projeto_estatistica.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import math\n",
    "import statistics as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "from scipy.stats import norm, chi2, lognorm, expon, gamma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estatística Descritiva"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Primeiramente, importamos o dataset com a organização dos dados por DIAS. Adicionamos a coluna organizada por ano para observar melhor os dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>Volume BTC</th>\n",
       "      <th>Volume USDC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-07-31</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23631.647504</td>\n",
       "      <td>23761.620475</td>\n",
       "      <td>23545.739397</td>\n",
       "      <td>23719.583576</td>\n",
       "      <td>3.176313e+04</td>\n",
       "      <td>1.342978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23795.133823</td>\n",
       "      <td>24661.013634</td>\n",
       "      <td>23500.000000</td>\n",
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       "      <td>2.137218e+06</td>\n",
       "      <td>88.572925</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-07-29</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23852.254868</td>\n",
       "      <td>24444.000000</td>\n",
       "      <td>23448.625662</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-07-28</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>22972.588882</td>\n",
       "      <td>24210.140953</td>\n",
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       "      <td>91.770726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-07-27</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>21272.485744</td>\n",
       "      <td>23100.000000</td>\n",
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       "        date    symbol          open          high           low  \\\n",
       "0 2022-07-31  BTC/USDC  23631.647504  23761.620475  23545.739397   \n",
       "1 2022-07-30  BTC/USDC  23795.133823  24661.013634  23500.000000   \n",
       "2 2022-07-29  BTC/USDC  23852.254868  24444.000000  23448.625662   \n",
       "3 2022-07-28  BTC/USDC  22972.588882  24210.140953  22643.084928   \n",
       "4 2022-07-27  BTC/USDC  21272.485744  23100.000000  21048.234917   \n",
       "\n",
       "          close    Volume BTC  Volume USDC  \n",
       "0  23719.583576  3.176313e+04     1.342978  \n",
       "1  23645.665322  2.137218e+06    88.572925  \n",
       "2  23774.368186  2.475382e+06   103.577541  \n",
       "3  23856.203587  2.131011e+06    91.770726  \n",
       "4  22961.245747  1.768529e+06    79.871174  "
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     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "btc_dataset = pd.read_csv('Poloniex_BTCUSDC_d.csv', skiprows=1, parse_dates=['date'])\n",
    "btc_dataset.drop(columns='unix', inplace=True)\n",
    "btc_dataset.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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       "      <th>date</th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>Volume BTC</th>\n",
       "      <th>Volume USDC</th>\n",
       "      <th>year</th>\n",
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       "      <th>0</th>\n",
       "      <td>2022-07-31</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23631.647504</td>\n",
       "      <td>23761.620475</td>\n",
       "      <td>23545.739397</td>\n",
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       "      <td>2022</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23795.133823</td>\n",
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       "      <td>2022</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-07-29</td>\n",
       "      <td>BTC/USDC</td>\n",
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       "      <td>2022</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-07-28</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>22972.588882</td>\n",
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       "      <td>2022</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-07-27</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>21272.485744</td>\n",
       "      <td>23100.000000</td>\n",
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       "      <td>2022</td>\n",
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       "        date    symbol          open          high           low  \\\n",
       "0 2022-07-31  BTC/USDC  23631.647504  23761.620475  23545.739397   \n",
       "1 2022-07-30  BTC/USDC  23795.133823  24661.013634  23500.000000   \n",
       "2 2022-07-29  BTC/USDC  23852.254868  24444.000000  23448.625662   \n",
       "3 2022-07-28  BTC/USDC  22972.588882  24210.140953  22643.084928   \n",
       "4 2022-07-27  BTC/USDC  21272.485744  23100.000000  21048.234917   \n",
       "\n",
       "          close    Volume BTC  Volume USDC  year  \n",
       "0  23719.583576  3.176313e+04     1.342978  2022  \n",
       "1  23645.665322  2.137218e+06    88.572925  2022  \n",
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   "source": [
    "btc_dataset['year'] = btc_dataset['date'].dt.year\n",
    "btc_dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A principio, vamos observar a variável do \"fechamento\", organizadas por ano."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
