retorno.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install turingquant\n",
    "# !pip install scipy\n",
    "# !pip install seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from turingquant.metrics import returns\n",
    "from turingquant.metrics import cumulative_returns\n",
    "from turingquant.metrics import cagr\n",
    "from turingquant.metrics import mar_ratio\n",
    "from turingquant.metrics import sharpe_ratio\n",
    "from turingquant.metrics import drawdown\n",
    "from turingquant.plot_metrics import plot_drawdown\n",
    "import yfinance as yf\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lista de Funções para Estudar\n",
    "\n",
    "* [returns]()\n",
    "* cumulative_returns \n",
    "* [cagr](https://www.suno.com.br/artigos/cagr/) \n",
    "* [mar_ratio](https://www.investopedia.com/terms/m/mar-ratio.asp)\n",
    "\n",
    "Para cada uma das funções:\n",
    "1. Estudar conceitos por trás\n",
    "2. Testar função com dados da Yahoo Finance\n",
    "3. Explicar resultados e fórmulas utilizadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  2 of 2 completed\n",
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "df = yf.download([\"ITUB3.SA\",\"PETR3.SA\"], start='2015-01-01', end='2023-01-01')\n",
    "df2 = yf.download([\"ITUB3.SA\"], start='2015-01-01', end='2023-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Adj Close</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Close</th>\n",
       "      <th colspan=\"2\" halign=\"left\">High</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Low</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Open</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>ITUB3.SA</th>\n",
       "      <th>PETR3.SA</th>\n",
       "      <th>ITUB3.SA</th>\n",
       "      <th>PETR3.SA</th>\n",
       "      <th>ITUB3.SA</th>\n",
       "      <th>PETR3.SA</th>\n",
       "      <th>ITUB3.SA</th>\n",
       "      <th>PETR3.SA</th>\n",
       "      <th>ITUB3.SA</th>\n",
       "      <th>PETR3.SA</th>\n",
       "      <th>ITUB3.SA</th>\n",
       "      <th>PETR3.SA</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>11.216163</td>\n",
       "      <td>3.803154</td>\n",
       "      <td>17.079889</td>\n",
       "      <td>9.00</td>\n",
       "      <td>17.426996</td>\n",
       "      <td>9.67</td>\n",
       "      <td>16.776859</td>\n",
       "      <td>9.00</td>\n",
       "      <td>17.421488</td>\n",
       "      <td>9.58</td>\n",
       "      <td>220704</td>\n",
       "      <td>12710900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>11.089526</td>\n",
       "      <td>3.494677</td>\n",
       "      <td>16.887053</td>\n",
       "      <td>8.27</td>\n",
       "      <td>17.129477</td>\n",
       "      <td>8.96</td>\n",
       "      <td>16.468319</td>\n",
       "      <td>8.25</td>\n",
       "      <td>16.782370</td>\n",
       "      <td>8.64</td>\n",
       "      <td>396214</td>\n",
       "      <td>25730600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>11.198072</td>\n",
       "      <td>3.405936</td>\n",
       "      <td>17.052341</td>\n",
       "      <td>8.06</td>\n",
       "      <td>17.267218</td>\n",
       "      <td>8.53</td>\n",
       "      <td>16.870523</td>\n",
       "      <td>7.78</td>\n",
       "      <td>16.887053</td>\n",
       "      <td>8.28</td>\n",
       "      <td>558112</td>\n",
       "      <td>29754600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>11.451335</td>\n",
       "      <td>3.570739</td>\n",
       "      <td>17.438017</td>\n",
       "      <td>8.45</td>\n",
       "      <td>17.900826</td>\n",
       "      <td>8.51</td>\n",
       "      <td>17.355371</td>\n",
       "      <td>8.08</td>\n",
       "      <td>17.382919</td>\n",
       "      <td>8.30</td>\n",
       "      <td>696234</td>\n",
       "      <td>34128300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>11.614151</td>\n",
       "      <td>3.811606</td>\n",
       "      <td>17.685949</td>\n",
       "      <td>9.02</td>\n",
       "      <td>17.895315</td>\n",
       "      <td>9.26</td>\n",
       "      <td>17.316805</td>\n",
       "      <td>8.52</td>\n",
       "      <td>17.592285</td>\n",
       "      <td>8.60</td>\n",
       "      <td>267894</td>\n",
       "      <td>37487300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Adj Close                Close                High            \n",
       "             ITUB3.SA  PETR3.SA   ITUB3.SA PETR3.SA   ITUB3.SA PETR3.SA   \n",
       "Date                                                                      \n",
       "2015-01-02  11.216163  3.803154  17.079889     9.00  17.426996     9.67  \\\n",
       "2015-01-05  11.089526  3.494677  16.887053     8.27  17.129477     8.96   \n",
       "2015-01-06  11.198072  3.405936  17.052341     8.06  17.267218     8.53   \n",
       "2015-01-07  11.451335  3.570739  17.438017     8.45  17.900826     8.51   \n",
       "2015-01-08  11.614151  3.811606  17.685949     9.02  17.895315     9.26   \n",
       "\n",
       "                  Low                Open            Volume            \n",
       "             ITUB3.SA PETR3.SA   ITUB3.SA PETR3.SA ITUB3.SA  PETR3.SA  \n",
       "Date                                                                   \n",
       "2015-01-02  16.776859     9.00  17.421488     9.58   220704  12710900  \n",
       "2015-01-05  16.468319     8.25  16.782370     8.64   396214  25730600  \n",
       "2015-01-06  16.870523     7.78  16.887053     8.28   558112  29754600  \n",
       "2015-01-07  17.355371     8.08  17.382919     8.30   696234  34128300  \n",
       "2015-01-08  17.316805     8.52  17.592285     8.60   267894  37487300  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Função _returns_\n",
    "\n",
    "Calcula a variação percentual de um ativo, dia a dia. Abaixo, exemplos de sua utilização."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get just the adjusted close price of days 01/01/2020 and 01/01/2022\n",
    "data = returns(df['Adj Close'].loc['2015-01-01':'2023-01-01'])\n",
    "sns.histplot(data);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- seria mais interessante automatizar"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparar este histograma com periodos mais curtos ou periodos onde a acao tenha ido melhor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# group returns by month: histograma de retornos mensais do ativo\n",
    "monthly_data2 = returns(df['Adj Close'].loc['2015-01-01':'2023-01-01']).groupby(pd.Grouper(freq='M')).sum()\n",
    "sns.histplot(monthly_data2);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Função _cumulative\\_returns_\n",
    "\n",
    "**Representa o retorno cumulativo de determinado ativo (ou portfolio) em um dado período.** <br><br>\n",
    "\n",
    "O retorno cumulativo é bastante útil para avaliar o desempenho geral de um investimento no horizonte de tempo determinado, sendo possível observar também as flutuações na cotação desse ativo ou portfolio<br><br>\n",
    "\n",
    "Sua fórmula é dada por: \n",
    "$Ret_{acum} = (1 + Ret_{t_{1}}) * (1 + Ret_{t_{2}}) * ... * (1 + Ret_{t_{n}})$\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# retorno logaritmico\n",
    "data_log = cumulative_returns(returns(df['Adj Close'].loc['2020-01-01':'2022-01-01']), 'log')   \n",
    "sns.histplot(data_log, \n",
    "             color = \"darkorange\",\n",
    "             kde = True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-01-02         NaN\n",
       "2020-01-03   -0.008282\n",
       "2020-01-06   -0.018489\n",
       "2020-01-07   -0.037864\n",
       "2020-01-08   -0.048062\n",
       "                ...   \n",
       "2021-12-23   -0.311456\n",
       "2021-12-27   -0.302115\n",
       "2021-12-28   -0.305714\n",
       "2021-12-29   -0.308810\n",
       "2021-12-30   -0.320715\n",
