{
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  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "language_info": {
   "name": "python"
  },
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   "enabled": false
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "UDU93-LZwySz",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "805afd3f-93c8-4478-c629-eaa7c9b4f495"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Requirement already satisfied: yfinance in /usr/local/lib/python3.11/dist-packages (0.2.61)\n",
      "Requirement already satisfied: pandas>=1.3.0 in /usr/local/lib/python3.11/dist-packages (from yfinance) (2.2.2)\n",
      "Requirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.11/dist-packages (from yfinance) (2.0.2)\n",
      "Requirement already satisfied: requests>=2.31 in /usr/local/lib/python3.11/dist-packages (from yfinance) (2.32.3)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.11/dist-packages (from yfinance) (0.0.11)\n",
      "Requirement already satisfied: platformdirs>=2.0.0 in /usr/local/lib/python3.11/dist-packages (from yfinance) (4.3.8)\n",
      "Requirement already satisfied: pytz>=2022.5 in /usr/local/lib/python3.11/dist-packages (from yfinance) (2025.2)\n",
      "Requirement already satisfied: frozendict>=2.3.4 in /usr/local/lib/python3.11/dist-packages (from yfinance) (2.4.6)\n",
      "Requirement already satisfied: peewee>=3.16.2 in /usr/local/lib/python3.11/dist-packages (from yfinance) (3.18.1)\n",
      "Requirement already satisfied: beautifulsoup4>=4.11.1 in /usr/local/lib/python3.11/dist-packages (from yfinance) (4.13.4)\n",
      "Requirement already satisfied: curl_cffi>=0.7 in /usr/local/lib/python3.11/dist-packages (from yfinance) (0.10.0)\n",
      "Requirement already satisfied: protobuf>=3.19.0 in /usr/local/lib/python3.11/dist-packages (from yfinance) (5.29.4)\n",
      "Requirement already satisfied: websockets>=13.0 in /usr/local/lib/python3.11/dist-packages (from yfinance) (15.0.1)\n",
      "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.11/dist-packages (from beautifulsoup4>=4.11.1->yfinance) (2.7)\n",
      "Requirement already satisfied: typing-extensions>=4.0.0 in /usr/local/lib/python3.11/dist-packages (from beautifulsoup4>=4.11.1->yfinance) (4.13.2)\n",
      "Requirement already satisfied: cffi>=1.12.0 in /usr/local/lib/python3.11/dist-packages (from curl_cffi>=0.7->yfinance) (1.17.1)\n",
      "Requirement already satisfied: certifi>=2024.2.2 in /usr/local/lib/python3.11/dist-packages (from curl_cffi>=0.7->yfinance) (2025.4.26)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.11/dist-packages (from pandas>=1.3.0->yfinance) (2025.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.11/dist-packages (from requests>=2.31->yfinance) (3.4.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.11/dist-packages (from requests>=2.31->yfinance) (3.10)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.11/dist-packages (from requests>=2.31->yfinance) (2.4.0)\n",
      "Requirement already satisfied: pycparser in /usr/local/lib/python3.11/dist-packages (from cffi>=1.12.0->curl_cffi>=0.7->yfinance) (2.22)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.11/dist-packages (from python-dateutil>=2.8.2->pandas>=1.3.0->yfinance) (1.17.0)\n"
     ]
    }
   ],
   "source": [
    "# run in first lines for installing\n",
    "!pip install yfinance\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Usando o pacote yfinance para capturar dados históricos"
   ],
   "metadata": {
    "id": "z7qT19kqUz0C"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# Using yfinance to get Petrobras prices\n",
    "petro = yf.Ticker(\"PETR4.SA\")\n",
    "\n",
    "# get historical market data\n",
    "# hist = petro.history(period = \"5y\")\n",
    "\n",
    "# other way is determining a period\n",
    "hist = petro.history(start=\"2007-01-01\", end=\"2022-11-03\")\n"
   ],
   "metadata": {
    "id": "IlGmzxyZw3px"
   },
   "execution_count": 2,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "# get stock info: some of the features of yfinance\n",
    "petro.info"
   ],
   "metadata": {
    "id": "DoJOAM9WRvIy",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "188d1530-2a36-4c87-d1a0-c79f4a209890"
   },
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'address1': 'Avenida Henrique Valadares, 28',\n",
       " 'city': 'Rio de Janeiro',\n",
       " 'state': 'RJ',\n",
       " 'zip': '20231-030',\n",
       " 'country': 'Brazil',\n",
       " 'phone': '55 21 3224 2401',\n",
       " 'website': 'https://petrobras.com.br',\n",
       " 'industry': 'Oil & Gas Integrated',\n",
       " 'industryKey': 'oil-gas-integrated',\n",
       " 'industryDisp': 'Oil & Gas Integrated',\n",
       " 'sector': 'Energy',\n",
       " 'sectorKey': 'energy',\n",
       " 'sectorDisp': 'Energy',\n",
       " 'longBusinessSummary': 'Petróleo Brasileiro S.A. - Petrobras explores, produces, and sells oil and gas in Brazil and internationally. It operates through three segments: Exploration and Production; Refining, Transportation & Marketing; and Gas & Low Carbon Energies. The Exploration and Production segment explores, develops, and produces crude oil, natural gas liquids, and natural gas primarily for supplies to the domestic refineries. The Refining, Transportation and Marketing segment engages in the refining, logistics, transport, acquisition, and export of crude oil; trading of oil products; and production of fertilizers, as well as holding interests in petrochemical companies. The Gas and Low Carbon Energies segment is involved in the logistic and trading of natural gas and electricity; transportation and trading of liquefied natural gas; generation of electricity through thermoelectric power plants; renewable energy business; low carbon business; and natural gas processing business, as well as production of biodiesel and its co-products. The company also engages in prospecting, drilling, refining, processing, trading, and transporting crude oil from producing onshore and offshore oil fields, and shale or other rocks, as well as oil products, natural gas, and other liquid hydrocarbons. In addition, it engages in the research, development, production, transportation, distribution, and trading of energy. The company was incorporated in 1953 and is headquartered in Rio de Janeiro, Brazil.',\n",
       " 'fullTimeEmployees': 41778,\n",
       " 'companyOfficers': [{'maxAge': 1,\n",
       "   'name': 'Ms. Magda Maria de Regina Chambriard',\n",
       "   'title': 'CEO & Non-Independent Director',\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Ms. Clarice  Coppetti',\n",
       "   'age': 60,\n",
       "   'title': 'Interim President, Chief Corporate Affairs Officer & Member of Executive Board',\n",
       "   'yearBorn': 1964,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Fernando Sabbi Melgarejo',\n",