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        "title": {
         "text": "VARIAÇÃO DO FECHAMENTO - BTC/USDC"
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   ],
   "source": [
    "fig=px.box(data_frame=btc_dataset, y='close', x='year', color='year')\n",
    "\n",
    "fig.update_layout(\n",
    "    title='VARIAÇÃO DO FECHAMENTO - BTC/USDC',\n",
    "    autosize=False,\n",
    "    width=1300,\n",
    "    height=500,\n",
    "    legend=dict(font=dict(size= 15)),\n",
    "    template='presentation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Podemos observar uma concentração maior/mais variação de outliers no ano de 2020, então resolvemos observar esses dados mais de perto para avaliar melhor esses outliers. Levantamos os seguintes questionamentos: esses valores são anomalias? Seriam eles fruto de algum \"dump and pump\" (prática que envolve inflar artificialmente o preço inserindo dinheiro e depois tirando rapidamente)? Ou são valores reais mesmo?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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        "title": {
         "text": "Variação do Fechamento - 2020"
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   "source": [
    "fig=px.line(data_frame=btc_dataset[btc_dataset['year']==2020], y='close', x='date', color='year')\n",
    "\n",
    "fig.update_layout(\n",
    "    title='Variação do Fechamento - 2020',\n",
    "    autosize=False,\n",
    "    width=1300,\n",
    "    height=500,\n",
    "    legend=dict(font=dict(size= 15)),\n",
    "    template='presentation')"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Observando a variação do fechamento dia a dia durante o ano de 2020, não podemos observar nenhuma evidência de práticas de \"pump and dump\" ou valores anomolos resultantes de erros de inserção de dados. O que observamos é realmente um crescimendo no valor do fechamento durante o ano de 2020. Por isso, concluimos que esses dados devem ser mantidos."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Depois decidimos observar a variação no volume de BTC negociado, também organizado por ano."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
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        "title": {
         "text": "VARIAÇÃO DO VOLUME - BTC/USDC"
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   ],
   "source": [
    "fig=px.box(data_frame=btc_dataset, y='Volume BTC', x='year', color='year')\n",
    "\n",
    "fig.update_layout(\n",
    "    title='VARIAÇÃO DO VOLUME - BTC/USDC',\n",
    "    autosize=False,\n",
    "    width=1300,\n",
    "    height=500,\n",
    "    legend=dict(font=dict(size= 15)),\n",
    "    template='presentation')"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Logo de cara, o que chamou nossa atenção foi o valor discrepante no máximo de 2021. Então, também decidimos observar esse valor de perto, para ter certeza que esse valor também não é fruto de algum erro na inserção de dados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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          "xaxis": {
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           }
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          "yaxis": {
           "title": {
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           }
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        },
        "title": {
         "text": "Variação Volume - 2021"
        },
        "width": 1300,
        "xaxis": {
         "anchor": "y",
         "domain": [
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         "title": {
          "text": "date"
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        "yaxis": {
         "anchor": "x",
         "domain": [
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         "title": {
          "text": "Volume BTC"
         }
        }
       }
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=px.line(data_frame=btc_dataset[btc_dataset['year']==2021], y='Volume BTC', x='date', color='year')\n",
    "\n",
    "fig.update_layout(\n",
    "    title='Variação Volume - 2021',\n",
    "    autosize=False,\n",
    "    width=1300,\n",
    "    height=500,\n",
    "    legend=dict(font=dict(size= 15)),\n",
    "    template='presentation')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Com o gráfico de linhas focado no ano, podemos observar que a possível anomalia aconteceu em maio, mais especificamente no dia 19 de maio, mas ainda fica dificil distinguir o que realmente aconteceu. Por isso, resolvemos pegar o outro dataset disponível no site, que organiza os dados em um intervalo de hora em hora."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "btc_dataset_hora = pd.read_csv('Poloniex_BTCUSDC_1h.csv', skiprows=1, parse_dates=['date'])\n",
    "btc_dataset_hora.drop(columns='unix', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>Volume BTC</th>\n",
       "      <th>Volume USDC</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-07-31 01:00:00</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23750.558747</td>\n",
       "      <td>23751.746164</td>\n",
       "      <td>23703.877009</td>\n",
       "      <td>23719.583576</td>\n",
       "      <td>4438.147502</td>\n",
       "      <td>0.187043</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-07-31 00:30:00</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23668.742550</td>\n",
       "      <td>23761.620475</td>\n",
       "      <td>23661.164532</td>\n",