       "Name: ITUB3.SA, Length: 495, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_log['ITUB3.SA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "lineplot() takes from 0 to 1 positional arguments but 2 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[39], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# line plot fica mais simples de visualizar\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m sns\u001b[39m.\u001b[39;49mlineplot(data_log\u001b[39m.\u001b[39;49mindex, data_log[\u001b[39m'\u001b[39;49m\u001b[39mITUB3.SA\u001b[39;49m\u001b[39m'\u001b[39;49m], color \u001b[39m=\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39mdarkorange\u001b[39;49m\u001b[39m\"\u001b[39;49m);\n\u001b[1;32m      3\u001b[0m sns\u001b[39m.\u001b[39mlineplot(data_log\u001b[39m.\u001b[39mindex, data_log[\u001b[39m'\u001b[39m\u001b[39mPETR3.SA\u001b[39m\u001b[39m'\u001b[39m], color \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mblue\u001b[39m\u001b[39m\"\u001b[39m);\n\u001b[1;32m      4\u001b[0m plt\u001b[39m.\u001b[39mlegend(labels\u001b[39m=\u001b[39m[\u001b[39m'\u001b[39m\u001b[39mITUB3.SA\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mPETR3.SA\u001b[39m\u001b[39m'\u001b[39m]);\n",
      "\u001b[0;31mTypeError\u001b[0m: lineplot() takes from 0 to 1 positional arguments but 2 were given"
     ]
    }
   ],
   "source": [
    "# line plot fica mais simples de visualizar\n",
    "sns.lineplot(data_log['ITUB3.SA'], palette = [\"darkorange\"]);\n",
    "sns.lineplot(data_log['PETR3.SA'], palette = [\"blue\"]);\n",
    "plt.legend(labels=['ITUB3.SA', 'PETR3.SA']);\n",
    "plt.gcf().set_size_inches(15, 8);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "e o retorno somado, a evoluca"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "tem dois tipos:\n",
    "- na pratica talvez seja mais usado o simples\n",
    "- o log pode ajudar em alguns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# retorno simples \n",
    "data_simp = cumulative_returns(returns(df['Adj Close'].loc['2020-01-01':'2022-01-01']), 'simp')   \n",
    "sns.histplot(data_simp, \n",
    "             color = \"darkblue\",\n",
    "             kde = True);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "log retorno pode ser interessante, e a soma dos retornos: quando tem uma queda para voltar para o mesmo patamar e o mesmo valor"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Função _cagr_\n",
    "\n",
    "**CAGR é a sigla para Compound Annual Growth Rate, ou seja, Taxa de Crescimento Anual Composta** <br><br>\n",
    "\n",
    "Geralmente apresentada em termos percentuais, nada mais é do que a taxa de crescimento anual que, se aplicada de forma constante ao longo do período considerado, resultaria no crescimento total observado na cotação de um ativo (ou portfolio). É bastante útil para comparar a performance de investimentos em diferentes horizontes de tempo, pois utiliza a mesma base de tempo (anual). Cabe ressaltar que o CAGR não mostra explicitamente a variação existente na cotação (o quanto flutuou o preço do ativo). Um CAGR de 10%, por exemplo, pode \"esconder\" momentos de retornos negativos e momentos de retorno muito positivos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'cagr' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m# CAGR anual - time_scale de 252 dias\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[39m# 2020 a 2022\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m cagr_anual_20_22 \u001b[39m=\u001b[39m cagr(returns(df2[\u001b[39m'\u001b[39m\u001b[39mAdj Close\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mloc[\u001b[39m'\u001b[39m\u001b[39m2020-01-01\u001b[39m\u001b[39m'\u001b[39m:\u001b[39m'\u001b[39m\u001b[39m2022-01-01\u001b[39m\u001b[39m'\u001b[39m]), time_scale \u001b[39m=\u001b[39m \u001b[39m252\u001b[39m)\n\u001b[1;32m      4\u001b[0m \u001b[39mprint\u001b[39m(cagr_anual_20_22)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'cagr' is not defined"
     ]
    }
   ],
   "source": [
    "# CAGR anual - time_scale de 252 dias\n",
    "# 2020 a 2022\n",
    "cagr_anual_20_22 = cagr(returns(df2['Adj Close'].loc['2020-01-01':'2022-01-01']), time_scale = 252)\n",
    "print(cagr_anual_20_22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.19421380625883722\n"
     ]
    }
   ],
   "source": [
    "# CAGR anual - time_scale de 252 dias\n",
    "# 2018 a 2020\n",
    "cagr_anual_18_20 = cagr(returns(df2['Adj Close'].loc['2018-01-01':'2020-01-01']), time_scale = 252)\n",
    "print(cagr_anual_18_20)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este valores obtidos mostram que o ativo \"ITUB3.SA\" mostrou um retorno anualizado médio de -21% no período 2018 - 2020. Ou seja, se um investidor tivesse comprado o ativo no primeiro dia de mercado do ano 2018 e tivesse vendido o ativo no último dia do ano 2020, o retorno anualizado médio teria sido um prejuiço de -21%.\n",
    "\n",
    "No período de 2018 - 2020, podemos ver que o desempenho do ativo foi bem melhor, pois o retorno anualizado médio (cagr) foi de 19%.\n",
    "\n",
    "Em resumo:\n",
    "- CAGR é porcentagem media que o investimento rendeu por ano\n",
    "- O CAGR é uma métrica importante para fazer a avaliação de uma estratégia de investimentos: Backtesting"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Função _mar\\_ratio_\n",
    "\n",
    "**MAR Ratio é a sigla para Minimum Acceptable Return Ratio, ou seja, Taxa de Retorno Mínimo Aceitável** <br><br>\n",
    "\n",
    "O seu cálculo leva em consideração duas coisas: 1) CAGR (explicado acima) e 2) Drawdown <br>\n",
    "O Drawdown representa o quanto a cotação de um ativo (ou portfolio) caiu do o seu pico anteriormente alcançado até seu vale subsequente. O Máximo Drawdown representa a maior queda (em termos percentuais) de um pico até seu vale subsequente, mostrando o impacto negativo que um investidor teria tido se tivesse comprado o ativo no pior pico possível e vendido no ponto mais baixo do Drawdown. <br><br>\n",
    "\n",
    "Finalmente, o MAR ratio pode ser expresso pela razão entre o CAGR e o Máximo Drawdown observados no período. É, portanto, uma medida de avaliação da relação risco-retorno, funcionando como uma métrica para ajustar o retorno de um investimento ao risco a que ele está exposto (tomando como base seu desempenho histórico)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.004302655272216676\n"
     ]
    }
   ],
   "source": [
    "# MAR Ratio para 2 anos (2020 - 2022)\n",
    "# time_window = 2; time_scale = 252\n",
    "\n",
    "marratio_20_22 = mar_ratio(returns(df2['Adj Close'].loc['2020-01-01':'2022-01-01']), time_window = 2, time_scale = 252)\n",
    "print(marratio_20_22)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0010287830388190514\n"
     ]
    }
   ],
   "source": [
    "# MAR Ratio para 1 ano (2020 - 2021)\n",
    "# time_window = 1; time_scale = 252\n",
    "\n",
    "marratio_20_21 = mar_ratio(returns(df2['Adj Close'].loc['2020-01-01':'2021-01-01']), time_window = 1, time_scale = 252)\n",
    "print(marratio_20_21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.008015418211259895\n"
     ]
    }
   ],
   "source": [
    "# MAR Ratio para 1 ano (2021 - 2022)\n",
    "# time_window = 1; time_scale = 252\n",
    "\n",
    "marratio_21_22 = mar_ratio(returns(df2['Adj Close'].loc['2021-01-01':'2022-01-01']), time_window = 1, time_scale = 252)\n",
    "print(marratio_21_22)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analisando os valores de **MAR Ratio** obtidos nos intervalos, nota-se que o período de 2020 a 2021 obteve um resultado maior que o de 2021 a 2022, apesar de ambos serem negativos. (??) Portanto, a relação de risco em relação ao retorno do investimento mostrou-se menos prejudicial ao investidor no primeiro período. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Função _sharpe\\_ratio_\n",
    "\n",