       "   'title': 'Chief Financial & Investor Relations Officer',\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Claudio Romeo Schlosser',\n",
       "   'age': 60,\n",
       "   'title': 'Chief Logistics, Commercialization & Markets Officer and Member of Executive Board',\n",
       "   'yearBorn': 1964,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. William Franca da Silva',\n",
       "   'age': 64,\n",
       "   'title': 'Chief Industrial Processes & Products Officer and Member of Executive Board',\n",
       "   'yearBorn': 1960,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Mauricio Tiomno Tolmasquim',\n",
       "   'age': 66,\n",
       "   'title': 'Chief Energy Transition & Sustainability Officer and Member of Executive Board',\n",
       "   'yearBorn': 1958,\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Ricardo Wagner de Araujo',\n",
       "   'title': 'Chief Governance & Compliance Officer',\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Ms. Renata  Baruzzi',\n",
       "   'title': 'Chief Engineering, Technology, & Innovation Officer',\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Hélio Siqueira Júnior',\n",
       "   'title': 'Acting General Counsel',\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0},\n",
       "  {'maxAge': 1,\n",
       "   'name': 'Mr. Bruno  Motta',\n",
       "   'title': 'Executive Manager of Communication & Brands',\n",
       "   'exercisedValue': 0,\n",
       "   'unexercisedValue': 0}],\n",
       " 'auditRisk': 2,\n",
       " 'boardRisk': 2,\n",
       " 'compensationRisk': 1,\n",
       " 'shareHolderRightsRisk': 7,\n",
       " 'overallRisk': 1,\n",
       " 'governanceEpochDate': 1746057600,\n",
       " 'executiveTeam': [],\n",
       " 'maxAge': 86400,\n",
       " 'priceHint': 2,\n",
       " 'previousClose': 31.4,\n",
       " 'open': 31.46,\n",
       " 'dayLow': 31.14,\n",
       " 'dayHigh': 31.53,\n",
       " 'regularMarketPreviousClose': 31.4,\n",
       " 'regularMarketOpen': 31.46,\n",
       " 'regularMarketDayLow': 31.14,\n",
       " 'regularMarketDayHigh': 31.53,\n",
       " 'dividendRate': 5.1,\n",
       " 'dividendYield': 16.25,\n",
       " 'exDividendDate': 1748908800,\n",
       " 'payoutRatio': 1.4075,\n",
       " 'fiveYearAvgDividendYield': 20.11,\n",
       " 'beta': 0.331,\n",
       " 'trailingPE': 8.177546,\n",
       " 'forwardPE': 3.8955224,\n",
       " 'volume': 20075100,\n",
       " 'regularMarketVolume': 20075100,\n",
       " 'averageVolume': 45034059,\n",
       " 'averageVolume10days': 37864920,\n",
       " 'averageDailyVolume10Day': 37864920,\n",
       " 'bid': 31.3,\n",
       " 'ask': 31.32,\n",
       " 'bidSize': 0,\n",
       " 'askSize': 0,\n",
       " 'marketCap': 419114811392,\n",
       " 'fiftyTwoWeekLow': 29.66,\n",
       " 'fiftyTwoWeekHigh': 40.76,\n",
       " 'priceToSalesTrailing12Months': 0.84456044,\n",
       " 'fiftyDayAverage': 32.949,\n",
       " 'twoHundredDayAverage': 36.1443,\n",
       " 'trailingAnnualDividendRate': 5.602,\n",
       " 'trailingAnnualDividendYield': 0.17840765,\n",
       " 'currency': 'BRL',\n",
       " 'tradeable': False,\n",
       " 'enterpriseValue': 732744187904,\n",
       " 'profitMargins': 0.09696,\n",
       " 'floatShares': 8112812836,\n",
       " 'sharesOutstanding': 5446499840,\n",
       " 'heldPercentInsiders': 0.0,\n",
       " 'heldPercentInstitutions': 0.56811,\n",
       " 'impliedSharesOutstanding': 13381699584,\n",
       " 'bookValue': 30.712,\n",
       " 'priceToBook': 1.0197968,\n",
       " 'lastFiscalYearEnd': 1735603200,\n",
       " 'nextFiscalYearEnd': 1767139200,\n",
       " 'mostRecentQuarter': 1743379200,\n",
       " 'earningsQuarterlyGrowth': 0.486,\n",
       " 'netIncomeToCommon': 48114999296,\n",
       " 'trailingEps': 3.83,\n",
       " 'forwardEps': 8.04,\n",
       " 'lastSplitFactor': '2:1',\n",
       " 'lastSplitDate': 1209340800,\n",
       " 'enterpriseToRevenue': 1.477,\n",
       " 'enterpriseToEbitda': 3.734,\n",
       " '52WeekChange': -0.15158063,\n",
       " 'SandP52WeekChange': 0.09362543,\n",
       " 'lastDividendValue': 0.909166,\n",
       " 'lastDividendDate': 1748908800,\n",
       " 'quoteType': 'EQUITY',\n",
       " 'currentPrice': 31.32,\n",
       " 'targetHighPrice': 48.5,\n",
       " 'targetLowPrice': 35.3,\n",
       " 'targetMeanPrice': 43.02727,\n",
       " 'targetMedianPrice': 43.0,\n",
       " 'recommendationMean': 1.58333,\n",
       " 'recommendationKey': 'buy',\n",
       " 'numberOfAnalystOpinions': 11,\n",
       " 'totalCash': 44038000640,\n",
       " 'totalCashPerShare': 3.417,\n",
       " 'ebitda': 196239998976,\n",
       " 'totalDebt': 370313986048,\n",
       " 'quickRatio': 0.408,\n",
       " 'currentRatio': 0.718,\n",
       " 'totalRevenue': 496251994112,\n",
       " 'debtToEquity': 93.137,\n",
       " 'revenuePerShare': 38.496,\n",
       " 'returnOnAssets': 0.08658,\n",
       " 'returnOnEquity': 0.12019,\n",
       " 'grossProfits': 246470000640,\n",
       " 'freeCashflow': 101453996032,\n",
       " 'operatingCashflow': 206894006272,\n",
       " 'earningsGrowth': 0.489,\n",
       " 'revenueGrowth': 0.046,\n",
       " 'grossMargins': 0.49666,\n",
       " 'ebitdaMargins': 0.39543998,\n",
       " 'operatingMargins': 0.35481998,\n",
       " 'financialCurrency': 'BRL',\n",
       " 'symbol': 'PETR4.SA',\n",
       " 'language': 'en-US',\n",
       " 'region': 'US',\n",
       " 'typeDisp': 'Equity',\n",
       " 'quoteSourceName': 'Delayed Quote',\n",
       " 'triggerable': False,\n",
       " 'customPriceAlertConfidence': 'LOW',\n",
       " 'shortName': 'PETROBRAS   PN      N2',\n",
       " 'earningsTimestamp': 1754593200,\n",
       " 'earningsTimestampStart': 1754593200,\n",
       " 'earningsTimestampEnd': 1754593200,\n",
       " 'earningsCallTimestampStart': 1754650740,\n",
       " 'earningsCallTimestampEnd': 1754650740,\n",
       " 'isEarningsDateEstimate': False,\n",
       " 'epsTrailingTwelveMonths': 3.83,\n",
       " 'epsForward': 8.04,\n",
       " 'epsCurrentYear': 7.42,\n",
       " 'priceEpsCurrentYear': 4.221024,\n",
       " 'fiftyDayAverageChange': -1.6290016,\n",
       " 'fiftyDayAverageChangePercent': -0.04944009,\n",
       " 'twoHundredDayAverageChange': -4.824299,\n",
       " 'twoHundredDayAverageChangePercent': -0.1334733,\n",
       " 'sourceInterval': 15,\n",
       " 'exchangeDataDelayedBy': 15,\n",
       " 'averageAnalystRating': '1.6 - Buy',\n",
       " 'cryptoTradeable': False,\n",
       " 'longName': 'Petróleo Brasileiro S.A. - Petrobras',\n",
       " 'corporateActions': [{'header': 'Dividend',\n",
       "   'message': 'PETR4.SA announced a cash dividend of 0.909 with an ex-date of Jun. 3, 2025',\n",
       "   'meta': {'eventType': 'DIVIDEND',\n",