       "      <td>23757.142858</td>\n",
       "      <td>11194.919103</td>\n",
       "      <td>0.471548</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-07-31 00:00:00</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23631.647504</td>\n",
       "      <td>23699.423626</td>\n",
       "      <td>23545.739397</td>\n",
       "      <td>23668.742550</td>\n",
       "      <td>16130.064947</td>\n",
       "      <td>0.684387</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-07-30 23:30:00</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23560.198121</td>\n",
       "      <td>23662.974411</td>\n",
       "      <td>23560.198121</td>\n",
       "      <td>23645.665322</td>\n",
       "      <td>1490.858574</td>\n",
       "      <td>0.063228</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-07-30 23:00:00</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23689.478250</td>\n",
       "      <td>23738.880032</td>\n",
       "      <td>23562.341240</td>\n",
       "      <td>23594.398784</td>\n",
       "      <td>826.181940</td>\n",
       "      <td>0.034931</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date    symbol          open          high           low  \\\n",
       "0 2022-07-31 01:00:00  BTC/USDC  23750.558747  23751.746164  23703.877009   \n",
       "1 2022-07-31 00:30:00  BTC/USDC  23668.742550  23761.620475  23661.164532   \n",
       "2 2022-07-31 00:00:00  BTC/USDC  23631.647504  23699.423626  23545.739397   \n",
       "3 2022-07-30 23:30:00  BTC/USDC  23560.198121  23662.974411  23560.198121   \n",
       "4 2022-07-30 23:00:00  BTC/USDC  23689.478250  23738.880032  23562.341240   \n",
       "\n",
       "          close    Volume BTC  Volume USDC  year  \n",
       "0  23719.583576   4438.147502     0.187043  2022  \n",
       "1  23757.142858  11194.919103     0.471548  2022  \n",
       "2  23668.742550  16130.064947     0.684387  2022  \n",
       "3  23645.665322   1490.858574     0.063228  2022  \n",
       "4  23594.398784    826.181940     0.034931  2022  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_dataset_hora['year'] = btc_dataset_hora['date'].dt.year\n",
    "btc_dataset_hora.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 47976 entries, 0 to 47975\n",
      "Data columns (total 10 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   date           47976 non-null  datetime64[ns]\n",
      " 1   symbol         47976 non-null  object        \n",
      " 2   open           47976 non-null  float64       \n",
      " 3   high           47976 non-null  float64       \n",
      " 4   low            47976 non-null  float64       \n",
      " 5   close          47976 non-null  float64       \n",
      " 6   Volume BTC     47976 non-null  float64       \n",
      " 7   Volume USDC    47976 non-null  float64       \n",
      " 8   year           47976 non-null  int64         \n",
      " 9   specific date  47976 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(6), int64(1), object(2)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "btc_dataset_hora['specific date'] = btc_dataset_hora['date'].dt.date\n",
    "btc_dataset_hora['specific date'] = btc_dataset_hora['specific date'].astype(str)\n",
    "btc_dataset_hora.info()"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "- Mais uma vez, não podemos encontrar nenhum evidência de que os dados estejam de alguma forma incorretos, então concluimos que deve ser mantido."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Achamos interessante adicionar um delta do valor máximo e do valor mínimo de cada dia, já que er auma informação que não constava. Também adicionamos para mais uma variável na tabela de correlação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .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>date</th>\n",
       "      <th>symbol</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>Volume BTC</th>\n",
       "      <th>Volume USDC</th>\n",
       "      <th>year</th>\n",
       "      <th>delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-07-31</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23631.647504</td>\n",
       "      <td>23761.620475</td>\n",
       "      <td>23545.739397</td>\n",
       "      <td>23719.583576</td>\n",
       "      <td>3.176313e+04</td>\n",
       "      <td>1.342978</td>\n",
       "      <td>2022</td>\n",
       "      <td>215.881078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-07-30</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23795.133823</td>\n",
       "      <td>24661.013634</td>\n",
       "      <td>23500.000000</td>\n",
       "      <td>23645.665322</td>\n",
       "      <td>2.137218e+06</td>\n",
       "      <td>88.572925</td>\n",
       "      <td>2022</td>\n",
       "      <td>1161.013634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-07-29</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>23852.254868</td>\n",
       "      <td>24444.000000</td>\n",
       "      <td>23448.625662</td>\n",
       "      <td>23774.368186</td>\n",
       "      <td>2.475382e+06</td>\n",
       "      <td>103.577541</td>\n",
       "      <td>2022</td>\n",
       "      <td>995.374338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-07-28</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>22972.588882</td>\n",
       "      <td>24210.140953</td>\n",
       "      <td>22643.084928</td>\n",
       "      <td>23856.203587</td>\n",
       "      <td>2.131011e+06</td>\n",
       "      <td>91.770726</td>\n",