    "De forma similar ao **MAR Ratio**, o **Sharpe Ratio** é uma métrica utilizada para estimar retornos ajustados ao risco de um ativo. A função utiliza como base a média histórica e o desvio padrão dos retornos em questão, comparando-os com um retorno _risk free_. Nesse caso, tomamos a **Ibovespa** como parâmetro de comparação sem riscos, ilustrando um investimento conservador. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.09200314560232274\n",
      "-0.8037771352580643\n"
     ]
    }
   ],
   "source": [
    "print(sharpe_ratio(returns(df2['Adj Close'].loc['2020-01-01':'2021-01-01']), risk_free=0, time_scale=252)) # 2020 a 2021 \n",
    "print(sharpe_ratio(returns(df2['Adj Close'].loc['2021-01-01':'2022-01-01']), risk_free=0, time_scale=252)) # 2021 a 2022"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como os valores de **Sharpe Ratio** estão negativos, podemos inferir que o desempenho do retorno _risk free_ foi melhor que o do ativo que estamos analisando. Apesar de ambos os valores serem menores que zero, percebe-se que a performace do ativo entre 2020 e 2021 gerou um valor de _sharpe ratio_ maior que a de 2021 a 2022, evidenciando que o investimento tornou-se menos vantajoso dado seu retorno e seu risco balanceados. (?)\n",
    "\n",
    "Tal resultado condiz com o encontrado nos cálculos do **MAR Ratio**, já que eles também ilustraram um menor desempenho no primeiro período analisado. "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TODO: O QUE REPRESENTAM ESTES VALORES OBTIDOS????"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- o maximo de retorno q estamos tendo considerando o risco"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "mar ratio ja usa o cagr:\n",
    "\n",
    "- comparar o mar ratio () de 2 acoes e do drawdown e do preco do ativo: automatizar !!!!\n",
    "\n",
    "- acrescentar garman class\n",
    "\n",
    "- graficos garman class, preco e drawdown\n",
    "\n",
    "- input: janela de tempo, codigos dos ativos"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Função Drawdown\n",
    "O **DrawDown** é uma métrica que expressa a taxa de oscilação entre o preço máximo e mínimo de uma ação em uma determinada janela de tempo. Para sua implementação:\n",
    "\n",
    "1. Retornos acumulados ao longo do tempo\n",
    "2. Para cada instante de tempo, fazer a proporção do maior retorno até então com o retorno acumulado\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Estudando a implementação dessa função da biblioteca _turingquant_, é exatamente isso que é feito:\n",
    "\n",
    "```python\n",
    "# Snippet extraído da biblioteca turingquant \n",
    "\n",
    "def drawdown(returns):\n",
    "    \"\"\"\n",
    "    Calcula o drawdown percentual para uma série de retornos.\n",
    "\n",
    "    Args:\n",
    "        returns (pd.Series): série de retornos para a qual será calculado o drawdown.\n",
    "    \n",
    "    Returns:\n",
    "        pd.Series: uma série com os valores percentuais do Drawdown.\n",
    "    \"\"\"\n",
    "\n",
    "    cum_returns = (1 + returns).cumprod() # retorno acumulado ao longo do tempo\n",
    "    peeks = cum_returns.cummax() # para cada instante de tempo, calcula o retorno máximo até então\n",
    "    drawdowns = pd.Series((cum_returns/peeks - 1)*100, # proporção entre o retorno acumulado e o máximo (em termos percentuais)\n",
    "                          name='Drawdown')\n",
    "    return drawdowns\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2020-01-02          NaN\n",
       "2020-01-03     0.000000\n",
       "2020-01-06    -1.020716\n",
       "2020-01-07    -2.938472\n",
       "2020-01-08    -3.928237\n",
       "                ...    \n",
       "2020-12-22   -12.316746\n",
       "2020-12-23   -10.981480\n",
       "2020-12-28    -9.614376\n",
       "2020-12-29    -9.964115\n",
       "2020-12-30   -11.203984\n",
       "Name: Drawdown, Length: 248, dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "ret = returns(df2['Adj Close'].loc['2020-01-01':'2021-01-01'])\n",
    "# Exemplo de utilização\n",
    "ddowns= drawdown(ret)\n",
    "ddowns"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A biblioteca também define uma função para plotar os drawdowns. Ela foi adaptada e executada a seguir:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Mime type rendering requires nbformat>=4.2.0 but it is not installed",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m~/repos/turing/turingquant-exemplos/env/lib/python3.10/site-packages/IPython/core/formatters.py:922\u001b[0m, in \u001b[0;36mIPythonDisplayFormatter.__call__\u001b[0;34m(self, obj)\u001b[0m\n\u001b[1;32m    920\u001b[0m method \u001b[39m=\u001b[39m get_real_method(obj, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mprint_method)\n\u001b[1;32m    921\u001b[0m \u001b[39mif\u001b[39;00m method \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 922\u001b[0m     method()\n\u001b[1;32m    923\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mTrue\u001b[39;00m\n",
      "File \u001b[0;32m~/repos/turing/turingquant-exemplos/env/lib/python3.10/site-packages/plotly/basedatatypes.py:834\u001b[0m, in \u001b[0;36mBaseFigure._ipython_display_\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    831\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mplotly\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mio\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mpio\u001b[39;00m\n\u001b[1;32m    833\u001b[0m \u001b[39mif\u001b[39;00m pio\u001b[39m.\u001b[39mrenderers\u001b[39m.\u001b[39mrender_on_display \u001b[39mand\u001b[39;00m pio\u001b[39m.\u001b[39mrenderers\u001b[39m.\u001b[39mdefault:\n\u001b[0;32m--> 834\u001b[0m     pio\u001b[39m.\u001b[39;49mshow(\u001b[39mself\u001b[39;49m)\n\u001b[1;32m    835\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    836\u001b[0m     \u001b[39mprint\u001b[39m(\u001b[39mrepr\u001b[39m(\u001b[39mself\u001b[39m))\n",
      "File \u001b[0;32m~/repos/turing/turingquant-exemplos/env/lib/python3.10/site-packages/plotly/io/_renderers.py:396\u001b[0m, in \u001b[0;36mshow\u001b[0;34m(fig, renderer, validate, **kwargs)\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    392\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mMime type rendering requires ipython but it is not installed\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    393\u001b[0m         )\n\u001b[1;32m    395\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m nbformat \u001b[39mor\u001b[39;00m Version(nbformat\u001b[39m.\u001b[39m__version__) \u001b[39m<\u001b[39m Version(\u001b[39m\"\u001b[39m\u001b[39m4.2.0\u001b[39m\u001b[39m\"\u001b[39m):\n\u001b[0;32m--> 396\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    397\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mMime type rendering requires nbformat>=4.2.0 but it is not installed\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    398\u001b[0m         )\n\u001b[1;32m    400\u001b[0m     ipython_display\u001b[39m.\u001b[39mdisplay(bundle, raw\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m    402\u001b[0m \u001b[39m# external renderers\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: Mime type rendering requires nbformat>=4.2.0 but it is not installed"
     ]
    },
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "fillpattern": {
          "shape": ""
         },
         "hovertemplate": "Date=%{x}<br>Drawdown=%{y}<extra></extra>",
         "legendgroup": "",
         "line": {
          "color": "#636efa"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "",
         "orientation": "v",
         "showlegend": false,
         "stackgroup": "1",
         "type": "scatter",
         "x": [
          "2020-01-02T00:00:00",
          "2020-01-03T00:00:00",
          "2020-01-06T00:00:00",
          "2020-01-07T00:00:00",
          "2020-01-08T00:00:00",
          "2020-01-09T00:00:00",
          "2020-01-10T00:00:00",
          "2020-01-13T00:00:00",
          "2020-01-14T00:00:00",
          "2020-01-15T00:00:00",
          "2020-01-16T00:00:00",
          "2020-01-17T00:00:00",
          "2020-01-20T00:00:00",
          "2020-01-21T00:00:00",
          "2020-01-22T00:00:00",