       "    'dateEpochMs': 1748919600000,\n",
       "    'amount': '0.909'}}],\n",
       " 'regularMarketTime': 1748289296,\n",
       " 'exchange': 'SAO',\n",
       " 'messageBoardId': 'finmb_409268',\n",
       " 'exchangeTimezoneName': 'America/Sao_Paulo',\n",
       " 'exchangeTimezoneShortName': 'BRT',\n",
       " 'gmtOffSetMilliseconds': -10800000,\n",
       " 'market': 'br_market',\n",
       " 'esgPopulated': False,\n",
       " 'regularMarketChangePercent': -0.25477684,\n",
       " 'regularMarketPrice': 31.32,\n",
       " 'marketState': 'POST',\n",
       " 'hasPrePostMarketData': False,\n",
       " 'firstTradeDateMilliseconds': 946900800000,\n",
       " 'regularMarketChange': -0.07999992,\n",
       " 'regularMarketDayRange': '31.14 - 31.53',\n",
       " 'fullExchangeName': 'São Paulo',\n",
       " 'averageDailyVolume3Month': 45034059,\n",
       " 'fiftyTwoWeekLowChange': 1.6599998,\n",
       " 'fiftyTwoWeekLowChangePercent': 0.05596763,\n",
       " 'fiftyTwoWeekRange': '29.66 - 40.76',\n",
       " 'fiftyTwoWeekHighChange': -9.439999,\n",
       " 'fiftyTwoWeekHighChangePercent': -0.23159958,\n",
       " 'fiftyTwoWeekChangePercent': -15.158063,\n",
       " 'trailingPegRatio': 0.2287}"
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "hist"
   ],
   "metadata": {
    "colab": {
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     "height": 455
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    "id": "T7vluQcSxrVf",
    "outputId": "6f78fa80-72d1-49ea-e2cf-e6bfbe9c1f30"
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   "execution_count": 4,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                Open       High        Low      Close  \\\n",
       "Date                                                                    \n",
       "2007-01-02 00:00:00-02:00   6.481254   6.539585   6.450143   6.522734   \n",
       "2007-01-03 00:00:00-02:00   6.501992   6.533102   6.223298   6.324406   \n",
       "2007-01-04 00:00:00-02:00   6.286814   6.319221   6.144227   6.183115   \n",
       "2007-01-05 00:00:00-02:00   6.118303   6.220707   5.846090   5.995159   \n",
       "2007-01-08 00:00:00-02:00   6.027566   6.110526   5.936828   6.065157   \n",
       "...                              ...        ...        ...        ...   \n",
       "2022-10-26 00:00:00-03:00  19.465428  20.020730  19.047459  19.531109   \n",
       "2022-10-27 00:00:00-03:00  19.590822  20.026704  19.507226  19.680386   \n",
       "2022-10-28 00:00:00-03:00  19.525141  19.775921  19.023578  19.447517   \n",
       "2022-10-31 00:00:00-03:00  18.277200  18.868328  17.411406  17.799520   \n",
       "2022-11-01 00:00:00-03:00  18.253316  18.313026  17.650246  17.829376   \n",
       "\n",
       "                              Volume  Dividends  Stock Splits  \n",
       "Date                                                           \n",
       "2007-01-02 00:00:00-02:00   10244800      0.225           0.0  \n",
       "2007-01-03 00:00:00-02:00   19898600      0.000           0.0  \n",
       "2007-01-04 00:00:00-02:00   21060200      0.000           0.0  \n",
       "2007-01-05 00:00:00-02:00   24864000      0.000           0.0  \n",
       "2007-01-08 00:00:00-02:00   19440200      0.000           0.0  \n",
       "...                              ...        ...           ...  \n",
       "2022-10-26 00:00:00-03:00  121334800      0.000           0.0  \n",
       "2022-10-27 00:00:00-03:00  111008800      0.000           0.0  \n",
       "2022-10-28 00:00:00-03:00  129745400      0.000           0.0  \n",
       "2022-10-31 00:00:00-03:00  262409400      0.000           0.0  \n",
       "2022-11-01 00:00:00-03:00  115944200      0.000           0.0  \n",
       "\n",
       "[3927 rows x 7 columns]"
      ],
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Dividends</th>\n",
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       "    <tr>\n",
       "      <th>2007-01-02 00:00:00-02:00</th>\n",
       "      <td>6.481254</td>\n",
       "      <td>6.539585</td>\n",
       "      <td>6.450143</td>\n",
       "      <td>6.522734</td>\n",
       "      <td>10244800</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2007-01-03 00:00:00-02:00</th>\n",
       "      <td>6.501992</td>\n",
       "      <td>6.533102</td>\n",
       "      <td>6.223298</td>\n",
       "      <td>6.324406</td>\n",
       "      <td>19898600</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-04 00:00:00-02:00</th>\n",
       "      <td>6.286814</td>\n",
       "      <td>6.319221</td>\n",
       "      <td>6.144227</td>\n",
       "      <td>6.183115</td>\n",
       "      <td>21060200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-05 00:00:00-02:00</th>\n",
       "      <td>6.118303</td>\n",
       "      <td>6.220707</td>\n",
       "      <td>5.846090</td>\n",
       "      <td>5.995159</td>\n",
       "      <td>24864000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-08 00:00:00-02:00</th>\n",
       "      <td>6.027566</td>\n",
       "      <td>6.110526</td>\n",
       "      <td>5.936828</td>\n",
       "      <td>6.065157</td>\n",
       "      <td>19440200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-26 00:00:00-03:00</th>\n",
       "      <td>19.465428</td>\n",
       "      <td>20.020730</td>\n",
       "      <td>19.047459</td>\n",
       "      <td>19.531109</td>\n",
       "      <td>121334800</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-27 00:00:00-03:00</th>\n",
       "      <td>19.590822</td>\n",
       "      <td>20.026704</td>\n",
       "      <td>19.507226</td>\n",
       "      <td>19.680386</td>\n",
       "      <td>111008800</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-28 00:00:00-03:00</th>\n",
       "      <td>19.525141</td>\n",
       "      <td>19.775921</td>\n",
       "      <td>19.023578</td>\n",
       "      <td>19.447517</td>\n",
       "      <td>129745400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-31 00:00:00-03:00</th>\n",
       "      <td>18.277200</td>\n",
       "      <td>18.868328</td>\n",
       "      <td>17.411406</td>\n",
       "      <td>17.799520</td>\n",
       "      <td>262409400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01 00:00:00-03:00</th>\n",
       "      <td>18.253316</td>\n",
       "      <td>18.313026</td>\n",
       "      <td>17.650246</td>\n",
       "      <td>17.829376</td>\n",
       "      <td>115944200</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3927 rows × 7 columns</p>\n",
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       "variable_name": "hist",