       "      <td>2022</td>\n",
       "      <td>1567.056025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-07-27</td>\n",
       "      <td>BTC/USDC</td>\n",
       "      <td>21272.485744</td>\n",
       "      <td>23100.000000</td>\n",
       "      <td>21048.234917</td>\n",
       "      <td>22961.245747</td>\n",
       "      <td>1.768529e+06</td>\n",
       "      <td>79.871174</td>\n",
       "      <td>2022</td>\n",
       "      <td>2051.765083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date    symbol          open          high           low  \\\n",
       "0 2022-07-31  BTC/USDC  23631.647504  23761.620475  23545.739397   \n",
       "1 2022-07-30  BTC/USDC  23795.133823  24661.013634  23500.000000   \n",
       "2 2022-07-29  BTC/USDC  23852.254868  24444.000000  23448.625662   \n",
       "3 2022-07-28  BTC/USDC  22972.588882  24210.140953  22643.084928   \n",
       "4 2022-07-27  BTC/USDC  21272.485744  23100.000000  21048.234917   \n",
       "\n",
       "          close    Volume BTC  Volume USDC  year        delta  \n",
       "0  23719.583576  3.176313e+04     1.342978  2022   215.881078  \n",
       "1  23645.665322  2.137218e+06    88.572925  2022  1161.013634  \n",
       "2  23774.368186  2.475382e+06   103.577541  2022   995.374338  \n",
       "3  23856.203587  2.131011e+06    91.770726  2022  1567.056025  \n",
       "4  22961.245747  1.768529e+06    79.871174  2022  2051.765083  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_dataset['delta'] = btc_dataset['high'] - btc_dataset['low']\n",
    "\n",
    "btc_dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_dataset_correlacao = btc_dataset[btc_dataset['year']==2021].drop(columns='year').corr()\n",
    "\n",
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "sns.heatmap(btc_dataset_correlacao, annot=True, vmin=-1, vmax=1, cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Adicionamos o delta pois a principio levantamos a hipotese de a diferença do valor máximo e valor mínimo (delta) estar relacionada com o valume comercializado no dia. Porém, ao observarmos a tabela de correlação, não podemos afirmar com certeza se há ou não, pois embora seja demonstrada uma correlação de 65%, pode haver ruído nos dados. Seria necessario fazer um estudo mais a fundo para determinar o que realmente está correlacionado. \n",
    "- É importante ressaltar que a alta de taxa de correlação entre os dados de abertura, máxima, mínima e fechamento podem estar relacionada com os fatos de que:\n",
    "As oscilações diarias acabam não sendo tão grandes por causa dos valores altos;\n",
    "O valor de abertura é bem próximo ao valor de fechamento pois um sucede o outro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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 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>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>Volume BTC</th>\n",
       "      <th>Volume USDC</th>\n",
       "      <th>year</th>\n",
       "      <th>delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>3.650000e+02</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.0</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47378.256432</td>\n",
       "      <td>48800.993615</td>\n",
       "      <td>45716.144672</td>\n",
       "      <td>47423.202145</td>\n",
       "      <td>1.245023e+07</td>\n",
       "      <td>283.176884</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>3084.848943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9826.230879</td>\n",
       "      <td>9824.410586</td>\n",
       "      <td>9784.133484</td>\n",
       "      <td>9776.534514</td>\n",
       "      <td>9.531012e+06</td>\n",
       "      <td>244.067330</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1683.866342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>28939.016219</td>\n",
       "      <td>29688.700013</td>\n",
       "      <td>28000.000000</td>\n",
       "      <td>29382.652722</td>\n",
       "      <td>1.545689e+06</td>\n",
       "      <td>29.912819</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>780.437941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38213.267797</td>\n",
       "      <td>39766.723489</td>\n",
       "      <td>36200.000000</td>\n",
       "      <td>38329.071002</td>\n",
       "      <td>5.521954e+06</td>\n",
       "      <td>115.503178</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>1970.704006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>47850.370460</td>\n",
       "      <td>49234.808808</td>\n",
       "      <td>46582.693875</td>\n",
       "      <td>47827.526938</td>\n",
       "      <td>9.635082e+06</td>\n",
       "      <td>209.793330</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>2728.221583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>56281.677146</td>\n",
       "      <td>57735.115685</td>\n",
       "      <td>54019.560203</td>\n",
       "      <td>56286.023442</td>\n",
       "      <td>1.696789e+07</td>\n",
       "      <td>381.467060</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>3620.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>67534.243288</td>\n",
       "      <td>69000.000000</td>\n",
       "      <td>66318.209901</td>\n",
       "      <td>67562.580742</td>\n",
       "      <td>8.196420e+07</td>\n",
       "      <td>2141.153853</td>\n",
       "      <td>2021.0</td>\n",