          "2020-01-23T00:00:00",
          "2020-01-24T00:00:00",
          "2020-01-27T00:00:00",
          "2020-01-28T00:00:00",
          "2020-01-29T00:00:00",
          "2020-01-30T00:00:00",
          "2020-01-31T00:00:00",
          "2020-02-03T00:00:00",
          "2020-02-04T00:00:00",
          "2020-02-05T00:00:00",
          "2020-02-06T00:00:00",
          "2020-02-07T00:00:00",
          "2020-02-10T00:00:00",
          "2020-02-11T00:00:00",
          "2020-02-12T00:00:00",
          "2020-02-13T00:00:00",
          "2020-02-14T00:00:00",
          "2020-02-17T00:00:00",
          "2020-02-18T00:00:00",
          "2020-02-19T00:00:00",
          "2020-02-20T00:00:00",
          "2020-02-21T00:00:00",
          "2020-02-26T00:00:00",
          "2020-02-27T00:00:00",
          "2020-02-28T00:00:00",
          "2020-03-02T00:00:00",
          "2020-03-03T00:00:00",
          "2020-03-04T00:00:00",
          "2020-03-05T00:00:00",
          "2020-03-06T00:00:00",
          "2020-03-09T00:00:00",
          "2020-03-10T00:00:00",
          "2020-03-11T00:00:00",
          "2020-03-12T00:00:00",
          "2020-03-13T00:00:00",
          "2020-03-16T00:00:00",
          "2020-03-17T00:00:00",
          "2020-03-18T00:00:00",
          "2020-03-19T00:00:00",
          "2020-03-20T00:00:00",
          "2020-03-23T00:00:00",
          "2020-03-24T00:00:00",
          "2020-03-25T00:00:00",
          "2020-03-26T00:00:00",
          "2020-03-27T00:00:00",
          "2020-03-30T00:00:00",
          "2020-03-31T00:00:00",
          "2020-04-01T00:00:00",
          "2020-04-02T00:00:00",
          "2020-04-03T00:00:00",
          "2020-04-06T00:00:00",
          "2020-04-07T00:00:00",
          "2020-04-08T00:00:00",
          "2020-04-09T00:00:00",
          "2020-04-13T00:00:00",
          "2020-04-14T00:00:00",
          "2020-04-15T00:00:00",
          "2020-04-16T00:00:00",
          "2020-04-17T00:00:00",
          "2020-04-20T00:00:00",
          "2020-04-22T00:00:00",
          "2020-04-23T00:00:00",
          "2020-04-24T00:00:00",
          "2020-04-27T00:00:00",
          "2020-04-28T00:00:00",
          "2020-04-29T00:00:00",
          "2020-04-30T00:00:00",
          "2020-05-04T00:00:00",
          "2020-05-05T00:00:00",
          "2020-05-06T00:00:00",
          "2020-05-07T00:00:00",
          "2020-05-08T00:00:00",
          "2020-05-11T00:00:00",
          "2020-05-12T00:00:00",
          "2020-05-13T00:00:00",
          "2020-05-14T00:00:00",
          "2020-05-15T00:00:00",
          "2020-05-18T00:00:00",
          "2020-05-19T00:00:00",
          "2020-05-20T00:00:00",
          "2020-05-21T00:00:00",
          "2020-05-22T00:00:00",
          "2020-05-25T00:00:00",
          "2020-05-26T00:00:00",
          "2020-05-27T00:00:00",
          "2020-05-28T00:00:00",
          "2020-05-29T00:00:00",
          "2020-06-01T00:00:00",
          "2020-06-02T00:00:00",
          "2020-06-03T00:00:00",
          "2020-06-04T00:00:00",
          "2020-06-05T00:00:00",
          "2020-06-08T00:00:00",
          "2020-06-09T00:00:00",
          "2020-06-10T00:00:00",
          "2020-06-12T00:00:00",
          "2020-06-15T00:00:00",
          "2020-06-16T00:00:00",
          "2020-06-17T00:00:00",
          "2020-06-18T00:00:00",
          "2020-06-19T00:00:00",
          "2020-06-22T00:00:00",
          "2020-06-23T00:00:00",
          "2020-06-24T00:00:00",
          "2020-06-25T00:00:00",
          "2020-06-26T00:00:00",
          "2020-06-29T00:00:00",
          "2020-06-30T00:00:00",
          "2020-07-01T00:00:00",
          "2020-07-02T00:00:00",
          "2020-07-03T00:00:00",
          "2020-07-06T00:00:00",
          "2020-07-07T00:00:00",
          "2020-07-08T00:00:00",
          "2020-07-09T00:00:00",
          "2020-07-10T00:00:00",
          "2020-07-13T00:00:00",
          "2020-07-14T00:00:00",
          "2020-07-15T00:00:00",
          "2020-07-16T00:00:00",
          "2020-07-17T00:00:00",
          "2020-07-20T00:00:00",
          "2020-07-21T00:00:00",
          "2020-07-22T00:00:00",
          "2020-07-23T00:00:00",
          "2020-07-24T00:00:00",
          "2020-07-27T00:00:00",
          "2020-07-28T00:00:00",
          "2020-07-29T00:00:00",
          "2020-07-30T00:00:00",
          "2020-07-31T00:00:00",
          "2020-08-03T00:00:00",
          "2020-08-04T00:00:00",
          "2020-08-05T00:00:00",
          "2020-08-06T00:00:00",
          "2020-08-07T00:00:00",
          "2020-08-10T00:00:00",
          "2020-08-11T00:00:00",
          "2020-08-12T00:00:00",
          "2020-08-13T00:00:00",
          "2020-08-14T00:00:00",
          "2020-08-17T00:00:00",
          "2020-08-18T00:00:00",
          "2020-08-19T00:00:00",
          "2020-08-20T00:00:00",
          "2020-08-21T00:00:00",
          "2020-08-24T00:00:00",
          "2020-08-25T00:00:00",
          "2020-08-26T00:00:00",
          "2020-08-27T00:00:00",
          "2020-08-28T00:00:00",
          "2020-08-31T00:00:00",
          "2020-09-01T00:00:00",
          "2020-09-02T00:00:00",
          "2020-09-03T00:00:00",
          "2020-09-04T00:00:00",
          "2020-09-08T00:00:00",
          "2020-09-09T00:00:00",
          "2020-09-10T00:00:00",
          "2020-09-11T00:00:00",
          "2020-09-14T00:00:00",
          "2020-09-15T00:00:00",
          "2020-09-16T00:00:00",
          "2020-09-17T00:00:00",
          "2020-09-18T00:00:00",
          "2020-09-21T00:00:00",
          "2020-09-22T00:00:00",
          "2020-09-23T00:00:00",
          "2020-09-24T00:00:00",
          "2020-09-25T00:00:00",
          "2020-09-28T00:00:00",
          "2020-09-29T00:00:00",
          "2020-09-30T00:00:00",
          "2020-10-01T00:00:00",
          "2020-10-02T00:00:00",
          "2020-10-05T00:00:00",
          "2020-10-06T00:00:00",
          "2020-10-07T00:00:00",
          "2020-10-08T00:00:00",
          "2020-10-09T00:00:00",
          "2020-10-13T00:00:00",
          "2020-10-14T00:00:00",
          "2020-10-15T00:00:00",
          "2020-10-16T00:00:00",
          "2020-10-19T00:00:00",
          "2020-10-20T00:00:00",
          "2020-10-21T00:00:00",
          "2020-10-22T00:00:00",
          "2020-10-23T00:00:00",
          "2020-10-26T00:00:00",
          "2020-10-27T00:00:00",
          "2020-10-28T00:00:00",
          "2020-10-29T00:00:00",
          "2020-10-30T00:00:00",
          "2020-11-03T00:00:00",
          "2020-11-04T00:00:00",
          "2020-11-05T00:00:00",
          "2020-11-06T00:00:00",
          "2020-11-09T00:00:00",
          "2020-11-10T00:00:00",
          "2020-11-11T00:00:00",
          "2020-11-12T00:00:00",
          "2020-11-13T00:00:00",
          "2020-11-16T00:00:00",
          "2020-11-17T00:00:00",
          "2020-11-18T00:00:00",
          "2020-11-19T00:00:00",
          "2020-11-23T00:00:00",
          "2020-11-24T00:00:00",
          "2020-11-25T00:00:00",
          "2020-11-26T00:00:00",
          "2020-11-27T00:00:00",
          "2020-11-30T00:00:00",
          "2020-12-01T00:00:00",
          "2020-12-02T00:00:00",
          "2020-12-03T00:00:00",
          "2020-12-04T00:00:00",
          "2020-12-07T00:00:00",
          "2020-12-08T00:00:00",
          "2020-12-09T00:00:00",
          "2020-12-10T00:00:00",
          "2020-12-11T00:00:00",
          "2020-12-14T00:00:00",
          "2020-12-15T00:00:00",
          "2020-12-16T00:00:00",
          "2020-12-17T00:00:00",
          "2020-12-18T00:00:00",
          "2020-12-21T00:00:00",
          "2020-12-22T00:00:00",
          "2020-12-23T00:00:00",
          "2020-12-28T00:00:00",
          "2020-12-29T00:00:00",
          "2020-12-30T00:00:00"
         ],
         "xaxis": "x",
         "y": [
          null,
          0,
          -1.020716093895302,
          -2.938472218101029,
          -3.9282373358530243,
          -4.8252504522711455,
          -5.969696437535921,
          -4.639658979453465,
          -5.103600654895734,
          -6.4645890891215085,
          -6.309921678554852,