       "summary": "{\n  \"name\": \"hist\",\n  \"rows\": 3927,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2007-01-02 00:00:00-02:00\",\n        \"max\": \"2022-11-01 00:00:00-03:00\",\n        \"num_unique_values\": 3927,\n        \"samples\": [\n          \"2013-02-22 00:00:00-03:00\",\n          \"2018-10-30 00:00:00-03:00\",\n          \"2017-10-19 00:00:00-02:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.0348858300029,\n        \"min\": 1.2631014775019687,\n        \"max\": 21.794116472675853,\n        \"num_unique_values\": 3882,\n        \"samples\": [\n          9.945166348079857,\n          7.680812513125406,\n          2.9923473513513015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.0793290684071297,\n        \"min\": 1.2841532213748683,\n        \"max\": 22.922632562888666,\n        \"num_unique_values\": 3868,\n        \"samples\": [\n          4.595282712303584,\n          6.34858896980602,\n          7.102157846332209\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.9894311384853607,\n        \"min\": 1.2390421711232067,\n        \"max\": 21.728434442163007,\n        \"num_unique_values\": 3877,\n        \"samples\": [\n          17.62516975402832,\n          13.010700246528433,\n          2.363803757197514\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.037300327458976,\n        \"min\": 1.2631012201309204,\n        \"max\": 22.5225772857666,\n        \"num_unique_values\": 3129,\n        \"samples\": [\n          7.779792308807373,\n          7.403769493103027,\n          8.23857307434082\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34298588,\n        \"min\": 0,\n        \"max\": 698950612,\n        \"num_unique_values\": 3848,\n        \"samples\": [\n          16106700,\n          18645600,\n          35977000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Dividends\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14563746162865623,\n        \"min\": 0.0,\n        \"max\": 6.732003,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          2.970249,\n          0.1773,\n          0.000461\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stock Splits\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03191534511615757,\n        \"min\": 0.0,\n        \"max\": 2.0,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2.0,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
      }
     },
     "metadata": {},
     "execution_count": 4
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# creating log returns\n",
    "log_prices = np.log(hist[\"Close\"])\n",
    "log_returns = log_prices.diff().dropna()\n",
    "\n",
    "# another way without using first differences and log\n",
    "log_returns2 = hist[\"Close\"].pct_change().dropna()"
   ],
   "metadata": {
    "id": "HlkcbU-vwLLj"
   },
   "execution_count": 5,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "log_prices"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 490
    },
    "id": "zwXc6SzQzVzc",
    "outputId": "6a32e9e3-559c-4f8f-ff7e-12401cbcc583"
   },
   "execution_count": 6,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
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       "Date\n",
       "2007-01-02 00:00:00-02:00    1.875294\n",
       "2007-01-03 00:00:00-02:00    1.844416\n",
       "2007-01-04 00:00:00-02:00    1.821822\n",
       "2007-01-05 00:00:00-02:00    1.790952\n",
       "2007-01-08 00:00:00-02:00    1.802560\n",
       "                               ...   \n",
       "2022-10-26 00:00:00-03:00    2.972009\n",
       "2022-10-27 00:00:00-03:00    2.979622\n",
       "2022-10-28 00:00:00-03:00    2.967719\n",
       "2022-10-31 00:00:00-03:00    2.879172\n",
       "2022-11-01 00:00:00-03:00    2.880847\n",
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-02 00:00:00-02:00</th>\n",
       "      <td>1.875294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-03 00:00:00-02:00</th>\n",
       "      <td>1.844416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-04 00:00:00-02:00</th>\n",
       "      <td>1.821822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-05 00:00:00-02:00</th>\n",
       "      <td>1.790952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-08 00:00:00-02:00</th>\n",
       "      <td>1.802560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-26 00:00:00-03:00</th>\n",
       "      <td>2.972009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-27 00:00:00-03:00</th>\n",
       "      <td>2.979622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-28 00:00:00-03:00</th>\n",
       "      <td>2.967719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-31 00:00:00-03:00</th>\n",
       "      <td>2.879172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01 00:00:00-03:00</th>\n",
       "      <td>2.880847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3927 rows × 1 columns</p>\n",
       "</div><br><label><b>dtype:</b> float64</label>"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "log_returns"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 490
    },
    "id": "oucdT6_SzWw4",
    "outputId": "dac09c5c-4e09-4a10-8392-d52600fcef35"
   },
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Date\n",
       "2007-01-03 00:00:00-02:00   -0.030878\n",
       "2007-01-04 00:00:00-02:00   -0.022594\n",
       "2007-01-05 00:00:00-02:00   -0.030870\n",
       "2007-01-08 00:00:00-02:00    0.011608\n",
       "2007-01-09 00:00:00-02:00   -0.031039\n",
       "                               ...   \n",
       "2022-10-26 00:00:00-03:00   -0.024760\n",
       "2022-10-27 00:00:00-03:00    0.007614\n",
       "2022-10-28 00:00:00-03:00   -0.011903\n",
       "2022-10-31 00:00:00-03:00   -0.088548\n",
       "2022-11-01 00:00:00-03:00    0.001676\n",
       "Name: Close, Length: 3926, dtype: float64"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-03 00:00:00-02:00</th>\n",
       "      <td>-0.030878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-04 00:00:00-02:00</th>\n",
       "      <td>-0.022594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-05 00:00:00-02:00</th>\n",
       "      <td>-0.030870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-08 00:00:00-02:00</th>\n",
       "      <td>0.011608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-09 00:00:00-02:00</th>\n",
       "      <td>-0.031039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-26 00:00:00-03:00</th>\n",
       "      <td>-0.024760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-27 00:00:00-03:00</th>\n",
       "      <td>0.007614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-28 00:00:00-03:00</th>\n",
       "      <td>-0.011903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-31 00:00:00-03:00</th>\n",
       "      <td>-0.088548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01 00:00:00-03:00</th>\n",
       "      <td>0.001676</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3926 rows × 1 columns</p>\n",
       "</div><br><label><b>dtype:</b> float64</label>"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "log_returns2"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 490