       "      <td>13468.725508</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               open          high           low         close    Volume BTC  \\\n",
       "count    365.000000    365.000000    365.000000    365.000000  3.650000e+02   \n",
       "mean   47378.256432  48800.993615  45716.144672  47423.202145  1.245023e+07   \n",
       "std     9826.230879   9824.410586   9784.133484   9776.534514  9.531012e+06   \n",
       "min    28939.016219  29688.700013  28000.000000  29382.652722  1.545689e+06   \n",
       "25%    38213.267797  39766.723489  36200.000000  38329.071002  5.521954e+06   \n",
       "50%    47850.370460  49234.808808  46582.693875  47827.526938  9.635082e+06   \n",
       "75%    56281.677146  57735.115685  54019.560203  56286.023442  1.696789e+07   \n",
       "max    67534.243288  69000.000000  66318.209901  67562.580742  8.196420e+07   \n",
       "\n",
       "       Volume USDC    year         delta  \n",
       "count   365.000000   365.0    365.000000  \n",
       "mean    283.176884  2021.0   3084.848943  \n",
       "std     244.067330     0.0   1683.866342  \n",
       "min      29.912819  2021.0    780.437941  \n",
       "25%     115.503178  2021.0   1970.704006  \n",
       "50%     209.793330  2021.0   2728.221583  \n",
       "75%     381.467060  2021.0   3620.000000  \n",
       "max    2141.153853  2021.0  13468.725508  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "btc_dataset[btc_dataset['year']==2021].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Gráficos mostrando a frequência de valores dos últimos quatro anos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([532., 233.,  72.,  29.,  88., 139., 122.,  67.,  81.,  29.]),\n",
       " array([ 3180.        ,  9615.42432884, 16050.84865768, 22486.27298652,\n",
       "        28921.69731536, 35357.1216442 , 41792.54597305, 48227.97030189,\n",
       "        54663.39463073, 61098.81895957, 67534.24328841]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.hist(btc_dataset['open'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([531., 233.,  72.,  31.,  87., 139., 122.,  67.,  81.,  29.]),\n",
       " array([ 3180.        ,  9618.25807425, 16056.5161485 , 22494.77422275,\n",
       "        28933.032297  , 35371.29037125, 41809.5484455 , 48247.80651975,\n",
       "        54686.064594  , 61124.32266825, 67562.5807425 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.hist(btc_dataset['close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([529., 237.,  68.,  33.,  83., 142., 118.,  69.,  84.,  29.]),\n",
       " array([ 3229. ,  9806.1, 16383.2, 22960.3, 29537.4, 36114.5, 42691.6,\n",
       "        49268.7, 55845.8, 62422.9, 69000. ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.hist(btc_dataset['high'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([538., 227.,  76.,  28., 105., 127., 133.,  61.,  71.,  26.]),\n",
       " array([ 3118.6       ,  9438.56099014, 15758.52198028, 22078.48297042,\n",
       "        28398.44396056, 34718.4049507 , 41038.36594084, 47358.32693098,\n",
       "        53678.28792112, 59998.24891126, 66318.2099014 ]),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "plt.hist(btc_dataset['low'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parte 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probabilidade e Inferência"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Parte 2 <br>\n",
    "Iremos testar se os nossos dados pertencem a uma distribuição exponencial, normal, lognormal ou gamma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "np_array = np.load('values_squad3.npy')\n",
    "df_base = pd.DataFrame(np_array, columns=['valores'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Para um intervalo de confiança de 68% nossos valores estariam entre \u001b[91m0.0996 e -0.0996\u001b[0m com uma média de\u001b[1m-8.4e-06\u001b[0m.\n",
      "Para um intervalo de confiança de 95% nossos valores estariam entre \u001b[91m0.1992 e -0.1992\u001b[0m com uma média de\u001b[1m-8.4e-06\u001b[0m.\n",
      "Para um intervalo de confiança de 99.7% nossos valores estariam entre \u001b[91m0.2988 e -0.2988\u001b[0m com uma média de\u001b[1m-8.4e-06\u001b[0m.\n"
     ]
    }
   ],
   "source": [
    "mean_ = float(np.mean(df_base,axis=0))\n",
    "std_ = float(np.std(df_base,axis=0))\n",
    "IC1 = mean_ + std_\n",
    "IC2 = mean_ - std_\n",
    "\n",
    "print('Para um intervalo de confiança de 68% nossos valores estariam entre {}{} e {}{} com uma média de{}{}{}.'.format('\\033[91m', round(IC1,4), round(IC2,4),'\\033[0m', '\\033[1m', round(mean_,7),'\\033[0m'))\n",
    "print('Para um intervalo de confiança de 95% nossos valores estariam entre {}{} e {}{} com uma média de{}{}{}.'.format('\\033[91m', round(IC1+std_,4), round(IC2-std_,4),'\\033[0m', '\\033[1m', round(mean_,7),'\\033[0m'))\n",
    "print('Para um intervalo de confiança de 99.7% nossos valores estariam entre {}{} e {}{} com uma média de{}{}{}.'.format('\\033[91m', round(IC1+2*std_,4), round(IC2-2*std_,4),'\\033[0m','\\033[1m', round(mean_,7),'\\033[0m'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agrupamento(np_array, step, sort_array= False):\n",
    "    if sort_array == True:\n",
    "        np_array = np.sort(np_array)\n",
    "    lista_media_aleatoria = []\n",
    "    for i in range(step,len(np_array)+1, step):\n",
    "        agrupamento = np_array[i-step:i]\n",
    "        media_agrupamentos = np.mean(agrupamento)\n",
    "        lista_media_aleatoria.append(media_agrupamentos)\n",
    "\n",