          -4.825250452271113,
          -6.4955198798456015,
          -8.75347433117798,
          -7.268789647948093,
          -5.289198856186472,
          -6.464589089121498,
          -7.6090283459131935,
          -8.413208719320586,
          -9.217442920512564,
          -7.9802045630755,
          -8.939065803995671,
          -8.459392958504885,
          -7.840447448842214,
          -7.7166839151073585,
          -8.118992777185973,
          -7.871418610404756,
          -7.004898933960879,
          -3.1056142177480317,
          -2.61045915571253,
          -4.343471594707982,
          -5.581362614033303,
          -6.076477305230366,
          -6.169316776714195,
          -5.240908604929828,
          -5.333754804886725,
          -7.619087413159809,
          -11.360865303168522,
          -11.423749612524059,
          -10.040214065025177,
          -10.024509808868986,
          -10.93683693000498,
          -10.024509808868986,
          -11.534567564063314,
          -14.397445387170805,
          -18.204085757619005,
          -16.190657474765036,
          -20.406301538001102,
          -28.428572514739226,
          -20.53211052755065,
          -28.428572514739237,
          -25.15676501204768,
          -31.543135601676543,
          -30.158886836031762,
          -33.61947510777837,
          -38.02385956923106,
          -35.41269392384567,
          -31.88915405801277,
          -27.547721190646378,
          -30.25326712784967,
          -28.176907436328634,
          -30.536414731776496,
          -34.644692550160606,
          -34.07802054781478,
          -37.068779730178804,
          -33.857676511566616,
          -30.111397273070985,
          -27.718806075515147,
          -28.379912197463476,
          -28.22247265599008,
          -28.128085635699087,
          -29.92253576233903,
          -30.74102086950309,
          -30.331761494738384,
          -30.646566564481358,
          -31.402106806032204,
          -33.13359861011279,
          -35.494720739098575,
          -34.73916704060158,
          -29.229933657928342,
          -28.474366502485214,
          -30.7410208695031,
          -33.52901751576297,
          -31.323841207228032,
          -32.26891580681828,
          -34.4740988438263,
          -32.77295245331231,
          -31.607338691754727,
          -34.15907173447186,
          -34.31661893151444,
          -32.30041851775373,
          -34.94667315022332,
          -32.6469483380436,
          -35.04118128302965,
          -34.63161912697659,
          -32.23744000977513,
          -31.827877853722065,
          -28.551575731016655,
          -31.00882082634666,
          -28.362572922350125,
          -30.00070043404708,
          -30.882776340239516,
          -28.912491026740106,
          -25.161118334126975,
          -22.765257098656853,
          -20.558580340626264,
          -18.887779363678902,
          -17.059350448012033,
          -17.91048883500729,
          -20.842246036979827,
          -22.859825787720855,
          -24.972001141418186,
          -23.395768853534815,
          -22.702197849001358,
          -20.71616790850731,
          -16.80714036328238,
          -20.211781381413406,
          -20.905318743581482,
          -22.60766280230275,
          -22.166308610972017,
          -23.994717341219662,
          -22.04022375402642,
          -24.341462472647933,
          -23.316285401104153,
          -22.496158546509893,
          -22.590781063358488,
          -20.00415415927631,
          -21.89679289349442,
          -20.666585790420346,
          -20.729692139387357,
          -20.28806880913364,
          -21.202845094468803,
          -20.47733402825007,
          -20.00415415927632,
          -21.171295284221834,
          -20.85583082411755,
          -20.035703969523276,
          -19.278656550003724,
          -20.19341937839274,
          -21.549832450927763,
          -21.928349432214468,
          -19.08937787394114,
          -19.184020576208958,
          -17.70144190505144,
          -18.742397245955246,
          -21.45518974865993,
          -20.460970381732547,
          -23.964472854749197,
          -23.93287594519069,
          -22.670365442885288,
          -24.05914247090931,
          -23.206954442114412,
          -23.77507306617127,
          -24.090699009629347,
          -26.142311377181883,
          -26.142311377181883,
          -27.625784935258324,
          -26.699146351892576,
          -26.98387514699193,
          -27.17369210090048,
          -27.047136250839664,
          -26.066407472426935,
          -26.35116990989167,
          -27.96459046845855,
          -27.26862412751053,
          -26.50933612645713,
          -28.628980085267298,
          -26.682056030283018,
          -26.966946308736162,
          -26.01724251967058,
          -24.78262153825953,
          -26.42879630375373,
          -26.840329902417682,
          -28.074971069247955,
          -29.056345781075866,
          -28.10661507811797,
          -28.86638752923275,
          -29.372900253818244,
          -29.309592050658985,
          -30.322617499829985,
          -30.76581529278316,
          -30.544213032070022,
          -31.873819867875696,
          -30.89242497062855,
          -31.050732485128584,
          -31.082383222471666,
          -32.47529153296351,
          -32.31700420388272,
          -31.98439559438424,
          -31.825993881261173,
          -31.66759889661117,
          -31.889362640678087,
          -32.11110619932577,
          -28.689724740185063,
          -28.94317959243352,
          -29.164909694135066,
          -29.671812670158882,
          -30.146990895635806,
          -31.287433439421175,
          -30.780584291181956,
          -28.43629680182892,
          -27.992782770641234,
          -25.078304287524354,
          -25.83858810259281,
          -25.141639404575912,
          -27.074083785503568,
          -29.95691825975043,
          -28.689724740185063,
          -30.368734454283498,
          -29.37017522222296,
          -26.612160651983665,
          -24.995422956190982,
          -24.710115512407206,
          -19.606265904007692,
          -17.482275351591316,
          -17.76758952384816,
          -19.95492865026225,
          -18.243110901451885,
          -15.992329002417083,
          -16.08742924085399,
          -18.021178945558127,
          -17.64077799181053,
          -17.418893135228274,
          -15.580210026595665,
          -16.436152543366212,
          -17.926105621013534,
          -17.19697463628065,
          -18.338224596834973,
          -15.08695912244027,
          -15.023516349819522,
          -14.991818513164922,
          -14.547739290238981,
          -14.29394128586372,
          -14.864926239450371,
          -13.976781250544622,
          -11.470943122669597,
          -11.998818749267059,
          -13.397695215376103,
          -12.793633088154543,
          -10.695325117464039,
          -10.695325117464039,
          -10.854292022325385,
          -12.857217158709844,
          -12.316745830516652,
          -10.9814803488552,
          -9.614375732604586,
          -9.96411503455107,
          -11.203984224960328
         ],
         "yaxis": "y"
        }
       ],
       "layout": {
        "legend": {
         "tracegroupgap": 0
        },
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "parcoords"
           }
          ],
          "pie": [
           {
            "automargin": true,
            "type": "pie"
           }
          ],
          "scatter": [
           {
            "fillpattern": {
             "fillmode": "overlay",
             "size": 10,
             "solidity": 0.2
            },
            "type": "scatter"
           }
          ],
          "scatter3d": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatter3d"
           }
          ],
          "scattercarpet": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattercarpet"
           }