    },
    "id": "zxY8cgv6onZi",
    "outputId": "7acd3103-6fa6-4ddd-e59c-893d6185277e"
   },
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Date\n",
       "2007-01-03 00:00:00-02:00   -0.030406\n",
       "2007-01-04 00:00:00-02:00   -0.022341\n",
       "2007-01-05 00:00:00-02:00   -0.030398\n",
       "2007-01-08 00:00:00-02:00    0.011676\n",
       "2007-01-09 00:00:00-02:00   -0.030562\n",
       "                               ...   \n",
       "2022-10-26 00:00:00-03:00   -0.024456\n",
       "2022-10-27 00:00:00-03:00    0.007643\n",
       "2022-10-28 00:00:00-03:00   -0.011833\n",
       "2022-10-31 00:00:00-03:00   -0.084741\n",
       "2022-11-01 00:00:00-03:00    0.001677\n",
       "Name: Close, Length: 3926, dtype: float64"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2007-01-03 00:00:00-02:00</th>\n",
       "      <td>-0.030406</td>\n",
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       "    <tr>\n",
       "      <th>2007-01-04 00:00:00-02:00</th>\n",
       "      <td>-0.022341</td>\n",
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       "    <tr>\n",
       "      <th>2007-01-05 00:00:00-02:00</th>\n",
       "      <td>-0.030398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-08 00:00:00-02:00</th>\n",
       "      <td>0.011676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-01-09 00:00:00-02:00</th>\n",
       "      <td>-0.030562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2022-10-26 00:00:00-03:00</th>\n",
       "      <td>-0.024456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-27 00:00:00-03:00</th>\n",
       "      <td>0.007643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-28 00:00:00-03:00</th>\n",
       "      <td>-0.011833</td>\n",
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       "    <tr>\n",
       "      <th>2022-10-31 00:00:00-03:00</th>\n",
       "      <td>-0.084741</td>\n",
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       "    <tr>\n",
       "      <th>2022-11-01 00:00:00-03:00</th>\n",
       "      <td>0.001677</td>\n",
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       "</div><br><label><b>dtype:</b> float64</label>"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# ploting the log returns\n",
    "plt.plot(log_returns);\n",
    "plt.title(\"Plot of Daily log-Returns of PETR4\");\n",
    "plt.ylabel(\"Percentage (%)\");"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "nGRw7zEQ14A6",
    "outputId": "c39f9d31-164b-4d9f-ff53-cc0b9da94599"
   },
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# ploting the prices\n",
    "plt.plot(log_prices);\n",
    "plt.title(\"Plot of Daily log-Prices of PETR4\");\n",
    "plt.ylabel(\"Log\");"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "UtoGthjCjYbc",
    "outputId": "4f76da5a-a91e-4cc7-fd02-a66af5c13064"
   },
   "execution_count": 10,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Volatility modeling in Python\n",
    "Vamos usar o pacote arch para estimar os modelos. Também podemos encontrar algumas postagens em que os modelos GARCH são estimados usando otimização (estilo solver), dado que são modelos determinísticos da volatilidade.\n",
    "Para mais detalhes do pacote:\n",
    "https://arch.readthedocs.io/en/latest/univariate/introduction.html\n"
   ],
   "metadata": {
    "id": "RiiAa6MxSoR5"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# analyzing the ACF of returns and quadratic returns\n",
    "# !pip install statsmodels\n",
    "from statsmodels.tsa.stattools import adfuller\n",
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "# ADF test: unit root\n",
    "adf = adfuller(log_prices)\n",
    "print('ADF Statistic: {}'.format(adf[0]))\n",
    "print('p-value: {}'.format(adf[1]))\n",
    "print('Critical Values:')\n",
    "for key, value in adf[4].items():\n",
    "    print('\\t{}: {}'.format(key, value))"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MfTkV-hmUQox",
    "outputId": "5b82fcfb-da54-4025-fa42-6129b1f8d3fb"
   },
   "execution_count": 11,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "ADF Statistic: -1.62842494696922\n",
      "p-value: 0.46831526608825136\n",
      "Critical Values:\n",
      "\t1%: -3.432019708703093\n",
      "\t5%: -2.8622777859108606\n",
      "\t10%: -2.567162732014412\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# ACF of log_prices\n",
    "plot_acf(log_prices, lags=60);"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "-CpuAxeoVl5f",
    "outputId": "05f2b9b7-6eff-4be7-87a6-8c8bb8b99b43"
   },
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# ACF of log_returns\n",
    "plot_acf(log_returns, lags=60);"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "a3iVFGrjVqU2",
    "outputId": "54db3d30-f899-42d7-b2dc-08694c0dd9ee"
   },
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# ACF of quadratic returns: see the conditional homoscedasticity\n",
    "plot_acf(log_returns**2, lags=60);"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "id": "pp0dwtgJVy-n",
    "outputId": "a2657030-7507-4312-c05e-431dd0667529"
   },
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# run first line for installing the library\n",
    "!pip install -q arch\n",
    "from arch import arch_model\n"
   ],
   "metadata": {
    "id": "VSGxykJsj2aC",
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "04b66863-d8ba-4eb2-cfc1-f21d5ca158d1"
   },
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/985.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.4/985.3 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m983.0/985.3 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m985.3/985.3 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25h"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# creating a ARCH(12) model with constant mean\n",
    "garch = arch_model(log_returns, vol='ARCH', p=12, rescale=False).fit(disp='off')\n",
    "print(garch.summary())"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "7p_46BA8WMbD",
    "outputId": "475b4c95-a9df-4f54-e8bd-cbd9e1c220fd"
   },
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                      Constant Mean - ARCH Model Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:                  Close   R-squared:                       0.000\n",
      "Mean Model:             Constant Mean   Adj. R-squared:                  0.000\n",
      "Vol Model:                       ARCH   Log-Likelihood:                8798.22\n",
      "Distribution:                  Normal   AIC:                          -17568.4\n",
      "Method:            Maximum Likelihood   BIC:                          -17480.6\n",
      "                                        No. Observations:                 3926\n",