    "    return pd.DataFrame(lista_media_aleatoria, columns=['médias agrupamento'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#analisando os dados em ordem crescente\n",
    "df_lista_media_sorted = agrupamento(np_array, 1, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_variable = norm()\n",
    "normal_values = normal_variable.rvs(size=100000)\n",
    "x = np.linspace(normal_variable.ppf(0.01), normal_variable.ppf(0.99), 100)\n",
    "\n",
    "fig, (axs1,axs2) = plt.subplots(2,1, figsize=(8,10))\n",
    "axs1.plot(x, normal_variable.pdf(x))\n",
    "axs1.hist(df_base, density=True, bins=20)\n",
    "axs2.scatter(df_lista_media_sorted,df_lista_media_sorted.index/len(df_lista_media_sorted), s=0.5, c='b');\n",
    "axs2.plot(x, normal_variable.cdf(x));\n",
    "axs1.grid()\n",
    "axs2.grid()\n",
    "axs1.set_title('Comparativo entre a distribuição dos dados importados e a normal Gaussiana')\n",
    "axs2.set_title('Comparativo entre curva acumulativa dos dados importados e da Gaussiana');\n",
    "#leptocúrtica simétrica"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, (axs2,axs1) = plt.subplots(2,1, figsize=(10,10))\n",
    "axs1.scatter(np_array, norm.cdf(np_array, loc = mean_, scale=std_), s=15); # cumulative density function\n",
    "axs1.scatter(df_lista_media_sorted,df_lista_media_sorted.index/len(df_lista_media_sorted), s=1, c='yellow')\n",
    "axs2.scatter(np_array, norm.pdf(np_array, loc = mean_, scale=std_), s=1); # probability density function\n",
    "axs2.hist(df_lista_media_sorted, density=True, bins=141, histtype='step', color='red')\n",
    "axs1.grid()\n",
    "axs2.grid()\n",
    "axs1.set_title('Comparativo entre curva acumulativa dos dados importados e da Gaussiana ajustada')\n",
    "axs2.set_title('Comparativo entre a distribuição dos dados importados e a normal Gaussiana ajustada')\n",
    "axs1.legend(['PDF Normal Ajustada', 'Curva acumulativa calculada']);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(5,8))\n",
    "plt.boxplot(np_array)\n",
    "plt.xlabel('')\n",
    "plt.title('Boxplot dos dados analisados')\n",
    "plt.ylabel('Variação da Base de Dados');\n",
    "plt.xticks([])\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 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>valores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>-0.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.099599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-0.395677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-0.066225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.067889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.379186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            valores\n",
       "count  20000.000000\n",
       "mean      -0.000008\n",
       "std        0.099599\n",
       "min       -0.395677\n",
       "25%       -0.066225\n",
       "50%        0.000454\n",
       "75%        0.067889\n",
       "max        0.379186"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_base.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#analisando os dados em ordem aleatória\n",
    "df_lista_media_aleatoria1 = agrupamento(np_array,4) \n",
    "df_lista_media_aleatoria2 = agrupamento(np_array,16) \n",
    "df_lista_media_aleatoria3 = agrupamento(np_array,64) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, (axs1,axs2,axs3,axs4) = plt.subplots(4,1, figsize=(10,10))\n",
    "axs1.scatter(df_base.index, df_base, s=2, c='r')\n",
    "axs2.scatter(df_lista_media_aleatoria1.index, df_lista_media_aleatoria1.values, s=2,c='y')\n",
    "axs3.scatter(df_lista_media_aleatoria2.index, df_lista_media_aleatoria2.values, s=2)\n",
    "axs4.scatter(df_lista_media_aleatoria3.index, df_lista_media_aleatoria3.values, s=2, c='g')\n",
    "axs2.set_ylim(-0.4,0.4)\n",
    "axs3.set_ylim(-0.4,0.4)\n",
    "axs4.set_ylim(-0.4,0.4);\n",
    "axs1.grid()\n",
    "axs2.grid()\n",
    "axs3.grid()\n",
    "axs4.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def forecast_constant(loc, n, scale=1.0):\n",
    "  return loc + np.random.normal(loc, scale, n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_forecast = [] \n",
    "np_forecast = np.array(forecast_constant(mean_, len(df_base), std_ ))\n",
    "df_forecast = pd.DataFrame(np_forecast.T)\n",
    "mean_tests = np.mean(np_forecast,axis=0)\n",
    "std_tests = np.std(np_forecast,axis=0)\n",
    "\n",
    "conf_tests = 1.96*std_/np.sqrt(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(df_forecast.index, df_forecast, s=2)\n",
    "plt.scatter(df_base.index, df_base, s=2, c='r', alpha= 0.3)\n",
    "plt.title('Criando uma simulação para comparar com os dados originais')\n",
    "plt.legend(['Dados originais','simulação']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_forecast = [] \n",
    "for i in range(1000):\n",
    "  X_forecast.append(forecast_constant(mean_, len(df_base), std_ ))\n",
    "\n",
    "np_forecast = np.array(X_forecast)\n",
    "df_forecast = pd.DataFrame(np_forecast.T)\n",
    "mean_tests = np.mean(np_forecast,axis=0)\n",
    "std_tests = np.std(np_forecast,axis=0)\n",
    "\n",
    "conf_tests = 1.96*std_/np.sqrt(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "plt.scatter(pd.DataFram80e(mean_tests).index, mean_tests, s= 2) \n",
    "plt.scatter(pd.DataFrame(mean_tests).index, mean_tests + conf_tests, s=1,color='orange') \n",
    "plt.scatter(pd.DataFram80e(mean_tests).index, mean_tests - conf_tests, s=1,color='orange');\n",
    "plt.scatter(df_base.index, df_base, s=2, c='r', alpha=0.1)\n",