          ],
          "scattergeo": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergeo"
           }
          ],
          "scattergl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattergl"
           }
          ],
          "scattermapbox": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scattermapbox"
           }
          ],
          "scatterpolar": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolar"
           }
          ],
          "scatterpolargl": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterpolargl"
           }
          ],
          "scatterternary": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "scatterternary"
           }
          ],
          "surface": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "surface"
           }
          ],
          "table": [
           {
            "cells": {
             "fill": {
              "color": "#EBF0F8"
             },
             "line": {
              "color": "white"
             }
            },
            "header": {
             "fill": {
              "color": "#C8D4E3"
             },
             "line": {
              "color": "white"
             }
            },
            "type": "table"
           }
          ]
         },
         "layout": {
          "annotationdefaults": {
           "arrowcolor": "#2a3f5f",
           "arrowhead": 0,
           "arrowwidth": 1
          },
          "autotypenumbers": "strict",
          "coloraxis": {
           "colorbar": {
            "outlinewidth": 0,
            "ticks": ""
           }
          },
          "colorscale": {
           "diverging": [
            [
             0,
             "#8e0152"
            ],
            [
             0.1,
             "#c51b7d"
            ],
            [
             0.2,
             "#de77ae"
            ],
            [
             0.3,
             "#f1b6da"
            ],
            [
             0.4,
             "#fde0ef"
            ],
            [
             0.5,
             "#f7f7f7"
            ],
            [
             0.6,
             "#e6f5d0"
            ],
            [
             0.7,
             "#b8e186"
            ],
            [
             0.8,
             "#7fbc41"
            ],
            [
             0.9,
             "#4d9221"
            ],
            [
             1,
             "#276419"
            ]
           ],
           "sequential": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ],
           "sequentialminus": [
            [
             0,
             "#0d0887"
            ],
            [
             0.1111111111111111,
             "#46039f"
            ],
            [
             0.2222222222222222,
             "#7201a8"
            ],
            [
             0.3333333333333333,
             "#9c179e"
            ],
            [
             0.4444444444444444,
             "#bd3786"
            ],
            [
             0.5555555555555556,
             "#d8576b"
            ],
            [
             0.6666666666666666,
             "#ed7953"
            ],
            [
             0.7777777777777778,
             "#fb9f3a"
            ],
            [
             0.8888888888888888,
             "#fdca26"
            ],
            [
             1,
             "#f0f921"
            ]
           ]
          },
          "colorway": [
           "#636efa",
           "#EF553B",
           "#00cc96",
           "#ab63fa",
           "#FFA15A",
           "#19d3f3",
           "#FF6692",
           "#B6E880",
           "#FF97FF",
           "#FECB52"
          ],
          "font": {
           "color": "#2a3f5f"
          },
          "geo": {
           "bgcolor": "white",
           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
           "subunitcolor": "white"
          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Drawdown ITUB3.SA"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Tempo"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Drawdown (%)"
         }
        }
       }
      },
      "text/html": [
       "<div>                        <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
       "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.20.0.min.js\"></script>                <div id=\"65fc9e02-f757-4a6a-aed8-8db61ba517ab\" class=\"plotly-graph-div\" style=\"height:100%; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"65fc9e02-f757-4a6a-aed8-8db61ba517ab\")) {                    Plotly.newPlot(                        \"65fc9e02-f757-4a6a-aed8-8db61ba517ab\",                        [{\"fillpattern\":{\"shape\":\"\"},\"hovertemplate\":\"Date=%{x}<br>Drawdown=%{y}<extra></extra>\",\"legendgroup\":\"\",\"line\":{\"color\":\"#636efa\"},\"marker\":{\"symbol\":\"circle\"},\"mode\":\"lines\",\"name\":\"\",\"orientation\":\"v\",\"showlegend\":false,\"stackgroup\":\"1\",\"x\":[\"2020-01-02T00:00:00\",\"2020-01-03T00:00:00\",\"2020-01-06T00:00:00\",\"2020-01-07T00:00:00\",\"2020-01-08T00:00:00\",\"2020-01-09T00:00:00\",\"2020-01-10T00:00:00\",\"2020-01-13T00:00:00\",\"2020-01-14T00:00:00\",\"2020-01-15T00:00:00\",\"2020-01-16T00:00:00\",\"2020-01-17T00:00:00\",\"2020-01-20T00:00:00\",\"2020-01-21T00:00:00\",\"2020-01-22T00:00:00\",\"2020-01-23T00:00:00\",\"2020-01-24T00:00:00\",\"2020-01-27T00:00:00\",\"2020-01-28T00:00:00\",\"2020-01-29T00:00:00\",\"2020-01-30T00:00:00\",\"2020-01-31T00:00:00\",\"2020-02-03T00:00:00\",\"2020-02-04T00:00:00\",\"2020-02-05T00:00:00\",\"2020-02-06T00:00:00\",\"2020-02-07T00:00:00\",\"2020-02-10T00:00:00\",\"2020-02-11T00:00:00\",\"2020-02-12T00:00:00\",\"2020-02-13T00:00:00\",\"2020-02-14T00:00:00\",\"2020-02-17T00:00:00\",\"2020-02-18T00:00:00\",\"2020-02-19T00:00:00\",\"2020-02-20T00:00:00\",\"2020-02-21T00:00:00\",\"2020-02-26T00:00:00\",\"2020-02-27T00:00:00\",\"2020-02-28T00:00:00\",\"2020-03-02T00:00:00\",\"2020-03-03T00:00:00\",\"2020-03-04T00:00:00\",\"2020-03-05T00:00:00\",\"2020-03-06T00:00:00\",\"2020-03-09T00:00:00\",\"2020-03-10T00:00:00\",\"2020-03-11T00:00:00\",\"2020-03-12T00:00:00\",\"2020-03-13T00:00:00\",\"2020-03-16T00:00:00\",\"2020-03-17T00:00:00\",\"2020-03-18T00:00:00\",\"2020-03-19T00:00:00\",\"2020-03-20T00:00:00\",\"2020-03-23T00:00:00\",\"2020-03-24T00:00:00\",\"2020-03-25T00:00:00\",\"2020-03-26T00:00:00\",\"2020-03-27T00:00:00\",\"2020-03-30T00:00:00\",\"2020-03-31T00:00:00\",\"2020-04-01T00:00:00\",\"2020-04-02T00:00:00\",\"2020-04-03T00:00:00\",\"2020-04-06T00:00:00\",\"2020-04-07T00:00:00\",\"2020-04-08T00:00:00\",\"2020-04-09T00:00:00\",\"2020-04-13T00:00:00\",\"2020-04-14T00:00:00\",\"2020-04-15T00:00:00\",\"2020-04-16T00:00:00\",\"2020-04-17T00:00:00\",\"2020-04-20T00:00:00\",\"2020-04-22T00:00:00\",\"2020-04-23T00:00:00\",\"2020-04-24T00:00:00\",\"2020-04-27T00:00:00\",\"2020-04-28T00:00:00\",\"2020-04-29T00:00:00\",\"2020-04-30T00:00:00\",\"2020-05-04T00:00:00\",\"2020-05-05T00:00:00\",\"2020-05-06T00:00:00\",\"2020-05-07T00:00:00\",\"2020-05-08T00:00:00\",\"2020-05-11T00:00:00\",\"2020-05-12T00:00:00\",\"2020-05-13T00:00:00\",\"2020-05-14T00:00:00\",\"2020-05-15T00:00:00\",\"2020-05-18T00:00:00\",\"2020-05-19T00:00:00\",\"2020-05-20T00:00:00\",\"2020-05-21T00:00:00\",\"2020-05-22T00:00:00\",\"2020-05-25T00:00:00\",\"2020-05-26T00:00:00\",\"2020-05-27T00:00:00\",\"2020-05-28T00:00:00\",\"2020-05-29T00:00:00\",\"2020-06-01T00:00:00\",\"2020-06-02T00:00:00\",\"2020-06-03T00:00:00\",\"2020-06-04T00:00:00\",\"2020-06-05T00:00:00\",\"2020-06-08T00:00:00\",\"2020-06-09T00:00:00\",\"2020-06-10T00:00:00\",\"2020-06-12T00:00:00\",\"2020-06-15T00:00:00\",\"2020-06-16T00:00:00\",\"2020-06-17T00:00:00\",\"2020-06-18T00:00:00\",\"2020-06-19T00:00:00\",\"2020-06-22T00:00:00\",\"2020-06-23T00:00:00\",\"2020-06-24T00:00:00\",\"2020-06-25T00:00:00\",\"2020-06-26T00:00:00\",\"2020-06-29T00:00:00\",\"2020-06-30T00:00:00\",\"2020-07-01T00:00:00\",\"2020-07-02T00:00:00\",\"2020-07-03T00:00:00\",\"2020-07-06T00:00:00\",\"2020-07-07T00:00:00\",\"2020-07-08T00:00:00\",\"2020-07-