      "Date:                Mon, May 26 2025   Df Residuals:                     3925\n",
      "Time:                        20:16:52   Df Model:                            1\n",
      "                                 Mean Model                                 \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "mu         8.0039e-04  3.917e-04      2.044  4.100e-02 [3.272e-05,1.568e-03]\n",
      "                               Volatility Model                              \n",
      "=============================================================================\n",
      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-----------------------------------------------------------------------------\n",
      "omega      2.3896e-04  3.730e-05      6.406  1.496e-10  [1.658e-04,3.121e-04]\n",
      "alpha[1]       0.1333  5.601e-02      2.381  1.728e-02    [2.357e-02,  0.243]\n",
      "alpha[2]       0.0870  2.515e-02      3.460  5.396e-04    [3.774e-02,  0.136]\n",
      "alpha[3]       0.0622  2.741e-02      2.271  2.316e-02    [8.520e-03,  0.116]\n",
      "alpha[4]       0.0945  2.799e-02      3.378  7.304e-04    [3.969e-02,  0.149]\n",
      "alpha[5]       0.0748  2.494e-02      3.000  2.697e-03    [2.594e-02,  0.124]\n",
      "alpha[6]       0.0732  2.989e-02      2.450  1.428e-02    [1.465e-02,  0.132]\n",
      "alpha[7]       0.0610  2.351e-02      2.594  9.482e-03    [1.491e-02,  0.107]\n",
      "alpha[8]       0.0277  3.094e-02      0.896      0.370 [-3.290e-02,8.836e-02]\n",
      "alpha[9]       0.0214  3.071e-02      0.698      0.485 [-3.875e-02,8.165e-02]\n",
      "alpha[10]      0.0427  2.613e-02      1.633      0.103 [-8.553e-03,9.386e-02]\n",
      "alpha[11]      0.0144  1.838e-02      0.781      0.435 [-2.168e-02,5.038e-02]\n",
      "alpha[12]      0.0295  2.484e-02      1.189      0.234 [-1.915e-02,7.823e-02]\n",
      "=============================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# creating a GARCH(1,1) model with constant mean\n",
    "garch = arch_model(log_returns, vol='GARCH', p=1, q=1, rescale=False).fit(disp='off')\n",
    "print(garch.summary())"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "KuS4ecWNWFnP",
    "outputId": "edf6d864-7bb1-46ec-edf7-7dc5f4aa4f32"
   },
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                     Constant Mean - GARCH Model Results                      \n",
      "==============================================================================\n",
      "Dep. Variable:                  Close   R-squared:                       0.000\n",
      "Mean Model:             Constant Mean   Adj. R-squared:                  0.000\n",
      "Vol Model:                      GARCH   Log-Likelihood:                8804.70\n",
      "Distribution:                  Normal   AIC:                          -17601.4\n",
      "Method:            Maximum Likelihood   BIC:                          -17576.3\n",
      "                                        No. Observations:                 3926\n",
      "Date:                Mon, May 26 2025   Df Residuals:                     3925\n",
      "Time:                        20:16:56   Df Model:                            1\n",
      "                                  Mean Model                                 \n",
      "=============================================================================\n",
      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-----------------------------------------------------------------------------\n",
      "mu         6.4826e-04  4.117e-04      1.574      0.115 [-1.587e-04,1.455e-03]\n",
      "                              Volatility Model                              \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "omega      1.7367e-05  6.608e-12  2.628e+06      0.000 [1.737e-05,1.737e-05]\n",
      "alpha[1]       0.1000  2.150e-02      4.651  3.307e-06   [5.786e-02,  0.142]\n",
      "beta[1]        0.8800  1.788e-02     49.226      0.000     [  0.845,  0.915]\n",
      "============================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# creating a GARCH(1,1) model with AR(1) mean\n",
    "garch = arch_model(log_returns,mean='ARX', lags=1, vol='GARCH', p=1, q=1, rescale=False).fit(disp='off')\n",
    "print(garch.summary())"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "UnW1IcRaYfJx",
    "outputId": "f32f0302-8a86-46d8-8151-887d6aaaf608"
   },
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                           AR - GARCH Model Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                  Close   R-squared:                       0.001\n",
      "Mean Model:                        AR   Adj. R-squared:                  0.000\n",
      "Vol Model:                      GARCH   Log-Likelihood:                8803.73\n",
      "Distribution:                  Normal   AIC:                          -17597.5\n",
      "Method:            Maximum Likelihood   BIC:                          -17566.1\n",
      "                                        No. Observations:                 3925\n",
      "Date:                Mon, May 26 2025   Df Residuals:                     3923\n",
      "Time:                        20:16:58   Df Model:                            2\n",
      "                                  Mean Model                                 \n",
      "=============================================================================\n",
      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-----------------------------------------------------------------------------\n",
      "Const      6.6950e-04  4.151e-04      1.613      0.107 [-1.441e-04,1.483e-03]\n",
      "Close[1]      -0.0284  1.720e-02     -1.649  9.922e-02 [-6.208e-02,5.356e-03]\n",
      "                              Volatility Model                              \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "omega      1.7390e-05  6.543e-12  2.658e+06      0.000 [1.739e-05,1.739e-05]\n",
      "alpha[1]       0.1000  2.164e-02      4.622  3.802e-06   [5.759e-02,  0.142]\n",
      "beta[1]        0.8800  1.796e-02     49.004      0.000     [  0.845,  0.915]\n",
      "============================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# creating a GARCH(1,1) model zero mean\n",
    "garch = arch_model(log_returns,mean='Zero', vol='GARCH', p=1, q=1, rescale=False, dist='StudentsT').fit(disp='off')\n",
    "print(garch.summary())"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QNcIyzBeVx7i",
    "outputId": "3c580997-144b-40d2-cf5d-11ecaa096521"
   },
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                          Zero Mean - GARCH Model Results                           \n",
      "====================================================================================\n",
      "Dep. Variable:                        Close   R-squared:                       0.000\n",