    "plt.title('média dos valores com desvio padrão para 1000 simulações');\n",
    "plt.legend(['Valores médios','desvio padrão']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "a) Para cada uma das distribuições, pesquise um estimador para o parâmetro θ. Em seguida,\n",
    "escreva um código para a estimação desses parâmetros."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "b) Faça o teste do qui-quadrado para verificar qual distribuição teórica mais se assemelha à\n",
    "distribuição dos dados. Não esqueça de montar o teste de hipótese."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribuição Normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parâmetro θ:\n",
      "Mean = -8.394894515616346e-06 Std = 0.09959690301282376\n",
      "\n",
      "Hipótese nula não rejeitada pois Chi_calc (167.36591349158851) < Chi_cut (167.51430499097768)\n"
     ]
    }
   ],
   "source": [
    "print(\"Parâmetro θ:\")\n",
    "mean_ = float(np.mean(df_base,axis=0))\n",
    "std_ = float(np.std(df_base,axis=0))\n",
    "print(f\"Mean = {mean_} Std = {std_}\\n\")\n",
    "normal_distribution = norm(loc= mean_, scale=std_)\n",
    "\n",
    "k = 141\n",
    "\n",
    "interval = np.linspace(np.min(np_array),np.max(np_array),k)\n",
    "\n",
    "hist, bins = np.histogram(np_array, bins=interval)\n",
    "norm_cdf = norm.cdf(interval, scale= std_)\n",
    "\n",
    "area_bins = norm_cdf[1:] - norm_cdf[:-1]\n",
    "frequencia_teorica = area_bins * np_array.shape[0]\n",
    "chi_calc = np.sum((frequencia_teorica - hist)**2/frequencia_teorica)\n",
    "\n",
    "chi_cut = chi2.ppf(0.95,df=k-1-1)\n",
    "\n",
    "if chi_calc > chi_cut:\n",
    "  print(f'Hipótese nula rejeitada pois Chi_calc ({chi_calc}) > Chi_cut ({chi_cut})')\n",
    "else:\n",
    "  print(f'Hipótese nula não rejeitada pois Chi_calc ({chi_calc}) < Chi_cut ({chi_cut})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribuição Lognormal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parâmetro θ:\n",
      "Scale = 0.007833233102440391, Mean = -12.721436104743017 Std = 12.721038807157175\n",
      "\n",
      "Hipótese nula rejeitada pois Chi_calc (6314606.0) > Chi_cut (167.51430499097768)\n"
     ]
    }
   ],
   "source": [
    "s,mean_l,std_l=lognorm.fit(np_array)\n",
    "print(\"Parâmetro θ:\")\n",
    "print(f\"Scale = {s}, Mean = {mean_l} Std = {std_l}\\n\")\n",
    "lognormal_distribution = lognorm(s=s,loc=mean_l,scale=std_l)\n",
    "\n",
    "k = 141\n",
    "interval = np.linspace(np.min(np_array),np.max(np_array),k)\n",
    "hist, bins = np.histogram(np_array, bins=interval)\n",
    "\n",
    "lognorm_cdf = lognorm.cdf(interval,s=s)\n",
    "\n",
    "area_bins = lognorm_cdf[1:] - lognorm_cdf[:-1]\n",
    "frequencia_teorica = area_bins * np_array.shape[0]\n",
    "\n",
    "chi_calc = np.sum((frequencia_teorica - hist)**2/(frequencia_teorica+1))\n",
    "\n",
    "chi_cut = chi2.ppf(0.95,df=k-1-1)\n",
    "\n",
    "if chi_calc > chi_cut:\n",
    "  print(f'Hipótese nula rejeitada pois Chi_calc ({chi_calc}) > Chi_cut ({chi_cut})')\n",
    "else:\n",
    "  print(f'Hipótese nula não rejeitada pois Chi_calc ({chi_calc}) < Chi_cut ({chi_cut})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribuição Exponencial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parâmetro θ:\n",
      "Mean = -0.39567698457224626 Std = 0.39566858967773066\n",
      "\n",
      "Hipótese nula rejeitada pois Chi_calc (39586.07062191132) > Chi_cut (167.51430499097768)\n"
     ]
    }
   ],
   "source": [
    "mean_e,std_e=expon.fit(np_array)\n",
    "print(\"Parâmetro θ:\")\n",
    "print(f\"Mean = {mean_e} Std = {std_e}\\n\")\n",
    "expon_distribution = expon(loc= mean_e, scale=std_e)\n",
    "\n",
    "k = 141\n",
    "\n",
    "interval = np.linspace(np.min(np_array),np.max(np_array),k)\n",
    "hist, bins = np.histogram(np_array, bins=interval)\n",
    "\n",
    "expon_cdf = expon.cdf(interval,loc=mean_e,scale=std_e)\n",
    "area_bins = expon_cdf[1:] - expon_cdf[:-1]\n",
    "\n",
    "frequencia_teorica = area_bins * np_array.shape[0]\n",
    "chi_calc = np.sum((frequencia_teorica - hist)**2/frequencia_teorica)\n",
    "\n",
    "chi_cut = chi2.ppf(0.95,df=k-1-1)\n",
    "\n",
    "if chi_calc > chi_cut:\n",
    "  print(f'Hipótese nula rejeitada pois Chi_calc ({chi_calc}) > Chi_cut ({chi_cut})')\n",
    "else:\n",
    "  print(f'Hipótese nula não rejeitada pois Chi_calc ({chi_calc}) < Chi_cut ({chi_cut})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribuição Gamma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parâmetro θ:\n",
      "Scale = 5171.838247679916, Mean = -7.169655155202056 Std = 0.0013862839619284669\n",
      "\n",
      "Hipótese nula rejeitada pois Chi_calc (177.71330706292565) > Chi_cut (167.51430499097768)\n"
     ]
    }
   ],
   "source": [
    "a,mean_g,std_g=gamma.fit(np_array)\n",
    "print(\"Parâmetro θ:\")\n",
    "print(f\"Scale = {a}, Mean = {mean_g} Std = {std_g}\\n\")\n",
    "gamma_distribution = gamma(loc=mean_g,scale=std_g,a=a)\n",
    "\n",
    "k = 141\n",
    "\n",
    "interval = np.linspace(np.min(np_array),np.max(np_array),k)\n",
    "\n",
    "hist, bins = np.histogram(np_array, bins=interval)\n",
    "gamma_cdf = gamma.cdf(interval,a,mean_g,std_g)\n",
    "area_bins = gamma_cdf[1:] - gamma_cdf[:-1]\n",
    "frequencia_teorica = area_bins * np_array.shape[0]\n",
    "\n",
    "chi_calc = np.sum((frequencia_teorica - hist)**2/frequencia_teorica)\n",
    "\n",
    "chi_cut = chi2.ppf(0.95,df=k-1-1)\n",