09T00:00:00\",\"2020-07-10T00:00:00\",\"2020-07-13T00:00:00\",\"2020-07-14T00:00:00\",\"2020-07-15T00:00:00\",\"2020-07-16T00:00:00\",\"2020-07-17T00:00:00\",\"2020-07-20T00:00:00\",\"2020-07-21T00:00:00\",\"2020-07-22T00:00:00\",\"2020-07-23T00:00:00\",\"2020-07-24T00:00:00\",\"2020-07-27T00:00:00\",\"2020-07-28T00:00:00\",\"2020-07-29T00:00:00\",\"2020-07-30T00:00:00\",\"2020-07-31T00:00:00\",\"2020-08-03T00:00:00\",\"2020-08-04T00:00:00\",\"2020-08-05T00:00:00\",\"2020-08-06T00:00:00\",\"2020-08-07T00:00:00\",\"2020-08-10T00:00:00\",\"2020-08-11T00:00:00\",\"2020-08-12T00:00:00\",\"2020-08-13T00:00:00\",\"2020-08-14T00:00:00\",\"2020-08-17T00:00:00\",\"2020-08-18T00:00:00\",\"2020-08-19T00:00:00\",\"2020-08-20T00:00:00\",\"2020-08-21T00:00:00\",\"2020-08-24T00:00:00\",\"2020-08-25T00:00:00\",\"2020-08-26T00:00:00\",\"2020-08-27T00:00:00\",\"2020-08-28T00:00:00\",\"2020-08-31T00:00:00\",\"2020-09-01T00:00:00\",\"2020-09-02T00:00:00\",\"2020-09-03T00:00:00\",\"2020-09-04T00:00:00\",\"2020-09-08T00:00:00\",\"2020-09-09T00:00:00\",\"2020-09-10T00:00:00\",\"2020-09-11T00:00:00\",\"2020-09-14T00:00:00\",\"2020-09-15T00:00:00\",\"2020-09-16T00:00:00\",\"2020-09-17T00:00:00\",\"2020-09-18T00:00:00\",\"2020-09-21T00:00:00\",\"2020-09-22T00:00:00\",\"2020-09-23T00:00:00\",\"2020-09-24T00:00:00\",\"2020-09-25T00:00:00\",\"2020-09-28T00:00:00\",\"2020-09-29T00:00:00\",\"2020-09-30T00:00:00\",\"2020-10-01T00:00:00\",\"2020-10-02T00:00:00\",\"2020-10-05T00:00:00\",\"2020-10-06T00:00:00\",\"2020-10-07T00:00:00\",\"2020-10-08T00:00:00\",\"2020-10-09T00:00:00\",\"2020-10-13T00:00:00\",\"2020-10-14T00:00:00\",\"2020-10-15T00:00:00\",\"2020-10-16T00:00:00\",\"2020-10-19T00:00:00\",\"2020-10-20T00:00:00\",\"2020-10-21T00:00:00\",\"2020-10-22T00:00:00\",\"2020-10-23T00:00:00\",\"2020-10-26T00:00:00\",\"2020-10-27T00:00:00\",\"2020-10-28T00:00:00\",\"2020-10-29T00:00:00\",\"2020-10-30T00:00:00\",\"2020-11-03T00:00:00\",\"2020-11-04T00:00:00\",\"2020-11-05T00:00:00\",\"2020-11-06T00:00:00\",\"2020-11-09T00:00:00\",\"2020-11-10T00:00:00\",\"2020-11-11T00:00:00\",\"2020-11-12T00:00:00\",\"2020-11-13T00:00:00\",\"2020-11-16T00:00:00\",\"2020-11-17T00:00:00\",\"2020-11-18T00:00:00\",\"2020-11-19T00:00:00\",\"2020-11-23T00:00:00\",\"2020-11-24T00:00:00\",\"2020-11-25T00:00:00\",\"2020-11-26T00:00:00\",\"2020-11-27T00:00:00\",\"2020-11-30T00:00:00\",\"2020-12-01T00:00:00\",\"2020-12-02T00:00:00\",\"2020-12-03T00:00:00\",\"2020-12-04T00:00:00\",\"2020-12-07T00:00:00\",\"2020-12-08T00:00:00\",\"2020-12-09T00:00:00\",\"2020-12-10T00:00:00\",\"2020-12-11T00:00:00\",\"2020-12-14T00:00:00\",\"2020-12-15T00:00:00\",\"2020-12-16T00:00:00\",\"2020-12-17T00:00:00\",\"2020-12-18T00:00:00\",\"2020-12-21T00:00:00\",\"2020-12-22T00:00:00\",\"2020-12-23T00:00:00\",\"2020-12-28T00:00:00\",\"2020-12-29T00:00:00\",\"2020-12-30T00:00:00\"],\"xaxis\":\"x\",\"y\":[null,0.0,-1.020716093895302,-2.938472218101029,-3.9282373358530243,-4.8252504522711455,-5.969696437535921,-4.639658979453465,-5.103600654895734,-6.4645890891215085,-6.309921678554852,-4.825250452271113,-6.4955198798456015,-8.75347433117798,-7.268789647948093,-5.289198856186472,-6.464589089121498,-7.6090283459131935,-8.413208719320586,-9.217442920512564,-7.9802045630755,-8.939065803995671,-8.459392958504885,-7.840447448842214,-7.7166839151073585,-8.118992777185973,-7.871418610404756,-7.004898933960879,-3.1056142177480317,-2.61045915571253,-4.343471594707982,-5.581362614033303,-6.076477305230366,-6.169316776714195,-5.240908604929828,-5.333754804886725,-7.619087413159809,-11.360865303168522,-11.423749612524059,-10.040214065025177,-10.024509808868986,-10.93683693000498,-10.024509808868986,-11.534567564063314,-14.397445387170805,-18.204085757619005,-16.190657474765036,-20.406301538001102,-28.428572514739226,-20.53211052755065,-28.428572514739237,-25.15676501204768,-31.543135601676543,-30.158886836031762,-33.61947510777837,-38.02385956923106,-35.41269392384567,-31.88915405801277,-27.547721190646378,-30.25326712784967,-28.176907436328634,-30.536414731776496,-34.644692550160606,-34.07802054781478,-37.068779730178804,-33.857676511566616,-30.111397273070985,-27.718806075515147,-28.379912197463476,-28.22247265599008,-28.128085635699087,-29.92253576233903,-30.74102086950309,-30.331761494738384,-30.646566564481358,-31.402106806032204,-33.13359861011279,-35.494720739098575,-34.73916704060158,-29.229933657928342,-28.474366502485214,-30.7410208695031,-33.52901751576297,-31.323841207228032,-32.26891580681828,-34.4740988438263,-32.77295245331231,-31.607338691754727,-34.15907173447186,-34.31661893151444,-32.30041851775373,-34.94667315022332,-32.6469483380436,-35.04118128302965,-34.63161912697659,-32.23744000977513,-31.827877853722065,-28.551575731016655,-31.00882082634666,-28.362572922350125,-30.00070043404708,-30.882776340239516,-28.912491026740106,-25.161118334126975,-22.765257098656853,-20.558580340626264,-18.887779363678902,-17.059350448012033,-17.91048883500729,-20.842246036979827,-22.859825787720855,-24.972001141418186,-23.395768853534815,-22.702197849001358,-20.71616790850731,-16.80714036328238,-20.211781381413406,-20.905318743581482,-22.60766280230275,-22.166308610972017,-23.994717341219662,-22.04022375402642,-24.341462472647933,-23.316285401104153,-22.496158546509893,-22.590781063358488,-20.00415415927631,-21.89679289349442,-20.666585790420346,-20.729692139387357,-20.28806880913364,-21.202845094468803,-20.47733402825007,-20.00415415927632,-21.171295284221834,-20.85583082411755,-20.035703969523276,-19.278656550003724,-20.19341937839274,-21.549832450927763,-21.928349432214468,-19.08937787394114,-19.184020576208958,-17.70144190505144,-18.742397245955246,-21.45518974865993,-20.460970381732547,-23.964472854749197,-23.93287594519069,-22.670365442885288,-24.05914247090931,-23.206954442114412,-23.77507306617127,-24.090699009629347,-26.142311377181883,-26.142311377181883,-27.625784935258324,-26.699146351892576,-26.98387514699193,-27.17369210090048,-27.047136250839664,-26.066407472426935,-26.35116990989167,-27.96459046845855,-27.26862412751053,-26.50933612645713,-28.628980085267298,-26.682056030283018,-26.966946308736162,-26.01724251967058,-24.78262153825953,-26.42879630375373,-26.840329902417682,-28.074971069247955,-29.056345781075866,-28.10661507811797,-28.86638752923275,-29.372900253818244,-29.309592050658985,-30.322617499829985,-30.76581529278316,-30.544213032070022,-31.873819867875696,-30.89242497062855,-31.050732485128584,-31.082383222471666,-32.47529153296351,-32.31700420388272,-31.98439559438424,-31.825993881261173,-31.66759889661117,-31.889362640678087,-32.11110619932577,-28.689724740185063,-28.94317959243352,-29.164909694135066,-29.671812670158882,-30.146990895635806,-31.287433439421175,-30.780584291181956,-28.43629680182892,-27.992782770641234,-25.078304287524354,-25.83858810259281,-25.141639404575912,-27.074083785503568,-29.95691825975043,-28.689724740185063,-30.368734454283498,-29.37017522222296,-26.612160651983665,-24.995422956190982,-24.710115512407206,-19.606265904007692,-17.482275351591316,-17.76758952384816,-19.95492865026225,-18.243110901451885,-15.992329002417083,-16.08742924085399,-18.021178945558127,-17.64077799181053,-17.418893135228274,-15.580210026595665,-16.436152543366212,-17.926105621013534,-17.19697463628065,-18.338224596834973,-15.08695912244027,-15.023516349819522,-14.991818513164922,-14.547739290238981,-14.29394128586372,-14.864926239450371,-13.976781250544622,-11.470943122669597,-11.998818749267059,-13.397695215376103,-12.793633088154543,-10.695325117464039,-10.695325117464039,-10.854292022325385,-12.857217158709844,-12.316745830516652,-10.9814803488552,-9.614375732604586,-9.96411503455107,-11.203984224960328],\"yaxis\":\"y\",\"type\":\"scatter\"}],                        {\"template\":{\"data\":{\"histogram2dcontour\":[{\"type\":\"histogram2dcontour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"choropleth\":[{\"type\":\"choropleth\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"histogram2d\":[{\"type\":\"histogram2d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmap\":[{\"type\":\"heatmap\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"heatmapgl\":[{\"type\":\"heatmapgl\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"contourcarpet\":[{\"type\":\"contourcarpet\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"contour\":[{\"type\":\"contour\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"surface\":[{\"type\":\"surface\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"},\"colorscale\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]]}],\"mesh3d\":[{\"type\":\"mesh3d\",\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}],\"scatter\":[{\"fillpattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2},\"type\":\"scatter\"}],\"parcoords\":[{\"type\":\"parcoords\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolargl\":[{\"type\":\"scatterpolargl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"bar\":[{\"error_x\":{\"color\":\"#2a3f5f\"},\"error_y\":{\"color\":\"#2a3f5f\"},\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"bar\"}],\"scattergeo\":[{\"type\":\"scattergeo\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterpolar\":[{\"type\":\"scatterpolar\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"histogram\":[{\"marker\":{\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"histogram\"}],\"scattergl\":[{\"type\":\"scattergl\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatter3d\":[{\"type\":\"scatter3d\",\"line\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattermapbox\":[{\"type\":\"scattermapbox\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scatterternary\":[{\"type\":\"scatterternary\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"scattercarpet\":[{\"type\":\"scattercarpet\",\"marker\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}}}],\"carpet\":[{\"aaxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"baxis\":{\"endlinecolor\":\"#2a3f5f\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"minorgridcolor\":\"white\",\"startlinecolor\":\"#2a3f5f\"},\"type\":\"carpet\"}],\"table\":[{\"cells\":{\"fill\":{\"color\":\"#EBF0F8\"},\"line\":{\"color\":\"white\"}},\"header\":{\"fill\":{\"color\":\"#C8D4E3\"},\"line\":{\"color\":\"white\"}},\"type\":\"table\"}],\"barpolar\":[{\"marker\":{\"line\":{\"color\":\"#E5ECF6\",\"width\":0.5},\"pattern\":{\"fillmode\":\"overlay\",\"size\":10,\"solidity\":0.2}},\"type\":\"barpolar\"}],\"pie\":[{\"automargin\":true,\"type\":\"pie\"}]},\"layout\":{\"autotypenumbers\":\"strict\",\"colorway\":[\"#636efa\",\"#EF553B\",\"#00cc96\",\"#ab63fa\",\"#FFA15A\",\"#19d3f3\",\"#FF6692\",\"#B6E880\",\"#FF97FF\",\"#FECB52\"],\"font\":{\"color\":\"#2a3f5f\"},\"hovermode\":\"closest\",\"hoverlabel\":{\"align\":\"left\"},\"paper_bgcolor\":\"white\",\"plot_bgcolor\":\"#E5ECF6\",\"polar\":{\"bgcolor\":\"#E5ECF6\",\"angularaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"radialaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"ternary\":{\"bgcolor\":\"#E5ECF6\",\"aaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"baxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"},\"caxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\"}},\"coloraxis\":{\"colorbar\":{\"outlinewidth\":0,\"ticks\":\"\"}},\"colorscale\":{\"sequential\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"sequentialminus\":[[0.0,\"#0d0887\"],[0.1111111111111111,\"#46039f\"],[0.2222222222222222,\"#7201a8\"],[0.3333333333333333,\"#9c179e\"],[0.4444444444444444,\"#bd3786\"],[0.5555555555555556,\"#d8576b\"],[0.6666666666666666,\"#ed7953\"],[0.7777777777777778,\"#fb9f3a\"],[0.8888888888888888,\"#fdca26\"],[1.0,\"#f0f921\"]],\"diverging\":[[0,\"#8e0152\"],[0.1,\"#c51b7d\"],[0.2,\"#de77ae\"],[0.3,\"#f1b6da\"],[0.4,\"#fde0ef\"],[0.5,\"#f7f7f7\"],[0.6,\"#e6f5d0\"],[0.7,\"#b8e186\"],[0.8,\"#7fbc41\"],[0.9,\"#4d9221\"],[1,\"#276419\"]]},\"xaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"yaxis\":{\"gridcolor\":\"white\",\"linecolor\":\"white\",\"ticks\":\"\",\"title\":{\"standoff\":15},\"zerolinecolor\":\"white\",\"automargin\":true,\"zerolinewidth\":2},\"scene\":{\"xaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"yaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2},\"zaxis\":{\"backgroundcolor\":\"#E5ECF6\",\"gridcolor\":\"white\",\"linecolor\":\"white\",\"showbackground\":true,\"ticks\":\"\",\"zerolinecolor\":\"white\",\"gridwidth\":2}},\"shapedefaults\":{\"line\":{\"color\":\"#2a3f5f\"}},\"annotationdefaults\":{\"arrowcolor\":\"#2a3f5f\",\"arrowhead\":0,\"arrowwidth\":1},\"geo\":{\"bgcolor\":\"white\",\"landcolor\":\"#E5ECF6\",\"subunitcolor\":\"white\",\"showland\":true,\"showlakes\":true,\"lakecolor\":\"white\"},\"title\":{\"x\":0.05},\"mapbox\":{\"style\":\"light\"}}},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Tempo\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Drawdown (%)\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Drawdown ITUB3.SA\"}},                        {\"responsive\": true}                    )                };                            </script>        </div>"
      ],
      "text/plain": [
       "Figure({\n",
       "    'data': [{'fillpattern': {'shape': ''},\n",
       "              'hovertemplate': 'Date=%{x}<br>Drawdown=%{y}<extra></extra>',\n",
       "              'legendgroup': '',\n",
       "              'line': {'color': '#636efa'},\n",
       "              'marker': {'symbol': 'circle'},\n",
       "              'mode': 'lines',\n",
       "              'name': '',\n",
       "              'orientation': 'v',\n",
       "              'showlegend': False,\n",
       "              'stackgroup': '1',\n",
       "              'type': 'scatter',\n",
       "              'x': array([datetime.datetime(2020, 1, 2, 0, 0),\n",
       "                          datetime.datetime(2020, 1, 3, 0, 0),\n",
       "                          datetime.datetime(2020, 1, 6, 0, 0), ...,\n",
       "                          datetime.datetime(2020, 12, 28, 0, 0),\n",
       "                          datetime.datetime(2020, 12, 29, 0, 0),\n",
       "                          datetime.datetime(2020, 12, 30, 0, 0)], dtype=object),\n",
       "              'xaxis': 'x',\n",
       "              'y': array([         nan,   0.        ,  -1.02071609, ...,  -9.61437573,\n",
       "                           -9.96411503, -11.20398422]),\n",
       "              'yaxis': 'y'}],\n",
       "    'layout': {'legend': {'tracegroupgap': 0},\n",
       "               'template': '...',\n",
       "               'title': {'text': 'Drawdown ITUB3.SA'},\n",
       "               'xaxis': {'anchor': 'y', 'domain': [0.0, 1.0], 'title': {'text': 'Tempo'}},\n",
       "               'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Drawdown (%)'}}}\n",
       "})"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "try:\n",
    "    fig = px.area(ddowns, x=ddowns.index, y='Drawdown', title='Underwater')\n",
    "    fig.update_xaxes(title_text='Tempo')\n",
    "    fig.update_yaxes(title_text='Drawdown (%)')\n",
    "    fig.update_layout(title='Drawdown ITUB3.SA')\n",
    "except ValueError as e:\n",
    "    print(\"Problemas com versão: \", str(e))\n",
    "\n",
    "fig"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A biblioteca também define uma função para plotar os drawdowns:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusões\n",
    "\n",
    "Com a utilização dessas funções, é possível obter informações sobre o desempenho histórico dos ativos, como a relação risco-retorno e o crescimento acumulado ao longo do tempo. Pode ser uma estratégia interessante usar essas funções para comparar e entender o comportamento dos ativos em momentos de crise e euforia, bem como para simular como estratégias de investimentos e portfolios teriam desempenhado no passado. Porém, é importante lembrar que retorno passado não é garantia de retorno futuro."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.11.3"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}