      "Mean Model:                       Zero Mean   Adj. R-squared:                  0.000\n",
      "Vol Model:                            GARCH   Log-Likelihood:                8969.36\n",
      "Distribution:      Standardized Student's t   AIC:                          -17930.7\n",
      "Method:                  Maximum Likelihood   BIC:                          -17905.6\n",
      "                                              No. Observations:                 3926\n",
      "Date:                      Mon, May 26 2025   Df Residuals:                     3926\n",
      "Time:                              20:17:00   Df Model:                            0\n",
      "                              Volatility Model                              \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "omega      1.2150e-05  1.010e-07    120.283      0.000 [1.195e-05,1.235e-05]\n",
      "alpha[1]       0.0832  1.119e-02      7.440  1.009e-13   [6.130e-02,  0.105]\n",
      "beta[1]        0.9050  8.850e-03    102.262      0.000     [  0.888,  0.922]\n",
      "                              Distribution                              \n",
      "========================================================================\n",
      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
      "------------------------------------------------------------------------\n",
      "nu             5.4672      0.489     11.184  4.873e-29 [  4.509,  6.425]\n",
      "========================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    " # creating a GARCH(1,1) model with constant mean and t-Student errors\n",
    "garch = arch_model(log_returns,\n",
    "                   vol='GARCH',\n",
    "                   power=1.0,\n",
    "                   dist='StudentsT',\n",
    "                   rescale='False').fit(disp='off')\n",
    "print(garch.summary())"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OEZDY8RPaM3U",
    "outputId": "72efb8f9-080d-4779-9a44-797a537c70c7"
   },
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                       Constant Mean - AVGARCH Model Results                        \n",
      "====================================================================================\n",
      "Dep. Variable:                        Close   R-squared:                       0.000\n",
      "Mean Model:                   Constant Mean   Adj. R-squared:                  0.000\n",
      "Vol Model:                          AVGARCH   Log-Likelihood:                8960.20\n",
      "Distribution:      Standardized Student's t   AIC:                          -17910.4\n",
      "Method:                  Maximum Likelihood   BIC:                          -17879.0\n",
      "                                              No. Observations:                 3926\n",
      "Date:                      Mon, May 26 2025   Df Residuals:                     3925\n",
      "Time:                              20:17:02   Df Model:                            1\n",
      "                                 Mean Model                                 \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "mu         7.2159e-04  3.563e-04      2.025  4.284e-02 [2.328e-05,1.420e-03]\n",
      "                               Volatility Model                              \n",
      "=============================================================================\n",
      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
      "-----------------------------------------------------------------------------\n",
      "omega      1.4891e-03  5.312e-03      0.280      0.779 [-8.921e-03,1.190e-02]\n",
      "alpha[1]       0.1119      0.146      0.766      0.444      [ -0.174,  0.398]\n",
      "beta[1]        0.8591      0.310      2.768  5.634e-03      [  0.251,  1.467]\n",
      "                              Distribution                              \n",
      "========================================================================\n",
      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
      "------------------------------------------------------------------------\n",
      "nu             5.4950      0.947      5.801  6.575e-09 [  3.639,  7.351]\n",
      "========================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    " # creating a GARCH(1,1) model with constant mean, t-Student errors and asymmetric shocks (GJR-GARCH)\n",
    "garch = arch_model(log_returns,\n",
    "                   vol='GARCH',\n",
    "                   p=1,\n",
    "                   o=1,\n",
    "                   q=1,\n",
    "                   power=1.0,\n",
    "                   dist='StudentsT',\n",
    "                   rescale='False').fit(disp='off')\n",
    "print(garch.summary())"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RglLPBqQcAF8",
    "outputId": "760f8a3e-e396-46e3-bb17-995c30850d82"
   },
   "execution_count": 21,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "                     Constant Mean - TARCH/ZARCH Model Results                      \n",
      "====================================================================================\n",
      "Dep. Variable:                        Close   R-squared:                       0.000\n",
      "Mean Model:                   Constant Mean   Adj. R-squared:                  0.000\n",
      "Vol Model:                      TARCH/ZARCH   Log-Likelihood:               -174551.\n",
      "Distribution:      Standardized Student's t   AIC:                           349114.\n",
      "Method:                  Maximum Likelihood   BIC:                           349152.\n",
      "                                              No. Observations:                 3926\n",
      "Date:                      Mon, May 26 2025   Df Residuals:                     3925\n",
      "Time:                              20:17:05   Df Model:                            1\n",
      "                                 Mean Model                                 \n",
      "============================================================================\n",
      "                 coef    std err          t      P>|t|      95.0% Conf. Int.\n",
      "----------------------------------------------------------------------------\n",
      "mu         7.4767e+06    549.386  1.361e+04      0.000 [7.476e+06,7.478e+06]\n",
      "                            Volatility Model                            \n",
      "========================================================================\n",
      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
      "------------------------------------------------------------------------\n",
      "omega          0.2019      0.216      0.935      0.350 [ -0.221,  0.625]\n",
      "alpha[1]   1.4089e-10      0.193  7.287e-10      1.000 [ -0.379,  0.379]\n",