    "\n",
    "if chi_calc > chi_cut:\n",
    "  print(f'Hipótese nula rejeitada pois Chi_calc ({chi_calc}) > Chi_cut ({chi_cut})')\n",
    "else:\n",
    "  print(f'Hipótese nula não rejeitada pois Chi_calc ({chi_calc}) < Chi_cut ({chi_cut})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Questão 3\n",
    "Séries Temporais"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    
   ],
   "source": [
    "from statsmodels.tsa.seasonal import seasonal_decompose\n",
    "import statsmodels.api as sm\n",
    "import pylab as py\n",
    "from statsmodels.graphics.gofplots import qqplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importação dos dados\n",
    "data = pd.read_csv('ipeadata_vendas_ventiladores.csv',sep=\";\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tratamento e limpeza de dados\n",
    "data = data.drop(columns = 'Unnamed: 2')\n",
    "data.rename(columns={'Vendas industriais - ventiladores - qde. - Unidade - Associação Nacional de Fabricantes de Produtos Eletroeletrônicos (Eletros) - ELET12_VENT12': 'Quantidade'}, inplace = True)\n",
    "data['Data'] = data['Data'].astype(str);\n",
    "data['Data'] = data['Data'].str.replace('.', '-', regex = False )\n",
    "data['Data'] = pd.to_datetime(data['Data'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mostre graficamente o comportamento da série temporal. Realce os valores extremos\n",
    "(mínimo e máximo). Existem registros atípicos?\n",
    "\n",
    "Aqui está a representação gráfica do comportamento da série temporal. Observando o gráfico é possível ver que não existem registros atípicos, baseado na não existência de dados anômalos e porque os resíduos mais extremos observados não resultam na identificação de Outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Os valores mínimo e máximo da série temporal respectivamente, são: 25553 e 506912. \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(x = data['Quantidade']);\n",
    "\n",
    "print(f\"Os valores mínimo e máximo da série temporal respectivamente, são: {data['Quantidade'].min()} e {data['Quantidade'].max()}. \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count        96.000000\n",
       "mean     215608.000000\n",
       "std      128336.733474\n",
       "min       25553.000000\n",
       "25%       96135.750000\n",
       "50%      193736.500000\n",
       "75%      318420.000000\n",
       "max      506912.000000\n",
       "Name: Quantidade, dtype: float64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Quantidade'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Componentes da série temporal:\n",
    "\n",
    "Tendência: Algo próximo a um senóide, mas sem períodos bem definidos.\n",
    "Sazonalidade: Podemos observar que a sazonalidade segue uma curva senóide com um achatamento no seus picos e vales.\n",
    "Resíduo: Está bem distribuído, uniformemente, em relação ao eixo x."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "result = seasonal_decompose(data['Quantidade'], model='additive', period= 12)\n",
    "fig = result.plot()\n",
    "fig.set_size_inches((15, 10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os resíduos da série seguem uma distribuição normal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuo = result.resid\n",
    "mean_ = np.mean(residuo)\n",
    "std_ = np.std(residuo)\n",
    "\n",
    "nbins_sturges = round(1 + 3.322*np.log10(residuo.count()))\n",
    "nbins_sqrt = round(np.sqrt(residuo.count()))\n",
    "\n",
    "plt.scatter(residuo, norm.pdf(residuo, loc = mean_, scale=std_), s=3); # probability density function\n",
    "plt.hist(residuo, density=True, bins = nbins_sqrt , histtype='step', color='red');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal_distribution = norm(scale=std_)\n",
    "\n",
    "k = nbins_sturges\n",
    "\n",
    "interval = np.linspace(np.min(residuo),np.max(residuo),k)\n",
    "\n",
    "hist, bins = np.histogram(residuo, bins=interval)\n",
    "norm_cdf = norm.cdf(interval, scale= std_)\n",
    "\n",
    "area_bins = norm_cdf[1:] - norm_cdf[:-1]\n",
    "frequencia_teorica = area_bins * residuo.shape[0]\n",
    "\n",
    "chi_calc = np.sum((frequencia_teorica - hist)**2/frequencia_teorica)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hipótese nula rejeitada pois Chi_calc (11.398737673696528) > Chi_cut (11.070497693516351)\n"
     ]
    }
   ],
   "source": [
    "chi_cut = chi2.ppf(0.95,df=k-1-1)\n",
    "\n",
    "if chi_calc > chi_cut:\n",
    "  print(f'Hipótese nula rejeitada pois Chi_calc ({chi_calc}) > Chi_cut ({chi_cut})')\n",
    "else:\n",
    "  print(f'Hipótese nula não rejeitada pois Chi_calc ({chi_calc}) < Chi_cut ({chi_cut})')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Após a aplicação do teste de hipótese (Chi-Quadrado), verificou-se que a hipótese nula é rejeitada, uma vez que o \"Chi\" calculado é maior que o \"Chi\" tabelado. O gráfico qqplot foi construído como método de conferência. A expectativa era de que os dados formassem um comportamento retilíneo, porém as extremidades apresentaram desvios. O \"QQplot\" corrobora com o \"Chi-Quadrado\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# O parâmetro line define a linha ideal da curva normal\n",
    "# 's' vem de solid (uma linha contínua) \n",
    "\n",
    "qqplot(result.resid,  line = 's') \n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "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": "3.8.10"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}