  27466.899510  15847.650833  108725.763428   \n",
       "\n",
       "            Total net worth  \n",
       "Date                         \n",
       "2017-02-01     49997.048993  \n",
       "2017-02-02     50360.719485  \n",
       "2017-02-03     51142.703515  \n",
       "2017-02-06     50471.428698  \n",
       "2017-02-07     50302.024201  \n",
       "...                     ...  \n",
       "2023-08-04    349540.013219  \n",
       "2023-08-07    347762.361876  \n",
       "2023-08-08    346199.059512  \n",
       "2023-08-09    348024.338764  \n",
       "2023-08-11    344191.814095  \n",
       "\n",
       "[1617 rows x 9 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data normalization (data scaling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "comparison = comparison.dropna()\n",
    "comparison_scaling = comparison / comparison.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IBOV</th>\n",
       "      <th>CMIG3.SA</th>\n",
       "      <th>CPLE6.SA</th>\n",
       "      <th>FESA4.SA</th>\n",
       "      <th>PETR3.SA</th>\n",
       "      <th>RANI3.SA</th>\n",
       "      <th>TAEE11.SA</th>\n",
       "      <th>UNIP6.SA</th>\n",
       "      <th>Total net worth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-02</th>\n",
       "      <td>0.996021</td>\n",
       "      <td>1.044554</td>\n",
       "      <td>1.020094</td>\n",
       "      <td>1.004662</td>\n",
       "      <td>0.986352</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.008276</td>\n",
       "      <td>0.994366</td>\n",
       "      <td>1.007274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-03</th>\n",
       "      <td>1.001820</td>\n",
       "      <td>1.076238</td>\n",
       "      <td>1.028258</td>\n",
       "      <td>1.002331</td>\n",
       "      <td>1.013648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.010115</td>\n",
       "      <td>0.992958</td>\n",
       "      <td>1.022914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-06</th>\n",
       "      <td>0.986998</td>\n",
       "      <td>1.063367</td>\n",
       "      <td>1.012245</td>\n",
       "      <td>0.995338</td>\n",
       "      <td>0.990695</td>\n",
       "      <td>0.996442</td>\n",
       "      <td>0.997241</td>\n",
       "      <td>1.007042</td>\n",
       "      <td>1.009488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-07</th>\n",
       "      <td>0.990175</td>\n",
       "      <td>1.058416</td>\n",
       "      <td>1.024804</td>\n",
       "      <td>1.002331</td>\n",
       "      <td>0.978288</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.990804</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.006100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-04</th>\n",
       "      <td>1.843235</td>\n",
       "      <td>4.255377</td>\n",
       "      <td>4.824753</td>\n",
       "      <td>8.444812</td>\n",
       "      <td>5.109959</td>\n",
       "      <td>4.865179</td>\n",
       "      <td>3.126330</td>\n",
       "      <td>39.951028</td>\n",
       "      <td>6.991213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-07</th>\n",
       "      <td>1.841261</td>\n",
       "      <td>4.190290</td>\n",
       "      <td>4.852869</td>\n",
       "      <td>8.180395</td>\n",
       "      <td>5.144149</td>\n",
       "      <td>5.059968</td>\n",
       "      <td>3.113481</td>\n",
       "      <td>39.354524</td>\n",
       "      <td>6.955658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-08</th>\n",
       "      <td>1.836788</td>\n",
       "      <td>4.253133</td>\n",
       "      <td>4.858493</td>\n",
       "      <td>8.048187</td>\n",
       "      <td>5.141041</td>\n",
       "      <td>4.920788</td>\n",
       "      <td>3.102047</td>\n",
       "      <td>39.114946</td>\n",
       "      <td>6.924390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-09</th>\n",
       "      <td>1.826285</td>\n",
       "      <td>4.365353</td>\n",
       "      <td>4.880986</td>\n",
       "      <td>8.001913</td>\n",
       "      <td>5.200098</td>\n",
       "      <td>4.971661</td>\n",
       "      <td>3.047579</td>\n",
       "      <td>39.080721</td>\n",
       "      <td>6.960898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-08-11</th>\n",
       "      <td>1.820981</td>\n",
       "      <td>4.448395</td>\n",
       "      <td>5.100292</td>\n",
       "      <td>7.468122</td>\n",
       "      <td>5.201652</td>\n",
       "      <td>4.948537</td>\n",
       "      <td>3.059878</td>\n",
       "      <td>37.863269</td>\n",
       "      <td>6.884243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1616 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                IBOV  CMIG3.SA  CPLE6.SA  FESA4.SA  PETR3.SA  RANI3.SA  \\\n",
       "Date                                                                     \n",
       "2017-02-01  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
       "2017-02-02  0.996021  1.044554  1.020094  1.004662  0.986352  1.000000   \n",
       "2017-02-03  1.001820  1.076238  1.028258  1.002331  1.013648  1.000000   \n",
       "2017-02-06  0.986998  1.063367  1.012245  0.995338  0.990695  0.996442   \n",
       "2017-02-07  0.990175  1.058416  1.024804  1.002331  0.978288  1.000000   \n",
       "...              ...       ...       ...       ...       ...       ...   \n",
       "2023-08-04  1.843235  4.255377  4.824753  8.444812  5.109959  4.865179   \n",
       "2023-08-07  1.841261  4.190290  4.852869  8.180395  5.144149  5.059968   \n",
       "2023-08-08  1.836788  4.253133  4.858493  8.048187  5.141041  4.920788   \n",
       "2023-08-09  1.826285  4.365353  4.880986  8.001913  5.200098  4.971661   \n",
       "2023-08-11  1.820981  4.448395  5.100292  7.468122  5.201652  4.948537   \n",
       "\n",
       "            TAEE11.SA   UNIP6.SA  Total net worth  \n",
       "Date                                               \n",
       "2017-02-01   1.000000   1.000000         1.000000  \n",
       "2017-02-02   1.008276   0.994366         1.007274  \n",
       "2017-02-03   1.010115   0.992958         1.022914  \n",
       "2017-02-06   0.997241   1.007042         1.009488  \n",
       "2017-02-07   0.990804   1.000000         1.006100  \n",
       "...               ...        ...              ...  \n",
       "2023-08-04   3.126330  39.951028         6.991213  \n",
       "2023-08-07   3.113481  39.354524         6.955658  \n",
       "2023-08-08   3.102047  39.114946         6.924390  \n",
       "2023-08-09   3.047579  39.080721         6.960898  \n",
       "2023-08-11   3.059878  37.863269         6.884243  \n",
       "\n",
       "[1616 rows x 9 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comparison_scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparison_scaling.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comparison_scaling[[\"IBOV\", \"Total net worth\"]].plot();"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.10.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}