      "gamma[1]       2.0000      0.194     10.318  5.856e-25 [  1.620,  2.380]\n",
      "beta[1]    1.0649e-12  5.526e-02  1.927e-11      1.000 [ -0.108,  0.108]\n",
      "                              Distribution                              \n",
      "========================================================================\n",
      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
      "------------------------------------------------------------------------\n",
      "nu             2.0790  2.661e-05  7.813e+04      0.000 [  2.079,  2.079]\n",
      "========================================================================\n",
      "\n",
      "Covariance estimator: robust\n"
     ]
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Forecasting volatility\n",
    "\n",
    "https://arch.readthedocs.io/en/latest/univariate/forecasting.html\n"
   ],
   "metadata": {
    "id": "NCEfB-Hc26ck"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "forecast = garch.forecast(horizon=1)\n",
    "forecast.variance"
   ],
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    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 112
    },
    "id": "jzEeGMRo24yE",
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   },
   "execution_count": 22,
   "outputs": [
    {
     "output_type": "execute_result",
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       "                                    h.1\n",
       "Date                                   \n",
       "2022-11-01 00:00:00-03:00  2.236021e+14"
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     "metadata": {},
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    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Value at Risk\n",
    "https://arch.readthedocs.io/en/latest/univariate/univariate_volatility_forecasting.html#Parametric-VaR"
   ],
   "metadata": {
    "id": "iR9g441X_O_z"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "am = arch_model(log_returns, vol=\"Garch\", p=1, o=0, q=1, dist=\"skewt\", rescale=False)\n",
    "res = am.fit(last_obs=\"2021-12-31\")\n",
    "forecasts = res.forecast(start=\"2022-01-01\", reindex=False)\n",
    "cond_mean = forecasts.mean[\"2022\":]\n",
    "cond_var = forecasts.variance[\"2022\":]\n",
    "q = am.distribution.ppf([0.01, 0.05], res.params[-2:])\n",
    "print(q)"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GdMiPusv_REY",
    "outputId": "7140d324-0fb4-4b6f-8c8d-36100655584c"
   },
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Iteration:      1,   Func. Count:      8,   Neg. LLF: 390658.38455198176\n",
      "Iteration:      2,   Func. Count:     25,   Neg. LLF: 2034024.2284916663\n",
      "Iteration:      3,   Func. Count:     42,   Neg. LLF: 16498.978725178757\n",
      "Iteration:      4,   Func. Count:     50,   Neg. LLF: 10573.706961983578\n",
      "Iteration:      5,   Func. Count:     57,   Neg. LLF: 32414.19603278839\n",
      "Iteration:      6,   Func. Count:     68,   Neg. LLF: 112992.94144688276\n",
      "Iteration:      7,   Func. Count:     82,   Neg. LLF: 298402.49523688585\n",
      "Iteration:      8,   Func. Count:     99,   Neg. LLF: 45317.94184255569\n",
      "Iteration:      9,   Func. Count:    107,   Neg. LLF: 24093.969671964063\n",
      "Iteration:     10,   Func. Count:    115,   Neg. LLF: 639752.9960794718\n",
      "Iteration:     11,   Func. Count:    132,   Neg. LLF: 5296.4436541080495\n",
      "Iteration:     12,   Func. Count:    139,   Neg. LLF: 1298916.2356498886\n",
      "Iteration:     13,   Func. Count:    155,   Neg. LLF: 70671.20098488516\n",
      "Iteration:     14,   Func. Count:    172,   Neg. LLF: 75000.91650616596\n",
      "Iteration:     15,   Func. Count:    189,   Neg. LLF: 15932.571624307933\n",
      "Iteration:     16,   Func. Count:    197,   Neg. LLF: 103958.72545870309\n",
      "Iteration:     17,   Func. Count:    205,   Neg. LLF: 87203.09357116537\n",
      "Iteration:     18,   Func. Count:    213,   Neg. LLF: 1075.6010042818489\n",
      "Iteration:     19,   Func. Count:    220,   Neg. LLF: 86427.4585183924\n",
      "Iteration:     20,   Func. Count:    229,   Neg. LLF: 96470.45294558411\n",
      "Iteration:     21,   Func. Count:    246,   Neg. LLF: 13809.65099206184\n",
      "Iteration:     22,   Func. Count:    257,   Neg. LLF: 445482.5913334888\n",
      "Iteration:     23,   Func. Count:    265,   Neg. LLF: 216461.23297600297\n",
      "Iteration:     24,   Func. Count:    278,   Neg. LLF: 2460.614347998372\n",
      "Iteration:     25,   Func. Count:    286,   Neg. LLF: 5759.802064512188\n",
      "Iteration:     26,   Func. Count:    296,   Neg. LLF: 1648382.8845535477\n",
      "Iteration:     27,   Func. Count:    304,   Neg. LLF: 192289.94299340903\n",
      "Iteration:     28,   Func. Count:    314,   Neg. LLF: -4995.750635278489\n",
      "Optimization terminated successfully    (Exit mode 0)\n",
      "            Current function value: -4995.750635030736\n",
      "            Iterations: 32\n",
      "            Function evaluations: 314\n",
      "            Gradient evaluations: 28\n",
      "[-2.28167218 -1.59717959]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "value_at_risk = -cond_mean.values - np.sqrt(cond_var).values * q[None, :]\n",
    "value_at_risk = pd.DataFrame(value_at_risk, columns=[\"1%\", \"5%\"], index=cond_var.index)\n",
    "ax = value_at_risk.plot(legend=False)\n",
    "xl = ax.set_xlim(value_at_risk.index[0], value_at_risk.index[-1])\n",
    "rets_2022 = log_returns[\"2022\":].copy()\n",
    "rets_2022.name = \"Log Return\"\n",
    "c = []\n",
    "for idx in value_at_risk.index:\n",
    "    if rets_2022[idx] > -value_at_risk.loc[idx, \"5%\"]:\n",
    "        c.append(\"#000000\")\n",
    "    elif rets_2022[idx] < -value_at_risk.loc[idx, \"1%\"]:\n",
    "        c.append(\"#BB0000\")\n",
    "    else:\n",
    "        c.append(\"#BB00BB\")\n",
    "c = np.array(c, dtype=\"object\")\n",
    "labels = {\n",
    "    \"#BB0000\": \"1% Exceedence\",\n",
    "    \"#BB00BB\": \"5% Exceedence\",\n",
    "    \"#000000\": \"No Exceedence\",\n",
    "}\n",
    "markers = {\"#BB0000\": \"x\", \"#BB00BB\": \"s\", \"#000000\": \"o\"}\n",
    "for color in np.unique(c):\n",
    "    sel = c == color\n",
    "    ax.scatter(\n",
    "        rets_2022.index[sel],\n",
    "        -rets_2022.loc[sel],\n",
    "        marker=markers[color],\n",
    "        c=c[sel],\n",
    "        label=labels[color],\n",
    "    )\n",
    "ax.set_title(\"Parametric VaR\")\n",
    "leg = ax.legend(frameon=False, ncol=3)\n"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "rHnIj3apAFm_",
    "outputId": "4c336a64-bfd0-4449-f387-05ab618edc89"
   },
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "ni5gkXvyAGo3"
   },
   "execution_count": null,
   "outputs": []
  }
 ]
}